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How can AI and Machine Learning help to increase production efficiency?

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What works with self-driving cars or smart buildings also works in industrial production. The use of sensors and the connected equipment and components in IoT give rise to facilities that are capable of autonomous control and self-maintenance. The more complex and dynamic the development and production environment, the faster conventional IT analysis and control methods reach their limits. And the more vulnerable production ecosystems are to attack. We use practical examples to demonstrate how AI methods can be used to manage this complexity and increase efficiency and how to make production environments more secure. We will share the example of Fujitsu’s production facilities in Augsburg as an example of an efficient production 4.0 environment.
Speaker:
Hugo Lerias
Gant Kinchin

Publié dans : Technologie
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How can AI and Machine Learning help to increase production efficiency?

  1. 1. 0 © Copyright 2017 FUJITSU Fujitsu Forum 2017 #FujitsuForum
  2. 2. 1 © Copyright 2017 FUJITSU How can Artificial Intelligence and Machine Learning help to increase production efficiency? Hugo Lerias Director, Industries, BAS EMEAI, Manufacturing, Automotive, Fujitsu Grant Kinchin Proposition Manager, Manufacturing EMEAI, Fujitsu
  3. 3. 2 © Copyright 2017 FUJITSU Highlights – Manufacturing Industry 4.0 Manufacturing Optimization using COLMINA IoT PlatformI4.0 CC Show Case – „Smart Factory“ Engineering Cloud IIoT & Edge Computing Industrial Analytics Managed Business Process integration (SAP) Fujitsu Glovia Globe Ranger Visualization – Intelligent Dashboard Knowhow Share or Digital Workspace for Product Design with AI Vertical Integration and Management of Manufacturing Site with IoT and MESShow Case „F|AIR Demonstrator“ Show Case „Image Recognition“ Fujitsu / Kuka Cooperation – Testing Robot with Industrial Edge Computing
  4. 4. 3 © Copyright 2017 FUJITSU FUJITSU Smart Manufacturing
  5. 5. 4 © Copyright 2017 FUJITSU Manufacturing Business of Fujitsu Fujitsu’s Major Manufacturing Solution Planning Distribution Procurement CAD for Machine iCAD CAD for electricity E-BLADE Analytical solution Electronic parts Solution Verification of Product VPS Management of Design Information PLEMIA Verification of Product Line GP4 Support of Procurement Solution Manufacturing Control/Manufacturing execution GLOVIA Distribution Solution Call center Solution Automation of factory Solution Robot Integration Factory IoT Solution  Fujitsu is the only vender in Japan who develop the application of Manufacturing for from Design through Production in self.  Fujitsu has offered the solutions of a variety of manufacturing for 30 years or more.  There are an adopted results to the customer of 10,000 companies or more.
  6. 6. 5 © Copyright 2017 FUJITSU Trend of Manufacturing Business  To digitalization that uses the advanced technology from the efficiency improvement of the individual business system お客様から問い合わせいただく主な内容 IoT technology AI technology Connected technology Contents of Response to Customers (Contents asked by Customers) 49% Utilize IoT to Make Factory Better Construct Next Generation Factory Others Utilization of AI to Improve Manufacturing Quality Maintenance, Detection of Indication of Machine Trouble Visualize Factory Utilization of AI to Improve Manufacturing Efficiency
  7. 7. 6 © Copyright 2017 FUJITSU To provide a comprehensive platform for Manufacturing companies which enables their digital transformation Fujitsu Manufacturing Digital Solution COLlaborative MONOZUKURI INnovation Agent COLMINA
  8. 8. 7 © Copyright 2017 FUJITSU COLMINA Platform COLMINA Platform Connecting technology FTCP · Ele-mecha CAD · Analysis tool · knowledge · Part library · Virtual verification... Standard profile · Work common · Electric · Mechanical · factory · SCM... AI technology Zinrai · Detection of abnormal processing quality · Detection of equipment failure Automated inspection by image recognition · Knowledge information structuring / search · Safety management support for workers ... IoT technology Data utilization · Structured / unstructured Accumulation of data, utilization? · Parallel distributed processing · Data processing · Data virtual integration IoT platform Standard protocol? REST, MQTT · Edge / cloud? Distributed control · Event detection / Notification  Public cloud type that can be used easily, Private cloud type that can be dealed with individual needs and on premises type is available
  9. 9. 8 © Copyright 2017 FUJITSU COLMINA Service : Intelligent Dashboard Monthly production amount Monthly power consumption CO₂ emissions amount Each production site Peak power Operation rate of facilities Personnel Visualization of operations Plant Line Process Comparison of cross-functional multiple lines Evaluation with 3 layers Visualization of multiple plants with dashboard Enable benchmarking and evaluation of each production lines through visualization
  10. 10. 9 © Copyright 2017 FUJITSUPatent is being applied by Fujitsu Picture of the site Detailed information Process Time Efficiency is good Occurrence of trouble Efficiency is bad COLMINA Service : Visualine  Centrally managing log data (e.g. PLC) of production lines  Increasing the productivity by 30% by finding causal relationships and making improvement
  11. 11. 10 © Copyright 2017 FUJITSU COLMINA Platform Utilize Integration, Accumulation, Analysis Data collection API Zinrai PF Design Production Operation Maintenance Big Data PFStandard Profile PF Manufacturing RDBNoSQL DB V-DB COLMINA Platform Mainitenance Product Design Factory Design 工程 Product Equipment Human Environment Recognition Forecast/ Inference Judgement Standard Adoptor / IoT Platform Defective fault/ Prevention Management of flow/ analysis Kaizen of work Production Indicator Visualize Experience transmission /Remote support Predictive Detection Image analysis Execution Results difference analysis
  12. 12. 11 © Copyright 2017 FUJITSU FUJITSU Image Recognition
  13. 13. 12 © Copyright 2017 FUJITSU Image Recognition AI  Image recoginiton algorithm is generated automaically in production equipment  Using generig program (machine learning).  When recognition accuracy tends to lower, re-generate it.
  14. 14. 13 © Copyright 2017 FUJITSU Using genetic algorithms to aenerate new Functions  The platform connected from the factory to the mainenence site in total is offered  Information in all factories is collected with a standard adapter and IoT-PF and the visualization of the iste and the process
  15. 15. 14 © Copyright 2017 FUJITSU FUJITSU Advanced Image Recognition
  16. 16. 15 © Copyright 2017 FUJITSU €€€€Price Pressures Fluctuating Demand Product Customisation Manufacturer Challenges Current QualityControl Constraint Usually still heavily worker dependent €High Cost = Time Consuming Training Overheads Risk of Human Error Manufacturing Priorities & Quality Control
  17. 17. 16 © Copyright 2017 FUJITSU Benefit Computer Vision Deep Learning Trained from data instead of programmed rules Fast adaptation to changing specifications / product variation Replicates worker judgement Highlights anything unexpected Learns from experience Easily applied to more complex image types (i.e. ultrasound) Classification of defects AI Deep Learning: a game changer for quality control Complex Defects Measurable Defects Angle Width Computer Vision Deep Learning Placement Jagged Edge Scratch Curvature Discoloration Computer Vision Deep Learning
  18. 18. 17 © Copyright 2017 FUJITSU FUJITSU Advanced Image Recognition Infrared Learn Review Analyse  Able to learn from a small training sample  Simple markup and ingestion of samples  Connectivity with any imaging system  Analysis of any image type  Tooling for QA team to assess and report findings  Feedback used to further refine recognition model  Simplifies application of Machine Learning  Proven to enable efficiency gains of ~80% Deep Learning
  19. 19. 18 © Copyright 2017 FUJITSU Applying AI for Wind Turbine Manufacture  Ultrasonic quality inspection generates massive amount of blade scan data for Quality Control  Scan data evaluation process now completely automated  High gains in time efficiency, enabling skilled operators to focus on the important part of the data  Cuts inspection times for windmill turbine blades from six hours to just one and a half hours ‘By adopting Fujitsu’s ground-breaking AI technology it takes only a quarter of the time previously required to perform an inspection’ Kenneth Lee Kaser, Head of SCM, Siemens Wind Power
  20. 20. 19 © Copyright 2017 FUJITSU FUJITSU Advanced Image Recognition: Features Fast implementation Adaptable web GUI Flexible deployment: on premise or cloud API layer for integration Audit of all decisions Trend analysis
  21. 21. 20 © Copyright 2017 FUJITSU Fast Implementation with XpressWay  Business case validated early to confirm benefits  Quick and simple to prove for any potential application  Low risk phased rollout  Continuous improvement through proactive support  Step 1: Feasibility study (~1 week)  Step 2: Proof of Value (~3-4 weeks)  Candidate Use Case Identification  Outline Business Case  Configuration & Test  Pilot  Continuous Improvement  Adapt & Extend
  22. 22. 21 © Copyright 2017 FUJITSU Highlights – Manufacturing Industry 4.0 Manufacturing Optimization using COLMINA IoT PlatformI4.0 CC Show Case – „Smart Factory“ Engineering Cloud IIoT & Edge Computing Industrial Analytics Managed Business Process integration (SAP) Fujitsu Glovia Globe Ranger Visualization – Intelligent Dashboard Knowhow Share or Digital Workspace for Product Design with AI Vertical Integration and Management of Manufacturing Site with IoT and MESShow Case „F|AIR Demonstrator“ Show Case „Image Recognition“ Fujitsu / Kuka Cooperation – Testing Robot with Industrial Edge Computing
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