Enhanced product design for generativeprocess planning

Daniel Kretz ∗ Tim Neumann ∗∗ Joerg Militzer ∗∗∗

Tobias Teich ∗∗∗∗

∗ University of Applied Sciences Zwickau, Academic Department ofEconomics, Chair of Business Informatics, Zwickau, 08056 Germany;

e-mail: daniel.kretz@fh-zwickau.de.∗∗ e-mail: tim.neumann@fh-zwickau.de∗∗∗ e-mail: joerg.militzer@fh-zwickau.de∗∗∗∗ e-mail: tobias.teich@fh-zwickau.de

Abstract: Product design and process planning are important stages of the product lifecyclebecause they mainly determine the success or failure of a new product. Process planning, whichis linking design and production phase, causes most of the resulting production costs. Gashedapplication landscapes, heterogenic systems and consequently different inconsistent productdata models are preventing an efficient product development especially across different stages ofthe product lifecycle (Sharma and Gao (2006)). This results in increased error-proneness, higherdevelopment effort, misinterpretations and miscalculations. For generative process planning e.g.for an exact quotation generation and continuously initializing the production after procurement,we utilize a completely computer-interpretable design model for products based on machiningparts. Therefore, the design model provides semantics about the geometric information whichis necessary for reasoning required manufacturing operations for automatic process planning.Furthermore, the design model includes complete product information which can be reused andextended during later product lifecycle stages. For continuous system integration and consistentproduct data representation we are utilizing the ISO standard 10303. Based on these aspects,we present our approach for generating process plans directly from a given product descriptionby retrieving and evaluating various process plans while considering the entire manufacturingenvironment.

Keywords: CAD/CAM, Integration, Automation, Production, Planning, ISO, Geometry.

1. INTRODUCTION

Small and medium-sized enterprises (SMEs) are a substan-tial part of the economy. In fact, many SMEs are suppliersof larger companies, especially in the area of machine-building industry or automotive engineering. Customerorientation, individual customer demands and coping withintensified cost pressure are powerful factors of successbut also great challenges. Flexibility, adherence to deliverydates and cost-efficient development and production aswell as the resulting quality are crucial aspects to becomeand stay competitive on the markets.

We want to focus our attention on products based onmachining parts because an enormous plenty of individualcustomer demands is typical in this area. SMEs are forcedto forecast the feasibility, the potential date of delivery andaccruing costs as fast and exact as possible to obtain po-tential orders and execute them cost-effectively afterwards.For innovative inquiries the preparation of quotations canbe very time-consuming. It requires intensive analysis andcomplex calculations while respecting the current man-

? The authors and co-authors appreciate the support of the Euro-pean Union via the European commission and their European SocialFund (ESF).

ufacturing environment. This procedure subsequently in-cludes the risk of miscalculation and additionally can bea misinvestment, if subsequently no order is placed. Gen-erative cost estimation reduces the required effort and thepotential error rate. But this is only possible, if requiredmanufacturing parameters like processes, effort, requiredresources or accruing costs can be derived automaticallyfrom a generated process plan. For generative process plan-ning, we require enhanced product models to derive therequired manufacturing parameters directly from designmodels which are assumed as inquiries.

Within the given context, we want to discuss deficienciesof product models created with common CAD systems andstate in which way the ISO standard 10303 covers theseissues and provides a solution for more efficient product de-velopment in section 2. Furthermore, we provide an insightof certain aspects of our work like feature dependencies,generating process plans, diversity of intermediate prod-ucts, the evaluation of process plans as well as alternativeplans in section 3 and our implementation architecture insection 4.

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2. ENHANCED PRODUCT MODEL

The design model contains a concrete definition and pro-vides an illustration of the final product. Usually, theproduct is designed by utilizing CAD systems (computeraided design) like CATIA or Autodesk Inventor. There arespecialized systems for each application area of the prod-uct lifecycle like computer aided process planning (CAPP),computer aided manufacturing (CAM) or computer aidedquality assurance (CAQ) which can be summarized as CAxsystems (Vajna et al. (2007)).

With CAD systems, the product can be modeled andvisualized two- or three-dimensional. Both kind of mod-els are important for further processing. Two-dimensionalmodels provide a specific point of view and they are perfectfor plotting. Three dimensional models are necessary forgetting an imagination of the resulting shape or simula-tions e.g. of the construction of assemblies. The geometricvisualization is always based on geometric primitives likepoints, lines, surface and topology information.

CAD systems support the construction of complex de-sign models but these models are absolutely insufficientfor automated process planning. In fact, the design lacksinterpretable information about the modeled geometricaspects of a part. There is no limitation for a consistent de-scription of common design aspects. Furthermore, besidethe consistent and interpretable geometric design model,we require extended product parameters like dimensions,tolerances, surface finish or material properties which needto be associated with the corresponding geometric aspects.

2.1 Feature modeling

Feature modeling is an approach for enriching geometricaspects with semantics and creating design models moreefficient and to extend them with the required informationfor further processing. In a nutshell, features are objectsfor the description of all necessary aspects of a product.There are form features for geometric design descriptionof the part as well as tolerance, material, functional orassembly features for extended product information.

Ehrlenspiel et al. (2007) defined features as objects forthe description of work pieces which represent functional,geometric and technological properties. Additionally, theycan also contain application-specific data like time or costs.Because of their semantics, the resulting shape of formfeatures and in general their significant properties can beforecasted. Feature based design addresses an approach ofdirectly instantiating form features, assigning geometricproperties and relationships to create the design model ofa part. Consequently, all required features, their semantics,and significant properties need to be defined before, e.g.within a feature library. Based on this feature library, wecan instantiate all required features and link them togetherto describe a complete product model.

The advantage of this approach is quite obvious: All in-stantiated features are based on a common feature li-brary. In consequence, their general meaning or abstractshape is already known. This results in a product modelwhich contains geometric objects which can be interpreted.Moreover, the complex design of either a part or a com-plete assembly can be created more efficient, because only

significant properties of the form features need to beparameterized. The resulting geometric representation isfinally derived from these properties.

There are two approaches to create a feature model.The first approach, destruction by machining features,describes the removal of feature-defined volumes froman initial stock. This concept can be associated withdestructive solid geometry. On the other side, synthesisby design features addresses the attachment of feature-defined volumes as explained by Xu (2009).

In fact, todays market-relevant CAD systems partiallysupport the design by features but their feature library islimited to certain, not standardized design features whichdo not support the description of all the required productproperties. Those features are neither standardized norcan be reused for further processing because they are onlycompletely mapped to vendor specific file formats.

During our researches we have recognized that there aresupported standardized exchange formats but transforma-tions into these were always lossy and did not provideour intended feature representation. Furthermore, the con-struction of the design model is not limited to featurebased design which also results in uninterpretable geomet-ric constructs.

2.2 ISO 10303

The ISO standard 10303 is titled ”‘Industrial automationsystems and integration - Product data representation andexchange”’ and provides reference models for each applica-tion context within the product lifecycle. Commonly thisstandard is associated with STEP, the ”‘Standard for theexchange of product model data”’ but it should not belimited to the exchange purpose. Each of the referencemodels contains the knowledge of long-term internationalresearches within different industries and branches andprovides information for the development of product mod-els containing complete product information.

Hence, ISO 10303 consist of several parts, each of themcovering a special issue of the product lifecycle. Thoseare defined in application protocols. Every applicationprotocol was developed upon common resources whichdefine building blocks for developing the data models.This approach supports the exchange of the product datathrough the entire product lifecycle and consequentlybetween all CAx systems which are based on the STEPstandard.

2.3 Application protocol 224

For our purposes, we have identified the application pro-tocol 224 (AP 224) - ”‘Mechanical product definition forprocess planning using machining features”’ as suitable.This protocol applies the destructive feature based designapproach for the design of a part. Therefore, the designerfirst needs to specify the minimal required initial volumeas a base shape either implicit or explicit. An implicit baseshape can be a cylindrical, a block or an ngon base shape.Afterwards the designer has to describe which volumesneed to be removed to get the final part shape. Because ofthe similarity of this design approach to the manufacturing

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of a part by turning, drilling, milling or cutting processes,those features are so called machining features. In fact,the AP 224 provides only design features, whose seman-tics imply manufacturing operations for the production ofthe part ISO (2006). To give an example, a round holefeature implies a boring or milling operation and an outerdiameter feature e.g. requires a turning operation. Figure1 illustrates some selected features of a safety bold todemonstrate the destructive feature based design approachand the efficient description of complex design aspects withfeatures and their significant properties.

Table 1. Feature based design of a safety bold

Cylindrical base shape• Base shape length: 88

mm• Placement:

· Point (0,0,0)· Vector (0,0,1)

• Diameter: 25 mm

Outer diameter• Placement:

· Point (0,0,20)· Vector (0,0,1)

• Diameter: 16 mm• Feature length:

20 mm

Round hole• Placement:

· Point(-12.5,0,61.5)

· Vector (1,0,0)• Bottom condition:

Through• Diameter: 9 mm• Hole depth: 25 mm

Open slot• Placement:

· Point(-5.5,12.5,78.5)

· Vector (0,1,0)• Linear path: 25 mm• Slot end type: Open• Square U profile

· Depth: 1.2 mm· Witdh: 5 mm· Angle: 90 ◦

3. GENERATIVE PROCESS PLANNING

Process planning is connecting the design and manufactur-ing stage of the product lifecycle. This phase is supportedby CAPP systems - computer aided process planning.The resulting document of this stage is a process plan,a concrete guideline describing an operation sequence forthe manufacturing of the part. To create a process plan, werequire a complete product description as previously dis-cussed. This includes geometric information, dimensions,material properties, tolerances, surface finish as well asfunctional and non-functional requirements. Furthermore,we need information about the entire manufacturing en-vironment from an enterprise resource planning system,e.g. available machines and their abilities, vacant capaci-ties, accruing costs, set-up times and duration of severalmachining operations (Prabhakar et al. (2004)).

With our development we are providing an approach forgenerative process planning directly from a feature based

CAD drawing as discussed in section 2. Figure 1 illustratesthe previously explained relationships.

3.1 Determining process plan alternatives

First of all we need to check the feasibility of the givenpart before generating a process plan alternative. To putit simply, a process plan alternative is another sequenceof concrete manufacturing operations for producing a partamongst many other alternatives. Therefore we have toexamine each manufacturing feature within the given fea-ture model to assess, whether there is at least one resourceavailable that could apply the feature. Alternatively, aresource could be also an outsourced process.

For this reason, we need to interpret the geometric infor-mation defined by the design features. Thus, we calculatedependencies between the manufacturing features whileconsidering the resource description, which also containsdecision logic of a process planner and associate a set ofsuitable manufacturing operations. For this purpose, theresource model provides a description of possible machinetools, clamping devices and other manufacturing relatedinformation for each request. Further details about thedevelopment of the resource model are published by Teichet al. (2010) as well as Gaese and Winkler (2010).

For generating the process plans we divide the manufac-turing features into dependent and independent ones. In-dependent features are without any dependency relating toother features and hence can be applied directly. Againstthis, dependent features require others to be machinedpreviously, to become an independent one. This can hap-pen, e.g. if the associated feature volume is inaccessiblefor machining because it is still covered by a part volume,which is subtracted when applying another feature.

To give an example, we assume that four manufacturingfeatures with different dependencies are given as illus-trated in table 2. The features are named from F1 toF4 and their dependencies are visualized as a set betweensquare brackets. Independent features are emphasized andselected features are marked with X. The application ofeach feature results into a new intermediate product andchanges the set of dependencies.

Table 2. Determining process plans

Variant 1

F1[F3,F4] F1[F3,F4] F1[F3] F1[ ] XF2[ ] XF3[ ] F3[ ] F3[ ] XF4[F2] F4[ ] X

Variant 2

F1[F3,F4] F1[F4] F1[F4] F1[] XF2[ ] F2[ ] XF3[ ] XF4[F2] F4[F2] F4[] X

At the beginning, there are two independent featuresnamely F2 and F3. Feature F1 depends on F3 and F4as well as feature F4 depends on F2. In process plan 1, weinitially select feature F2 randomly. Hence the dependencyset of F4 changes and it becomes an independent feature.Afterwards the features F3 and F4 are independent, so we

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Fig. 1. Integrating CAD and CAPP

need to select again randomly. Process plan alternative 2starts with feature F3 and afterwards the sequence is de-termined by the dependencies. The resulting sequences are{F2-F4-F3-F1} and {F3-F2-F4-F1}. Lastly the sequence{F2-F3-F4-F1} is also possible.

As demonstrated by this example, there are several pointswere we need to decide randomly which feature should beapplied next. Prospectively we are going to control therandom selection with an ant colony optimization (ACO)algorithm (Dorigo and Stuetzle, 2004). Hence, we have toweight each path to the next possible feature. The weightfactor is determined by the consequences of the selectionto the environment. E.g. in variant 1 we have initiallyselected feature F2 and need to decide between F3 andF4. If feature F3 is similar and associates identical ma-chining operations like the previously selected F2 then theselection of F3 could result in a more efficient alternativee.g. with reduced set-up and manufacturing times or lowerproduction costs. This example assumes that feature F4is absolutely distinct. The weighting always depends onthe defined optimization criterion and the evaluation ofprevious alternatives.

Further examples suppose, that an operation manufac-tures exactly one complete feature. In fact, a manufac-turing operation can also affect more than one feature orparts of feature volumes at the same time. In consequence,the applicable machining operations of other features canchange. Moreover the selection of a clamping shape caninfluence the resulting sequence and shapes of intermediateproducts crucial. To get around this problem, we need aniterative generation of the process plan. Iterative means,that we also have to consider neighbored features in oneiteration and not only the current one which should beapplied. This aspect requires additional information fromthe resource model which indicates whether neighboredfeatures were influenced and whether their applicable ma-chining operations changed. This request has to be re-peated until the selected feature is completely applied andthe volume of the final part remains unaffected.

To give an example, Table 3 illustrates valid and invalid in-termediate products. The first picture visualizes a selectedfeature of the safety bold. It is an independent featurebut is quite obvious that the manufacturing of this featurewithout considering neighbors is absolutely inefficient. Ifwe utilize a turning operation from the left then we couldapply this feature. But we remove a volume that is notaffected by any other machining feature and thereforebelongs to the final part. So this has to be prohibited.Consequently the turning operation should be executedfrom the right side and, depending on the machine, shouldcomplete both features with one operation. If we selectthe smaller outer diameter first then the greater diametershould be affected in the same way.

3.2 Evaluating and optimizing process plan alternatives

The goodness of a process plan depends on different as-pects. First, there is the current state of the manufacturingenvironment, e.g. workload, availability of machines andother resources or set-up times. Second, there are externalrequirements which have to be met, like due dates, qualityof processing, or a limit of accruing costs. In fact, thoseparameters are determined after scheduling the currentprocess plan. Because we want to evaluate a selectedalternative to compare them with each other, we cannotschedule this within an ERP system. This requires a sim-ulation of the scheduling of a process plan alternative. Forexample, an ERP system like SAP provides an AdvancedPlanner and Optimizer (APO) module but this cannotsimulate the scheduling of a process plan independent froman order. For that reason we have implemented a modulewhich simulates the scheduling for a defined manufactur-ing environment.

Additionally, there are different parameters with distinctimportance that should be considered. To give an example,there could be some due dates which should be compliedat all costs. Alternatively we have defined due dates butalso limited costs, because otherwise the execution of anorder is not economic. All of the introductory mentioned

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Table 3. Variants of manufacturing a machin-ing feature

Selected feature: Valid butineffective

Invalid

Valid

Valid

parameters are important for the evaluation of a processplan. In fact, it is not possible to satisfy all requirements atany time. Finally we need to parametrize the consequencesif the target is not met. All in all we have a multi-objectiveoptimization problem which can be optimized with geneticalgorithms.

Genetic Algorithms (GAs) are a solution for optimizationand approximation. They were first introduced by JohnHolland in 1975 (Reeves and Rowe, 2003). GAs are a com-puter simulation of the natural evolutionary process. Toimplement a GA, we initialize a random population with adefined number of individuals. Each individual representsa solution candidate for the problem so the initializationof the entire population is a random exploration of thesearch space. After the initialization, each candidate isevaluated and the goodness is assigned as fitness value tothe candidate.

Relating to the natural model, the problem is encodedwithin the genotype, a problem representation as a chro-mosome, based on a sequence of primitive types like bytes,characters or numbers. Consequently, the genotype has tobe decoded into the phenotype, a real-world representationof the problem, to evaluate the candidate. Individuals of apopulation mate as partners to generate the offspring. Thepartner selection depends on the fitness value, so betterindividuals are preferred for reproduction.

Reproduction utilizes special crossover operators whichrecombine the genotype of the involved parents to gen-erate the offspring. In general, those operators combinedwith a fitness-relative partner selection emphasize bet-ter sequences after each iteration. Additionally, there aremutation operations which influence the genotype of asingle individual to avoid premature convergence of thepopulation (Vanyi (2004)).

4. IMPLEMENTATION

Based on the previous explanations we have developeda modular system architecture as illustrated in figure 2.The AP224 CAD module provides a feature based drawingas discussed in section 2. Therefore we provide a featurelibrary based on the application protocol 224 of the ISO10303. We have implemented the geometry from featureapproach which derives a geometric representation fromform features with the Open CASCADE 3D geometrykernel. Furthermore, the presentation layer which providesthe user interface for the CAD module to instantiate andparameterize product features was developed with the QTframework. Both components provide the opportunity tobe ported to different operating systems

The drawing is directly provided via the communicationlayer to the process plan generation module. This modulederives a dependency graph as previously discussed insection 3 and is still under development. The resourcemodel provides suitable manufacturing parameters andoperations for requested manufacturing features. Finally,the scheduling module utilizes a multi-chromosome geneticalgorithm to schedule and evaluate the generated processplans and alternatives. Required information about themanufacturing environment is provided by an ERP sys-tem.

To achieve a flexible and extensible communication layerwe have utilized CORBA as middleware realization. Thisresults in distinct interfaces and supports the involvementof modules in different programming languages and tobe open for different platforms. Furthermore, we have adistributed application which hides the calculation effortof the process plan generation and scheduling module fromthe user.

5. CONCLUSION

This paper provides a discussion about the potential ofthe feature technology for innovating the product develop-ment especially for machining parts and generative processplanning in our point of view. One important aspect isthe ability to retrieve existing information directly fromthe design model and reuse it in succeeding stages of theproduct lifecycle. Therefore we focused our research on theISO 10303 because its AP 224 provides a feature definitionfor the destructive FBD approach especially for machiningparts and additionally their semantics indicate applicablemachining operations. In this way we could access theexperiences and effort of international researches of severalyears and additionally support the exchange in a standard-ized manner.

We discussed our approaches for the generative processplanning and our current implementation to innovate theproduct development starting with the fundamental de-sign phase. Therefore we have emphasized that the designmodel contains required parameters for the manufacturingof a part. For the automatic generation of process plans,e.g. for generating quotations or producing the part, wehave illustrated how to derive process plan alternativeswhile considering geometric or machining dependencies.The generation of process plan sometimes requires a ran-dom selection because there is no determination by de-

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Fig. 2. Architecture of the complete system

pendencies. In this scope, the development of the ACOalgorithm is still in progress. The evaluation of processplans is realised by the simulation of operations schedulingand genetic optimization.

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Gaese, T. and Winkler, S. (2010). Development of aresource model within the scope of automatic generationof replies to customer requests. Proceedings of the 6thCIRP-Sponsored International Conference on DigitalEnterprise Technology, 971–982.

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Vanyi, R. (2004). Object oriented design and implemen-tation of a general evolutionary algorithm. Conferencefor Genetic and Evolutionary Computation - GECCO2004, 1275–1286.

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