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How IoT combined with engineering simula6ons may revolu6onize product development Ma$s Karlsson, Johan Ölvander, Marcus Oledal * Departement of Management and Engineering, Linköping University EDR & Medeso AB, Göteborg *
Ø New companies are exploiting a ”blind spot” in traditional business models
Ø These companies create customer value based on existing infrastructure
Ø Internet is the enabler It is time for product manufactures to leverage on the possibilities of IoT!
SAY BIG DATA ONE MORE TIME…
Big Data Analy6cs
System Engineering
Cloud & Collabora6on
Technology drivers
IoT
DigitalizaPon and Industry 4.0
Design principles of Industrie 4.0
• Interoperability: Connect and communicate • VirtualizaPon: Linking sensor data with simulaPon models • DecentralizaPon: Cyber-‐physical systems make decisions
on their own • Real-‐Time Capability: provide derived insights
immediately • Service OrientaPon: Offering of services • Modularity: Flexible adaptaPon by replacing or expanding
modules
Cyber-‐Physical Systems needs new design tools
6C´s of a Cyber-‐Physical System
• Connection (sensor and networks) • Cloud (computing and data on demand) • Cyber (model and memory) • Content/context (meaning and correlation) • Community (sharing and collaboration) • Customization (personalization and value)
C1: IoT layout (sensors and network)
A pracPcal example
Smart Products and System of Systems
Smart Products and System of Systems
It is sPll a tractor…
The EvoluPon of “Big Data” and Engineering SimulaPon
Evolu&on of Data Analy&cs
Service Data
Analy6cs
Simula6on
Trigger Filter
DescripPve – What happened?
PrescripPve – What should be done?
Service track
Prod
uct
developm
ent
Incremental DisrupPve
PredicPve – When will it happen?
IoT and Engineering SimulaPon
Funding: Vinnova Dnr 2015-‐02509 Innova&ve IoT toolkit for CAE simula&ons
PlaForm overview: TreLab test database Windows 8.1
.NET 4.0
ANSYS Workbench 16.2 (SimulaPon plakorm)
IronPython 2.7 (win32) Python 2.7 (win32)
pycURL 7.19 (win32)
h$ps://api.trelab.fi
IoT Toolkit (ACT IronPython)
ANSYS Mechanical (FEM Frontend)
IoT Toolkit (ACT XML)
Funding: Vinnova Dnr 2015-‐02509 Innova&ve IoT toolkit for CAE simula&ons
Economy
Comfort
Reach
Performance Load
Sport
An example: Industrial Robot
Design automaPon of industrial robots: State of the art
• Performance (CT) • Weight • Cost • Lifetime • …..
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Design variables
Objective function
Optimization
Simulation results
Figure of merit
Simulation models
• Geometry • Material thickness • Motors • Gearboxes • Control parameter • …..
Integrated modelling: Mul& disciplinary
Fixed rigid body
Even link
Odd link
Spring Connection
Dynamic modeleling (Dymola)
Geometric modeling (CATIA) Structual modeling (FEM)
OpPmizaPon results: Mul& objec&ve
Big Data based opPmizaPon
Cyber-‐physical model
Optimally customized products
Identified user requirements
IoT – Big Data input • Loads, constraints • Boundary condiations
Big Data based opPmizaPon
SL 40893ML 9206
40.26.0
W kgCT s=
=
54.56.0
W kgCT s=
=
SL 36347ML 9373
62.85.2
W kgCT s=
=
56.95.6
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Product Development methods
The road ahead…
• Filter out relevant information from data
• Develop the trigger to transform information into knowledge
• Fast turnaround of virtual prototypes
But, it´s still a tractor that needs to be realized in HW
www.liu.se
[email protected] Division of Machine Design
Division of Applied Thermodynamics and Fluid Mechanics
