Courses

University of Tennessee, Knoxville

Courses Assigned for Graduate Programs

IE 516 Statistical Methods in Industrial Engineering

Offer time: Fall 22, 23, 24, 25

Course description: Probability and statistics basis, central limit theory, sample distribution and statistical inference, historical review of machine learning and artificial intelligence, advanced linear regression and regularization techniques, Python programming.
Referenced books
1. Hayter, A. (2002). Probability and Statistics for Engineers and Scientists, (2nd Ed), Duxbury Publishing, Belmont, CA.
2. Friedman, J., Hastie, T. and Tibshirani, R., 2001. The Elements of Statistical Learning (Vol. 1, No. 10). New York: Springer Series in Statistics.

IE 542 Design of Experiments for Engineering Managers

Offer time: Spring 22, 23, 24, 25
Course description: Principles of experimental design and data collection, response surface methodology, scientific problem-solving paradigms in science and engineering, statistics vs physics, hands-on FEM/CFD simulators in manufacturing as case studies for group projects.
Referenced books
1. Douglas C. Montgomery (2013). Design and Analysis of Experiments, (8th Ed), Wiley. ISBN 978-1118-14692-7.1.
2. Hayter, A. (2002). Probability and Statistics for Engineers and Scientists, (2nd Ed), Duxbury Publishing, Belmont, CA.

New Courses Development for Undergraduate and Ph.D. Students

IE 444/544 Manufacturing Systems Modeling and Analysis

Offer time: Spring 24, 25
Course description: Introduces modern manufacturing through the lens of systems modeling and analysis. The main topics of this course will cover a broad range of concepts, including history of industrial evolution; holistic view of manufacturing from humans, materials, processes, machines, and systems; manufacturing operations management; system and data driven research paradigms; dynamic systems modeling and analytics methods; digital twin techniques; machine learning and artificial intelligence for manufacturing. Real-world and hands-on examples, including subtractive and additive manufacturing, green hydrogen production, manufacturing enterprise management, are used to illustrate, reinforce, and facilitate the adoption of systems modeling and analytics techniques in manufacturing.
Topics
1. Introduction to Manufacturing
2. Manufacturing Processes
3. Manufacturing Systems
4. America's Cutting Edge (ACE) Online Training Module
5. Manufacturing Enterprise Systems and Management
6. Introduction to Dynamic Systems
7. Systems Dynamics Modeling and Analysis
8. Systems Design and Simulation
9. Systems Control and Optimization
10. Systems Performance and Management
11. Advanced Manufacturing + AI
12. Case Studies on Subtractive and Additive Manufacturing
Referenced books
1. Tlusty, Jeorge., 2000. Manufacturing Processes and Equipment. Upper Saddle River, NJ: Prentice-Hall.
2. Bryson, A.E. and Ho, Y.C., 2018. Applied Optimal Control: Optimization, Estimation, and Control. Routledge.
3. Schmitz, T. and Smith, K.S., Machining Dynamics: Frequency Response to Improved Productivity, Second Edition, Springer, New York, NY, 2019.
Development/modification process: This course was 1st time offered in Spring 2024 as IE 491, catalog change requested in 2024 to be co-listed for graduate students as IE 444/544; 2nd time offered in Spring 2025; 3rd time offered in Spring 2026. Catalog name was changed to IE 444/544 in 2024. I developed this course from scratch, have updated it in the ISE undergraduate course catalog, and will offer it each Spring. This course is the only manufacturing processes and engineering course for ISE undergraduate and graduate students.

IE 612 Statistical Learning for Complex Systems

Offer time: Spring 23
Course description: Introduces theory, algorithms, and practice of statistical learning and focus on its interaction and integration with complex systems in science and engineering. With an emphasis of predictability, computability and stability, this course will provide new learning-based tools being developed for modeling, design, control, estimation, and optimization of complex systems including time, event and decision-driven dynamic systems, manufacturing processes and systems, and scientific experimentation. Real-world and hands-on examples are used to illustrate the methods taught and further reinforce and facilitate the integration of statistical learning and systems theory in science and engineering.
Topics
1. Complex Systems Basics and Why Statistical Learning
2. Basic Knowledges of Probability, Statistics, Algebra and Complexity
3. ML/AI through the Lens of Statistical Learning Theory and Approximation Theory
4. Supervised Learning
  - Linear and Nonlinear Methods
  - Neural Network
  - Function Learning/Symbolic Regression
5. Frequentist Statistics and Bayesian Statistics for Learning
6. Systems Modeling, Analysis, Control, Optimization and UQ for Design and Operations
7. Learning for Continuous Variable Dynamic Systems (CVDS)
  - Elements of CVDS
  - Case Study: Machining Dynamics and Additive Friction Stir Deposition
8. Learning for Discrete Event Dynamic Systems (DEDS)
  - Elements of DEDS and Hybrid Systems
  - Case Study: Production Systems Operations
9. Learning for Scientific Experimentation
  - Elements of Scientific Experimentation
  - Case Study: Optimal Experiment Design
Referenced books
1. Thurner, S., Hanel, R. and Klimek, P., 2018. Introduction to the Theory of Complex Systems. Oxford University Press.
2. Cassandras, C.G. and Lafortune, S., 2008. Introduction to Discrete Event Systems. Springer.
3. Vapnik, V., 2000. The Nature of Statistical Learning Theory. Springer.
4. Bryson, A.E. and Ho, Y.C., 2018. Applied Optimal Control: Optimization, Estimation, and Control. Routledge.
Development/modification process: This course was 1st time offered in Spring 2023 as IE 691, catalog change requested in 2024 as IE 612; 2nd time is expected to be offered in Spring 2026. I developed this course from scratch, have updated it in the ISE Ph.D. course catalog, and will offer it every two or three years. About 15 Ph.D. Students from Industrial Engineering, Mechanical Engineering, Materials Science and Engineering, and Electrical Engineering were involved when first offered.

University of Tennessee Space Institute

Courses Assigned for Online Engineering Management Program

EM 516 Statistical Methods in Industrial Engineering

Offer time: Fall 20, 21

EM 542 Design of Experiments for Engineering Managers

Offer time: Spring 21

University of Wisconsin-Madison

ISyE 643 Manufacturing Systems Performance and Analysis

Guest lecturer, Spring 18

America's Cutting Edge (ACE) Program

"Can Machine Tools Think?" - This question has been in Dr. Shi's mind since 2022, when he became an affiliated faculty member of UTK Machine Tool Research Center and work closely with his mentor and lifelong friend Dr. Tony Schmitz. Through years of efforts, Dr. Shi has devoted all his career to Establishing a Machine-Tool-Process-Materials Convergence Framework to Make Machine Tools Think, including machining and Additive Friction Stir Deposition (AFSD), and AFSD-based Hybrid Manufacturing. The research accomplishments are utilized for the development of new engineering workforce training courses in collaboration with America's Cutting Edge (ACE). Dr. Shi's vision or grand aim is to arouse interests in manufacturing, construct and deliver a multi-layer, vertical integration of knowledge to educate and train the hierarchical modern U.S. manufacturing workforce.

Manufacturing mind and physics: Can machine tools think?

New Module for ACE Program: Hybrid Manufacturing. To vertically advance all levels of the manufacturing workforce, a new module of AFSD-based Hybrid Manufacturing will be developed and incorporated into the ACE workforce training program, a national initiative to restore the prominence of the U.S. machine tools sector. Dr. Shi appreciates the great efforts and support from ACE and Dr. Tony Schmitz for enabling the partnership with ACE initiative. This will significantly reduce barriers aligns with Dr. Shi's personal vision to extend advanced manufacturing knowledge and opportunity to all people. New courses for online and in-person training will be developed as below.

New Module for ACE Online Training Course. The online training course will be a 3-hour based curriculum, including four modules: 1) introduction to machining, additive manufacturing, AFSD machine tool, and metrology using structured light scanning; 2) basics of mechatronics and thermo-mechanics in manufacturing processes; 3) machine tool instructions for parameters selection and tool path generation; and 4) research findings of hybrid manufacturing. Course lectures will be delivered through videos. Basic model, simulation and parameter selection capabilities for AFSD + machining will be provided as simple hand-on tools to the student with appropriate functionalities of process parameters adjustment and visualizations. Checkpoint quizzes will be included to assess learning and progress.

New Module for ACE In-person raining Course. Dr. Shi will unify knowledge and action by developing and coupling AFSD in-person training content with the one-week in-person ACE CNC machining course. Trainee will learn the online courses first. Afterwards, the in-person training content will include cohorts of about 10 trainees and will span two days within the five-day training period. Trainees will receive an introduction to AFSD processes, the machine and its operations. Crucially, trainees will acquire experience using the AFSD machine. Together with the skills learned in 3-day CNC machining training, they will deposit the preform of one part (an aluminum piston block) for the oscillating piston air-engine. This preform will be later CNC machined and assembled to make the piston air-engine.

At the conclusion, trainees will receive an online or in-person certificate to signal to employers their knowledge and skills acquired for hybrid manufacturing integrating new AFSD technology, metrology, and CNC machining.

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