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Artificial Intelligence In Manufacturing

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Video at: https://www.linkedin.com/video/live/urn:li:ugcPost:6705141260845412352/

In this talk, we will review some of the challenges related to Industry 4.0 or Factory of Future, and how can Artificial Intelligence help address them.
Examples include the use of semantic interoperability and integration to support the use of sensor collected data in decision making, the use of computer vision to identify deviations in the process and manage quality, and the use of predictive algorithms for device maintenance.

Video at: https://www.linkedin.com/video/live/urn:li:ugcPost:6705141260845412352/

In this talk, we will review some of the challenges related to Industry 4.0 or Factory of Future, and how can Artificial Intelligence help address them.
Examples include the use of semantic interoperability and integration to support the use of sensor collected data in decision making, the use of computer vision to identify deviations in the process and manage quality, and the use of predictive algorithms for device maintenance.

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Artificial Intelligence In Manufacturing

  1. 1. AI in Manufacturing neXt LIVE with Dr. Amit Sheth
  2. 2. I II III IV V VI VII AMIT SHETH, PhD • Founding director of the university-wide Artificial Intelligence Institute at UofSC (AIISC) • Core research on AI topics such as knowledge infused learning and neuro-symbolic computing, • AIISC has translational research with nearly al of the colleges at UofSC • Fellow of IEEE, AAAI and AAAS Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing2
  3. 3. I II III IV V VI VII OUTLINE I. AIISC Introduction II. AI in Manufacturing III. Knowledge Graph/Ontology IV. Computer Vision in Manufacturing V. Predictive Maintenance VI. NLP and Conversational AI VII. Applications of AI in Manufacturing Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing3
  4. 4. AIISC Introduction Section I 4
  5. 5. I II III IV V VI VII I | AIISC DRIVERS AND DISTINCTIONS • To be recognized as the top institution in interdisciplinary AI, AI applications and impact in Southeast US, and among the top in its chosen of selected AI subareas ● Exceptional Student Outcomes ○ Education: 20+ courses in AI ● High impact from translational research ● Apply AI and realize impact across the university and state ● High engagement with communities and industry Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing5 I
  6. 6. I II III IV V VI VII I | UNIVERSITY-WIDE MANDATE ● College of Medicine (5) ● College of Nursing (2) ● College of Arts & Science (2) ○ Hazard & Vulnerability Res Inst ○ Institute of Mind and Brain ● College of Pharmacy ○ Colorectal Cancer ○ Digestive Inflammation Index ● College of Information & Communication ● College of Engineering & Computing ○ Civil and Environmental ○ Mechanical & Aerospace ○ Computer Sc & Engg ● College Education ○ ALL4SC ● College of Public Health Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing6 Practically all our work involves real world challenges, real-world data, interdisciplinary collaborators, path-breaking research and innovations, real-world deployments, real world use, and measurable real world impact. I
  7. 7. AI in Manufacturing Section II 7
  8. 8. I II III IV V VI VII II | BIG CHANGES IN MANUFACTURING NEED AI ● Automation supported by myriad of technologies including Robots; IoT, Digital Twins ● Strategic changes in Supply Chain - massive disruptions, hiccups in globalization ● Sustainability - traceability and accountability Result: Data Tsunami -> Analytics [CAGR of 30.9% over the forecast period, 2020- 2025: ResearchAndMarkets.com] Possible Solution: AI can help (Recommendation/Planning/Decision Making) [AI in manufacturing is expected to grow at a CAGR of 57.2% during 2020 and 2027: MarketsAndMarkets.com] Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing8 II
  9. 9. I II III IV V VI VII II | BIG CHANGES IN MANUFACTURING NEED AI Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing9 “Industry 4.0 is the information-intensive transformation of manufacturing (and related industries) in a connected environment of big data, people, processes, services, systems and IoT-enabled industrial assets with the generation, leverage and utilization of actionable data and information as a way and means to realize smart industry and ecosystems of industrial innovation and collaboration.” From: https://www.i-scoop.eu/industry-4-0/ II
  10. 10. I II III IV V VI VII II | BIG CHANGES IN MANUFACTURING NEED AI Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing10 Information is cheap. Understanding is expensive. Karl Fast, Professor of UX Design, Kent State University AI is about converting data into knowledge, insights and actions. II
  11. 11. I II III IV V VI VII II | WHAT IS EXPECTED FOR FACTORY OF FUTURE (FOF) Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing11 1. Detect defects throughout the production process. 2. Deploy predictive maintenance to reduce downtime. 3. Respond to real-time changes in demand across the supply chain. 4. Validate whether intricate goods like microchips have been perfectly produced. 5. Reduce costs of small-batch or single-run goods, enabling greater customization. 6. Improve employee satisfaction by shifting mundane tasks to machines. II From: Luke A. Renner, How Can Artificial Intelligence Be Applied in Manufacturing?
  12. 12. I II III IV V VI VII II | AI IN MANUFACTURING (WHY? -> BENEFITS) • Direct Automation • 24/7 production • Safety • Low operational cost • Greater efficiency • Quality control • Quick decision making https://www.rowse.co.uk/blog/post/7-manufacturing-ai-benefits Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing12 https://www.industryweek.com/technology-and-iiot/article/22027119/benefits-of- ai-on-manufacturing-a-visual-guide II
  13. 13. I II III IV V VI VII II | MANUFACTURING FAILURES: SEVERITY OF THE PROBLEM Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing13 II
  14. 14. I II III IV V VI VII II | AI IN MANUFACTURING Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing14 “EVEN THOUGH AI HAS BECOME ONE OF THE HOTTEST TOPICS IN MANUFACTURING TODAY, MOST MANUFACTURERS ARE AT THE START OF THE ADOPTION CURVE. “ THOMAS LEESON “BY THE TIME A LATE ADOPTER HAS DONE ALL THE NECESSARY PREPARATION, EARLIER ADOPTERS WILL HAVE TAKEN CONSIDERABLE MARKET SHARE; THEY’LL BE ABLE TO OPERATE AT SUBSTANTIALLY LOWER COSTS WITH BETTER PERFORMANCE. IN SHORT, THE WINNERS MAY TAKE ALL AND LATE ADOPTERS MAY NEVER CATCH UP.” VIKRAM MAHIDHAR & THOMAS H. DAVENPORT, HBR, DEC 2019 II
  15. 15. I II III IV V VI VII II | AI IN MANUFACTURING Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing15 KEY AI SUBAREAS Conversational AI Machine & Deep Learning Natural Language Processing (NLP) Computer Vision Robotics Knowledge Graph (Ontology) II
  16. 16. Knowledge Graph/Ontology Section III 16
  17. 17. I II III IV V VI VII III | TYPICAL NW ARCHITECTURE FOR FOF: EDGE, FOG, CLOUD Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing17 Figure: Li et al, Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay, 2019 III
  18. 18. I II III IV V VI VII II | INDUSTRY NEXT MANUFACTURING @ MCNAIR, UOFSC Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing18 Digital Cell Actual Cell II
  19. 19. I II III IV V VI VII III | CONNECTED MANUFACTURING: SMART IOT AS SOLUTION Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing19 III http://wiki.aiisc.ai/index.php/Smart_Data
  20. 20. I II III IV V VI VII III | SEMANTICS AT DEVICE AND FACTORY FLOOR NW PROTOCOL LEVELS Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing20 Reference: Gyrard, Amelie, Pankesh Patel, Amit P. Sheth, and Martin Serrano. "Building the web of knowledge with smart iot applications." IEEE Intelligent Systems 31 (5), 2016) P. Desai, A. Sheth, P. Anantharam: Semantic Gateway as a Service Architecture for IoT Interoperability, 2015. ● Moving computation and intelligence closer to data generation. ● Semantic Gateway as a service for interoperability between devices that are using different protocols. III
  21. 21. I II III IV V VI VII III | DATA INTEROPERABILITY Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing21 III
  22. 22. I II III IV V VI VII III | ANALOGY WITH HUMAN HEALTH Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing22 III
  23. 23. I II III IV V VI VII III | TYPES OF INTEROPERABILITY Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing23 Interoperability of ● NWs & protocols ● Data Data interop: ● Domain independent ● Domain specific SenML, SSN Semantic annotation Liu et al, Device-Oriented Automatic Semantic Annotation in IoT, 2017 III
  24. 24. I II III IV V VI VII III | ROLE OF ONTOLOGY/KG FOR INTEROPERABILITY: SSN Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing24 Semantic annotation/ labeling help with shared meaning/uniform interpretation of data SSN ontology provides framework for semantic annotation of sensor/device data; Similarly application/ domain specific ontology/knowledge graph can support semantic annotation wrt to the application/domain/task III
  25. 25. I II III IV V VI VII III | DIKW: DATA, ANNOTATION, ABSTRACTION, ACTION Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing25 Adapted from: Gyrard, et al, Building the Web of Knowledge with Smart IoT Applications (Extended Version), 2016 ISA 95 model III
  26. 26. I II III IV V VI VII III | FACTORY OF FUTURE (FOF) NETWORKING Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing26 Intizar Ali, Pankesh Patel, John Breslin, “Middleware for Real-Time Event Detection and Predictive Analytics in Smart Manufacturing”, 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2019. III
  27. 27. I II III IV V VI VII III | USE OF KNOWLEDGE GRAPHS IN SMART MANUFACTURING Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing27 VISUAL SENSORS Security Cameras, Drones, Inspection Cameras PHYSICAL SENSORS INDUSTRIAL SENSORS Load cell, Accelerometer, Optical sensor, Potentiometer, RTD temperature sensor HEAT MAP SENSOR Infra-Red heat map sensor DIGITAL TWIN Factory Configuration Process simulators Loops Manufacturing Process Manufacturing Knowledge Representation MANUFACTURING ONTOLOGY MANUFACTURING KG Downstream Tasks ADAPTIVE KG UPDATE MODULE Calculating Ont. + KG update MANUFACTURING SCENE/ EVENT UNDERSTANDING FAULT DETECTION Events Features of Interests Computer Vision + Signal Processing Module KG facts extraction and infusion Enhanced Fault Detection Feedback III
  28. 28. I II III IV V VI VII III | REVISITING ARCHITECTURE: DATA TO ABSTRACTION Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing28 III
  29. 29. Computer Vision in Manufacturing Section IV 29
  30. 30. I II III IV V VI VII IV | COMPUTER VISION EXAMPLES IN FOF • Predictive maintenance of machinery: Using IoT sensors to monitor the production line in real-time to reduce unscheduled downtime and increase productivity • Inspection of defectives: Monitor the assembly lines and identify the defective components • Accurate assembly of components: Alert system for misassembly or mid- operation failure. • Quality control for products: Eg: Acquire Automation implements machine vision that permits manufacturers to inspect bottles in a complete 360- degree view to verify that products are placed in the correct packaging • Health and safety: Deep learning-based AI to track the movement of people and predict where the machines are going to be to avoid dangerous interactions Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing30 IV
  31. 31. I II III IV V VI VII IV | INSPECTION SYSTEM Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing31 • Visual sensors(IoT) are deployed to monitor defects • They generate a lot of data and it is difficult to manage large volumes of data • A system to handle and make sense out of such large volumes of data is necessary • Convolutional Neural Networks can be used for defect classification • Besides defect type, the degree of defect can also be quantified IV
  32. 32. I II III IV V VI VII IV | DEFECT DETECTION Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing32 L Li et al, Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing, IEEE Transactions on Industrial Informatics, 14 (10), October 2018 IV
  33. 33. I II III IV V VI VII IV | VIDEO ANALYTICS FOR INDUSTRY 4.0 : DRONE AND SAFETY Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing33 ● Health, Safety and Environment (HSE) inspection ● "Bird eye" view ● Camera to capture images, evidences Drone@ Construction site ● Grid inspection ● Camera to capture any potential issues ● Remote inspection for worker safety Drone@ grid inspection Image source: https://bit.ly/2RgKxeL https://bit.ly/2zKXN4N IV
  34. 34. Predictive Maintenance Section V 34
  35. 35. I II III IV V VI VII V | INTELLIGENT PREDICTIVE MAINTENANCE FOR FAULT DIAGNOSIS Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing35 ML/DL Algorithms used for Fault Diagnosis How AI Affects the Future Predictive Maintenance: A Primer of Deep Learning Li et al, ML algorithms used: SVM, Decision Trees, ANNs, Self-Organizing-Maps and other Statistical Machine Learning techniques V
  36. 36. I II III IV V VI VII V | PREDICTIVE MAINTENANCE BASED ON DEEP LEARNING Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing36 Wang and Wang, How AI Affects the Future Predictive Maintenance: A Primer of Deep Learning, 2018 V Prognostics: probabilities that the system can fail in different time horizon/ Maintenance decision.
  37. 37. NLP and Conversational AI Section VI 37
  38. 38. I II III IV V VI VII VI | CHATBOT AND SMART MANUFACTURING Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing38 Image source: : https://bit.ly/2zLZ9Mo Chatbot features ● Easy to use ● Real-time interactions with devices ● Questions- answer structure ● Natural communication ● Continuous improvement over time ● Personalized relation with engineers (context, history) ● Helping maintenance crews to verify factory's condition ○ Field operation – "What is the temperature reading of a motor #1 of floor #3?" ● Feedback from users on trial runs ○ Improved customer-manufacturer relationship ● Scalable VI
  39. 39. I II III IV V VI VII VI | TAKEAWAY • Manufacturing is a data rich environment. More automation and new manufacturing add to the growth of data • Different area of AI provide ability to improve decision making from different types of data, and for different applications • AI is at the center of the future differentiation and progress in manufacturing Dr Harik's neXt LIVE with Dr. Amit Sheth on AI in Manufacturing39 VI
  40. 40. THANK YOU! neXt LIVE with Dr. Ramy Harik For more information email harik@cec.sc.edu Slide layout by Alex Brasington

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