Doctoral College Cyber-Physical Production Systems...Digital Assistance Systems in Cyber-Physical Assembly Systems 1 D. Gerhard, S. Schulte (Eds.): Final Report of the Doctoral College

Detlef GerhardStefan Schulte (Eds.)

Doctoral CollegeCyber-Physical Production Systems

Final Report

C PP S

http://dc-cpps.tuwien.ac.at/

http://dc-cpps.tuwien.ac.at/

Editors

Detlef GerhardTU Wien, Mechanical Engineering Informatics andVirtual Product DevelopmentA-1060 Wien, Getreidemarkt [email protected]

Stefan SchulteTU Wien, Distributed Systems GroupA-1040 Wien, Argentinierstrasse 8/[email protected]

Copyright c© 2017 for the individual papers by the papers authors. Copying per-mitted only for private and academic purposes. This volume is published andcopyrighted by its editors.

mailto:[email protected]

mailto:[email protected]

Preface

According to the OECD, about 18.7% of the Austrian gross value added (GVA)in 2016 has been created by the industrial sector, i.e., the manufacturing sectorwithout energy and construction, showing the significance of this economic sectorfor the national income. Similar numbers can be observed for the European Union(19.32% of the GVA in 2015) and the Euro area (20.07% of the GVA in 2015).From 1995 to 2015, the compound annual growth rate (CAGR) was 3.2% for thecomplete industrial sector in Austria (including manufacturing, manufacturing-oriented services, and construction), leading to a GVA of 153 billion e in 2015,out of which 66 billion e can be attributed to manufacturing and 67 billion ecan be attributed to manufacturing-oriented services.

These numbers show that the manufacturing industry forms the backbone ofthe Austrian and European economy, even more if taking into account multipliereffects in related economic sectors, e.g., Information and Communication Tech-nologies (ICT) or the transport sector. Since Austria and many EU membersare high-wage countries, European companies are under constant competitionwith business rivals from developing countries, which can take advantage of lowlabor and production costs. Therefore, Austrian and European manufacturersare forced to decrease their production costs, raise their productivity, increasethe degree of utilization of their resources, and provide a variety of sophisticatedproducts in a short time frame to remain competitive.

Especially, European manufacturers have to be able to overcome limitationsby rigidity and enable changeability of their products, processes, and assets. Sincethe term Industry 4.0 has been first coined in 2011, massive activities have ledto a start in the transformation of the European manufacturing landscape whichaim at achieving these goals. In particular, many of the most substantial trans-formations in the manufacturing domain can be traced back to the proliferationof ICT and Internet technologies.

The emergence of so-called Cyber-Physical Systems (CPS), which are basi-cally smart networked (embedded) technical systems consisting of sensors, actu-ators, and controllers within physical structures, was attended by an innovationjump from mechatronics to self-optimizing systems, which collect as well as ana-lyze information from the environment and are able to adapt to changing contextor user requirements. The application of CPS in the manufacturing domain in-troduced Cyber-Physical Production Systems (CPPS).

While CPS can be employed in many different areas (e.g., ambient assistedliving, smart buildings, smart grids, smart cities), CPPS are basic building blocksto realize Smart Factories. There are also special requirements on CPS compo-nents and subsystems resulting from the production environment, e.g., smartnetworked systems need a high data rate communication, which is robust andflexible in harsh environments with strong electromagnetic interference. Within

the Doctoral College CPPS (DC-CPPS)1 at TU Wien, special focus was put onthose requirements.

Since its start in 2015, DC-CPPS has been a major research activity in thefield of Industry 4.0 and especially led to wide recognition of these topics inAustria. To the best of our knowledge, the doctoral college was one out of onlytwo dedicated colleges on Industry 4.0 in the German-speaking area and theonly one bringing together researchers from mechanical engineering, computerscience, and information technology.

Within DC-CPPS, ten PhD students collaborated on research topics in thefield of CPPS and worked on their individual PhD thesis topics. In this final re-port, the students present selected results from the research they have conductedduring the last three years. The research conducted within the doctoral collegeled to 46 peer-reviewed research papers so far, with further papers currently un-der review. Amongst other accomplishments, two of the involved PhD studentswon the Best Paper Award at the 35th International Conference on Computer-Aided Design and at the Scientific Conference on Advances in Wireless and Opti-cal Communications. In addition, the doctoral college was extensively covered bythe “Future” magazine supplement of Wiener Zeitung in May 2017. Furthermore,the doctoral college has been the kick-off and booster for various complementaryresearch projects, e.g., the FFG Competence Center for Excellent TechnologiesAustrian Center of Digital Production2, the European H2020 projects CREMA(Cloud-based Rapid Elastic Manufacturing)3 and FACTS4WORKERS (FACTo-rieS for WORKERS )4, and the Austrian transfer project DigiTrans 4.0 5. In allof these projects, faculty members from DC-CPPS are the scientific leaders andPrincipal Investigators (PIs).

Overall, DC-CPPS was a big success, both on the level of the individual stu-dents and from the university’s point of view. First, the combination of expertisefrom different CPPS-related research fields brought together experts at the rightpoint of time and put TU Wien into a favorable position to establish itself as theleading Industry 4.0 research institution in Austria. Second, the PhD studentswere provided with an excellent multidisciplinary education that enables themto pursue international research careers or to assume high-profile positions inindustrial research – both are desperately necessary to keep Austria’s positionin the global economy.

Vienna, December 2017 Detlef GerhardStefan Schulte

1 http://dc-cpps.tuwien.ac.at/home/2 http://www.acdp.at/3 http://www.crema-project.eu4 http://facts4workers.eu/5 http://www.digitrans.at/

http://dc-cpps.tuwien.ac.at/home/

http://www.acdp.at/

http://www.crema-project.eu

http://facts4workers.eu/

http://www.digitrans.at/

Involved Faculty

Scientific Head

Prof. Dr. Detlef Gerhard E307 – Institute for Engineering Designand Logistics Engineering

Coordinator

Assistant Prof. Dr. Stefan Schulte E184 – Institute of Information Systems

Collaborating Key Faculty Members

Assistant Prof. Dr. Ezio Bartocci E182 – Institute of Computer EngineeringProf. Dr. Stefan Biffl E188 – Institute of Software Technology

and Interactive SystemsProf. Dr. Friedrich Bleicher E311 – Institute for Production Engineer-

ing and Laser TechnologyProf. Dr. Schahram Dustdar E184 – Institute of Information SystemsProf. Dr. Radu Grosu E182 – Institute of Computer EngineeringProf. Dr. Gerti Kappel E188 – Institute of Software Technology

and Interactive SystemsProf. Dr. Wolfgang Kastner E183 – Institute of Computer Aided Au-

tomationProf. Dr. Burkhard Kittl E311 – Institute for Production Engineer-

ing and Laser TechnologyDr. Marta Sabou E188 – Institute of Software Technology

and Interactive SystemsProf. Dr. Wilfried Sihn E330 – Institute of Management ScienceAssistant Prof. Dr. Manuel Wimmer E188 – Institute of Software Technology

and Interactive SystemsProf. Dr. Horst Zimmermann E354 – Institute of Electrodynamics, Mi-

crowave and Circuit EngineeringProf. Dr. Tanja Zseby E389 – Institute of Telecommunications

PhD Students

Dipl.-Wirtsch.-Ing. Philipp Hold Planning and Evaluation of Digital Assis-tance Systems in Cyber-Physical AssemblySystems

Ahmed M. Ismail, M. Sc. Service-Oriented Manufacturing Infras-tructure

Dipl.-Ing. Christian Krieg Reactive Cyber-Security for Cyber-Physical Production Systems

Solmaz Mansour Fallah, M. Sc. A Multi Agent based Tool Cycle Manage-ment

Dinka Milovancev, M. Sc. Communication at High Data Rates inHarsh Production Environments

Angelika Musil, B. Sc., DI / M. Sc. Classification and Architectural Design ofCollective Intelligence System Variations

Olena Skarlat, M. Sc., B. Sc., B. Sc. Cloud Manufacturing – Resource Provi-sioning in Fog Computing

Guodong Wang, M. Sc., B. Sc. Smart Attributes for Cyber-Physical Pro-duction Systems

Dipl.-Ing. Paul Weienbach Virtual Engineering Design of Cyber-Physical Production Systems

Dipl.-Ing. Sabine Wolny, B. Sc. A Runtime Model for SysML

Contents

Planning and Evaluation of Digital Assistance Systems inCyber-Physical Assembly Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Philipp Hold

Service-Oriented Manufacturing Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . 7Ahmed M. Ismail

Reactive Cyber-Security for Cyber-Physical Production Systems . . . . . . . . 12Christian Krieg

A Multi Agent based Tool Cycle Management . . . . . . . . . . . . . . . . . . . . . . . . 16Solmaz Mansour Fallah

Communication at High Data Rates in Harsh Production Environments . 20Dinka Milovancev

Classification and Architectural Design of Collective IntelligenceSystem Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Angelika Musil

Cloud Manufacturing – Resource Provisioning in Fog Computing . . . . . . . . 30Olena Skarlat

Smart Attributes for Cyber-Physical Production Systems . . . . . . . . . . . . . . 34Guodong Wang

Virtual Engineering Design of Cyber-Physical Production Systems . . . . . . 39Paul Christian Weißenbach

A Runtime Model for SysML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Sabine Wolny

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Planning and Evaluation of Digital AssistanceSystems in Cyber-Physical Assembly Systems

Philipp Hold

Supervisor: Prof. Dr. Wilfried Sihn,E330 – Institute of Management Science

1 Planning and Evaluation Model of Digital AssistanceSystems

Currently used methods in industrial engineering are aimed to improve and toevaluate productivity effects of work systems [1]. An aspect that is largely missingin state-of-the-art industrial engineering research is a systematic identificationof information needs, the design of appropriate information systems and theirevaluation in regard to increase productivity. A recent review of the scientificliterature reveals that planning and evaluation of Digital Assistance Systems(DAS) is not addressed properly in scientific research. In particular, there is alack in a systematic analysis of relations between the characteristics of assemblytasks, qualitative and quantitative evaluation of their complexity and derivationof requirements for DAS [2].

This fact has been also proved by the related work analysis performed in thecourse of this thesis. Thereby 11,411 relevant scientific publications from pre-selected journals of the years 2012 to 2017 were analyzed. The results show thatmostly general technical research questions are addressed. The study identified140 publications within the thematic clusters ‘planning of digital assistance sys-tems’ , ‘evaluation of assistance systems’ , ‘investment decisions’ , ‘productivityeffects’ . The first schematic approaches and models are developed in the con-text of cognitive factory planning [2, 3]. However, it has to be emphasized thatthese approaches and models do not address an adequate measurable basis foran investment decision [4], especially for small and medium size companies inearly phases of assembly planning and engineering.

Claeys et al. [5] discuss a schematic concept of how product complexity, work-place complexity and human intrinsic and extrinsic factors determine a need fordigital and visual assistance support. Thereby the identification of digital as-sistance need is focused only on a qualitative assessment, and the approachcan be used only after an assembly system is implemented. In addition, onlypartial technical functions of DAS, mostly focusing on information representa-tion forms, are addressed. However, this is performed without a need analysisof the technical component Design of DAS from a holistic view and based onwork task requirements, e.g., in regard to needed (sensor) information from thereal shop floor (physical) to support individual operator capabilities, concern-ing individual assembly activities [6]. As for the specific (data) information and

Digital Assistance Systems in Cyber-Physical Assembly Systems 1

D. Gerhard, S. Schulte (Eds.): Final Report of the Doctoral College Cyber-PhysicalProduction Systems, published at https://dc-cpps.tuwien.ac.at

https://dc-cpps.tuwien.ac.at

interfaces to connected physical objects (and also established human interac-tion) of the working system with data processing modules, this issue is onlyaddressed rudimentarily. Furthermore, the high level of abstraction of currentlyexisting approaches and models do not allow an operational implementation ina real industrial surrounding. However, benefits and productivity effects of DASare described only in a qualitative way, i.e., an economic investment decisioncannot be sustained on this basis. It has to be pointed out, that planning andevaluating of DAS is rarely addressed in the scientific discussion with regard toquantity-calculated benefits concerning individual work systems based on indi-vidual operator performance levels and concerning individual environmental andorganizational influences.

2 Technical, Human and Productivity Relation Effects

Characteristics and description parameters of human operators to perform awork task in a work system consist of different determinants. These determinantsin general are classified into four central categories [7]:

Constituent characteristics: Gender, anatomy, culture, and heredity.Disposition characteristics: Personality, age, intelligence, body weight, health

status, and rhythmological influence.Qualification and individual competency characteristics: Work load, fa-

tigue, motivation, acceptance, satisfaction, and mood.Adaptation characteristics: Experiences, knowledge, skills, education, and

hard competences.

The determinants influence human operator performance during the execu-tion of work tasks in different ways. Concerning the fact that these determinantshave partly an enhancing influence and partly a limiting influence onto the hu-man performance, a holistic view is required. In order to measure the influencesonto the productivity of human and work system by DAS, primary descriptionparameters have been identified: (i) handling / operation time [8], (ii) humanerror probability (HEP) [9], and learning time [10].

Table 1 illustrates selected technical components of DAS, based on a relatedwork study, clustered into six defined technical pools:

T-Pool: Tools, that can be integrated (e.g., tool of assembly systems) into as-sistance systems and which can be controlled by DAS.

D-Pool: Devices of assistance systems.TS-Pool: Sensory systems to connect the physical operation level of the shop

floor with the digital information processing level.IR-Pool: Forms to represent information.II-Pool: Information sources by which the system can be supplied with basic

information about the work task execution.IP-Pool: Different forms of information systems, categorized by technical readi-

ness levels that depends to the other pools.

2 Philipp Hold

Table 1. Description Parameters of DAS Components

Cluster of technicalcomponents of DAS

Description of parameters for selection of technical componentsof DAS

T-Pool Ability to dialog with toolsAbility to interact with tools

D-Pool Degree of mobilityAccess to work information

TS-Pool Time of information provisionIR-Pool Real versus virtual worker informationII-Pool Source of informationIP-Pool Depending on the technical components of T-Pool, D-Pool, TS-

Pool, IR-Pool and II-Pool

Fig. 1. Fundamental Taxonomy of the Model

As shown in Table 1, DAS can be described in terms of their technical sub-functions with regard to the following features [4, 11].

In order to quantify the aforementioned description parameters, the followinginformation sources are usable:

1. Information on the basis of the Methods Time Measurement (MTM) – asystem of certain times [6], and

2. Information on the basis of product information, which come out of CADdata, as well as from corresponding parts lists.

Furthermore, an additional description of the parameter ‘system usability’ [12]establishes a correlation to employee acceptance of the technical design of theassistance system. For this purpose, subcomponents are analyzed and evalu-ated concerning possible technical compositions. The results are described in a


Table 2. Values of Description Parameters (Assembly Line)

Characteristic Scale Value

Product Complexity (low) 0 – 15 (high) 8.59Degree of Mobility (low) 0 – 4 (high) 4.00Cognitive Load of Work (low) 1 – 0 (high) 0.00Degree of Flexibility (low) 0 – ∞ (high) 1.00Source of Information (low) 0 – ∞ (high) 1.00Time of Information Provision (low) 1 – 0 (high) 1.00Access to Worker Information (low) 0 – 4 (high) 1.00Real vs. Virtual Worker Information (low) 0 – 15 (high) 8.59Ability to Dialog (low) 0 – 4 (high) 0.00Interaction with Tools (low) 0 – 4 (high) 0.80

technology database. The correlation taxonomy is illustrated schematically inFig.1 [13].

To discuss how the results influence the productivity and investment require-ments, the individual subcomponents, their technical combination possibilitiesand investment efforts are described. Hence, the target function, based on aconventional amortization formula, correlates necessary investments for DASconfiguration variants with the expected effects of adequate achievable produc-tivity. Therefore, the value of the expected monetary return is calculated asthe difference between the determinants (initial situation: MTMp, HEPp andLERNp) and the expected influences, based on the identified DAS components(target situation: MTMe, HEPe and LERNe). The best human and productivityoriented solution is obtained by a designed genetic algorithm [14].

3 Implementation and First Results

The cyber-physical assembly system [15] of the TU Wien Pilot Factory Industry4.0 (PF) extends along three assembly stations, where the developed model isimplemented as a ‘proof-of-concept’ demonstrator. As an adequate data inputfor the developed model, MTM codes and information based on CAD files wereinitially prepared. With regard to the technical requirements, taking into accountindividual human performance competences, the results have been generated bythe mathematical framework of the model (see Table 2).

Out of 32,767 technical combination possibilities, the DAS component com-position is selected as the best DAS design (see Fig. 2). The solution is identifiedby the algorithm with a production quantity of 9,000 pieces (see Table 3).

A first evaluation study (n=30) in the PF shows good results, which tend toconfirm the correctness of the developed model semantics.

4 Philipp Hold

Fig. 2. Selected Components of the Use Case

Table 3. Values of Description Parameters (Assembly Line)

Situation w/oDAS

Situation withDAS

Selected Assembly Station Assembly Line Assembly LineLearning Time in sec. 140.590 102.630Operating Time beyond Learning Time in sec. 14,709.259 14,709.259HEP in sec. 4,920.749 13.560Total Time in sec. 19,770.597 14,825.449

Savings Potential in Euro 54,946.09Investment in Euro 43,977.00

Time of amortization in years 0.80

4 Outlook

This work illustrates the potential of DAS in the context of cyber-physical as-sembly systems and describes how DAS can be planned and evaluated in termsof productivity effects taking into account human and work systems character-istics. For this, the information is gathered and processed in the early stages ofproduct development. In the course of the work in DC-CPPS, the model hasbeen formalized and developed into an IT-based solution, which is implementedas a ‘proof-of-concept’ demonstrator in a first use case. According to a work task,product, human performance information, several probability calculation meth-ods were introduced, i.e., (i) the probability of human errors is calculated by themeans of the method ‘Human Error Probability’ , and (ii) ‘learning and trainingtimes’ are calculated by the means of the method ‘Learning Times’ . By compar-ing the determined values of technical components of DAS with influences on thehandling and operation time, on the human error probability and on the learningand training time, investment decisions could be quantified. The described modelis being currently under further development and evaluation in the FFG-project“MMAssist”. In this project, the work with the model will consist of two maindirections, i.e., (i) the model will be evaluated in more than five industry usecases (along the application areas: manufacturing, assembly, and maintenance)


and (ii) the semantics of the model will be modified and adapted in order toplan and evaluate technical assistance systems, e.g., human-robotic-systems.

References

1. Bokranz, R., Landau, K.: Handbuch Industrial Engineering: Produk-tivitatsmanagement mit MTM, Band 2: Anwendung. 2 edn. Schaffer-Poeschel,Stuttgart, Germany (2012)

2. Wiesbeck, M.: Struktur zur Reprasentation von Montagesequenzen fur die situa-tionsorientierte Werkerfuhrung. Utz, Munchen, Germany (2014)

3. Fast-Berglund, A., Akerman, M., Karlsson, M., Hernandez, V.G., Stahre, J.: Cog-nitive Automation Strategies – Improving Use-efficiency of Carrier and Content ofInformation. Procedia CIRP 17 (2014) 67–70

4. Hold, P., Ranz, F., Sihn, W., Hummel, V.: Planning Operator Support in Cyber-Physical Assembly Systems. IFAC-PapersOnLine 49(32) (2016) 60– 65

5. Claeys, A., Hoedt, S., Soete, N., Landeghem, H.V., , Cottyn, J.: Frameworkfor Evaluating Cognitive Support in Mixed Model Assembly Systems. IFAC-PapersOnLine 48(3) (2015) 924–929

6. Hold, P., Ranz, F., Sihn, W. In: Konzeption eines MTM-basierten Bewertungsmod-ells fur digitalen Assistenzbedarf in der cyber-physischen Montage. GITO, Berlin,Germany (2016) 295–322

7. Schlick, C.M., Bruder, R., Luczak, H.: Arbeitswissenschaft. Springer, Berlin, Hei-delberg, Germany (2010)

8. Kuhlang, P., Britzke, B., MTM-Institut, eds.: Modellierung menschlicher Arbeitim Industrial Engineering: Grundlagen, Praxiserfahrungen und Perspektiven. er-gonomia, Stuttgart, Germany (2015)

9. Kern, C., Refflinghaus, R.: Assembly-specific database for predicting human reli-ability in assembly operations. Total Qual. Manag. Bus. Excell. 26(9–10) (2015)1056–1070

10. Jeske, T.: Entwicklung einer Methode zur Prognose der Anlernzeit sensumo-torischer Tatigkeiten. Shaker, Aachen, Germany (2013)

11. Lusic, M., Fischer, C., Bonig, J., Hornfeck, R., Franke, J.: Worker Information Sys-tems: State of the Art and Guideline for Selection under Consideration of CompanySpecific Boundary Conditions. Procedia CIRP 41 (2016) 1113–1118

12. Brooke, J.: SUS: a ‘quick and dirty’ usability scale. Usability Evaluation in Industry189 (1996) 4–7

13. Hold, P., Erol, S., Reisinger, G., Sihn, W.: Planning and Evaluation of DigitalAssistance Systems. Procedia Manufacturing 9 (2017) 143 – 150

14. Gunther, S.: Design for Six Sigma: Konzeption und Operationalisierung von al-ternativen Problemlosungszyklen auf Basis evolutionarer Algorithmen. Gabler,Wiesbaden, Germany (2010)

15. Erol, S., Jager, A., Hold, P., Ott, K., Sihn, W.: Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production. Procedia CIRP54(Supplement C) (2016) 13 – 18 6th CIRP Conf. on Learning Factories.

6 Philipp Hold

Service-Oriented Manufacturing Infrastructure

Ahmed M. Ismail

Supervisor: Prof. Dr. Wolfgang Kastner,E183 – Institute of Computer Aided Automation

Out of the four layers structuring the research topics of the DC-CPPS, thisdissertation applies itself between the abstraction and physical layers. Thus, itis concerned with the design and implementation of infrastructural systems forresilient machine-to-machine (M2M) communication in Cyber-Physical Produc-tion Systems (CPPS).

In general, manufacturing strategies are typically used by an enterprise togive assurances that its decisions match with its requirements and vision. Severalstrategies have been developed and applied over the years, including the well-known lean and agile manufacturing paradigms [1]. Currently, the focus hasshifted towards smart manufacturing. This is a strategy that aims to mitigate themodern challenges to manufacturing enterprises through tech-centric solutions.Therefore, it often involves the adoption of technologies such as service-orientedarchitectures (SOA), smart sensors, and big data solutions in the pursuit of acompetitive advantage in the manufacturing market.

This dissertation applies itself in congruence with the concepts of smart man-ufacturing. Thus, a total of eight research papers have investigated the appli-cation of SOA, M2M communication middleware systems, overlay networkingsolutions, and other technologies that improve the agility, resilience, and inter-operability of enterprise infrastructure.

In [2, 3], the various elements involved in the development of service-orientedsolutions for CPPS are investigated. The current state of manufacturing infras-tructure is detailed to establish a base level of understanding on enterprise net-work architectures, constraints, and technologies. The challenges involved arediscerned and the features of service-oriented solutions are discussed to high-light how they may be used to alleviate them. The service-oriented referencearchitectures of five major European Union (EU) research projects are surveyedto highlight their main characteristics. Realizations of these architectures areanalyzed to discern compatible technologies for the guarantee of system-wideinteroperability. An architecture analysis framework developed by Angelov etal. in [4] is also applied to the five architectures to determine the fluency withwhich they may be translated into concrete implementations. The results showthat the architectures are either over- or under-specified, and, in certain cases,are missing critical elements needed for implementation. By extrapolating pre-vious results presented in [4], this implies that the analyzed architectures arevulnerable to low adoption rates and criticisms by stakeholders. Rather thancontributing to the proliferation of available SOAs through the developmentof another competing architecture, these results are instead used to justify theselection of a mature and widely accepted service-oriented technology for the

Service-Oriented Manufacturing Infrastructure 7



basis of the remainder of the dissertation. Specifically, the choice is made forthe Open Platform Communications (OPC) Unified Architecture (UA) M2Mcommunications specifications family.

Enhancements for OPC UA are presented in [5–8]. A summary of these con-tributions can be seen in Fig. 1. The first of these, [5], addresses the coordina-tion of redundant OPC UA servers. The OPC UA specifications family may beused to build large distributed systems, as typically found for SCADA systems.These normally have several coordination requirements to allow the different in-dependent and concurrently running components to operate safely. In the caseof redundant OPC UA servers, this includes needs to address space synchro-nization and replication, failure detection, and resource fencing. This is sinceOPC UA necessitates that redundant OPC UA servers expose an identical ad-dress space to all connected clients. Failure detection and automated fail-overmeasures are needed because certain redundancy modes allow only a specificnumber of active servers connected to downstream devices at a time. As coor-dination is a task common to many distributed systems, Yahoo! developed andopen sourced a coordination service, Apache ZooKeeper, that provides strongguarantees for consistency, ordering, and durability, and implements a numberof primitives that allow for the rapid development of coordination functions [9].The use of such a service reduces the time and cost needed to implement andmeet the coordination requirements of an application allowing developers to fo-cus on the application logic instead. Thus, this work presented an integratedsystem of OPC UA and Apache ZooKeeper that meets the aforementioned co-ordination demands of redundant OPC UA servers. A detailed description ofthe architecture, data model, and components of the resulting system is given.An open source prototype is developed using the open62541 and ZooKeeper Clibraries. The resulting system demonstrates the system’s ability to provide run-time address space synchronization, failure detection, automated fail-overs, andcontention resolution.

In [6], the communication mechanisms of OPC UA’s client-server architec-ture are investigated. This communication primarily takes the form of servicecalls (SC) and can effectively be considered remote procedure calls (RPC). Thus,OPC UA operates using client-side push-based communication. This leaves OPCUA servers open to request overloads as too many SCs may be submitted to aserver within a short timespan. In trying to process these requests the servermay exhaust its available resources and subsequently enter a failed or degradedstate. The loss of a service in an online manufacturing system is considered to behighly undesirable and may cause extensive financial, human, and environmentallosses. Measures from the standard specifications to counter this vulnerabilityare found to include the use of redundant OPC UA servers and service level indi-cators. Effectively, these amount to capacity planning and simple load balancing.Therefore, they do not alter the push-based communication mechanism of OPCUA and the vulnerability of servers to request overloads persists. Thus, [6] pro-poses the use of a rate throttling service to mediate SCs on behalf of servers andshield them from traffic bursts that may have unwanted consequences. Similar

8 Ahmed M. Ismail

ZooKeeperEnsemble

Legacy OPC UA Servers

zkUA Failover Controller

zkUA ServerzkUA Proxy

zkUA System

Address Space Replication

Ephemeral locks

Address Space Replication

ZooKeeperEnsemble

OPC UA NativeBack Channel

OPC UAServer

ServiceCall Request

Request Queues

ServiceCall Response

OPC UAClient

ServiceCall Request

Request Rate Throttling System

Direct Communication

OPC UAApplicationHop n Hop n+1

Cooperative Networking System

OPC UAApplication

OPC UAApplication

Local discoveryLocal discovery

Legacy System

Fig. 1. A Summary of the Main Contributions to OPC UA [5–8]


to [5], this service is modeled on the use of Apache Zookeeper. The architecture,data model and communication flow are detailed and an open source prototypebased on the open62541 and ZooKeeper C libraries is made publicly available.The prototype also implements a safety measure that allows clients to circum-vent the queuing service in case of emergencies, when SCs must be immediatelyprocessed regardless of the state of the queue.

Finally, both [7, 8] continue addressing the resiliency of OPC UA systems bydeveloping an alternate transport layer based on P2P networking technologieswith the goal of creating cohesive and failure-resilient communication systems.Cooperative P2P overlay networking is selected as the most appropriate tech-nology domain for this purpose based on the requirements of manufacturing sys-tems. This is because this field allows for the development of networks made up ofseveral overlays. These can then be used to create expanded systems of networks,inter-system traffic engineering, and inter-system content-sharing. Thus, partic-ipating infrastructure may be organized into self-contained systems of cooper-ative nodes. These systems would be able to dynamically configure themselvesto restrict or facilitate message passing between nodes to meet the changingpolicies and requirements of a manufacturing enterprise. The result would be anenterprise-wide communication system that is compatible with the constraintsof a manufacturing system and is resilient to both node failures and networkchurn. To demonstrate this system, a service-oriented application is developedthrough the conversion of a vanilla P2P networking protocol, Chimera [10], intoa cooperative systems protocol. The system is evaluated through a prototypicalimplementation in C and tested as virtual deployments on 64-bit Xen Projectservers and 32-bit embedded devices. It is important to note that the resultingprotocol is in fact middleware-agnostic and can therefore be used by any ap-plication, including non-OPC UA ones, that share the same common need forsurvivable communication systems.

In sum, this dissertation addresses the smart-manufacturing paradigm asapplied to M2M communication infrastructure. Through the use of SOAs andthe implementation of versatile services for coordination, SC rate management,and failure-resistant communication, an approach is outlined for the develop-ment of more flexible, agile, and resilient distributed systems in manufacturingenterprises. Given the prominent and beneficial features of these technologies,and others belonging to smart manufacturing, it is expected that they becomedominant aspects of manufacturing systems in the coming years.

References

1. Correa, H.: Agile manufacturing as the 21st century strategy for improving man-ufacturing competitiveness. In Gunasekaran, A., ed.: Agile Manufacturing: The21st Century Competitive Strategy. Elsevier Science Ltd, Oxford (2001) 3 – 23

2. Ismail, A., Kastner, W.: Service-oriented architectures for interoperability in in-dustrial enterprises. In Biffl, S., Gerhard, D., Luder, A., eds.: Multi-DisciplinaryEngineering for Cyber-Physical Production Systems. Springer International Pub-lishing AG, Oxford (May 2017)

10 Ahmed M. Ismail

3. Ismail, A., Kastner, W.: Surveying the Features of Industrial SOAs. In: 2017Annual IEEE Industrial Electronics Societys 18th International Conference on In-dustrial Technology (ICIT). (March 2017) 1–8

4. Angelov, S., Grefen, P., Greefhorst, D.: A framework for analysis and design ofsoftware reference architectures. Information and Software Technology 54(4) (April2012) 417–431

5. Ismail, A., Kastner, W.: Coordinating Redundant OPC UA Servers. In: 2017 22ndIEEE International Conference on Emerging Technologies and Factory Automation(ETFA). (September 2017) In press.

6. Ismail, A., Kastner, W.: Throttled Service Calls in OPC UA. In: 2018 19th IEEEInternational Conference on Industrial Technology. (February 2018) 1–8 Underreview.

7. Ismail, A., Kastner, W.: Co-operative peer-to-peer systems for industrial mid-dleware. In: 2016 IEEE World Conference on Factory Communication Systems(WFCS). (May 2016) 1–8

8. Ismail, A., Kastner, W.: Discovery in soa-governed industrial middleware withmdns and dns-sd. In: 2016 IEEE 21st International Conference on Emerging Tech-nologies and Factory Automation (ETFA). (Sept 2016) 1–8

9. Hunt, P., Konar, M., Junqueira, F.P., Reed, B.: ZooKeeper: Wait-free Coordinationfor Internet-scale Systems. In: USENIX Annual Technical Conference. Volume 8.(2010)

10. Allen, M.S., Alebouyeh, R.: Chimera: A library for structured peer-to-peer appli-cation development. Technical report, UC Santa Barbara (2006)


Reactive Cyber-Security for Cyber-PhysicalProduction Systems

Christian Krieg

Supervisor: Prof. Dr. Tanja Zseby,E389 – Institute of Telecommunications

Reactive cyber security for Cyber-Physical Production System (CPPS) de-scribes methods and techniques to react to attacks against CPPS that are un-known. We investigated how cyber security for CPPS is different from cybersecurity for traditional networked computing systems. Besides the fact that thephysical world interacts with the cyber world, we discovered that also othercharacteristics of CPPS open a wider variety of new threat models and attackvectors. Because CPPS are connected to the Internet down to the field level,an attacker would have excessive control over the entire infrastructure once shetakes over the network. One efficient way to achieve this would be a hardwareTrojan attack.

The high number of interconnected devices in a Cyber-Physical System (CPS)provides an attractive attack surface to a potential adversary. Power efficiencyand area constraints suggest to use systems on chip (SoCs) to implement CPSnodes, including standard intellectual property (IP) cores in the SoC design flow.Due to the highly distributed nature of CPSs we suggest that high numbersof identical hardware cores are spread throughout the globe. Injecting Trojanfunctionality to these hardware cores creates a powerful foothold for furthersoftware-based attacks on the target systems. Network connectivity inherent toCPSs permits remote exploitation by the adversary.

With traditional factories, a hardware Trojan attack is hardly reasonable,because attack vectors exist that demand less effort with higher impact, suchas attacks over the corporate network or on the software level. Such attacksare targeted at, for instance, stealing trade secrets or installing ransomware onpersonal computers. While industrial control systems (ICSs), and supervisorycontrol and data acquisition (SCADA) systems facilitate rigorous control fromthe business floor down to the shop floor, several attack vectors have been intro-duced to impact the physical processes controlled by machines. When attachedto the Internet, such systems can be directly accessed, often without properpassword protection or security-driven configuration [1]. Recent trends in man-ufacturing, such as the Industry 4.0 initiative by the German government, callfor even more integration of information technology, such that each individualsensor and actuator is connected to the Internet [2]. While an attack over thenetwork still is an effective attack vector against a manufacturing system, moresophisticated attacks are possible with increasing intelligence of sensors and ac-tuators. With SoCs, technologies are available that allow to create powerful,highly integrated and energy-efficient systems to be embedded into the physicalcontext of the factory. With such systems getting more powerful, the software

12 Christian Krieg



running on them will be more generic such that real-time operating systems willrun applications for sensing and data conditioning instead of directly running onthe hardware. Due to time-to-market and code reusability, the hardware itselfwill also be made of standard components available as IP cores. Thus, if attack-ing these standard hardware components, high impact can be achieved becauseof the highly distributed nature of these sensors that are built into factories ofmulti-national corporations. We assume that an attack against the hardware iscrafted such that it supports a higher-level attack, e.g., by escalating privilegesor by opening a back door into the factory. This way, a hardware Trojan itselfwill serve as attack vector into the factory, thereby enabling attacks against thephysical domain (e.g., to damage machines or produced goods) or the cyberdomain (e.g., to steal data). The advantage of a hardware Trojan attack overhigher-level attacks is that it is very hard to detect, and, once detected, it is veryhard to recover from such an attack, as hardware is a physical good and prob-ably must be replaced by a legitimate version, which can get extremely costly.While system hardware can differ for different sensor and actuator types, thesame chip design may be used in a huge amount of different devices. Therefore,if different factories use hardware with the same IP, one has malware availableat multiple places.

Application

Control

Device

Fig. 1. Transition from Traditional, Hierarchical Control Applications to the CPS-based Automation Model (source: [3], [4])

Figure 1 illustrates the shift from traditional to future CPS-based automationmodels. The left side shows that with traditional systems, a strict hierarchy isimplemented from the application (or enterprise resource planning (ERP)) layerdown to the physical layer where sensors and actuators interact with the physicalprocesses. Between the layers, different interfaces and protocols are used, suchas Ethernet for the upper layers and field buses for the lower layers. On the rightside, the future model is shown, where the borders between the layers are brokensuch that sensors and actuators also do control tasks, directly interacting overthe Internet [3]. Any node “speaks the same language”, i.e., is connected to thenetwork via the Internet protocol.

In order to highlight the vulnerability of the underlying hardware systemsof a CPPS, we investigated how hardware Trojans can be implemented on ar-chitectures that most probably will be used in future CPPS. We focused onfield-programmable gate array (FPGA) architectures because they provide the

Reactive Cyber-Security for CPPS 13

?=C1

?=C2

?=C3

G1

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zro/oneL1

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opcode[0]

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Compromisedpayload(simple)

Triggersignal(smart)

Compromisedpayload(smart)

smart maliciousrequires superuser

simple maliciousrequires superuser

Fig. 2. Example Attack on an Instruction Decoder Using a Malicious Lookup Table [5]

flexibility to update hardware routines as needed (e.g., for an updated versionof a high-speed control algorithm).

We implemented two potent hard-to-detect hardware Trojans. In the firstattack scenario, a malicious design tool injects malicious hardware in form of alookup table that is triggered by reconfiguring the lookup table when the finalbitstream is written [5]. Figure 2 shows a demonstration example that attacksthe instruction decoder of a microprocessor such that a privileged instructioncan be executed without superuser privileges. The malicious parts of the designare L1 and L2.

The second implementation of malicious hardware makes use of handling‘X’ values differently in simulation and in hardware. ‘X’ is used in hardwaresimulation in cases when the simulator cannot predict the value of a signalin an electronic design (which could be either logic ‘0’ or logic ‘1’). If an ‘X’is encountered post-synthesis, it is common practice in FPGA design flows toreplace an ‘X’ either by ‘0’ or ‘1’ in order to avoid X-propagation. Such practiceis called X-optimism. We exploit this common practice of X-optimism by forcingthe assignment of such a signal to be ‘0’ during simulation, which is then turnedinto a ‘1’ in hardware. This way, we created a trigger signal that is hard to detectby functional verification methods.

In ongoing research, we focus on the detection of such hardware Trojan at-tacks. We investigate the properties of an abstract graph model of the hardwaredesigns in order to reason whether a given structure acts maliciously or not. Thisway, we contribute to the security of future cyber-physical infrastructures, byfocusing on future attack vectors that are potentially able to lead to catastrophicfailures of entire production sites.

14 Christian Krieg

‘X’ generator

Togglecircuit

‘0’ generator

‘1’ generator

Rectifier circuit

D Q

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M1

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= 0C1

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6= 0C2

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6

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O

T

Fig. 3. Trigger Circuit that Exploits X-Optimism [6]

References

1. infracritical: Project shine (shodan intelligence extraction) findings report. Findingsreport (10 2014)

2. Sauer, O.: Developments and trends in shopfloor-related ict systems. In: Indus-trial Engineering and Engineering Management (IEEM), 2014 IEEE InternationalConference on. (Dec 2014) 1352–1356

3. Lueth, K.L.: Will the industrial internet disrupt the smart factory of the future?(2015) http://iot-analytics.com/industrial-internet-disrupt-smart-factory/.

4. rtSOA Project: rtsoa - a data driven, real time service oriented architecture forindustrial manufacturing (2017) TU Munchen.

5. Krieg, C., Wolf, C., Jantsch, A.: Malicious lut: A stealthy fpga trojan injected andtriggered by the design flow. In: Proceedings of the 35th International Conferenceof Computer Aided Design (ICCAD). (11 2016)

6. Krieg, C., Wolf, C., Jantsch, A., Zseby, T.: Toggle mux: How x-optimism can leadto malicious hardware. In: Proceedings of the 54th Design Automation Conference(DAC) 2017. (2017)

Reactive Cyber-Security for CPPS 15

A Multi Agent based Tool Cycle Management

Solmaz Mansour Fallah

Supervisor: Prof. Dr. Friedrich Bleicher,E311 – Institute for Production Engineering and Laser Technology

1 Introduction

Current scientific efforts, such as Industry 4.0 (Germany) or ‘advanced manu-facturing’ (USA), address the development of (self-)describable, modular andself-acting entities. These entities are to be combined to production systems, tofulfill the goal of reconfigurable flexible production systems. Those efforts arebundled under auspices of Cyber-Physical Systems (CPS). CPS are embeddedsystems, which possess not only a physical layer (sensors, actuator, etc.), butalso a virtual representation of this layer in a digital network. In the productionarea, CPS can be combined to Cyber-Physical Production Systems (CPPS) inorder to satisfy the need of reconfigurable production systems.

Taking the entangled dependency of all resources and the complexity alongthe production process into account, the prior manifested goal can not be reachedby only one target. We need to consider several subordinate targets in differentareas along the supply chain and on different abstraction level of the enterprise.Those subordinated targets manage complex systems, establishing proper stan-dardization, delivering reference architectures, providing comprehensive broad-band infrastructure, and rethinking work in the digital era [1]. The first threesubordinated targets have an issue in common, namely the missing linkage.

To address the problem of the missing linkage through the different areasand layers of the production enterprise, solutions can be divided in technologicaland abstract ones. A technological solution could be establishing interfaces orprotocols like MTConnect or OPC UA, for example. An abstract solution wouldbe for instance an information model or a reference architecture. The lattercategory captures the complexity along the production process into a model for acommon understanding, as a first step towards a digital transformation. A digitaltransformation does not only digitize the data flow through the process, butalso redesigns the process itself [2]. To realize this transformation and establisha shared understanding, the system including its processes has to be capturedin a model and described. This shared understanding, so called semantic, is thefoundation of any adequate communication.

Our work addresses the abstract target of managing a complex sub-produc-tion process, the tool cycle, as a demonstrator. We aim to keep our demonstrationprocess as realistic as possible, since we do not want to build an own prototypeof demonstrator, which is too far from practice. Instead, we aim to build for thetool cycle a general semantics, which will serve as a foundation for our multiagent system (MAS). We will make use of already established techniques and

16 Solmaz Mansour Fallah



technologies (OPC UA, object-oriented modeling, the java agent developmentframework – JADE) in order to provide an abstract solution of the process it-self. We have chosen a subsection of the tool management (TM) process, the toolcycle, as demonstrator. Tool management is a supporting division of a produc-tion enterprise with a high amount of boundary points with other processes ondifferent enterprise layers (manufacturing execution systems – MES, enterpriseresource planning – ERP). The tool cycle handles required process steps beforeand after the tool use. It is a proper demonstration process, for it has severaltask areas in different layers of the production. The tool cycle manages the dataflow of tools and tool components, as well as the flow of the physical itemsthrough the production. In addition, it has interfaces with the tool managementsoftware solution, presetting and measuring machines and with machine tools.There are existing information models of tools listing several components andtheir categories like the DIN 4000. The ISO 13399 standards record cutting tooldata representation and exchange. However, those standards capture the physi-cal entities and their data exchange, but not the tool management process stepsor itself. The goal of this doctoral thesis is to build a MAS capturing the servicesof the tool management and the flow of production tools, with possible inter-faces to OPC UA or MTConnect. To reach this goal and define service-orientedfunction blocks, existing standards and models are analyzed and consolidated.

2 Problem Statement

Tool management is a cross-sectional process that goes beyond a wide range ofareas of a manufacturing company (see Fig. 1). The range of tool managementtasks covers the scope of ERP, MES, and machine control. Current proprietarysoftware do exist. Those software solutions rely on a variety of interfaces withother programs, like computer aided manufacturing (CAM) programs, ERP andproduct data management (PDM) programs. In order to use all functions of toolmanagement, those interfaces need to be maintained with a high effort. Besidesthe interface issue and the fact, that those solutions are proprietary, the controlarchitecture of such tool management systems is hierarchical and rigid.

3 Approach

We aim to implement a service-oriented control architecture by using MAS. Wehave chosen all functions of tool cycle, a subsection of tool management, as ademonstration process. Tool cycle includes tool disposition, tool supply and tooluse. As Fig. 1 illustrates, functions of tool cycle span planning periods betweenmid- and short-term. Its tasks handle mid-size amounts of data with a hightopicality. The tool cycle manages operations of virtual and physical entities,outside and machine-internal. Due to the listed characteristics of the tool cycle,it is a decent fit for a demonstration process. The goal of this doctoral thesis is tobuild an MAS capturing and managing the functions of the tool cycle, through

A Multi Agent based Tool Cycle Management 17

Fig. 1. Schema Tool Management, [3] (Digital Revision and Translation: Solmaz Man-sour Fallah 2017)

the production process. To reach this goal existing standards and models will beanalyzed and consolidated, in order to define service-oriented function blocks.

In the first step, the necessary processes of a tool cycle are recorded. Thequestion is, “What tasks must be fulfilled for a functioning tool cycle?” Theseindividual tasks must be described in their purpose, content and scope. In doingso, an exact definition of functions in the tool cycle is considered and builds uponour semantics. In the course of the content delineation, it is examined whether therespective function can stand alone. As a self-contained function, it must be ableto offer its necessary functionalities. This criterion is used to determine whetherit is a partial of another function or an individual function. This difference canbe made after a closer look at the necessary function inputs and outputs. If afunction f2 needs the output of a preceding function, and if it returns its outputto this function f1, then it will most likely be a sub-function of f1. Recordingthe necessary function input and its format and quality is therefore important.Solitary functions are elementary building blocks, which means they are self-contained in their functionality. Such elements, called services, are fundamentalbuilding blocks of the service-oriented architecture (SOA) paradigm.

The principle of an SOA is to put the services of a process in the center,instead of the process itself or its stakeholder. To realize this paradigm, pro-cesses are broken down into elementary components, which can be reassembledaccording to their functionality. The concept of breaking entities into encapsu-lated units, as described before, is also reflected in the conceptual idea of CPS.We aim to implement a service-oriented control architecture of a tool cycle as a

18 Solmaz Mansour Fallah

MAS. An agent as a single entity will offer its functionality as service to otherentities and inquire their services.

Baker [4] defines an agent as a self-directed software object. We define anagent as an autonomous, self-deciding software object, which can, but does nothave to be bound to a physical layer (CPS). It shall be an object with its ownvalue system, capable of communicating with other agents, and acting continu-ously upon its own initiative [5].

In 1996, the nonprofit organization FIPA (foundation for intelligent physi-cal agents) was founded. FIPA aims to define a set of standards for an agentplatform and standards for agent communication. An agent platform is a systemwithin agents could execute and interact with each other. The most known FIPAstandard is the FIPA-ACL (FIPA agent communication language) [6].

We do not aim to expand the FIPA standards, or other agent technology.Our focus is on the process design and implementation of an agent-based toolcycle, so we use JADE as an agent framework. JADE is an agent frameworkimplemented in Java. JADE facilitates an agent platform under the FIPA stan-dard [7]. It provides an implementation of the FIPA-ACL. Jade was developedby Telecom Italia and is an open-source software, with an active community,involving universities and companies. As intended in the FIPA-ACL, JADE alsosupports XML-based syntax and offers an XML codec as add-on.

References

1. Kagermann, H., Helbig, J., Hellinger, A., Wahlster, W.: Recommendations for im-plementing the strategic initiative INDUSTRIE 4.0: Securing the future of Ger-man manufacturing industry; final report of the Industrie 4.0 Working Group.Forschungsunion (2013)

2. Hofmann, J.: Die digitale Fabrik: Auf dem Weg zur digitalen Produktion Industrie4.0. Beuth Verlag (2016)

3. Geib, T.: Geschaftsprozeßorientiertes Werkzeugmanagement. Springer-Verlag(2013)

4. Baker, A.D.: A survey of factory control algorithms that can be implemented in amulti-agent heterarchy: dispatching, scheduling, and pull. Journal of ManufacturingSystems 17(4) (1998) 297–320

5. Csaji, B.C., Monostori, L., Kadar, B.: Reinforcement learning in a distributedmarket-based production control system. Advanced Engineering Informatics 20(3)(2006) 279–288

6. Poslad, S.: Specifying protocols for multi-agent systems interaction. ACM Trans-actions on Autonomous and Adaptive Systems (TAAS) 2(4) (2007) 15

7. Bellifemine, F.L., Caire, G., Greenwood, D.: Developing multi-agent systems withJADE. Volume 7. John Wiley & Sons (2007)

A Multi Agent based Tool Cycle Management 19

Communication at High Data Rates in HarshProduction Environments

Dinka Milovancev

Supervisor: Prof. Dr. Horst Zimmermann,E354 – Institute of Electrodynamics, Microwave and Circuit Engineering

1 Introduction

Optical wireless communication (OWC) is a new paradigm in wireless commu-nications. In the upcoming industrial era known as Cyber-Physical ProductionsSystems (CPPS) a highly networked environment is necessary. In some sensi-tive industrial areas (airplanes, hospitals, petrochemical plants, mines etc.) theusability of radio frequency (RF) communication can be limited. OWC can beused in these areas as it is a safe technology which offers high-speed, immunity toelectromagnetic interference, real-time capability, security and unlicensed band-width [1].

Limited receiver sensitivity represents a major challenge for reaching Gbit/sspeed and good coverage [2]. In order to overcome these challenges, avalanchephotodiode (APD) receivers are proposed as a solution due to their superiorsensitivity compared to PIN receivers. The main focus of this work is testing anddeveloping fully integrated APD receivers. An integrated solution offers manybenefits in terms of reliability, chip size and production costs at the expense thata trade-off between the suitable technology for optical and electrical parts mustbe made.

2 High Speed OWC System

For the system setup, a commercially available transmitter (680 nm VCSEL anda laser driver from Maxim Integrated) was used which has a highly collimatedbeam (0.53 mrad HWHM). The beam steering was done via a MEMS mirror.At receiver side, APD receivers were employed developed in a standard 0.35 µmBiCMOS technology.

The steering of the beam in the experiments was done manually since fullyautomated tracking version of an OWC system would be out of the scope ofthis research. A highly directed beam together with steering capabilities wouldallow multiple optical links with high data rate to be used in production halls,see Fig. 1. If one of the beams is blocked, the other available station could beredirected toward the desired receiver.

20 Dinka Milovancev



111000011110101010101010101000001110101100111010110101110110011101000

111000011110101010101010101000001110101100111010110101110110011101000

MEMS

LASER

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APD

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production line production lineCommunication unit

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Ethernet Ethernet

Ethernet Ethernet111000011110101010101010101000001110101100111010110101110110011101000

111000011110101010101010101000001110101100111010110101110110011101000

Fig. 1: Optical Wireless Communication in a Production Hall

Outputbuffer

OPamp

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DummyTIA

LimitingAmplifiers

Vsub

n++p–well n–well p–well

p-- epitaxial layer

p–substrate

Anode CathodeAPD diameter

Oxidep++

n–well

Fig. 2: Structure of the Avalanche Photodiode and Receiver Block Diagram

2.1 Integrated APD Receivers

Based on the existing APD receivers in 0.35 µm BiCMOS technology [3, 4], firstcommunication experiments were conducted in order to gain insight about themain design parameters and limitations. The structure of the APD had separatemultiplication zone (n++/p-well) and absorption zone (p−− epi), see Fig. 2.Special guard rings were implemented to protect electrical circuits from highoperating voltages of the APD. The most critical part of the receiver is thetransimpedance amplifier (TIA) designed for low noise. A replica of the TIA(dummy) was made which together with an operational amplifier and RC lowpass filters works as an offset compensation and gives a differential input for thelimiting amplifiers.

2.2 High Speed OWC System

The APD receiver with 200 µm diameter avalanche photodiode (200APD) [3]reached sensitivities of -32.2 dBm at 2 Gbit/s (-35.5 dBm at 2 Gbit/s) and thereceiver with 400 µm diameter APD (400APD) reached -30.6 dBm at 2 Gbit/s

Communication at High Data Rates in Harsh Production Environments 21

Vout

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Vcont3

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modulated p–well n–well n–well

p++

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p-- epitaxial layer

p–substrate

Anode CatodeAPD diameterOxide

Fig. 3: Modulation Doped APD and Cascoded TIA Schematic

(-34.6 dBm at 1 Gbit/s) [4] for BER=10−9. The receivers had issues with achiev-ing the full bandwidth and optimum multiplication at the same time due to thespecific electric field distribution.

Based on the gained knowledge, new receivers were designed in the sametechnology. In order to achieve full bandwidth and optimum multiplication atthe same time, the layout of the APD was modified by perforating the p-wellso that its average doping level is reduced (modulation doping), see Fig. 3.The drawback was a higher APD biasing voltage (50-57 V) compared to non-modulation doped receivers ( 30 V). The diameters of the APDs were sized up to600 µm and 800 µm to decrease the irradiance (W/µm2) needed for proper work.The receiver design was customized for large photodiode areas (large junctioncapacitances) by using a cascoded TIA topology, see Fig. 3. The bandwidth ofthe receiver depended on the controlling voltages (Vcont) which could adjust thefeedback capacitance and resistance. The 600 µm diameter APD receiver hadreached a sensitivity of -31.8 dBm at 1 Gbit/s and -29.7 dBm at 2 Gbit/s [5],the maximum measured bandwidth was 850 MHz. The 800 µm diameter receiverreached a sensitivity of -32 dBm at 1 Gbit/s and -28.8 dBm at 2 Gbit/s, themaximum bandwidth achievable bandwidth was 900 MHz.

2.3 OWC Experiments

Optical communication experiments were done inside our faculty facilities undernormal ambient lightning (500 lux).

The first experiments were done with the existing 200APD and 400APDreceivers. The maximum error free (BER<10−9) transmission distance was 11m at 1 Gbit/s, and 6.5 m at 2 Gbit/s for a 200APD receiver [6, 7], see Fig. 4a. The400APD receiver reached 20m at 1 Gbit/s and 12 m at 2 Gbit/s [7, 8], compared

22 Dinka Milovancev

1.E-10

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Fig. 4: Result Plots

to an integrated 200APD receiver produced in HV CMOS [9] the increase of atransmission distance is 2.8 at 1 Gbit/s.

The absence of any kind of optics increased the receiving angle of receiverto 20±2◦, which is an improvement compared to a 9◦ obtained with a PINreceiver [10] which needed a lens at the receiver side. The background lightimmunity was tested using an adjustable cold light source from Euromex.

Fig. 4c shows that the 200APD receiver can operate up to 6000 lux at adistance of 6 m at (BER=10−10), the 400APD could work up to 2000 lux at thedistance of 11 m (BER=10−10) 2 Gbit/s [7, 8], see Fig. 4b.

The second set of OWC experiments is done with the modulation doped largearea APD receivers. Fig. 4c shows the measured Q-factor versus transmittingdistance at 2 Gbit/s for a 600 µm APD receiver, an error free transmission couldbe achieved up to 14 m where the Q-factor is 6 which relates to a theoretical biterror rate of 1 ∗ 10−9.

Fig. 4d shows the measured values of Q-factor at 2 Gbit/s for 800 µm di-ameter APD receiver in dependence on the relative distance to a transmitterside. Here the maximum error free distance (Q=6) increased to 16 m. Furtherexperiments regarding different data rates, receiving angle and background lightimmunity are yet to be done.

Communication at High Data Rates in Harsh Production Environments 23

3 Conclusion

The performances of the APDs, of the APD receivers, transmitter and opticalwireless link have been verified experimentally. The designed receivers couldsupport data rates up to 2 Gbit/s even with high photodiode diameters. Thecombination of high transconductance of bipolar transistors and high gain ofAPD resulted in highly sensitive receivers which led to a considerable increase ofthe transmission distances. These data rates and transmission ranges could besuitable for future application in the industrial environment enabling real-timeprocessing in CPPS. Additionally, the receivers can sustain large amounts ofambient light. The absence of any kind of optics and opto-electrical integrationon the same wafer could make it more cost-effective for future mass productionthan hybrid receivers (wire bonding of the photodiode to receiver chip). Howeverthere is still long way from this initial research to a commercial product; newindustry platforms are needed to drive and promote optical technologies andapplications.

References

1. Ghassemlooy, Z., Popoola, W., Rajbhandari, S.: Optical Wireless Communications:System and Channel Modelling with MATLAB. 1 edn. CRC Press (2012)

2. Wolf, M., Li, J., Grobe, L., O’Brien, D., Minh, H.L., Bouchet, O.: Challengesin Gbps Wireless Optical Transmission. In: International Conference on MobileLightweight Wireless Systems (MOBILIGHT). (2010) 484–495

3. Juki, T., Steindl, B., Enne, R., Zimmermann, H.: 200 µm APD OEIC in 0.35 µmBiCMOS. Electronics Letters 52(2) (2016) 128–130

4. Juki, T., Steindl, B., Zimmermann, H.: 400 µm Diameter APD OEIC in 0.35 µmBiCMOS. IEEE Photonics Technology Letters 28(18) (Sept 2016) 2004–2007

5. Milovancev, D., Jukic, T., Steindl, B., Zimmermann, H.: Optical wireless mono-lithically integrated receiver with large-area APD and dc current rejection. In:Scientific Conf. Advances in Wireless and Optical Communications (RTUWO),Riga, Latvia, IEEE (2017) NN–NN

6. Milovancev, D., Jukic, T., Brandl, P., Steindl, B., Zimmermann, H.: OWC usinga monolithically integrated 200 µm APD OEIC in 0.35 µm BiCMOS technology.Optics Express 24(2) (2016) 918–923

7. Milovancev, D., Jukic, T., Steindl, B., Hofbauer, M., , Enne, R., Schneider-Hornstein, K., Zimmermann, H.: Optical Wireless Communication with MonolithicAvalanche Photodiode Receivers. In: 30th IEEE Photonics Conference, Orlando,USA, IEEE (2017) 25–26

8. Milovancev, D., Jukic, T., Brandl, P., Steindl, B., Zimmermann, H.: Optical wire-less communication using a fully integrated 400 µm diameter APD receiver. TheJournal of Engineering (2017) 1–6

9. Brandl, P., Juki, T., Enne, R., Schneider-Hornstein, K., Zimmermann, H.: Opti-cal Wireless APD Receiver With High Background-Light Immunity for IncreasedCommunication Distances. IEEE Journal of Solid-State Circuits 51(7) (July 2016)1663–1673

10. Brandl, P., Schidl, S., Zimmermann, H.: PIN Photodiode Optoelectronic IntegratedReceiver Used for 3-Gb/s Free-Space Optical Communication. IEEE Journal ofSelected Topics in Quantum Electronics 20(6) (Nov 2014) 391–400

24 Dinka Milovancev

Classification and Architectural Design ofCollective Intelligence System Variations

Angelika Musil

Supervisor: Prof. Dr. Stefan Biffl,E188 – Institute of Software Technology and Interactive Systems

Co-Supervisors: Assoc. Prof. Dr. Danny Weyns (KU Leuven), Dr. Marta Sabou

1 Introduction

In the last decades, Collective Intelligence Systems (CIS) as new forms of social,Web-based information systems have experienced popularity among users andsubstantially influenced our daily life and today’s businesses. This kind of socio-technical platforms accesses and harnesses the collective knowledge and work ofconnected people by providing a Web-based environment for a user community toshare, distribute and retrieve topic-specific information in an efficient way. Well-known examples of successful systems include Facebook, Twitter, Wikipedia,YouTube, and GitHub, but they represent only a fraction of the wide range ofsuch existing user contribution-driven Web platforms in our modern knowledge-driven society. Today, they are adopted in a growing number of various appli-cation domains and their usefulness has been recognized in particular by orga-nizations, since these systems provide macro- and micro-level benefits to onlinecommunities of various scales [1]. One example domain where organization-levelCIS have recently increased in relevance is Industry 4.0 and Cyber-Physical Pro-duction Systems (CPPS) where challenges arise from multi-disciplinary CPPSengineering processes which involve the collaboration of teams from multipledisciplines, such as mechanical, electrical, and software engineering. These chal-lenges can be addressed by a CIS that enhances (software) engineering methodsand tools with collective intelligence to overcome the existing aggregation, inte-gration, coordination and communication complexities of distributed knowledgeand works in such large, multi-disciplinary projects [2]. In addition, CIS providebottom-up, self-organizational knowledge transfer as well as awareness, effec-tive discoverability and efficient management of business-critical knowledge [2],which is illustrated in Fig. 1.

2 Research Motivation

Although CIS have seen wide adoption across organizations and society, thesoftware architectural knowledge about the CIS domain that goes beyond im-plementation or technical architectures remains still a topic of research. In orderto provide the usefulness and benefits of a CIS and to improve existing work-flows, software architects need a complete understanding about (1) underlying

Classification and Architectural Design of CIS Variations 25



Aggregation of

CPPS-specific

Knowledge

of Interest Content

Distribution

& Awareness

Knowledge

Integration

Engineering

Team

Collective Intelligence System

Knowledge

Sharing

Knowledge Transfer

& Coordination

Fig. 1. Overview of a Multi-Disciplinary Engineering Scenario with a CIS as Coordi-nator and Knowledge Integrator [3]

models, mechanisms, and features all kinds of CIS have in common, and (2) ex-isting system variations and their effects to make better design decisions. So farthere is a lack of such adequate architectural knowledge which makes it difficultfor software architecture researchers and practitioners alike to predict the effectsof design decisions on a particular system’s capabilities and behavior. In addi-tion, based on a consolidated knowledge basis, methodological support is neededto provide useful assistance and guidelines for architecting CIS that are well-tailored towards specific stakeholder needs and a respective application domainand organization, such as the CPPS domain. Current approaches do not ad-dress systematic design and management of CIS-specific concerns and elementsin architecture descriptions and thus are still a challenge for software architects.

3 Research Focus

In the context of this research, existing challenges are addressed by investigatingarchitectural design variations of CIS and methodological architecting supportfocusing on identified feature variations in CIS. Thereby, this research workbuilds on the architectural basis pattern of CIS [4] which incorporates the essenceof all these systems in a minimal system description and a basic metamodel, butneeds to be differentiated to more specific ones.

4 Classification of CIS

In an investigation of a number of CIS in the field, we conducted a detailed sur-vey and review of existing variations among previously identified architecture-significant concepts, principles, characteristics and functions that all CIS havein common in order to explore different CIS subfamilies with an altered featureset. With respect to the CIS architecture basis pattern [4], we identified five

26 Angelika Musil

major pattern variants [5] along two dimensions with respect to the relationshipbetween key elements across and within two layers of the system. These majorpattern variants allow describing architecture-significant specifics of CIS sub-families, such as social networking services, wikis, or social media platforms [5].

Although the identified variants are the result of a detailed investigation ofa number of CIS in the field and increase the knowledge about CIS variations,further systematic reviews of CIS in a wider scope are essential to conclusivelyvalidate the identified architecture-significant variants and to investigate addi-tional variable factors so that more patterns can be derived leading to a largeCIS family description [5]. By increasing the design knowledge about variationdimensions in CIS, we provide the foundation for developing a systematic systemclassification model for the CIS domain which is a goal in this research. Followingthis line, we investigate the capability of self-adaptation as one further pivotalaspect of CIS in more detail. A CIS architecture realizes a perpetual feedbackloop connecting human actors with the reactive computational coordination en-vironment and consisting of two essential phases (see Fig. 2): aggregation anddissemination of knowledge and information [4]. This resulting feedback loopwith continuous adding, updating and restructuring of information enables aCIS to be adaptable and resilient [1].

Actors

Bottom-up

Content Aggregation

Feedback & Information

Dissemination

Collective Intelligence System

Knowledge

Integration

Knowledge Transfer

& Coordination

Continuous

Flow

Fig. 2. CIS Process with Content Aggregation and Feedback of Information (Adaptedfrom [1])


5 Self-Adaptation in CIS

One commonly applied approach to realize adaptation in CIS is self-organization[1]. In order to identify further potential self-adaptation solutions for CIS, wego beyond CIS and look at Cyber-Physical Systems (CPS), and consequentlyalso CPPS, which represent a well-known system family with self-adaptationcapabilities. The close relation to the physical environment, humans-in-the-loopand dealing with complex workflows and multiple heterogeneous knowledge, likein the CIS domain, imply a high level of uncertainty as a critical factor to betaken into account in CPS architecture design which is addressed by differentself-adaptation mechanisms [3]. To get a consolidated overview of existing designknowledge and best practices on self-adaptation strategies used to handle uncer-tainty challenges and concerns in CPS as well as to identify promising approachesfor CIS, we reviewed state-of-the-art in literature using a systematic mappingstudy method. The collected knowledge was synthesized and analyzed to de-rive three recurring adaptation patterns whereby each pattern serves a differentpurpose and combines different adaptation mechanisms on multiple layers [3].

6 Variability Management in CIS

In line with the goals of this research, we address a further dimension of CISvariations. In ongoing work we investigate the variability aspect of CIS in moredetail which represents a critical aspect in the complex evolution process of CIS.Planning the evolution of such a platform is a challenge for software architects,since they have to continuously deal with multiple uncertainties which affectthe system during operation. In a first step, we conducted a literature review, asurvey of existing CIS, and an interview study with companies successfully oper-ating CIS to identify if variability is a concern in CIS, what kind of variability ishandled at the moment, existing problems and challenges as well as best practiceswith regard to variability management in CIS. We identified a lack of consol-idated design knowledge about the variability solution space specific in thesesystems. Often variability is added in an ad-hoc manner as a reaction to certainmajor incidents. Furthermore, there is a lack of methods to support softwarearchitects and platform stakeholders to address this variability with reasonableeffort and systematically describe it. Based on these results, we developed a novelarchitecture viewpoint for continuous variability management according to theISO/IEC/IEEE 42010 standard [6], which provides a variability-specific view onCIS architectures. This viewpoint aims to support software architects across theCIS life-cycle in the design, planning and description of needed variability. Toevaluate the viewpoint’s applicability and usefulness, we conducted a case studywith a group of experienced master students [7]. Preliminary results show thatthe viewpoint’s models are well-structured, particularly useful and applicable,and that they cover well the scope to handle different variability problems inCIS.

28 Angelika Musil

References

1. Musil, J., Musil, A., Biffl, S.: Introduction and Challenges of Environment Archi-tectures for Collective Intelligence Systems. In Weyns, D., Michel, F., eds.: AgentEnvironments for Multi-Agent Systems IV. Volume 9068 of LNCS. Springer Inter-national Publishing (2015) 76–94

2. Musil, A., Musil, J., Biffl, S.: Towards Collective Intelligence System Architec-tures for Supporting Multi-Disciplinary Engineering of Cyber-Physical ProductionSystems. In: Proceedings of the 1st International Workshop on Cyber-PhysicalProduction Systems (CPPS), IEEE (2016) 1–4

3. Musil, A., Musil, J., Weyns, D., Bures, T., Muccini, H., Sharaf, M.: Patterns for Self-Adaptation in Cyber-Physical Systems. In Biffl, S., Luder, A., Gerhard, D., eds.:Multi-Disciplinary Engineering for Cyber-Physical Production Systems. SpringerInternational Publishing (2017) 331–368

4. Musil, J., Musil, A., Biffl, S.: SIS: An Architecture Pattern for Collective IntelligenceSystems. In: Proceedings of the 20th European Conference on Pattern Languagesof Programs (EuroPLoP ’15), ACM (2015) 20:1–20:12

5. Musil, A., Musil, J., Biffl, S.: Major Variants of the SIS Architecture Pattern forCollective Intelligence Systems. In: Proceedings of the 21st European Conferenceon Pattern Languages of Programs (EuroPLoP ’16), ACM (2016) 30:1–30:11

6. ISO/IEC/IEEE 42010: Systems and Software Engineering - Architecture Descrip-tion. (2011)

7. Musil, A., Musil, J., Weyns, D., Biffl, S.: Protocol for Case Study on: ContinuousVariability Management in Collective Intelligence Systems. Technical report (apr2017)


Cloud Manufacturing – Resource Provisioning inFog Computing

Olena Skarlat

Supervisor: Assistant Prof. Dr. Stefan Schulte,E184 – Institute of Information Systems

Cloud Manufacturing is a novel concept of networked manufacturing basedon the principles of cloud computing, business process management, and theInternet of Things (IoT) [1]. The basic idea of Cloud Manufacturing is the inte-gration of single, distributed steps of manufacturing processes as if the completemanufacturing was performed on the same shop floor [2]. Such single steps ofmanufacturing processes correspond to specific manufacturing services respon-sible for certain operations of the shop floor, pieces of equipment, or actionsof human operators [3]. Cloud Manufacturing can be summarized using threelevels of applications in the IoT [4]: (i) the interconnection between machines(machine data identification, access and control), (ii) within a manufacturing en-terprise (product-oriented data, supply chains, local services), and (iii) betweenenterprises (manufacturing networking and service management).

IoT technologies like Cyber-Physical Systems (CPS), smart objects, and sen-sor networks emit vast amounts of data in manufacturing scenarios. This data isconsumed by distributed manufacturers. The problems on the intersection of IoTand Cloud Manufacturing are caused by different levels of heterogeneity of man-ufacturing assets: multi-domain (sharing of cooperative resources), multi-level(managing manufacturing environment, e.g., design, engineering, manufactur-ing, and marketing), and multi-granularity (describing the capabilities of CPSobjects). There are no fundamental standards that combine volatile functional-ities and structure of CPS objects to ensure interoperability, and there is alsoa necessity to support manufacturing process monitoring [5]. Based on theseconsiderations, the following research challenges were formulated in Skarlat etal. [6, 3] which shaped the work over the topic: (i) shop floor interoperability, (ii)abstraction and virtualization of manufacturing assets, (iii) service compositionand resource provisioning, (iv) flexibility and elasticity, and (v) an executionenvironment for manufacturing processes.

So far, cloud computing has been named as the primary enabler of CloudManufacturing with regard to the provisioning of computational resources [1,7]. However, manufacturers tend to create private clouds to process and storedata within their own premises [8, 9]. With the advent of the recent paradigmfog computing, it is possible to go one level further using IoT devices whichare already available at the manufacturing shop floor (e.g., CPS, smart objects,sensor nodes, gateways) to execute some services from manufacturing processesinstead of using private or public cloud-based resources. To realize fog comput-ing in Cloud Manufacturing, the resources of such IoT devices available at theshop floor need to be virtualized and subsequently integrated into a distributed

30 Olena Skarlat



fog fog colony

cloudstorage

fog colony

fog cellfog cell

IoT device

IoT device

IoT device

IoT device

IoT device

IoT device

compute units

cloud–fog control middleware

fog cell fog cell

fog orchestration control node fog orchestration control node

Fig. 1. Fog Landscape Overview

infrastructure, the so-called fog landscape. Based on these virtualized resources,it is possible to deploy and execute services corresponding to manufacturingprocesses in the fog landscape.

During the course of our work, the general topic was narrowed and the fol-lowing main research questions were identified:

What are the mechanisms to provide virtualization of IoT resources? In orderto integrate the available resources of IoT devices into the fog landscape, it isnecessary to design and develop IoT resource virtualization mechanisms. Virtu-alization of IoT resources in fog computing means the creation and support ofcompute units inside available IoT resources able to execute specified services.As IoT devices are less powerful than cloud resources, the decision was madenot to make use of full-scale virtual machines, but apply light-weight containers,i.e., instances which contain instructions and execute services.

What are the methodologies and instrumentation to realize the software envi-ronment that manages the fog landscape and executes IoT services? To establishthe coordinated control over the physical and virtual IoT infrastructure, a fogcomputing framework is being implemented. This framework provides techno-logical advancements in the fields of IoT resource virtualization, management ofthe fog landscape, and of resource provisioning in the fog.

How to achieve optimal fog resource provisioning? To use available resourcesof the volatile fog landscape and perform optimization, resource provisioningmechanisms and algorithms have been designed and implemented. For that, thecontrol mechanisms in the fog computing framework have been developed to(i) analyze resource utilization within the fog landscape, (ii) create a serviceplacement plan to allocate resources for services, and (iii) perform infrastructuralchanges and monitoring in the fog landscape.

In the course of the work, we focus on the implementation of a fog comput-ing framework that provides a coordinated control over the physical and virtualcomputational infrastructure of a fog landscape called FogFrame. This frame-work is intended to become a testbed for researchers giving the opportunity toimplement and evaluate various policies and mechanisms in the real-world fog

Cloud Manufacturing 31

computing environment based on Raspberry Pi units. The framework providesmechanisms to execute services and control the fog landscape, including place-ment and migration of services and reconfiguration of the infrastructure. Theframework consists of three levels of communications: between IoT devices andthe fog landscape, within the fog landscape, and between the fog landscape andthe cloud.

To explain the architecture of a fog landscape, we use the notion of fogcolonies (Fig. 1). At the bottom of a fog colony, there are ‘thin’ IoT devices,which do not have any computational capabilities, e.g., sensors and actuators.These IoT devices are connected to ‘fat’ IoT devices, which have computationalpower and execute services in the fog colony. We call such ‘fat’ IoT devices fogcells. In each colony there is exactly one head fog cell called fog control node. Theresponsibility of the fog control node is to control and orchestrate fog cells in itscolony and to perform dynamic service placement. Such a hierarchical constructof IoT devices, i.e., sensors and actuators connected to fog cells, and fog cellsconnected to a fog control node forms a fog colony. Fog colonies are intercon-nected with each other via their corresponding fog control nodes. The commu-nication level between a fog landscape and the cloud is provided by a cloud-fogmiddleware. Every fog colony connects to the cloud-fog middleware via its fogcontrol node. Cloud-fog middleware is responsible for processing IoT applicationrequests from connected fog colonies and managing cloud resources. However, ifno cloud connection exists, fog colonies have to be able to work autonomously. Atfirst, such an architecture was simulated by the means of modeling frameworksCloudSim [10] and iFogSim [11] and extensively evaluated [12, 13]. Recently, thearchitecture and functionalities of a fog landscape has been implemented in theFogFrame framework.

Based on this concept of fog colonies, we are able to orchestrate fog cells andto provide suitable resource provisioning and service placement approaches, i.e.,a solution on how to place services on virtualized resources in a fog landscape.For this, we formalize an optimization problem that maximizes the utilization ofexisting resources in the fog and adheres to the QoS parameters of services. Tosolve the proposed optimization problem, we apply different approaches, namelythe exact optimization method and its approximation through a greedy first fitheuristic and a genetic algorithm. Also, we compare the results to a classicalapproach that neglects fog resources and runs all services in a centralized cloud.All these findings are summarized in Skarlat et al. [14].

In future work, we will continue to improve the system model for resourceprovisioning in terms of cost of fog resources and reliability and availability ofservices. The architecture can be enhanced by fault tolerance mechanisms toaccount for mobility in the fog landscape. Parallel heuristic algorithms have tobe investigated in order to find a viable substitution for the exact optimizationmethod. Another aspect of our future work is the systematic observation of a foglandscape to obtain real-world network data to evaluate the behavior of resourceprovisioning approaches.

32 Olena Skarlat

References

1. Wu, D., Greer, M.J., Rose, D.W., Schaefer, D.: Cloud Manufacturing: Strategicvision and state-of-the-art. J. Manuf. Syst. 32 (Oct. 2013) 564–579

2. Schulte, S., Hoenisch, P., Hochreiner, C., Dustdar, S., Klusch, M., Schuller, D.:Towards Process Support for Cloud Manufacturing. In: 18th IEEE Int. EnterpriseDistributed Object Computing Conf., Ulm, Germany, IEEE (2014) 142–149

3. Skarlat, O., Borkowski, M., Schulte, S.: Towards a Methodology and Instrumen-tation Toolset for Cloud Manufacturing. In: 1st Int. Workshop on Cyber-PhysicalProduction Systems, CPS Week 2016, Vienna, Austria, IEEE (April 2016) 1–4

4. Tao, F., Cheng, Y., Xu, L.D., Zhang, L., Li, B.H.: CCIoT-CMfg: Cloud Com-puting and Internet of Things-Based Cloud Manufacturing Service System. IEEETransactions on Industrial Informatics 10(2) (2014) 1435–1442

5. Wu, D., Greer, M.J., Rosen, D.W., Schaefer, D.: Cloud manufacturing: drivers,current status, and future trends. In: Proc. ASME International Manufacturing Sci-ence and Engineering Conference, Madison, Wisconsin, USA, ASME (10–14 Jun.2013)

6. Skarlat, O.: Elastic Manufacturing Process Landscapes. In: 8th ZEUS Workshop(ZEUS 2016). Volume 1562., Vienna, Austria, CEURWorkshop Proceedings (2016)45–48

7. Xu, X.: From Cloud Computing to Cloud Manufacturing. Robot. Comput. Integr.Manuf. 28(1) (Feb. 2012) 75–86

8. Kubler, S., Holmstrom, J., Framling, K., Turkama, P.: Technological Theory ofCloud Manufacturing. In: Service Orientation in Holonic and Multi-Agent Manu-facturing. Studies in Computational Intelligence (640), Springer (2016) 267–276

9. Georgakopoulos, D., Jayaraman, P.P., Fazia, M., Villari, M., Ranjan, R.: Internetof Things and Edge Cloud Computing Roadmap for Manufacturing. IEEE CloudComput. 3(4) (2016) 66–73

10. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim:a toolkit for modeling and simulation of cloud computing environments and eval-uation of resource provisioning algorithms. Softw. Pract. Exp. 41 (2011) 23–50

11. Gupta, H., Dastjerdi, A.V., Ghosh, S.K., Buyya, R.: iFogSim: A toolkit for mod-eling and simulation of resource management techniques in the Internet of Things,Edge and Fog computing Environments. Softw. Pract. Exp. 47 (2017) 1275–1296

12. Skarlat, O., Schulte, S., Borkowski, M., Leitner, P.: Resource Provisioning for IoTServices in the Fog. In: 9th IEEE Int. Conf. on Service Oriented Computing andApplications (SOCA 2016), Hong Kong, China, IEEE (2016) 32–39

13. Skarlat, O., Nardelli, M., Schulte, S., Dustdar, S.: Towards QoS-aware Fog ServicePlacement. In: 1st IEEE Int. Conf. on Fog and Edge Computing (ICFEC 2017),Madrid, Spain, IEEE (2017) 89–96

14. Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized IoTservice placement in the fog. Service Oriented Computing and Applications (2017)1–17

Cloud Manufacturing 33

Smart Attributes forCyber-Physical Production Systems

Guodong Wang

Supervisor: Prof. Dr. Radu Grosu,E182 – Institute of Computer Engineering

1 Research Motivation

Production systems are extensively equipped with sensors, actuators and con-trollers, and the automation pyramid is linked through a communication networkinto an either strictly centralized or to a strictly decentralized IT structure. Afixed set of products and services is typically offered, and their realization is care-fully planned in advance: unfortunately, too sequential, too comprehensive andtoo hardware or product-specific. An alternative method is to equip them withmonitors and to predict emergent behaviors at runtime, e.g., to predict the failuretime of machines or different components. This approach makes Cyber-PhysicalSystems (CPS) self-aware and opens up new perspectives to design smart sys-tems. For example, in the automotive scenario, the increasing failure rates ofmicrochips, due to continuously shrinking of devices, and the usage of unreliablesources of information (e.g., information sent by other vehicles) require fast er-ror detection, fault-tolerant system designs and new planning strategies. Someof these problems can be solved by knowledge-based techniques like autonomousreconfiguration and substitution of faulty components. In this work, we focus ondesigning an artificial neural network-based general solution framework in orderto achieve the partial goal of Industry 4.0, i.e., to design flexible and intelligentmanufacturing systems. In the following sections, we first briefly introduce ourresearch results, then explain how these proposed work can be applied in theremaining sections.

2 Brief Summary of the Research Results

A conceptual overview of applying deep learning techniques for Cyber-PhysicalProduction Systems (CPPS) is presented in Fig. 1. Accordingly, we developed anefficient high dimension industrial data compression platform called AECP (AnAutomated Auto-encoder Correlation-based Health Monitoring and PrognosticTool for Machine Bearings) [1]. Industrial data always contains plenty of noise.With the help of AECP, noise reductions can be easily achieved. Users just needto adopt simple mathematical models for specific problems.

Accurately modeling execution procedure of actuators is essential for Indus-try 4.0. We have developed a mixture Echo-State Network (ESN) for millingprocedures [2]. It turns out it is much more accurate than existing methods.

34 Guodong Wang



EnvironmentSmart Agent Cloud

Actuator

Sensors

Fig. 1. General Technical Framework for CPPS

“Zoom-in-zoom-out” (ZIZO) is then proposed as a novel hyper-parameters toolfor exploring the global reconfiguration of ESNs [3]. For some special industryfields, e.g., semiconductor manufacturing, there is the so-called black box prob-lem, e.g., after hundreds steps, we may get some broken products while thereis no clue which step is the root of the problem. Because the production line isnot allowed to be exposed to the outside. In order to tackle this problem, wedeveloped a novel Bayesian network-based method [4]. The study case is selectedfrom the semiconductor field.

3 Details of the Research Results

3.1 An Automated Auto-encoder Correlation-based HealthMonitoring and Prognostic Tool for Machine Bearings

In almost all industries, health management of machines is notably essential.A key subsidiary of health management is condition-based monitoring (CBM)where one prognoses an abnormal status of a machine based on extracted fea-tures from a group of implemented sensors and parameters. The CBM proceduretherefore includes two steps: 1) Feature extraction during a run-to-failure exper-iment and 2) Data processing for predicting the degradation starting point andmonitoring the defect propagation during the test.

This work studies an intelligent ultimate technique for health-monitoringand prognostic of common rotary machine components, particularly bearings.During a run-to-failure experiment, rich unsupervised features from vibrationsensory data are extracted by a trained sparse auto-encoder. Then, the corre-lation of the extracted attributes of the initial samples (presumably healthy at

Smart Attributes for CPPS 35

the beginning of the test) with the succeeding samples is calculated and passedthrough a moving-average filter. The normalized output is named auto-encodercorrelation-based (AEC) rate which stands for an informative attribute of thesystem depicting its health status and precisely identifying the degradation start-ing point. We show that the AEC technique well-generalizes in several run-to-failure tests. AEC collects rich unsupervised features from the vibration datafully autonomous. We demonstrate the superiority of the AEC over many otherstate-of-the-art approaches for the health-monitoring and prognostic of machinebearings.

3.2 Mixture Echo-State Network Method for Modeling MillingProcess

Milling is the machining process of using rotary cutters to remove material froma workpiece progressing (or feeding) in a direction and an angle with respectto the axis of the tool [5]. It covers a wide variety of different operations andmachines varying from small individual parts to large, heavy-duty gang millingoperations. Due to the up-streaming property of the drilling process, a smoothtool surface always implies a high quality of production, otherwise, the productsare unacceptable. For instance, a dull tool may tear the surface and decreasethe fatigue resistance of the workpiece. A worn tool can cause more frictionwhich will increase the cutting temperatures and lead to resource waste. Toolwear monitoring and prediction has attracted a considerable amount of researchattention in the past decades, as tool wear has a great effect on the final productsas previously mentioned. Therefore, efficient solutions should be proposed tosolve this problem. There are many types of tool wear, for example, roundingwear of the cutting edge, crater wear on the rake face, friction caused flank toolwear and so forth. Several parameters for causing wear have been investigated,e.g., the cutting speed, feeding rates, or cutting depths.

To tackle this problem, we propose a new method for predicting the wearcondition of end-milling tools. First, we adopt statistic-analysis techniques toanalyze the collected data. Second, we select interesting features based on thePearson correlation coefficient. Finally, those features are applied as inputs toan ESN to predict the subsequent tool wear condition. The experimental resultsand theoretical analysis both demonstrate that the proposed method performsbetter than naive feed-forward neural networks and time-series neural networks.

3.3 ZIZO: Automatically exploring the global reconfiguration spacefor Echo-State Networks

ESNs are a distinct architecture for recurrent neural networks (RNNs). The greatadvantage of ESNs is that they offer an easy way to train RNNs. To make full useof ESNs, one needs to first identify their global (hyper-) parameters. These areinput scaling, leaking rate (for leaky ESNs), spectral radius, and the size of theESNs. The most recommended way to get their optimal (or suboptimal) values isby trial-and-error. However, in practice, this method has a very low efficiency. In

36 Guodong Wang

order to tackle this problem, we propose a novel ZIZO algorithm for generatingthe global parameters automatically. The proposed technique consists of twomajor parts. First, we generate random ranges for the parameters of ESNs.Then, based on bootstrap-sampling, we search the optimal solution within thefixed specific ranges. To evaluate the proposed method, we use two different datasets, which are collected from the literature. The obtained results demonstratethe efficiency and accuracy of ZIZO.

3.4 A Novel Bayesian Network for Fault Identification

One of the great challenges in the semiconductor manufacturing industry isfault detection in various production steps [6]. Verification of semiconductorchips consists of presilicon fault diagnostic approaches [7–9], post-silicon faultdetection phases [10–12], and defect prognostic during the fabrication process ofthe chip. Particularly, the fabrication process is comprised of many process stepson a variety of different products. To reduce yield loss during the manufacturingprocess, tool abnormalities should be detected early through process monitoring.Today, Statistical Process Control and Advanced Process Control methods areused to monitor and control processes and equipment to avoid critical deviations.However, many critical deviations on product level are only found later afterparameter control monitoring and wafer test measurements on the chip level.An automatic approach for finding correlations between critical deviations inwafer-test data at the end of the process and process-control data early in theprocess would help to identify the critical processes together with the machineparameters. The results could be used to improve the process control setup andconfigurations, leading to earlier detection of deviations in the production flow.

In this work, we propose a novel fault prognostic method based on Bayesiannetworks. The network is designed such that it can process both discrete and con-tinuous variables, to represent the correlations between critical deviations and toprocess quality control data based on a divide-and-conquer strategy. Such a net-work enables us to perform high-precision multi-step prognostics on the status ofthe fabrication process given the current state of the sensory info. Additionally,we introduce a layer-wise approach for efficient learning of the Bayesian net-work’s parameters. We evaluate the accuracy of our prognostic model on a waferfabrication dataset where our model performs precise next-step fault prognosticby using the control sensory data.

4 Conclusion

Cyber-Physical Production Systems are looking forward to gain more Self-Xproperties. The core idea is to become flexible to dynamically changing run-time environments. Deep neural networks are the fundamental of modern smarttechnologies. They provide powerful tools for automatically extracting usefulknowledge and generating deep representation for industry sensory data. In thisproject, we have developed several novel tools for practical applications. Variousexperiments demonstrated the performance of our methods.

Smart Attributes for CPPS 37

References

1. Hasani, R.M., Wang, G., Grosu, R.: An automated auto-encoder correlation-basedhealth-monitoring and prognostic method for machine bearings. arXiv preprintarXiv:1703.06272 (2017)

2. Wang, G., Grosu, R.: Milling-tool wear-condition prediction with statistic analysisand echo-state networks. In: Proceedings of S2M’16, the International Conferenceon Sustaniable Smart Manufacturing

3. Wang, G., Sassi, M.A.B., Grosu, R.: Zizo: A novel zoom-in–zoom-out search al-gorithm for the global parameters of echo-state networks. Canadian Journal ofElectrical and Computer Engineering 40(3) (2017) 210–216

4. Wang, G., Hasani, R.M., Zhu, Y., Grosu, R.: A novel bayesian network-basedfault prognostic method for semiconductor manufacturing process. In: IndustrialTechnology (ICIT), 2017 IEEE International Conference on, IEEE (2017) 1450–1454

5. Nieto, P.G., Garcia-Gonzalo, E., Lasheras, F.S., de Cos Juez, F.J.: Hybrid pso–svm-based method for forecasting of the remaining useful life for aircraft enginesand evaluation of its reliability. Reliability Engineering & System Safety 138 (2015)219–231

6. Stanisavljevic, D., Spitzer, M.: A review of related work on machine learning insemiconductor manufacturing and assembly lines. In: SAMI@ iKNOW. (2016)

7. Lowe, W.M.: Transaction based windowing methodology for pre-silicon verification(June 6 2000) US Patent 6,073,194.

8. Adir, A., Copty, S., Landa, S., Nahir, A., Shurek, G., Ziv, A., Meissner, C., Schu-mann, J.: A unified methodology for pre-silicon verification and post-silicon valida-tion. In: Design, Automation & Test in Europe Conference & Exhibition (DATE),2011, IEEE (2011) 1–6

9. Hasani, R.M., Haerle, D., Grosu, R.: Efficient modeling of complex analog inte-grated circuits using neural networks. In: Ph. D. Research in Microelectronics andElectronics (PRIME), 2016 12th Conference on, IEEE (2016) 1–4

10. Chang, K.h., Markov, I.L., Bertacco, V.: Automating post-silicon debugging andrepair. In: Computer-Aided Design, 2007. ICCAD 2007. IEEE/ACM InternationalConference on, IEEE (2007) 91–98

11. Mitra, S., Seshia, S.A., Nicolici, N.: Post-silicon validation opportunities, challengesand recent advances. In: Proceedings of the 47th Design Automation Conference,ACM (2010) 12–17

12. Lee, D., Kolan, T., Morgenshtein, A., Sokhin, V., Morad, R., Ziv, A., Bertacco, V.:Probabilistic bug-masking analysis for post-silicon tests in microprocessor verifica-tion. In: Design Automation Conference (DAC), 2016 53nd ACM/EDAC/IEEE,IEEE (2016) 1–6

38 Guodong Wang

Virtual Engineering Design of Cyber-PhysicalProduction Systems

Paul Christian Weißenbach

Supervisor: Prof. Dr. Detlef Gerhard,E307 – Institute for Engineering Design and Logistics Engineering

1 Engineering Design of CPPS

Anyone who wishes to engineer an interconnected, real-time, adaptive, decen-tralized and self-optimizing production and logistic system (Cyber-Physical Pro-duction Systems – CPPS) is immediately faced with new fields of technology shehas to consider. Additional to conventional domains (mechanical design, electri-cal/electronic engineering and software development) and their subdomains (e.g.,manufacturing technology, sensor technology, control engineering, PLC program-ming), new fields (artificial intelligence for smart functionalities, industrial In-ternet, security for highly interconnected machines, real-time massive parallelprocessing for shop floor analytics, etc.) move closer to the core of the designtask.

From the necessity to consider more domains, varying problems arise. Dif-ferent domains have different development and deployment procedures leadingto a wide range of methods, tools, and model types that can be selected andadapted. Naturally this “engineers toolbox” itself advances. The greater com-plexity of CPPS makes it harder to identify and focus on the essential parts andincrease the communication and coordination overhead. This results in slowerdevelopment speed and leading to missed opportunities, or opportunities takenby competitors.

From a virtual engineering design perspective, this leads to the main chal-lenge in creating CPPS: The integration of the domain-specific processes. Schappilists this as “efficient interdisciplinary teamwork” in his critical factors for suc-cess in product development [1]. Conway [2] also stresses the importance of moreefficient communication among designers in the technology of system manage-ment.

The stated goal of this PhD project was to work on methods and tools for Vir-tual Engineering Design of CPPS in order to improve on the previously identifiedmain challenge. The work in this project resulted in two main contributions. Ac-cording to the goal, one in the area of development methods and the other one inthe area of tool support for virtual engineering design. Additionally and besideother things, a talk on agile methods in CPS development has been given andteaching materials on IT for mechanical engineering students have been created.

This report provides an overview on which levels this main challenge has beentackled. The description is roughly structured after the two main contributionsthat have been made.

Virtual Engineering Design of CPPS 39



2 Methodical Support for Interdisciplinary EngineeringTeams in Early Design Stages

X1, X2, X3

X1, X2, X4

X1, X4, X5

X2, X5, X6

Sf. X1

Sf. X2

Sf. X3

Time saved

X2, X5, X6

Iterative concept

development

Sacrifice for X

concept development

Design Space Timeline

XnImportant aspectConcept selected for realizationInitial idea

Fig. 1. Scalability of SfX

In order to support design teams in early stages of CPPS development, amethod to map the design space has been drafted. The method falls into thecategory of creativity techniques. Its value proposition is speeding up and collec-tively understanding the design space and its trade-offs better. The underlyingassumption is that knowing/understanding the main design trade-off early onhelps to mitigate a large class of design iterations and therefore helps to saveengineering resources, e.g., time. This is illustrated in Fig. 1.

The method described in [3] seeks to be an alternative to brainstorming, butaims for better characteristics in terms of effort for learning the method (i.e.,fewer behavioral rules), scaling to multiple teams, team member solution adher-ence, and tangibility of opportunistic design choices. Considering engineering asfinding the right solution in a design space, then Sacrifice for X (SfX) is figu-ratively mapping out the area where the assumed solution lies. Mapping it oneaspect at a time, or one aspect per team.

If one thinks of Design for X (DfX) as concurrently integrating different as-pects into a design, then SfX can be considered as its counterpart where all butone aspect can be excluded. Everything, especially design constraints, can be

40 Paul Christian Weißenbach

sacrificed for one aspect X, hence the name “Sacrifice for X”. Knowledge, guide-lines and toolboxes from DfX are reusable, which makes SfX a good companionmethod for teams exercising DfX: SfX for design space explorations and DfX forsolution realization. Furthermore, the applicability, benefits and caveats of thepresented method are discussed.

A state of the art evaluation suggested that there is no lack of methodsto support engineering teams, but the methods adaption is low. Therefore, themethod was designed to be simple, scalable, easy to learn and hands on aspossible.

3 Engineering Support Directly from Manufacturers andin Real-time

The second main contribution [4] dives further into the technical realm of virtualengineering. Often, a product engineer is not an expert in all production tech-nologies available for his product design, still she needs to find an overall designwhich leverages the technology as efficient as possible. The goal this contributionworks toward, is to provide feedback on production technology in real time, onthe design the engineer is working on and within the engineers design tools (e.g.,the virtual CAD model), and directly from the production technology experts(e.g., the suppliers).

The origin of the feedback should be knowledge bases maintained by manufac-turers. Therefore, the feedback provider is from the perspective of the engineeringdesigner behind a network. These “remote design checks” introduce additionalaspects regarding latency and intellectual property protection, which are incor-porated into the proposed distributed software architecture depicted in Fig. 2.Feedback is provided directly in engineers’ authoring tools (CAD software). Thecontribution includes a proof of concept implementation. Architecture and proofof concept are evaluated and discussed on the basis of a use case based on theproduction technology of aluminum extrusion.

According to a study done by Abramovici and Herzog [5], real-time decisionsupport is the second most named requirement (after “interdisciplinarity”) onengineering methods for smart products and services. Overall 61 percent dorather not and do not agree that today’s IT tools are suited for the challengesof what they refer to as engineering 4.0 (in style of “Industry 4.0”). The studymade multiple recommendations. The ones this approach tries to follow are:

– “Greater use of feedback from production and product use for optimizingengineering processes”, by a focus on feedback from manufacturing.

– “Development of an intercompany knowledge management”, by a distributedservice-oriented knowledge based engineering system.

– The service-oriented approach facilitates the “adaptable IT architectures,stronger cooperation with external partners and downstream partners (pro-duction, sales and product use)” recommendation.

Virtual Engineering Design of CPPS 41

CAD

BROWSER

Engineer's Workstation

Company Network

INTERNALSERVICE

EXTERNALSERVICE

PUBLICREGISTRY

CADCONNECTOR

Internet

EXTERNALSERVICE

EXTERNALSERVICE

EXTERNALSERVICE

FEEDBACKINTERMEDIARY

ServiceRegistration

ServiceRegistration

FeedbackRequest

ConsolidatedFeedback

ExchangeList ofServices

EXTERNALSERVICE

Profile withFeedback Incorporated

Fig. 2. Proposed Software Architecture

Taking this approach further, the proposed architecture allows different busi-ness models for manufacturers on top of it. For instance, a manufacturer couldcharge for API calls or build a product service system, by giving the generatedfeedback for free, if the consumer of the service orders the actual parts (or tool)for the design with them. From the perspective of an API consumer it wouldbe especially beneficial to compare feedback from different suppliers in order todifferentiate them on their capabilities.

References

1. Schappi, B., Andreasen, M.M., Kirchgeorg, M., Radermacher, F.J.: Handbuch Pro-duktentwicklung. Hanser Munchen (2005)

2. Conway, M.E.: How do committees invent. Datamation 14(4) (1968) 28–313. Weißenbach, P.C.: Sacrifice for X: Design Space Mapping. Procedia CIRP 50 (2016)

324–3294. Weißenbach, P.C., Gerhard, D.: Towards real-time feedback on manufacturability

for engineering designers directly from manufacturers. DS 87-5 Proceedings of the21st International Conference on Engineering Design (ICED 17) Vol 5: Design forX, Design to X, Vancouver, Canada, 21-25.08.2017

5. Abramovici, M., Herzog, O.: Engineering im Umfeld von Industrie 4.0: Ein-schatzungen und Handlungsbedarf. Herbert Utz Verlag

42 Paul Christian Weißenbach

A Runtime Model for SysML

Sabine Wolny

Supervisor: Assistant Prof. Dr. Manuel Wimmer,E188 – Institute of Software Technology and Interactive Systems

1 Introduction

Industry 4.0 or the fourth industrial revolution is at the moment one of the mostdiscussed topics according to the support of complex industrial processes by ICTtechnologies. The goal is to achieve the realization of a Smart Factory. The designof the system, the workflow and the interaction of people and machines should beassisted by the integration and combination of Cyber-Physical Systems (CPS),Internet of Things (IoT) and Internet of Services (IoS) [1].

Following the model-driven engineering (MDE) paradigm, models should beused as a driver throughout the development process and finally leading to anautomated generation of systems [2]. In the current state-of-practice in MDE [3],models are used as an abstraction and generalization of a system to be developed.By definition, a model never describes reality in its entirety, rather it describes ascope of reality for a certain purpose in a given context. Thus, models are usedas prescriptive models for creating and designing systems. It has to be empha-sized that engineers typically have the desirable behavior in mind when modelinga system, since they are not aware of some deviations that may take place atruntime. Thus, for later phases in the system’s lifecycle, additional model typesare needed to better understand how the system is actually realized and howit is operating in a certain environment. Such models are descriptive models.Also in context of Industry 4.0 such type of models are mentioned: planningand explanation models [4]. Planning models are the basis for creating complexsystems like prescriptive models, while explanatory models (descriptive mod-els) allow analysis of complex systems at runtime [4]. To design such modelsa modeling language is needed. A promising approach is provided by the Sys-tems Modeling Language (SysML) [5]. SysML is an OMG1 modeling standard tosupport designing, analyzing and verifying complex systems, which may includesoftware and hardware components. SysML reuses parts of UML, additionallyoffers new language elements [6], and allows modeling a wide variety of systemsfrom different perspectives as the behavioral, structural or requirement view. Byconducting a systematic mapping study of 537 paper about SysML in the yearsfrom 2005 until 2016, we found out that SysML is often used during the de-sign phase but less in the implementation phase. In addition, there is rarely anyapproach where the runtime information is used again for the design models.Certainly, prescriptive models and descriptive models should not stand alone,1 http://www.omg.org/

A Runtime Model for SysML 43



Log Models

Realization Layer

Automation Layer

Model Layer

Metamodel LayerSysML

«conformsTo»

SysML Model

Code Generator

Logging Language

Log Models

«conformsTo»

Design Time Runtime

«refersTo»

Execution Platform

Code

«validates, extends»

Legend:

«dependency type»

Input/Output

Log ModelsLog ModelsLog ModelsModel Profiles

Fig. 1. Unifying Conceptual Framework

but used together (i) to improve the quality of design models through runtimeinformation by incorporating knowledge from the system’s operation, (ii) to dealwith the evolution of these models, and (iii) to better anticipate the unforeseen.

2 Approach

SysML defines appropriate models (i) to represent communication processes andinteractions between components over time, (ii) to define rules for the control ofthe system, and (iii) to identify interaction patterns therefrom. Our approach isbased on the unifying conceptual framework for execution-based model profilingdescribed in [7]. Fig. 1 gives an overview of the used framework.

On the left hand side, the perspective at design time is shown. SysML is usedas modeling language to model a specific domain, e.g., a production system onthe Model Layer. This model describes the structure and behavior of a system.Based on model transformation techniques [3], code is generated by the codegenerator on the Automation Layer. This code is executable and can be deployedon an execution platform (see Fig. 1, Realization Layer).

On the right hand side, the perspective at runtime is shown. On the Meta-model layer, there is a logging language which refers to SysML. This logginglanguage captures the operational semantics of SysML, i.e., everything that ischanging in the running system, e.g., variable values, actual states of systemcomponents or sequences of the called operations. In order to observe changes,these elements should be provided with the «observe» stereotype (see Section 3,Fig. 3). Thereby the code generator produces a line of logging code (i.e., changes

44 Sabine Wolny

stm StateMachine

StateMachine

Init

entry / Init

do / ReadSensors

PiCar

Straight

entry / Steer

SteerStraight()

Drive

entry / Forward

DriveForward()

Avoid

entry / Drive

DriveForward()

entry / SteerLeft

SteerLeft()

Stop

entry / Stop

Stop()

Reverse

entry / Back

DriveBackward()

entry / ResetReed

ClearTraveledDistance()

Steer Opposit

entry / Right

SteerRight()

wait5Sec[self.motor.traveled

Distance >8]

wait2Sec /ClearTraveledDistance()

[self.motor.traveledDistance >8] /ClearTraveledDistance()

[self.distance.

CheckFront(self.minDist)][not self.distance.

CheckFront(self.minDist)]

[self.motor.traveledDist

ance >8]

wait5Sec

Fig. 2. SysML State Machine of the PiCar

are observable). At runtime, the code is executed and the changes are loggedand stored in form of so-called log models (see Fig. 1). The log models conformto the logging language and can be transformed into model profiles for furtheranalysis in diverse tools such as process mining tools, runtime verification toolsand monitoring tools. Model profiling is a continuous process to improve modelsat design time through runtime information.

In our approach, SysML sequence diagrams are used as model profiles togive an insight about chronological component communication and possible de-sign patterns hidden in the system behavior. This is especially relevant for thosemodel-driven engineered systems which do not explicitly have interaction dia-grams as part of the design model. For observing actual interactions of the systemcomponents at runtime, each log model is transformed into a sequence diagram.This makes it possible (i) to analyze runtime models in relation to design models,(ii) to reason about the precision of design models, and (iii) to identify hiddenactivities or unexpected changes. In our mapping study, this reasoning was alsoidentified as an open issue.

3 Case Study

For demonstration purposes, we use a mobile robot. In particular, the robot is aRaspberry Pi-based car, the so-called PiCar, which is developed by our projectpartner LieberLieber Software GmbH in the Christian Doppler Laboratory onModel-Integrated Smart Production. Its software is a fully model-driven engi-neered system. The system is enhanced with execution-based model profilingcapabilities, performing live experiments, collecting runtime information in formof log models and transforming them into sequence diagrams.


SysML Design Language

StateMachine

State

«observe»

currentState0…1

incoming0…*

outgoing0…*

Logging Language

Log

observationStart: StringobservationEnd: String

LogEntry

id: StringtimeStamp: String

0…*

AttributeValueChange

ProcessInstanceid: StringstartTime: StringendTime: String

0…*

relatedTo 1...1

Transitionname: Stringguard : String

name : Stringstart : Booleanend : Boolean

0…*

«observe»

currentTransition 0…1

firedTransition 1…1 changed

CurrentState1…1

predecessor1…1

successor1…1

currentValue: String

Eventtype : String value : String

Operation

name: String type : StringParams: String [0..*] expression : String

0…*

calledO

peratio

n1

..1

calls 0...*

triggeredBy0...*

OperationCall

«observe»calls 0…*

TransitionFiring

CurrentStateChange

Fig. 3. Logging Language for the SysML State Machine of the PiCar

The PiCar consists of several components, namely four distance sensors, amotor control and a servo control. Two distance sensors are placed on the frontside of the car and two distance sensors are placed on the rear side of the car.These sensors indicate any obstacles in the surrounding of the car by increasingvoltage at the related sensor. The motor control is used to control the drivingdirections forward and backward, the speed and the traveled distance of the car.To control the steering direction, the servo control is used.

The designed SysML state machine in Fig. 2 formally describes the PiCarcomponents and their implemented methods and transitions. In detail, the modelspecifies the behavior of the PiCar. At initialization of the car, all distancesensors are initialized and read continuously. The movement starts with steeringstraight and driving forward until any obstacle appear in front of the car. Ifthere is an obstacle in the rear of the car, it stops. Otherwise it starts thereversing process by driving backward until a given time (5s) or distance of8 reed units is passed. Afterwards the car steers to the right while continuingdriving backward. Again, after a given time (2s) or a distance (8 reed units)is passed, the steering is set to left and the driving direction to forward. Ifany obstacle appears while driving forward, the reversing process is restarted.Otherwise the car steers straight after 5s or a distance of 8 reed units is passed,which transitions to the first state after initialization.

As described in the section before, the logging language is based on the op-erational semantics of SysML. Fig. 3 shows all values that can be changed atruntime: AttributeValueChange, OperationCall, TransitionFiring, CurrentState-Change. The logging language itself is structured as follows. The Log represents alogging session of a running software system with a registered observationStart

46 Sabine Wolny

DistanceSensorCar MotorControl ServoControl

InitSensors

InitializeMotor

InitializeServo

SteerTo, direction=7

SteerTo, direction=7

InitializeServo

InitializeMotor

InitReader

SteerStraight

SteerStraight

InitAll, None

Fig. 4. Starting fragment of the generated sequence diagram

and an observationEnd. It consists of process instances related to the SysMLstate machine. Every ProcessInstance has a unique id, startTime, and endTimeattributes and consists of log entries with id and timeStamp for ordering purpose.The LogEntry could be either an AttributeValueChange, a CurrentStateChange,a TransitionFiring or a OperationCall. In our approach, the registered oper-ation calls at runtime form the crucial input for the sequence diagram generation.

From a technical realization perspective, the SysML state machine of themobile robot is used to generate executable Python code by a code generator.The operations calls of the generated code are additionally provided with logginginformation. The data is saved in a CSV-formatted log file. For this purpose, thestructure of the logging calls is defined by tagged values and operation calls aremarked with the defined stereotype «observe». For the log transformation intoa SysML sequence diagram we use a special tool, the UML Miner Tool [8]. Thistool requires the following data for each logging line:

– Case ID : number of the process instance, which the current operation callbelongs to

– Timestamp: time of the operation call– Lifeline: object (component), which operation was called– Operation: name of the operation called


– Message parameters: operation parameters or return values– Message type: ‘REQ’ for request message, i.e., operation start, ‘RES’ for

response message, i.e., operation end

The generated logs are loaded into the UML Miner tool, which transformsthem into a SysML sequence diagram in XMI format. The XMI file is importedinto the Enterprise Architect modeling tool2 and visualized as a SysML sequencediagram (see an extract in Fig. 4).

In our case study, the PiCar state machine is the prescriptive model andthe generated sequence diagram is the descriptive model. Thus, it is possibleto manually draw conclusions for design models out of runtime models. We arecurrently working on the automatic augmentation of runtime information toimprove design models.

References

1. Hermann, M., Pentek, T., Otto, B.: Design Principles for Industrie 4.0 Scenarios:A Literature Review. Working Paper, TU Dortmund (01/2015) (2015)

2. de Lara, J., Guerra, E., Cuadrado, J.S.: Model-driven engineering with domain-specific meta-modelling languages. Software and System Modeling 14(1) (2015)429–459

3. Brambilla, M., Cabot, J., Wimmer, M.: Model-Driven Software Engineering in Prac-tice. Synthesis Lectures on Software Engineering. Morgan & Claypool Publishers(2012)

4. Bundesverband Informationswirtschaft, Telekomunikation und neue Medien e.V.;Verband Deutscher Maschinen- und Anlagenbau e.V.; Zentralverband Elektrotech-nik und Elektronikindustrie e.V.: Umsetzungsstrategie Industrie 4.0 Ergebnisberichtder Plattform Industrie 4.0. (in german). Proceedings of the Institute for SystemProgramming (April 2015) 47–61

5. Friedenthal, S., Moore, A., Steiner, R.: A Practical Guide to SysML. Second edn.Morgan Kaufmann (2012)

6. Korff, A.: Modellierung von eingebetteten Systemen mit UML und SysML. SpringerSpektrum Akademischer Verlag (2008 (in german))

7. Mazak, A., Wimmer, M.: On Marrying Model-driven Engineering and ProcessMining: A Case Study in Execution-based Model Profiling. In Ceravolo, P., Guetl,C., Rinderle-Ma, S., eds.: Proceedings of the 6th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2016), Graz, Austria, December15-16, 2016. Volume 1757 of CEUR Workshop Proceedings., CEUR-WS.org (2016)78–88

8. Davydova, K., Shershakov, S.: Mining Hierarchical UML Sequence Diagrams fromEvent Logs of SOA systems while Balancing between Abstracted and Detailed Mod-els. Proceedings of the Institute for System Programming 28 (2016) 85–102

2 https://www.sparxsystems.de

48 Sabine Wolny

Bibliography 49

Bibliography

Until now, 46 publications have resulted from the research efforts conducted withDC-CPPS. Out of these, 27 papers were published at international conferencesand workshops, 13 in journals, 5 as book chapters, and 1 paper was invited to aconference. In the following list, all published papers are presented:

[1] L. Berardinelli, S. Biffl, A. Luder, E. Matzler, T. Mayerhofer, M. Wim-mer, and S. Wolny. Cross-disciplinary engineering with AutomationMLand SysML. Automatisierungstechnik, 64(4):253–269, 2016.

[2] S. Erol and P. Hold. Keeping track of the physical in assembly pro-cesses. In IEEE 20th Int. Enterprise Distributed Object Computing Work-shop (EDOCW), pages 1–4, Vienna, Austria, 2016. IEEE.

[3] S. Erol, A. Jager, P. Hold, K. Ott, and W. Sihn. Tangible Industry 4.0: AScenario-Based Approach to Learning for the Future of Production. Pro-cedia CIRP, 54(Supplement C):13–18, 2016. 6th CIRP Conf. on LearningFactories.

[4] S. M. Fallah, S. Wolny, and M. Wimmer. Towards model-integrated service-oriented manufacturing execution system. In 1st Int. Workshop on Cyber-Physical Production Systems at CPS Week 2016, pages 1–5, Vienna, Aus-tria, 2016. IEEE.

[5] R. M. Hasani, G. Wang, and R. Grosu. An Automated Auto-encoderCorrelation-based Health-Monitoring and Prognostic Method for MachineBearings. arXiv preprint arXiv:1703.06272, 2017.

[6] R. M. Hasani, G. Wang, and R. Grosu. Towards Deterministic and Stochas-tic Computations with the Izhikevich Spiking-Neuron Model. In Int. Work-Conf. on Artificial Neural Networks, pages 392–402. Springer, 2017.

[7] P. Hold and W. Sihn. Towards a model to identify the need and the economicefficiency of digital assistance systems in cyber-physical assembly systems.In 1st Int. Workshop on Cyber-Physical Production Systems at CPS Week2016, pages 1–4, Vienna, Austria, 2016. IEEE.

[8] P. Hold, F. Ranz, and W. Sihn. Konzeption eines MTM-basierten Bew-ertungsmodells fur digitalen Assistenzbedarf in der cyber-physischen Mon-tage. In Megatrend Digitalisierung: Potenziale der Arbeits- und Betrieb-sorganisation, pages 295–322. GITO, Berlin, Germany, 2016.

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[10] P. Hold, S. Erol, G. Reisinger, and W. Sihn. Planning and Evaluationof Digital Assistance Systems. Procedia Manufacturing, 9(Supplement C):143–150, 2017. 7th Conf. on Learning Factories, CLF 2017.

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[20] S. Mansour Fallah. Multi Agent Based Control Architectures. In 26thDAAAM Int. Symposium, pages 1166–1170. DAAAM, 2016.

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[23] D. Milovancev, T. Jukic, B. Steindl, M. Hofbauer, , R. Enne, K. Schneider-Hornstein, and H. Zimmermann. Optical Wireless Communication withMonolithic Avalanche Photodiode Receivers. In 30th IEEE Photonics Conf.,pages 25–26, Orlando, USA, 2017. IEEE.

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Doctoral College Cyber-Physical Production Systems...Digital Assistance Systems in Cyber-Physical Assembly Systems 1 D. Gerhard, S. Schulte (Eds.): Final Report of the Doctoral College