INDUSTRIE 4.0: CHALLENGES AND CONCEPTS

Transcription

1 INDUSTRIE 4.0: CHALLENGES AND CONCEPTS Michael Lickefett May,

2 The Fraunhofer-Gesellschaft Research for the Market Applied research of direct utility to the economy and to the benefit of society Institutes and independent research units: 67 Annual research budget in billion : 2.3 Staff 23,000 Fraunhofer innovations: Service robot Multimedia phones 3D at home Mp3 LED Biosensors 2

3 Fraunhofer IPA as a technology consultant and innovation driver Third-largest institute of the Fraunhofer-Gesellschaft; based in Stuttgart 1,000 employees I 73.4 million euros operating budget I 22.3 million euros industrial revenues Expertise in manufacturing engineering and automation since 1959 Note: key figures for

4 Fraunhofer IPA as a partner of the industry Over 1,000 projects with industrial customers per year Goal: to improve the competitiveness of manufacturing companies with a focus on the strategic cornerstones of mass sustainability and mass personalization 4

5 Fraunhofer IPA with an international network Field offices and subsidiaries in Germany, Austria and Hungary Fraunhofer Project Center for Electroactive Polymers at AIST Kansai in Japan One third of all projects outside Germany 5

6 Technical equipment and laboratories In tune with the times Application Center Industrie 4.0 Motion laboratory BioPoLis Biomanufacturing laboratory Factory planning cockpit Electroplating laboratory Intervention room Coating technology center Production laboratory Cleanrooms & cleanliness rooms Robotics experimentation area Synthesis & reactor park 6

7 Business units and field of work An interdisciplinary organization Director Prof. Dr.-Ing. Thomas Bauernhansl 7

8 Überschrift Kapitel International comparison the race has begun 8

9 Ranking of Nations Developing countries are gaining on industrialized countries Top 15 manufacturing countries, ranked by global share of nominal gross value added of production (purchasing power parity) United States United States United States China 2. Japan Japan China United States 3. Germany China India India 4. France Germany Japan Japan 5. Italy France Germany Germany 6. United Kingdom India Russia Russia 7. China United Kingdom Brazil Indonesia 8. Brazil Italy France Brazil 9. India Brazil United Kingdom United Kingdom 10. Mexico Russia Italy France 11. Spain Mexico Indonesia Mexico 12. Canada Indonesia Mexico Italy 13. Indonesia Spain Spain Turkey 14. South Korea Canada South Korea South Korea 15. Australia South Korea Canada Saudi Arabia Quelle: International Monetary Fund, abgerufen am 30. April 2017 (englisch), Report for Selected Countries and Subjects (BIP nach Kaufkraftparität). Abgerufen am 2. Februar 2018 (amerikanisches Englisch). Report for Selected Countries and Subjects (BIP-Veränderung zum Vorjahr). Abgerufen am 3. Februar 2018 (amerikanisches Englisch). 9

10 Manufacturing cost index 2014 Manufacturing cost converge - globally and timely +20 % -20 % Quelle: Boston Consulting Group

11 The Digital World of Today and Tomorrow Internet of Everything Access-Economy Holistic global integration as base for new business ecosystems More than 3 billion people used the internet in billion things were connected in 2015 via internet. In 2020 the number is expected to rise up to 50 billion. Internet services are uncounted. Example: Apple Apple store: > 1 million apps were downloaded more than 75 billion times New economic activities arise: Shared economy Prosumer Industrie 4.0 source: The Internet of Things, MIT Technology Review, statista, cisco 11

12 Areas of Industrie 4.0 implementation Enterprise of the future New Businessmodels Realtime Information I 4.0 Personalized Products Industrie 4.0 has the target to. create dynamic, realtime optimized, self organizing intercompany Value Networks, realize individual customer demands at a cost level of mass production. Smart Production Connectivity In Anlehnung an: Plattform Industrie 4.0 (2014) Industrie 4.0 Whitepaper FuE-Themen 12

13 New ecosystems networking and shared economy pg 13

14 IoT and IIoT Platform Provider Cloud-based platforms as backbone of Manufacturing Ecosystems Consumers, Business and IT Manufacturing, Production GE PREDIX 14

15 Structure of ecosystems Integrated design from Front to Back End Back End Focus Value Adding Focus Market Position Front End Value Adding System Ecosystem Manufacturing Network Factory X Prosumer 15

16 Businessmodel-Innovation Schunk egrip Since beginning of 2015 tailor-made gripper can be ordered from Schunk by CAD file of the object to be handled. Reduction of purchase time and high benefit for the customer by integrating him into the design process Communication via Online-Platform Manufacturing with 3D-Printing by partner company Materialise Customer Schunk Product Design Platform Materialise Manufacturing [Schunk GmbH; Materialise] 17

17 Personalized Products 18

18 Change of Product Architecture The ability to manage complexity effectively becomes a key competitive advantage degree of integration cyber-physical mechatronical mechanical complicated simple complex Minimal complexity, maximum personalization and economies of scale Customer is part of the personalization process and pays for it Innovation focus: eco-system, user-friendliness, design Success factor: openness standard mass products individualized regionalized, personalized degree of personalization sources: Wildemann, H.: Wachstumsorientiertes Kundenbeziehungsmanagement statt König-Kunde-Prinzip; Seemann, T.: Einfach produktiver werden complexity im Unternehmen senken; Bildquellen: apple.de 19

19 Überschrift Kapitel Digital Shadow element of Smart Factory 20

20 Our future scenario of shop floor production Decentralized self-organization of smart factories in real-time In two hours I have to be delivered! I will be able to work on Saturday I ve to leave early. Who can operate my orders? Capacity is fullybooked till Friday Saturday I won t be able to work Hooper will be empty soon, please refill! New big customer order: Additional shift on Saturday necessary Cyber-physical systems (e.g. machines, facilities) Have an identity Communicate with each other and their environment Configurate themselves (Plug and Produce) Store information Have easy-to-use human machine interfaces Decentralized selforganization in realtime 21

21 Use of Robotic CPS in intralogistics Mobile assistant within the low-wage sector Mobile Robots refill assembly areas and return empty boxes. Mobile Robots with sufficient capacity move through the supermarket and put goods in the boxes Mobile manipulation (omnidirectional) 2. Storage facility 3. Ability to grasp container 4. 3D Environment sensing (stereo vision, 3D sensor) 5. Can be used without any fence in an industrial environment

22 Robots will be mobile, flexible and safe Example: SEW Eurodrive freely navigating DTS (carries the robot for bin picking) 3D-camera system ensenso N20 KUKA Agilus Magnetic gripper Cut-pieces Point cloud Bin with cut-pieces Mobile platform Inductive power transmission source: IPA 23

23 All Objects in a Factory will be Mobile as Far as Possible Example: Audi R8 freely navigating AGV (navigation as a service) source: audi-mediaservices.com 24

24 Each object in the factory becomes smart ibin Intelligent bins order their fillings autonomous The quantity can be obtained via the built-in camera, the information will then be transmitted to the other IT-systems (e.g. ERP) Based on: Fraunhofer IML, Prof. Dr. Michael ten Hompel 25

25 Previous IT-Architectures dissolve Within the cloud: from the pyramid to the network Previous Historical precise hierarchical structured model Future Service orientation broad service orientation (XaaS) Service orientated IT-Architecture (SoA) Dehierarchization Dissolution of hierarchal structures New functions based on services QC App-ization App-development by end user Simulation in real-time Open standardization Efficiency benefits/ synergies by IT-Clouds Focus on information/ semantics ERP: Enterprise-Resource-Planning; MES: Manufacturing Execution System; QC: Quality Control; CAx: Computer-Aided x 26

26 Fraunhofer IPA Cloud-Architecture for Industry 4.0 Virtual Fort Knox: supported, secure Cloud Platform Legend: S AS IS CS CPS Service Aggregated Service Integration Service Cloud Service Cyber-Physical-System mos Manufacturing Operating System 27

27 Sense&Act Rule based approach for production and logistics management Features Simple way to define own/individual rules for interlinking the production entities Monitoring of sensor values Automatic triggering of predefined actions Actuator Rules ERP Wenn Bestand If stock product 7815<12 Produkt 7815 < 12 Stck pcs, then create new order dann Generiere Bestellauftrag product 7815:12 Produkt 7815: 12 Stck WennIf Maschine Machine7564 = 7564= mechanical Störung Mechanik Failure, then dann inform Benachrichtige N. Hofer N. Hofer Benefits Flexible interlinking / integration Simple adaptation to company specific requirements and situations Enables rule based production Flexible/ transformable production IT Sensor 28

28 Sense&Act Decentralized configuration of rules by users Central contextual system optimization If this (Sense) then that (Act)! send to Ms. N. Hofer! create a maintenance order! If Machine 7542 is down 29

29 Instant MES-Apps Tracking App Product traceability on Shopfloor (e.g. batch, machine, worker via NFC) Tracking App Product traceability on Shopfloor (e.g. batch, machine, worker via NFC) Master data App Administration of resources (e.g. machines, workers, work processing sheets, ) 30

30 KPI App Key Performance Indicators Process planning and monitoring Active involvement for remote and self-control of manufacturing processes User friendly (simple, fast and dynamic) operation by Drag & Drop Individual configuration of KPI charts Storage of templates for predefined situations Integration of different databases and decentralized data provision Comparison As is / Should be Configuration 31

31 Digital Shadow of Production Emulating models to ensure one peace flow Fields of application Factory operation Maintenance Factory planning Product data Factory structure data Infrastructure data Process data Resource data Motion data Objectives Support for people and machines Real-time production system Time Digital shadow Design task Meta model Data pool Information model Work data Near real-time factory model Analysis, simulation, evaluation Taking measures Sensor data Physical factory Context data of factory objects: type place state time relations Localization system storage systems, transport systems, machines, parts, material, tools, products, workers, assembly line 32

32 Augmented Reality Description of components and machines Personnel training using context-based descriptions and tutorials Support by error diagnostics Linking between components and control elements Remote assistant on plant level Image transmission from tablet to smartphones Assistance by maintenance activities Automated maintenance protocols Sensor evaluation 33

33 Cyber physical production support Adaptive assembly workplace Flexible, intuitive and personalized support in the assembly of complex products Pick-by-Light-System for supporting workers and capturing the correct material Put-to-Light-System for visualizing and control the correct product part position Flexible connection to material supply by variable height and position of the rear port Fault-free assembly and simultaneously the least effort for learning the assembly operations 34

34 Mixed Reality - a new way of scale 1:1 training and planning 36

35 Automated Detection of Dependencies Between processes and deriving optimization potential Through minimally invasive process monitoring via camera without elaborate system integration feature-based configuration and recognition of conditions in the videos via adaptive evaluation algorithms Benefits near real-time process analysis with direct assignment of the cause for loss detection and quantitative evaluation of potential for process optimization permanent transparency through forwarding errors and machine condition to operators and planers 37

36 Business potential of Industry 4.0 Experts expect an increase in performance of % on value added Potential benefits Pilot project of Bosch in which the whole shipping process of the inplant logistics centre was redesigned in a Industy 4.0 project. -10 % Milkruns +10 % Productivity -30 % reduce of stock Quelle: IPA/Bauernhansl, Bosch 38

37 Thank you for your attention. Michael Lickefett Head of department Factory Planning and Production Management Coordinator China Activities Phone Mobile