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DM 810

DM 810 - Intelligent Manufacturing

A course that sets out to develop a basic understanding of machine intelligence and explore modern intelligent manufacturing systems. Through lectures, group discussion, assigned readings, and projects the course participants will examine how knowledge-based systems (KBSs) and artificial neural networks (ANN) can improve the performance of machine tools, work cells, and overall manufacturing enterprises. Students will also explore new fabrication processes that have emerged because of advances in software.

Course Leader: George Knopf, PhD, P.Eng., Western University

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. Through lectures, group discussions, assigned readings, and group midterm project the participants will develop a basic understanding of machine intelligence and explore how knowledge-based systems and adaptive artificial neural networks can improve the performance of machine tools, work cells, and overall manufacturing enterprises. The second module will explore automated fabrication and manufacturing processes that have emerged because of advances in software and computing hardware. The students will exploit the principles of machine intelligence and flexible manufacturing by undertaking an individual final project.

Course Objectives

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 machine learning 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. Students require a laptop each day of each module and may be required to access interactive tools for simplified AI programming.

Module 1

Day 1 - Introduction to Intelligent Manufacturing
  • Course perspective (focus on product & process)
  • Brief history of manufacturing and automation
  • Manufacturability and assembly (DFMA)
  • What is intelligence?
  • Intelligent systems in manufacturing
Day 2 - Smart Products and Processes
  • Integrating physical systems with computing technology
  • Introduction to machine intelligence and knowledge engineering
  • Decision trees and Knowledge-Base Systems (KBS)
  • Fuzzy (soft) logic process control
  • Development of midterm project proposal
Day 3 - Machine Learning
  • Principles of artificial neural networks (ANNs)
  • Multilayered perceptron, radial basis function networks, and self-organizing feature maps
  • Learning algorithms (Backpropagation, LMS)
  • Applications of KBS and ANNs to intelligent manufacturing
  • Brief presentation of midterm project proposals

Between Modules

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

Module 2

Day 4 - Industry 4.0
  • “Internet-of-Everything” and the Smart Factory
  • Enabling intelligent manufacturing through communication and control
  • Applications of wireless technology (Bluetooth, RFID)
  • Introduction to the final project
  • Preparation of midterm project presentations
Day 5 - Technologies Supporting Industry 4.0
  • Presentation of midterm projects
  • Additive manufacturing
  • Flexible fabrication
  • Printable electronics: principles and opportunities
  • Preparation of proposal for final project
Day 6 - Future of Intelligent Manufacturing
  • Manufacturing challenges in a globally competitive environment
  • Intelligent manufacturing in healthcare (striving for quality)
  • Case-study discussion
  • Brief presentation of final project proposals
  • Wrap-up

After Module:

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

Course Evaluation

  • 10% - Midterm project proposal (group)
  • 20% - Midterm project presentation (group)
  • 30% - Midterm project report (group)
  • 10% - Final project proposal (individual)
  • 30% - Final project report (individual)

Course Materials

Course notes and supplemental reading material are provided to complement the lectures in Module 1 and Module 2.


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