7TH CONFERENCE ON LEARNING FACTORIES, CLF 2017

Transcription

1 7TH CONFERENCE ON LEARNING FACTORIES, CLF 2017 The Digital Twin: Demonstrating the potential of real time data acquisition in production systems Thomas H.-J. Uhlemann Christoph Schock Christian Lehmann Stefan Freiberger Rolf Steinhilper 1 Source: Peshkova\shutterstock.com

2 Agenda Motiv ation Digital Tw in Learning Factory Conclus ion & Outlook 2

3 Agenda Motiv ation Digital Tw in Learning Factory Conclus ion & Outlook 3

4 Motivation Within a 2015 VDMA survey Only 10% of those surveyed have implemented a comprehensive acquisition of process and machine data Only a third of them applied the gained data in a production control feedback The realization of a CPPS should Enhance transparency in the production system Allow near-real time production control Demonstrators are essential for an implementation in SME Learning factories Learning scenarios 4

5 Motivation Source: Lichtblau, K et al.: Industrie 4.0-Readiness. IMPULS-Stiftung für den Maschinenbau, den Anlagenbau und die Informations-technik,Aachen, Köln,

6 State of Technology Motion Data in production via RFID Manual acquisition Value Stream Mapping common method for process analysis and improvement Material flow simulation based on manually acquired data is state of the art Source: Kalsu Erlach Wertstromdesign Der Weg zur schlanken Fabrik; Springer Verlag Berlin Heidelberg

7 Deficits Manual acquisition of motion data is widely used Conflicts with real-time availability Punctual acquisition of data Limits the potential of simulation Slow standardization of data acquisition in production systems Hinders agile and adaptable system implementations Heterogeneous machinery parks in most productions systems Add-on solution required Sources: Sashkin, Piyapong Wongkam, Trimitrius, mjaud; shutterstock.com 7

8 Agenda 1 2 Motiv ation Digital Tw in Learning Factory Production System Data Layer Inform ation & Optim ization 3 Conclus ion & Outlook 8

9 Digital Twin Learning Factory Learning environment in the learning factory Bayreuth Learning module for the Digital Twin compared with Value Stream Mapping 9

10 Agenda 1 2 Motiv ation Digital Tw in Learning Factory Production System Data Layer Inform ation & Optim ization 3 Conclus ion & Outlook 10

11 Production System Production System Data Layer Information & Optimization Acquisition data via Machine Vision (identify types of products) Sensor based tracking (Routes of employees & mobile production devices) 11

12 Production System Machine Vision Cost efficient machine vision solution using Adapted consumer market hardware Open source software Image recognition via convolutional neuronal network using Tensor Flow Google Inception v3 Production System Data Layer Information & Optimization 12

13 Production System Sensor based tracking Commercially available indoor-tracking solution Active sensors, weight 15g Anchors powered via PoE Accuracy <10 cm No Ethernet required No interference with WLAN signals Real-time visualization Data export will be realized using Near-real-time updated xml file Cloud based data transfer Production System Data Layer Information & Optimization source: kinexon-industries.com 13

14 Agenda 1 2 Motiv ation Digital Tw in Learning Factory Production System Data Layer Inform ation & Optim ization 3 Conclus ion & Outlook 14

15 Data Layer Volatile Data Movement of employees Movement of mobile production devices Material flow Moment of production Machine assignment Transport routes Production System Data Layer Information & Optimization Non-volatile Data Layout Ground Plan Products Parts list Purpose of the production machine Qualification of employees source: exopixel, Mindscape studio; on shutterstock.com 15

16 Data Layer Example Seperation of Volatile, time dependent Data Non-volatile, non-time dependent data system-inherent data security Production System Data Layer Information & Optimization M3 PG M3 PG M3 PG M3 G x30y50 G M3 W time stamp M3 Production machine code PG Product code G2 Production device x30y50 2D-coordinates W4 employee source: exopixel, Mindscape studio; on shutterstock.com 16

17 Agenda 1 2 Motiv ation Digital Tw in Learning Factory Production System Data Layer Inform ation & Optim ization 3 Conclus ion & Outlook 17

18 Information & Optimization Merging of volatile and non-volatile data to generate Digital Shadow Set of parameters for material flow simulation is extracted Production System Data Layer Information & Optimization 18

19 Information & Optimization Example M3 PG M3 PG M3 PG M3 G x30y50 G M3 W4 Production System Data Layer Information & Optimization M3 CNC milling machine PG pump housing G2 forklifter W4 John Doe, Responsible for machine setup Three pump housings machined Cycle period 20 min 10 min delay before transporting the machined pump housings Stock overfilled Setup starts with a 20 min delay 19

20 Information & Optimization Simulation Realizing the Digital Twin in Tecnomatix PlantSimulation Model capable of experimenting Near-real time updates of model parameters Production System Data Layer Information & Optimization 20

21 Agenda Motiv ation Digital Tw in Learning Factory Conclus ion & Outlook 21

22 Conclusion & Outlook Concept for the realization of the Digital Twin using Machine vision Sensor based tracking has been presented System-inherent data security through separation of Volatile data and Non-volatile data Near-real time linkage between production system and material flow simulation for Enhanced transparency Near-real time production planning and control 22

23 Conclusion & Outlook Next Steps Training of neuronal network for various products Developing interface between sensor based tracking and Digital Shadow Implementing learning concept for the learning factory in Bayreuth Validating security concept through attacks on the system by Fraunhofer Institute for Applied Information Technology 23

24 Thank you for your attention. M.Sc. Christian Lehmann Tel: Source: Brian A Jackson; shutterstock.com Dipl.-Ing. Thomas H.-J. Uhlemann thomas.uhlemann@uni-bayreuth.de Tel: