Artificial Intelligence In Manufacturing

AI in Manufacturing
neXt LIVE with Dr. Amit Sheth

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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

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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

AIISC Introduction
Section I
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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
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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
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.
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AI in Manufacturing
Section II
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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]
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“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/
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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.
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II | WHAT IS EXPECTED FOR FACTORY OF FUTURE (FOF)
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.
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From: Luke A. Renner, How Can Artificial Intelligence Be Applied in Manufacturing?

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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
https://www.industryweek.com/technology-and-iiot/article/22027119/benefits-of-
ai-on-manufacturing-a-visual-guide
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II | MANUFACTURING FAILURES: SEVERITY OF THE PROBLEM
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II | AI IN MANUFACTURING
“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
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II | AI IN MANUFACTURING
KEY AI
SUBAREAS
Conversational
AI
Machine & Deep
Learning
Natural
Language
Processing (NLP)
Computer
Vision
Robotics
Knowledge Graph
(Ontology)
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Knowledge Graph/Ontology
Section III
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III | TYPICAL NW ARCHITECTURE FOR FOF: EDGE, FOG, CLOUD
Figure: Li et al, Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay, 2019
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II | INDUSTRY NEXT MANUFACTURING @ MCNAIR, UOFSC
Digital Cell
Actual Cell
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III | CONNECTED MANUFACTURING: SMART IOT AS SOLUTION
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http://wiki.aiisc.ai/index.php/Smart_Data

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III | SEMANTICS AT DEVICE AND FACTORY FLOOR NW PROTOCOL LEVELS
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.
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III | DATA INTEROPERABILITY
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III | ANALOGY WITH HUMAN HEALTH
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III | TYPES OF INTEROPERABILITY
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
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III | ROLE OF ONTOLOGY/KG FOR INTEROPERABILITY: SSN
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
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III | DIKW: DATA, ANNOTATION, ABSTRACTION, ACTION
Adapted from: Gyrard, et al,
Building the Web of Knowledge with Smart IoT Applications (Extended Version), 2016
ISA 95
model
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III | FACTORY OF FUTURE (FOF) NETWORKING
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.
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III | USE OF KNOWLEDGE GRAPHS IN SMART MANUFACTURING
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
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III | REVISITING ARCHITECTURE: DATA TO ABSTRACTION
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Computer Vision in Manufacturing
Section IV
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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
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IV | INSPECTION SYSTEM
• 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
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IV | DEFECT DETECTION
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
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IV | VIDEO ANALYTICS FOR INDUSTRY 4.0 : DRONE AND SAFETY
● 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
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Predictive Maintenance
Section V
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V | INTELLIGENT PREDICTIVE MAINTENANCE FOR FAULT DIAGNOSIS
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
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V | PREDICTIVE MAINTENANCE BASED ON DEEP LEARNING
Wang and Wang, How AI Affects the Future Predictive Maintenance: A Primer of Deep Learning, 2018
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Prognostics: probabilities
that the system can fail in
different time horizon/
Maintenance decision.

NLP and Conversational AI
Section VI
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VI | CHATBOT AND SMART MANUFACTURING
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
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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
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THANK YOU!
neXt LIVE with Dr. Ramy Harik
For more information email harik@cec.sc.edu
Slide layout by Alex Brasington

Artificial Intelligence In Manufacturing

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