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Incremental Learning Approach for an Industrial Inspection System - PowerPoint PPT Presentation

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Incremental Learning Approach for an Industrial Inspection System MATLAB Expo 2019, Bern Dr. Jianyong Wen & Ralph Stephan, Stubli Sargans AG MATLAB Expo 2019, Bern Stubli at a Glance 2 2019-05-23 Stubli Sargans AG MATLAB Expo

  1. Incremental Learning Approach for an Industrial Inspection System MATLAB Expo 2019, Bern Dr. Jianyong Wen & Ralph Stephan, Stäubli Sargans AG

  2. MATLAB Expo 2019, Bern Stäubli at a Glance 2 2019-05-23 Stäubli Sargans AG

  3. MATLAB Expo 2019, Bern Company Presentation 3 2019-05-23 Stäubli Sargans AG

  4. MATLAB Expo 2019, Bern Three activities – four divisions 4 2019-05-23 Stäubli Sargans AG

  5. MATLAB Expo 2019, Bern Stäubli Textile Our Textile manufacturing products and services range from ▪ Shedding solutions for frame weaving and Jacquard weaving to ▪ Carpet weaving systems, ▪ Weaving systems for technical fabrics, ▪ Automation solutions for sock knitting machines, ▪ Automated weaving preparation systems . 5 2019-05-23 Stäubli Sargans AG

  6. MATLAB Expo 2019, Bern Stäubli Weaving Preparation Systems ▪ Located in Sargans, SG, since 1994 ▪ ~120 employees (~30 in R&D) ▪ Product lines ▪ Drawing-in ▪ Tying ▪ Leasing ▪ Inspection 6 2019-05-23 Stäubli Sargans AG

  7. MATLAB Expo 2019, Bern Drawing-In 7 2019-05-23 Stäubli Sargans AG

  8. MATLAB Expo 2019, Bern Tying 8 2019-05-23 Stäubli Sargans AG

  9. MATLAB Expo 2019, Bern Leasing, Reading-in and other Equipment 9 2019-05-23 Stäubli Sargans AG

  10. MATLAB Expo 2019, Bern Inspection 10 2019-05-23 Stäubli Sargans AG

  11. MATLAB Expo 2019, Bern From Idea to Product Idea Model Feedback Design Data Model Collection Verification Field SoC Application Integration Integration Test 11 2019-05-23 Stäubli Sargans AG

  12. MATLAB Expo 2019, Bern Challenges in Embedded Application ▪ Real-time industrial fabric inspection systems face challenges of a great number of pattern variations, fast and easy training process. ▪ Strongly imbalanced datasets ▪ Limitation of hardware resources in embedded systems 12 2019-05-23 Stäubli Sargans AG

  13. MATLAB Expo 2019, Bern Approach ▪ The incremental model developed at Stäubli Sargans AG consists of two process stages, combining machine learning and deep learning models. Image Pre - processing Incremental Learning Sub- model Initial Learning Sub-model 13 2019-05-23 Stäubli Sargans AG

  14. MATLAB Expo 2019, Bern Development Process Developing models HDL Code generation Optimization parameters Verification Functionality test Efficiency and Speed Test 14 2019-05-23 Stäubli Sargans AG

  15. MATLAB Expo 2019, Bern Preprocessing Debayer Segmentation Resizing Filtering Feature extraction 15 2019-05-23 Stäubli Sargans AG

  16. MATLAB Expo 2019, Bern Initial Learning Sub-Model ▪ Classification based on extracted features from pre-processing Gray Level Co- Statistical Edges Lawmasks Corners Occurrence Matrix Features Classifier 16 2019-05-23 Stäubli Sargans AG

  17. MATLAB Expo 2019, Bern Incremental Sub-Model CNN with 15 Layers Class FC Conv. Relu Output Input Soft- Batch Maxpool max Image Norm 17 2019-05-23 Stäubli Sargans AG

  18. MATLAB Expo 2019, Bern Incremental Learning Model in MATLAB 18 2019-05-23 Stäubli Sargans AG

  19. MATLAB Expo 2019, Bern Simulink Design and Simulation 19 2019-05-23 Stäubli Sargans AG

  20. MATLAB Expo 2019, Bern HDL-Code Generation and Resource Utilization Analysis 20 2019-05-23 Stäubli Sargans AG

  21. MATLAB Expo 2019, Bern Advantages ▪ Applicability ❖ The CNN sub-model improves classification accuracy during production process (incremental learning). ▪ Efficiency ❖ Based on the initial sub-model, the complexity of the CNN sub-model can be reduced (resource and speed efficiency). 21 2019-05-23 Stäubli Sargans AG

  22. MATLAB Expo 2019, Bern Achievements and Outlook Idea Model Feedback Design future past Data Model Collection Verification Field SoC present Application Integration Integration Test 22 2019-05-23 Stäubli Sargans AG

  23. MATLAB Expo 2019, Bern Benefits and Challenges Idea Model Feedback Design ▪ Challenges Data Model Collection Verification ▪ matching of tools and data sets ▪ debugging with blackbox IP Field SoC ▪ limited computing power Application Integration Integration ▪ large databases Test 23 2019-05-23 Stäubli Sargans AG

  24. MATLAB Expo 2019, Bern Benefits and Challenges Idea Model Feedback Design ▪ Benefits Data Model Collection Verification ▪ fast and simple code generation ▪ step-by-step implementation and Field SoC verification (controlled progress) Application Integration Integration ▪ consistent and comparable Test intermediate results ▪ validation of results together with customers 24 2019-05-23 Stäubli Sargans AG

  25. MATLAB Expo 2019, Bern Concluding Remarks Idea Model Feedback Design ▪ There are no limit to imagination Data Model Collection Verification ▪ Limits are given by Field SoC ▪ Our knowledge and implementation capacity Application Integration Integration ▪ The capabilities and limitations of our tools Test 25 2019-05-23 Stäubli Sargans AG

  26. Thank You! www.staubli.com

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