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DM 810

DM 810 - Intelligent Manufacturing

The objectives of this course are to develop a basic understanding of machine intelligence and explore modern tools in designing intelligent manufacturing systems. Through the lectures, on-site visit, reading assignments, and course project(s) the participants will examine how knowledge-based systems (KBSs) and learning systems can effectively improve the performance of machine tools, work cells, and overall manufacturing enterprises.

Course Leader: George Knopf, Western Engineering

Course Outline

Intelligent Manufacturing involves a variety of software technologies and novel flexible fabrication processes that enable efficient and cost effective automation for small, medium and large-scale discrete product manufacturing. These advanced technologies provide opportunities and introduce challenges to traditional product development engineers, system designers, and manufacturing personnel. The first module will focus on intelligent systems in manufacturing and the application of artificial intelligence on improving product design, system control, process planning and fault diagnostics. The second module will explore automated fabrication and manufacturing processes that have emerged because of advances in software and computing hardware. These processes include flexible robotic assemble, 3D printing, printed electronics and laser microfabrication.

Course Objectives

The objectives of this course are to develop a basic understanding of machine intelligence and explore modern intelligent manufacturing systems. At the end of the course, each student should be able to:

  • Describe how machine intelligence can improve manufacturing enterprises
  • Discuss the role of knowledge engineering
  • Explain the fundamental principles of knowledge-based systems (KBS), fuzzy logic (FL) systems, and artificial neural networks (ANN)
  • Apply simple AI algorithms to intelligent control, signal processing, pattern classification, production planning and scheduling
  • Critically evaluate intelligent and flexible manufacturing automation
  • Assess the suitability of emerging flexible fabrication technologies

Activities and Schedule

Each student is expected to attend all sessions and participate fully in all discussions, assignments and projects. Active participation is critical because a significant portion of class time has been allocated towards group discussions and assignments. The individual class participation grade will be based, in large part, upon the student’s demonstrated knowledge and in-class remarks.

Module 1

Day 1 - Intelligent Manufacturing and Product Design
  • Welcome and brief history of manufacturing and automation
  • Human factors in manufacturing
  • Design for Manufacturability and Assembly (DFMA)
  • Introduction to role of machine intelligence in manufacturing enterprises
  • Components of intelligent manufacturing systems
Day 2 - Machine Intelligence
  • Introduction to machine intelligence and knowledge engineering
  • Structure of rule-based systems
  • Representing human expertise and reasoning
  • Fuzzy (soft) logic process control
  • Development of midterm project proposal
  • Welcome and brief history of manufacturing and automation
Day 3 - Adaptive Learning Systems
  • Introduction to adaptive linear filters and artificial neural networks (ANNs)
  • Common ANN architectures (feed-forward, radial basis function, self-organizing feature map)
  • Adaptation and learning
  • Engineering applications of neural networks (curve fitting, process modeling and control, pattern classification, task assignment and scheduling, optimization)
  • Preliminary work on midterm project
Day 4 - Applications of Intelligent Manufacturing
  • Expert systems in engineering design, part fabrication and product assembly
  • Selection of development tools
  • Brief in-class presentation of proposed midterm projects

Note: Module I will conclude at 12:00pm.

Between Modules

Midterm project report (group) and preparation of presentation on the implementation of an AI technique (KBS, FL, ANN) for an engineering design or manufacturing application.

Module 2

Final project report (individual) and supplementary material on an advanced fabrication technology or automated system that improves the efficiency or enhances an identified manufacturing process. The completed project is to be electronically transmitted to the course instructor within 3 weeks of the last lecture in Module 2.

Day 1 - Implementation of AI Techniques
  • Midterm project presentations
  • Data base design: object oriented and relational; introduction to fuzzy sets
Day 2 - Flexible Fabrication Processes
  • Computer assisted technologies for design and manufacture
  • CAD/CAM and virtual reality tools
  • 3D printing and rapid prototyping
  • Flexible robotic assembly
  • Development of final project proposal
Day 3 - Emerging Flexible Fabrication Technologies
  • Mechatronic system integration
  • Printed electronics: fundamental principles and opportunities
  • laser material processing and microfabrication
  • reparation of final project proposal (written)
Day 4 - Future of Intelligent Manufacturing
  • Manufacturing challenges in a globally competitive environment
  • Case-study discussion
  • Wrap-up and submission of short written proposal

Course Evaluation

  • 30% - Mid-term project report (group)
  • 20% - Mid-term project presentation (group)
  • 10% - Written proposal for final project (individual)
  • 30% - Final project report (individual)
  • 10% - Class participation (individual)

Biography of Course Leader

George Knopf, P.Eng.
gkknopf@uwo.ca

George Knopf is a Professor in the Department of Mechanical and Materials Engineering at Western University. His research interests include bioelectronics, biosensors, laser material processing, and micro-optical transducers. Dr. Knopf’s current work involves the development of conductive graphene-based inks and novel fabrication processes for printing electronic circuitry on a variety of mechanically flexible substrates (polymers, paper, silk). He has acted as a technical reviewer for numerous academic journals, conferences, and granting agencies and has co-chaired several international conferences. In recent years, he has also co-edited two books entitled “Smart Biosensor Technology” and “Optical Nano and Micro Actuator Technology.

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University of Western Ontario
Queen's University