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Factory 4.0 Toolkit for Smart Manufacturing Training

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Manufacturing Division Session: Make-it!

Tagged Division

Manufacturing

Page Count

10

Permanent URL

https://peer.asee.org/37176

Download Count

457

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Paper Authors

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Joseph Dennis Cuiffi Pennsylvania State University, New Kensington

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Dr. Joseph Cuiffi is the Program Coordinator for the Electro-Mechanical Engineering Technology program at Penn State New Kensington. He is a graduate of Penn State with an honors B.S. and a Ph.D. in the Department of Engineering Science and Mechanics, focused on semiconductor processing. His current interests are in Smart Manufacturing education and workforce development.

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Haifeng Wang Pennsylvania State University, New Kensington

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Dr. Haifeng Wang has received degrees of Doctor of Philosophy in Electrical Engineering (2014), Masters of Science in Control (2006) and Bachelors of Science in Electrical Engineering (2002). Currently, he is an IEEE member and IEEE Pittsburgh Section Executive and Administrative Committee Secretary. His expertise includes control system, power electronics, embedded system, image processing and machine learning.

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Josephine Heim Pennsylvania State University, New Kensington

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Brian W. Anthony Massachusetts Institute of Technology

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Brian Anthony is the Director of the Master's of Engineering in Manufacturing Program and Co-director of the Medical Electronic Device Realization Center at MIT. He has more than 20 years of product realization experience, including instrumentation and measurement solutions for manufacturing systems and medical diagnostics and imaging systems.

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Sangwoon Kim

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David Donghyun Kim Massachusetts Institute of Technology

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David Donghyun Kim received a Bachelor of Applied Science in Mechanical Engineering with Management Science Option from the University of Waterloo and received Master of Science and Ph.D. degrees in Mechanical Engineering from MIT. He is interested in mechanical design for robotic systems. His fundamental research background in CAD/CAM, mainly focusing on 5-axis CNC milling, allowed him to design with manufacturing in mind. He invented and developed multiple mechatronics systems pushing the limits on the current industrial standards.

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Abstract

Factory 4.0 Toolkit for Smart Manufacturing Training The rapid pace of technology development in the field of smart manufacturing has left educational systems scrambling to keep pace and adapt learning outcomes, resulting in inadequate preparedness and readiness of workforce at all levels. Often, smart manufacturing training materials are either broad and conceptual or a specific technical deep dive with little context. We have developed an educational toolkit that leverages an inexpensive, bench scale extrusion platform to provide lab activities and feature-rich data to explore fundamental concepts of smart manufacturing in a production context for an audience of both undergraduate engineering students and current manufacturing workforce members. Through investigation of the mock production platform and associated data, concepts and applications of modern data-driven tools are explored in the topic areas of data collection and the industrial internet of things, data analytics and predictive modeling for production data, simulation and digital twinning, and process and manufacturing systems optimization. The activities culminate in the exploration of advanced feedback control algorithms and optimization of operating conditions, balancing throughput, quality, and power consumption, using digital twins. The combination of overview conceptual materials along with in-depth activities on an actual process allows us to tailor the scope of the specific training to the intended audience. Select modules of the Factory 4.0 toolkit were delivered in an undergraduate course and in a training workshop for manufacturing personnel. Pre- and post-attitude surveys, along with participant comments, were used to assess the training approach and content. We found that the proper technical scope is critical for a given audience and that all types of manufacturing personnel, from technicians and engineers to operations and management, benefit from foundational smart manufacturing concepts and examples. We also found that for technical materials, student audiences required more of the fundamental instrumentation and statistical analysis topics, while current technical practitioners desired specific deep dives into data analytics, digital twinning, and process optimization after introductory overviews. Both educational experiences exposed a need for preparedness in programming and statistical analysis software tools to take advantage of these smart manufacturing concepts.

Cuiffi, J. D., & Wang, H., & Heim, J., & Anthony, B. W., & Kim, S., & Kim, D. D. (2021, July), Factory 4.0 Toolkit for Smart Manufacturing Training Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37176

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