Taking an Experiential Learning Approach to Industrial IoT Implementation for Smart Manufacturing through Course Work and University-Industry Partnerships
- Conference
- Location
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Baltimore , Maryland
- Publication Date
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June 25, 2023
- Start Date
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June 25, 2023
- End Date
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June 28, 2023
- Conference Session
- Tagged Division
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Manufacturing Division (MFG)
- Tagged Topic
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Diversity
- Page Count
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15
- DOI
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10.18260/1-2--42432
- Permanent URL
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https://peer.asee.org/42432
- Download Count
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138
Paper Authors
Eunseob Kim Purdue University
Eunseob Kim is a Ph.D. student in the School of Mechanical Engineering at Purdue University, IN, USA. He received his BS degree in Mechanical Engineering from Gyeongsang National University, Korea in 2013, and his MS degree in Mechanical and Aerospace Engineering from Seoul National University, Korea in 2016. His research interests include smart monitoring, sound recognition, and artificial intelligence application for manufacturing.
Lucas Wiese
Purdue University at West Lafayette (COE)
orcid.org/0009-0008-3620-0035
I am a PhD student at Purdue University in the Computer & Information Technology department with a focus in AI education efforts and responsible AI development. I work in the Research On Computing in Engineering and Technology Education lab under Prof. Alejandra J. Magana.
Hector Will Oakland City University
I am an assistant professor in Creative Technologies and Mathematics. My research interests are at the intersection of Science, Engineering, Technology, and Learning. I have experience developing learning materials for emerging topics such as Machine Learning and Quantum Computing using novel technologies.
Alejandra J. Magana
Purdue University at West Lafayette (COE)
orcid.org/0000-0001-6117-7502
Alejandra J. Magana, Ph.D., is the W.C. Furnas Professor in Enterprise Excellence in the Department of Computer and Information Technology with a courtesy appointment at the School of Engineering Education at Purdue University. She holds a B.E. in Informa
Martin Jun Purdue University
Dr. Martin Jun is a Professor of the School of Mechanical Engineering at Purdue University, West Lafayette, IN, USA. Prior to joining Purdue University, he was an Associate Professor at the University of Victoria, Canada. He received the BSc and MASc degrees in Mechanical Engineering from the University of British Columbia, Vancouver, Canada in 1998 and 2000, respectively. He then received his PhD degree in 2005 from the University of Illinois at Urbana-Champaign in the Department of Mechanical Science and Engineering. His main research focus is on advanced multi-scale and smart manufacturing processes and technologies for various applications. His sound-based smart machine monitoring technology led to a start-up company on smart sensing. He has authored over 150 peer-reviewed journal publications. He is an ASME fellow and Area Editor of Journal of Manufacturing Processes. He is also the recipient of the 2011 SME Outstanding Young Manufacturing Engineer Award, 2012 Canadian Society of Mechanical Engineers I.W. Smith Award for Outstanding Achievements, and 2015 Korean Society of Manufacturing Technology Engineers Damwoo Award.
Abstract
As IoT and AI continue to reshape industrial processes and product lifecycles, the need for retraining current workers and attracting future ones to the manufacturing industry has grown. Nationwide, The US manufacturing sector is expected to have 2.1M unfilled jobs by 2030, a shortage that will be led by gaps in filling and retaining skilled positions. The problem further intensifies because although the manufacturing workforce growth results in new jobs and higher wages, manufacturers face challenges in recruiting well-qualified workers and professionals. While reskilling and upskilling efforts will be needed for the current workforce, particularly in the floor plant, new jobs and occupations will emerge. These new jobs will require professionals and future managerial employees to have strong data science skills in order to effectively design and oversee future AI-enabled manufacturing systems. However, a critical gap exists between traditional analytic/numeric engineering education and computer science/AI development that can provide skills to effectively enact and manage the full data science cycle.
To take steps toward preparing engineering graduates to effectively work with data, starting from data collection through sensors to data analysis and insight enabled by dashboards, [Midwestern] University designed and implemented a graduate course in partnership with local industries. The course titled Industrial IoT Implementation for Smart Manufacturing provides an introduction to the industrial internet of things (IoT) implementation on real production machines for smart manufacturing. It is a practical lab/project course that allows engineering students to implement IoT sensors and devices on real production machines at local manufacturing companies, collect data, perform data analytics for the company’s benefit, and demonstrate the visualization of the analyzed data. Students worked with a local manufacturing company to support the implementation of sensors and devices to a production machine, collection of data, analytics, and visualization.
Kolb’s Experiential Learning model will be used as an explanatory approach to describe the course design and implementation. In the final version of this study, we will fully unpack the learning objectives of the course and how those were enacted by the students through the laboratory assignments and the final project implemented at a particular company. Specifically, we will describe how students enacted a learning process where new knowledge and skills resulted from the combination of grasping and transforming their experience through the acquisition of abstract concepts that were then practiced through laboratory experiences and then applied flexibly in a real-world industry situation. We will also describe some of the projects students’ implemented at local companies and the potential benefits or outcomes resulting from those implementations.
Kim, E., & Wiese, L., & Will, H., & Magana, A. J., & Jun, M. (2023, June), Taking an Experiential Learning Approach to Industrial IoT Implementation for Smart Manufacturing through Course Work and University-Industry Partnerships Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42432
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