2025 ASEE Annual Conference & Exposition

Development of Digital Laboratory Modules Using Computer Simulation For Enhanced Learning Experience in Manufacturing Education

Presented at Manufacturing Division (MFG) Technical Session 5

The complexity of modern manufacturing environments, characterized by interactions among various entities, variability, and randomness, presents significant challenges for learners. Understanding these dynamics is essential, but traditional classroom-only focused education often falls short in providing students with practical insights. Hands-on experimentation is vital for students to observe interactions and experience process manipulations, yet such experimental setups can be costly and impractical for many institutions. This paper presents the development of digital laboratory modules to enhance student's learning experience in manufacturing education through computer simulation techniques. Three modules were created to address complex manufacturing issues: production design under demand uncertainty, manufacturing layout design, and predictive maintenance. These modules allow users to control process parameters, design experiments, run simulations, and observe outcomes, promoting informed decision-making without wasting resources. This approach is particularly valuable for resource-constrained industries, facilitating rapid decision-making and process efficiency. Each module is based on case studies with background information, problem statements, datasets, and expected results. The paper will detail the development process and case studies and provide experimentation guidelines for using the modules effectively in educational settings.

Authors

Dr. Md Fashiar Rahman http://orcid.org/0000-0002-0437-2587 University of Texas at El Paso [biography]

Dr. Md Fashiar Rahman is an Assistant Professor of the Industrial, Manufacturing and Systems Engineering (IMSE) Department at The University of Texas at El Paso. He holds a Ph.D. degree in Computational Science Program. He has years of research experience in different projects in the field of image data mining, machine learning, deep learning, and computer simulation for industrial and healthcare applications. In addition, Dr. Rahman has taught various engineering courses in industrial and manufacturing engineering. His research area covers advanced quality technology, AI application in smart manufacturing, health care applications, computational intelligence/data analytics, and decision support systems.
Prof. Tzu-liang Bill Tseng University of Texas at El Paso [biography]

Dr. Bill Tseng is a Professor and Chair of the Department of Industrial, Manufacturing, and Systems Engineering at UTEP. He is also a Director of the Research Institute for Manufacturing & Engineering Systems, the host institute of TMAC.
Dr. Richard Y Chiou Drexel University [biography]

Dr. Richard Chiou is Professor within the Engineering Technology Department at Drexel University College of Engineering, Philadelphia, USA. He received his Ph.D. degree in the G.W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology.
Jakia Sultana University of Texas at El Paso
Mr. S M Atikur Rahman University of Texas at El Paso [biography]

S M Atikur Rahman is a Research Assistant in the Department of Aerospace and Mechanical Engineering (AME) at the University of Texas at El Paso (UTEP). Currently, he is pursuing his PhD degree in Mechanical Engineering at UTEP. He has completed his B.Sc. degree in Industrial and Production Engineering (KUET_Bangladesh) and MSc in Industrial Engineering (UTEP_USA). Mr. Atikur is working in the field of Simulation (AnyLogic), Deep Learning, and Smart Manufacturing Systems for Industrial Applications. Previously, he had Job experience in the field of Sourcing & Procurement, Production, and Supply chain in different companies in Bangladesh for more than seven years. His GitHub account is https://github.com/Atik1219. and email is atik.kuet.09@gmail.com.
Selim Molla University of Texas at El Paso

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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