Industry 4.0 Digitalisation as Day-to-Day Business

Transcription

1 Industry 4.0 Digitalisation as Day-to-Day Business What is today feasible for the company in the future? Prof. Jivka Ovtcharova INSTITUTE FOR INFORMATION MANAGEMENT IN ENGINEERING International Conference on Sustainable Manufacturing (ICSM 2016) I Shanghai I China I November 5 th, 2016 Karlsruhe Institute of Technology (KIT) The Research University in the Helmholtz Association

2 Main questions Why are we in the field of Industry 4.0? Why digitalisation everything changes? Where are potentials for the business? How it looks in the implementation? What is already possible in cooperation with us? Pictures: Lifecycle Engineering Solutions Center 2 November 5 th, 2016

3 Main questions Why are we in the field of Industry 4.0? Why digitalisation everything changes? Where are potentials for the business? How it looks in the implementation? What is already possible in cooperation with us? Pictures: Lifecycle Engineering Solutions Center 3 November 5 th, 2016

4 Profile in brief 2009 Karlsruhe Institute of Technology The Research University in the Helmholtz Association 1825 Karlsruhe University (TH) 1956 Helmholtz Research Center Focused interdisciplinary collaboration: EDUCATION RESEARCH INNOVATION Division I Biology, Chemistry and Process Engineering Division II Informatics, Economics and Society Division III Mechanical and Electrical Engineering Division IV Natural and Built Environment Division V Physics and Mathematics 4 November 5 th, 2016

5 Division III - optional clusters of institutes Product / Production Engineering and Logistics Automation Applied Materials Mechanics & Fluid Machinery Nano and Micro Systems Power Plant Technology Information & Communication Electrical Energy Vehicle Technology Digitalisation Competence Legend: assignment of institutes Mechanical Engineering University Electrical Engineering Helmholtz Association Mixed 5 November 5 th, 2016

6 Triad of digitalisation under one roof Institute for Information Management in Engineering at KIT Lifecycle Engineering Solutions Center at KIT Research Division Intelligent Systems and Production Engineering at FZI Digital Transformation Research (lat. assistants transformare) is the 20 process of a holistic Promotions (1st Advisor) 28 change of information models, business processes as well as humanmachine-interaction Student using assistants advanced digital technologies 30 and External / Industry Ph.D. students 12 services. EDUCATION RESEARCH INNOVATION 6 November 5 th, 2016

7 Digital transformation 360 (Big) Data Analytics Engineering Data Modeling and Simulation KNOWLEDGE Human- Maschine Interaction Virtual & Experimental Validation EDUCATION VALUE (Engineering) Data Acquisition LIFECYCLE Knowledge Screening RESEARCH INNOVATION Feedback Management Practical Implementation 7 November 5 th, 2016

8 Main principal (Big) Data Analytics Engineering Data Modeling and Simulation KNOWLEDGE Human- Maschine Interaction Virtual & Experimental Validation VALUE (Engineering) Data Acquisition LIFECYCLE Knowledge Screening Feedback Management Practical Implementation Digital transformation view of the whole, at any moment! 8 November 5 th, 2016

9 Lifecycle Engineering Fields of competence: Strategic Portfolio Planning Product Lifecycle Information Management Model-based Systems Engineering Computer-Aided Design and Simulation Real-time Tooling and Execution Business Process Management Complete data acquisition from multiple unstructured data sources and end-to-end process simulation 9 November 5 th, 2016

10 Knowledge Management Fields of competence: Recording of Various Machine Parameters Control for Balanced Load Distribution Fast Knowledge Extraction Reduced Energy Load Peaks Self Learning Big Data Analysis Historical Data Processing for Forecasting Energy Consumption Measurement Extensive (big) data analytics - from data collection to knowledge extraction, distribution and quality/consistency evaluation 10 November 5 th, 2016

11 Smart Immersive Environments Fields of competence: Virtual Reality Engine PolyVR PolyVR New Interaction Methods for VR/AR Algorithms for Dynamic Virtual Scenes Optimization of Human-Machine-Interaction Multi Dimensional Service Prototyping Extensive 3D-Scanning and Reconstruction Dynamic and scalable SW/HW solution for VR- and 3D- Scannig incl. tracking systems, haptic and mobile devices 11 November 5 th, 2016

12 Lifecycle Engineering Solutions Center: Infrastructure Enabling IT-Infrastructure Content Creation Lab Training and Development with CAx/PLM Virtual Environments Lab Stereoscopic 7-channel VR projection system, ART tracking system Communication Lab Shared space for lectures, project meetings and workshops Value Creation Lab Demonstration and transfer of research results Energy Experience Lab Mixed reality platform solutions for energy efficiency Lifecycle Engineering Solutions Center Cooperation Lab Spatial and technical cooperation platform Tea Lab Creativity pool Mixed Reality Lab Mobile projection, ART tracking system and haptic devices 12 November 5 th, 2016

13 Industry 4.0 Collaboration Lab Target Group: Small- and medium-sized companies in Automation, Manufacturing and Services Goal: Strengthening of competitiveness through extensive digitalisation, networking and realtime-enabled solutions for day-to-day business Solution: Investigation and qualification platform Solving business problems individually and pragmatically, through action 13 November 5 th, 2016 Opening Ceremony September 24, 2014 Hannover Messe April 14, 2015 Source: Industry 4.0 Collaboration Lab

14 Main questions Why are we in the field of Industry 4.0? Why digitalisation everything changes? Where are potentials for the business? How it looks in the implementation? What is already possible in cooperation with us? Pictures: Lifecycle Engineering Solutions Center 14 November 5 th, 2016

15 Degree of Innovation The challenge Industry 4.0 Industry 1.0 Mass production Industry 2.0 Mass distribution Industry 3.0 World economy Industry 4.0 Smart economy Factory? Mechanisation hydro- and steam power Electrification electrical power and vehicle mobility Automation computer and information technology End of the 18th century Beginning of the 20th century Beginning of the 70's Today Cyberisation smart devices and humans, real time interconnections Time Source: Industry 4.0 Collaboration Lab 15 November 5 th, 2016

16 Human-centred technologies Virtual, augmented and mixed reality Largely accessible high-speed network technologies Entertainment and gaming industry, 3D movies and TVs, Microsoft HoloLens, Oculus Rift, Samsung Gear VR headsets Novel input devices Kinect, Leap Motion, wearables 3D scanning and printing software, services Source: Lifecycle Engineering Solutions Center The next platform where anyone can experience anything they want. Zuckerberg I Source: IndustryWeek, 25 February November 5 th, 2016

17 Human-centred engineering methods Team workplace Decision-oriented Real time processing Space motion interaction 17 November 5 th, 2016 Pictures: Lifecycle Engineering Solutions Center

18 Industry 4.0 value creation loop HUMAN EXPERIENCE (Front-Side) Realistic (logical, intuitive) human perception and human-machinecommunication in real time and in space DATA AND TECHNOLOGY (Back-Side) Sensors, Network, Factory Infrastructure, Common Data Models and Algorithms Video: Microsoft HoloLens 18 November 5 th, 2016 Source: Deloitte analysis

19 Main questions Why are we in the field of Industry 4.0? Why digitalisation everything changes? Where are potentials for the business? How it looks in the implementation? What is already possible in cooperation with us? Pictures: Lifecycle Engineering Solutions Center 19 November 5 th, 2016

20 Fundamental change of production and work Standardisation is a main challenge Source: Accenture 20 November 5 th, 2016

21 Reference Architecture Model (RAMI 4.0) Added Value Lifecycle Phases Network of Functions Consolidation of guidelines, norms and standards Source: Plattform Industrie 4.0, RAMI 4.0 VDI, VDE and ZVEI 21 November 5 th, 2016

22 Reference Architecture Model (RAMI 4.0) Added Value Lifecycle Phases Network of Functions I4.0 component network as a first RAMI 4.0 Model Source: Plattform Industrie 4.0, RAMI 4.0 VDI, VDE and ZVEI 22 November 5 th, 2016

23 Reference Architecture Model (RAMI 4.0) Added Value Lifecycle Phases Network of Functions Management shell as a container for all relevant I4.0 component data Source: Plattform Industrie 4.0, RAMI 4.0 VDI, VDE and ZVEI 23 November 5 th, 2016

24 Reference Architecture Model (RAMI 4.0) Added Value Real World Virtual Twin Lifecycle Phases Network of Functions Digital (Virtual) World Real-time handling of production and service systems throughout the life cycle Source: Plattform Industrie 4.0, RAMI 4.0 VDI, VDE and ZVEI 24 November 5 th, 2016

25 Plug & Produce data management solution Source: Industry 4.0 Collaboration Lab 25 November 5 th, 2016

26 Main digitalisation steps 1. Complete data acquisition from multiple unstructured data sources 2. Intelligent data processing for Internet of Things ((IoT) 3. Operational intelligence algorithms for multidimensional big data 4. Automatic data quality check 5. Real-time data analysis and visualisation, also on mobile devices 6. Subject-oriented (human-centered) business processes 7. Continuous qualification of all parties involved living digitalisation for the day-to-day business Source: Industry 4.0 Collaboration Lab 26 November 5 th, 2016

27 Main questions Why are we in the field of Industry 4.0? Why digitalisation everything changes? Where are potentials for the business? How it looks in the implementation? What is already possible in cooperation with us? Pictures: Lifecycle Engineering Solutions Center 27 November 5 th, 2016

28 Platform business coordination on the market airbnb Ailibaba coordination on the platform without platforms high development and management costs Trend to "winner-takes-all markets" with platforms from B2C to B2B Figures: Holger Schmidt 28 November 5 th, 2016

29 Industry 4.0 by Trumpf An example of a customer order XY (2x20 punch parts) Sum: 97min (5min being used for punching/production) Source: Andre Mahl, FA Service 29 November 5 th, 2016

30 AXOOM software & business platform concept ONE platform (cloud), MANY partner solutions Source: Andre Mahl, FA Service 30 November 5 th, 2016

31 AXOOM software & business platform concept Connecting software, machines and processes with the individual human Source: Andre Mahl, FA Service 31 November 5 th, 2016

32 Condition monitoring with AXOOM Video: TRUMPF 32 November 5 th, 2016

33 Industry 4.0 at TRUMPF: examples Source: Andre Mahl, FA Service 33 November 5 th, 2016

34 Industry 4.0 by SAP Source: SAP 34 November 5 th, 2016

35 HANA software & business platform concept Source: SAP 35 November 5 th, 2016

36 fromthings In their new business model, Kaeser doesn t sell the compressor itself but compressed air priced by air volume. The compressor itself stays with Kaeser. For that, Kaeser agrees on service level agreements (SLA) with its customers to guarantee the availability of the compressor and its compressed air (energy consumption, compressed air quality, operational status). Our predictive maintenance solution has enabled us to improve service levels for customers. to Outcomes

37 fromthings In Harley-Davidson s new manufacturing facility, every machine is a connected device, and every variable is continuously measured and analyzed. It can build 1,700 bike variations on one production line and ship a customized bike approximately every 90 seconds. Harley-Davidson now manufactures 25% percent more motorcycles with 30% fewer people, and the locked schedule to build a motorcy cle has been slashed from 21 days to 6 six hours. Reduced production lead time for customized motorbikes from 21-day cycle to 6 hours to Outcomes

38 Main questions Why are we in the field of Industry 4.0? Why digitalisation everything changes? Where are potentials for the business? How it looks in the implementation? What is already possible in cooperation with us? Pictures: Lifecycle Engineering Solutions Center 38 November 5 th, 2016

39 Business and technical process optimisation Source: Thomas Mücke 39 November 5 th, 2016

40 Example: Real-time tool management Use Case: Process optimization and networking taking account of resource flows Goal: Optimizing the design and NC processes via web interface due to technological data from the manufacturing process Solution: Real-time readout of machine data Kinematic CAM simulation with real tooling Source: Industry 4.0 Collaboration Lab 40 November 5 th, 2016

41 callback order data s work plan NC-number Bill of material Setup sheet -> PDF CAD-modells NC-programs masterdata Cutting conditions, mean time, tool-life Ordering Good receipts Stock transactions Setup sheet ->PDF NC-programs CAM-models Optimized component tool data s order state Work order Masterdata s tools, incl. graphics tool-lists masterdata s tools analytics MDE DNC tool data Cutting conditions, mean time, tool-life machine Net requirements of tools crib presetting Pre calculation Work order CAD/ NC data Information tool data Source: Industry 4.0 Collaboration Lab 41 November 5 th, 2016

42 Example: Skill-based propagation Use Case: Skill-based propagation of plug & produce"-devices for smart reconfigurable manufacturing systems Goal: Increase production system flexibility using collaboration of cyber-physical assets that offer different skills Solution: Cloud-based Asset Management System for manufacturing execution Source: Industry 4.0 Collaboration Lab 42 November 5 th, 2016

43 Example: Situation-driven simulation Use Case: Radioactive contamination in the natural gas and oil production Goal: End-to-end process simulation for decontamination of pipes of different sizes Solution: Vibration method for decontamination Virtual prototype and experimental setup Real-time simulation reducing real laboratory tests Qualification and training 43 November 5 th, 2016 Source: Industry 4.0 Collaboration Lab

44 Example: Virtual twin of a milling machine Use Case: Tool configuration and manufacturing Goal: Manual and automatic operation and configuration of tool machines via a 3D Web interface in the Internet Solution: Virtual twin of the machine and automatic or manual control with a haptic device (six degrees of freedom) in real time Source: Industry 4.0 Collaboration Lab 44 November 5 th, 2016

45 Example: Virtual twin for factory planning Use Case: Factory configuration and planning Goal: Validation of large-scale integrated production lines in real time Solution: Integration of semantics, kinematics and actuators of 3D models in AutomationML Control over interactive website directly in the virtual world Connection of mobile devices Source: Industry 4.0 Collaboration Lab 45 November 5 th, 2016

46 Example: 3D energy experience Use Case: Energy efficiency management in buildings, cities and factories Goal: Learning data analytics and management of energy experience in immersive environments Solution: Real-time coupling of virtual and real building models Web-based energy efficiency measurement in real time and in space 46 November 5 th, 2016 Source: Industry 4.0 Collaboration Lab

47 Example: Real-time 3D reconstruction Use Case: Scalable 3D acquisition and analysis of objects, tools or components Goal: Generation of exact volume models in real time performing semantic analysis Solution: 47 November 5 th, 2016 Real-time large scale reconstruction of extended and bounded 3D objects and environments using automatic feature extraction and time efficient algorithms Dynamic model update Video: Source: Siemens AG

48 The future is now, work with us! Current plug & produce solutions through real-time data at KIT: Prof. Dr. Dr.-Ing. Dr. h.c. Jivka Ovtcharova Head of Institute Karlsruhe Institute of Technology (KIT) (IMI) Zirkel 2, Bldg , Room Karlsruhe, Germany Phone: Fax: November 5 th, 2016