Virtual On line
June 22, 2020
June 22, 2020
June 26, 2021
Instructional technologies - Simulations, VR, Remote Education
Manufacturing
10
10.18260/1-2--34519
https://peer.asee.org/34519
399
Dr. Tseng is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research focuses on the computational intelligence, data mining, bio- informatics and advanced manufacturing. Dr. Tseng published in many refereed journals such as IEEE Transactions, IIE Transaction, Journal of Manufacturing Systems and others. He has been serving as a principle investigator of many research projects, funded by NSF, NASA, DoEd, KSEF and LMC. He is currently serving as an editor of Journal of Computer Standards & Interfaces.
Md Fashiar Rahman is currently a doctoral student at the University of Texas at El Paso in Computational Science Program. He earned Master of Science in Computational Science at the University of Texas at El Paso (UTEP) in 2018. He has worked on a number of projects in the field of image data mining, machine learning and deep learning for industrial inspection and quality control. His research interests are in big data analytics, application of machine learning and deep learning for digital quality control. Email: mrahman13@miners.utep.edu
Dr. Richard Chiou is Associate Professor within the Engineering Technology Department at Drexel University, Philadelphia, USA. He received his Ph.D. degree in the G.W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. His educational background is in manufacturing with an emphasis on mechatronics. In addition to his many years of industrial experience, he has taught many different engineering and technology courses at undergraduate and graduate levels. His tremendous research experience in manufacturing includes environmentally conscious manufacturing, Internet based robotics, and Web based quality. In the past years, he has been involved in sustainable manufacturing for maximizing energy and material recovery while minimizing environmental impact.
Ivan Arturo Renteria Marquez was born in Ciudad Juárez, Chihuahua. In the spring of 2005 he entered the Mechatronic engineering program at Tecnológico de Monterrey with an academic scholarship. In the spring of 2011 he graduated with distinction. In the spring of 2012 he entered the PhD fast track program in Electrical and Computer Engineering offered at the University of Texas at El Paso. In the spring of 2016 he obtained his Philosophy Degree with specialty in Electrical engineering. He currently works as Research Assistant Professor in the Industrial Manufacturing and Systems Engineering Department at University of Texas at El Paso.
Aditya Akundi is currently affiliated to the Manufacturing and Industrial Engineering Department, at the University of Texas Rio Grande Valley. He earned a Bachelor of Technology in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, India. He earned a Master of Science in Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). and a Ph.D. in Electrical and Computer Engineering, with a concentration in Industrial and Systems Engineering (ISE) in 2016. His research is focused on understanding Complex Technical and Socio-Technical Systems from an Information Theoretic approach. He has worked on a number of projects in the field of Electrical & Computer Engineering, Systems Engineering, Additive Manufacturing and Green Energy Manufacturing. His research interests are in Systems Engineering & Architecture, Complex systems, Systems testing and Application of Entropy to Complex Systems.
Dr. Lopes' research interests focus on additive manufacturing and its applications. His research also includes Service Systems Engineering applications to additive manufacturing, healthcare, and energy systems. He is also interested in the application of lean manufacturing principles to improve additive manufacturing processes and operations.
In recent time, manufacturing environment are bracing higher end technologies to achieve high productivity, efficiency and effectiveness. Due to the adoption of the sophisticated technologies, the system is becoming very complex and stochastic. Moreover, the various subsystems and components add an extra level of difficulty to understand the entire manufacturing process, which cannot be described only through theoretical explanation. As a powerful tool, computer simulation can be integrated to design and visualize the stochastic and complex system. This paper uses the advantage of simulation to describe a series classroom activities using SIMIO software, which are deployed in industrial systems simulation course in the University of Texas at El Paso. Beside the authors show a noble approach to demonstrate the simulation using the virtual reality (VR) technology in Unity platform. Integration of these simulation based classroom activities provide three-fold benefit to students’ learning process; (1) students can experience the real time situation by imitating a manufacturing environment, (2) it provides an effective way to evaluate the design and operational performance of manufacturing systems, and (3) it motivates the students to be engaged in the classroom activities besides learning the theoretical knowledge.
Tseng, T. B., & Rahman, M. F., & Chiou, R., & Renteria-Marquez, I. A., & Akundi, A., & Lopes, A. J., & Senthilkumar, J. (2020, June), Embedding Computer Simulation-based Classroom Activities to Enhance the Learning Experience for Manufacturing Systems Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34519
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