1/23/2014. Smart Factories. Grant Challenges and evolutions in engineering. Smart production and smart factories

Transcription

1 Smart Factories Fritz Klocke Professor in Production Engineering Chair for manufacturing technology Werkzeugmaschinenlabor WZL - RWTH Aachen University and Head of the Fraunhofer Institute for Production Technology IPT, Aachen acatech - INAE Workshop Delhi, India January 22 to 24., 2014 Outline Grant Challenges and evolutions in engineering Smart production and smart factories Sensors, data processing and automation as enabler for smart factories Conclusion Seite 1 1

2 Outline - backup Pilot Smart Factories in Research Case 1: From plants to vaccines Case 2: From cells to tissue / organ Case 3: From a demonstration lab to industry Seite 2 Industrial society in strained relations Environment, climate, recources Overall balance Growth through sustainable innovation Economy growth welfare Individual and collective needs Source: Acatech, oct Seite 3 2

3 Megatrends Energy Mobility Environment Resource Efficiency Energy Efficiency More Goods with Fewer Resources Health Communications Safety Source: Fraunhofer-Gesellschaft Seite 4 Megatrends Energy Mobility Environment Work interdisciplinary to go for new shores! Engineering science might bring in own ideas, materialize conceptions and has to implement solutions! Health Communications Safety Source: Fraunhofer-Gesellschaft Seite 5 3

4 Changes in Production Environment Seite 6 Product variety: Multi material design Titanium Titan, PMC Titan, Nickel, MMC Nickel, CMC Nickel, (Titan,) TiAl Nickel Fan NDV HDV BK HDT NDT Steel New materials and design Titan-Aluminide PMC: Polymer Matrix Composites MMC: Metal Matrix Composites CMC:Ceramic Metal Composites p (bar) T (K) , , , ,4 740 Source: MTU Aero Engines Seite 7 4

5 Multi material design and geometry integration: high degree of product variety Low pressure stage 1-3 (Blisk) Source: MTU Aero Engines Christmas tree design Integrated design High degree of product variety Steady increase of production system parameters Seite 8 Global: über Flüge täglich Bis 12:45 Uhr: Starts und Landungen durch Triebwerke Seite 9 Quelle: DLR,

6 Worldwide Manufacturing Network Seite 10 Organization of shop floor production: Small batches, customized products Perception Uncertainty, unclear situation Source: Cessna AG, Garmin Ltd. and Schuh, G.:Fachkongress Industrie 4.0: Von der Strategie zur Praxis, Esslingen, den, Dec Seite 11 6

7 Product variety, worldwide production, uncertainty. Adaptability, resilience and productivity Variable market needs Adaptive manufacturing / factory Product variety Adaptability, resilience Adaptive process and sequence productivity Distinct process Variable component sequence Handle uncertainty, customized products appropriately to run production on a consistent level with highest productivity Variable feature Adaptive manufacturing step Parameter Seite 12 "If you always do what you've always done, you'll always get what you've always got. Source: Library of Congress, Henry Ford Seite 13 7

8 Key question Are smart factories a better way to meet the challenges? Seite 14 Outline Grant Challenges and evolutions in engineering Smart production and smart factories Sensors, data processing and automation as enabler for smart factories Conclusion Seite 15 8

9 Smart Factories Characteristics and Perspectives cyber Single Source of Truth ERP- Systems PLM/Engineering- Systems IT-Globalization Big Data Assessing and Storage in the cloud Data mining, safety, security High Speed Computing Local data storage Productivity and Economic Efficiency through Collaboration Cognitive System physical Cooperation Software Business Communities Social Communities Adaptation by sensors Intuitively, reliability IT-Openness Cost-efficiency Robustness Automation Hardware Seite 16 Outline Grant Challenges and evolutions in engineering Smart production and smart factories Sensors, data processing and automation as enabler for smart factories Conclusion Seite 17 9

10 Sensors for smart factories cyber Single source of Truth PLM/Engineering -Systems Process strategies Virtual monitoring and control systems Telemonitoring and remote control IT-Globalisation Big Data Data mining, security High Speed Computing Automatic calibration of process models Lokale Speicher physical Cooperation Software Business Communities Technical discussion New sensors Lab on the chip for production Wireless Dynamometer Field sensors Sensor networks Fast process and machine diagnostic SAW-Sensors Automation Quelle: Fraunhofer CMI, USA Hardware Seite 18 Sensors for Production State of the art and new developments Processes Spindle Encoder Dynamometer Machine Tools Piezo Foils NC Controller Lab on the chip for coolants SAW-Sensors Encoders Drive Sensors RFID Sensors Logistics Indoor and Outdoor GPS Systems Image source: KIT, Kistler, DS Technolgie, Siemens, Weiss Spindeln, Measurement Specialist, Fraunhofer, Knoll Maschinenbau, Schaeffler, jena-tac Seite 19 10

11 High resolution data from the shop floor Machine and process integrated sensors Manufacturing media sensors Cameras Foto: Sick Laser Tracker RFID Bar Codes Lab. on the chip Laser Scanner The use of shop floor sensors is steadily increasing. The data recorded enhance transparency and might be used for on-site optimization of processes and sound decisions and to feed holistic process chain models Image source: Trumpf company, Sick company Seite 20 Lab on a chip for molecular diagnostics PCR chamber DNA purification Mixers Bacteria lysis Fluorescence Detection Source: Sauer-Budge et al Lab Chip, 2009, 9, , Fraunhofer CMI, Boston Fluid dynamics Low cost and manufacture Integrated functions Sample input Mixers Bacterial lysis Nucleic acid isolation PCR (thermal cycling) Optical detection Automated instrument Demonstrated for bacteria Extending to influenza (RNA virus) in human nasopharyngeal aspirates sensitive lab-on-a-chip for detecting TB in urine Seite 21 11

12 Sensors for smart factories cyber Single source of Truth PLM/Engineering -Systems Process strategies Virtual monitoring and control systems Telemonitoring and remote contro Calibration IT-Globalisation Big data Smart data Data mining, security High Speed Computing Holistic process and factory models of models Scenarios Local storage physical Cooperation Software Business Communities Technical discussion New sensors, Lab on the chip Wireless Dynamometer Field sensors Sensor networks Fast process and machine diagnostic SAW-Sensors Automation Quelle: Fraunhofer USA Hardware Seite 22 Big data for production plants New & more Sensors Access rights Data transfer and storage Data reduction and analysis Sensor Data Transactions Big Data Enterprise Data Public Data Challenges Data privacy Scalability Data integrity Quality of data Reduction of data Data connectivity and avaibility Social Media Robust algorithms Identification of characteristic values Source: clinthuijbers.wordpress.com, chip.de Seite 23 12

13 Cloud based simulation and real time computing for process analyses, process layout, and model based process control and optimization Parallel computing of complex manufacturing processes and process sequences Real time scenario planning Model based real time process control and optimization Real time scenario planning Generate options for decision making Holistic modelling platform for self optimization and to provide options for men based decision making Schuh, G.:Fachkongress Industrie 4.0: Von der Strategie zur Praxis, Esslingen, den, Dec Seite 24 Smart Factories Case 1: Multi Machine coupling intercompany and cross company cyber Single Source of Truth Roughing Finishing Process strategies Polishing IT-Globalization Real time multi-physics modelling Electric field Fluid dyn. Heat transfer E-chemical. removal rates Geometry physical Feed rate v f / (mm/min) µm 1 Electrolytes Material library Math tools Faraday min. 1 Faraday max. Experiment Current 0 density J / (A/mm²) J / (A/mm²) Cooperation Software v f / (mm/min) 2 z 0 z 1 t / s z / mm U / V Automation t / s t / s Voltage a. Current Short circuit detection Temperature control Workpiece geometry Hardware Seite 25 13

14 Smart Factories Case 1: Multi Machine coupling intercompany and cross company IT-Globalization Flow Velocity 45 m/s Temperature distribution 324 K 0 m/s 308 K Voltage distribution 17 V Current density distribution 1,8 A/mm² 0 V Fully coupled three dimensional multiphysical simulation Cloud computing for different CAx- Strategies 0 A/mm² Virtual designing and testing of new cathode geometries Usage of material and electrolyte databases Seite 26 Smart Factories Case 2: Multiphysics process chain modelling No CAM Path optimization NC-simulation Default of set-points x =... (t) a =... (t) y =... (t) b =... (t) z =... (t) Optimized NC-program OK Yes Process evaluation Stability Tolerances Surfaces mech Mech. process behaviour Process model Coupled multibody simulation Work piece model F(t) Hybrid process modeling Empirical models f F(t) x d total = mech + therm therm Thermal process behaviour Temperature distribution Thermal machine model Work piece model T(t) FEM-model Seite 27 14

15 Modelling of production: different approaches Do complex production targets need always complex solutions? Not necessarily. This approach is only promising, if there is a judge basis of experience and empirical values. Only in this case we might be in a good position to judge boundary conditions appropriately. What else can we do? Use heuristics. Use rule based models. Deal uncertainty intuitively. Source: sciencedirect.com Seite 28 Sensors for smart factories cyber Single source of truth PLM/Engineering -Systems Process strategies Virtual monitoring and control systems Tele-monitoring and remote control IT - Globalization Big Data Data mining, security High Speed Computing Automatic calibration of process models storager Business Communities Technical discussion New sensors Lab on the chip for production Wireless Dynamometer Field sensors Sensor networks Fast process and machine diagnostic SAW-Sensors Source: Fraunhofer CMI, USA physical Cooperation Automation Software Hardware Seite 29 15

16 CAx-Framework CAM-Planning Operations Engagement Conditions Force Cal. Data Analyses NC Profiler Multi Body Simulation User- Interaktion VNCK Seite 30 Crowd sourcing & life cycle management Case 4: Trusted Cloud Tool manufacturer Practical experience Application oriented technology values Geometry Information Sensor Data Transactions Crowd sourcing Social Media Life time Tool state Enterprise Data Public Data Factory 3 Factory 1 Quality data Factory 2 Challenges Data privacy Make data anonymous Data masking Prevention of traceability Data integrity Data format Completeness of data Access rights Quality of data Existence of Meta data Calibration of sensors Boundary conditions Scalability Seite 31 16

17 Crowd sourcing & life cycle management Master Plan: Trusted Cloud Industrial I Private Cloud Tool manufacturer Public cloud services Small and mid size enterprises Cloud based Lifecycle Management for Tools Tool reconditioning Public cloud services Trusted Cloud Added value for the company through collaboration Industrial II Private Cloud Life cycle Information Seite 32 Outline Grant Challenges and evolutions in engineering Smart production and smart factories Sensors, data processing and automation as enabler for smart factories Conclusion Seite 33 17

18 In a nutshell Smart factory Cyber Physical Networks Virtual factory Fusion of real and virtual environment What will you get? Productivity, economic efficiency, profitability Virtual Compony networks Virtual Customer integration From the shop floor to top level management Fully electronically integrated business processes, intercompany and across companies and customers Inter- and across companies customers Bildquellen: WZL, Siemens, BMBF Single source of truth Seite 34 Thank You! Seite 35 18

19 Outline - backup Pilot Smart Factories in Research Case 1: From tobacco plants to vaccines Case 2: From cells to tissue/organ Case 3: From a research demonstration fab to industry Seite 36 Characteristics of pilot smart factories in research Interdisciplinary research Location / Sup supplier Disciplines Source: Fraunhofer Gesellschaft, WZL, Facebook, Xing, Skype Fully automated Processes New sensors Incorporation of social networks From big data to smart data Holistic models Multi physics modelling High speed parallel computation Open access for everybody One single source of truth Seite 37 19

20 Smart Factories in Research Case 1 - Pilot factory: From plants to vaccines Quality, time, costs Collection of critical process data (variable cell growth,..) Process optimization early error recognition (contaminations,..) Minimization of rejections Quality control (non destructive) Seite 38 Automated production of vaccines in tabacco plants Info from pathogen Recombined Sow seeds Treat seedlings Grow in greenhouse Harvest and disrupt leaves Source: Fraunhofer CMI, Fraunhofer IPT, Fraunhofer IME Seite 39 20

21 Smart Factories in Research Case 2 - Pilot factory: From a cell to tissue / organs Quality, time, costs Collection of critical process data Process optimization modul- Zellisolationsmodul - early error recognition (contaminations,..) Minimization of rejections Quality control (non destructive) Seite 40 Production unit for artificial skin equivalents Cell isolation module Zellisolationsmodul Tissue cultivation Gewebe- module aufbau- modul Cell expansion Zellexpansionsmodul modul Source: Fraunhofer IGB. IPA, IPT, Fraunhofer Foundation Seite 41 21

22 Project highlight» StemCellFactory «CAD model of the StemCellFactory Objectives Automated cell isolation of mesenchymal stem cells from bone marrow and fibroblasts from skin Automation of a manually established process for the generation of induced pluripotent stem cells ips Automated differentiation of ips into neuronal stem cells (and cardiac cells) Methodology Re-engineering and optimization of each laboratory process to enable complete process automation Choice and integration of all necessary commercially available functional modules Design and development of specific functional components and handling solutions Development and integration of metrology for inline monitoring of all cell culture processes Envisaged outcome Development and prototype production line of an fully automated, modular demonstrator for the reproducible production of ips derived cell products ips cell clone on feeder cells Seite 42 Smart Factories in Research Case 3 - Pilot factory: From a demonstration lab to industry Products) Prototyp I Assembly Prototype II MAXecart Range of use Off-road und fun sport High gradient streets Event - concept (Geocaching-Tours) Product Innovation 250 Watt Pedelec-E- Drive 3 power levels Step less variable gearbox Variable adjustment of drivers position Source: Schuh, G., Anlauffabrik, Campus, RWTH Aachen StSc Bodengruppe StreetScooter Body StSc Vorderwagenprofile Product innovation Space frame-structure Low cost budget Seite 43 22

23 Shop Floor of demonstration fab is interconnected with 3 different ERP Systems. Target figures are provided centrally ERP-Systems and IT-Architecture Lager, Warehouse Warenein- u. Shipping ausgang, area Verpackung Belchbearbeitung Sheet metal (Zuschnitt) forming Orders Logistcs Schedules Innovation-Lab Environment Welding Fügen II Welding Fügen I Deckenkran Blechbearbeitung metal Sheet (Biegen forming u. Assembly Montage Schweißen) Orders Orders Source: Schuh, G., Anlauffabrik, Campus, RWTH Aachen Seite 44 Future developments have to be received as they evolve! But we can do something, that our future evolves as we feel like it! Acc. to Curt Goetz, German author ( ) Seite 45 23