Data Analytics Short Courses for Reskilling and Upskilling Indiana's Manufacturing Workforce

Ted J. Fiock; Jonathan Mohn; John Mack; Charilaos Mousoulis; Eunseob Kim; Lucas Wiese; Alejandra J. Magana; Martin Jun; Ali Shakouri

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Manufacturing Division (MFG) Poster Session
Tagged Division: Manufacturing Division (MFG)
Page Count: 9
DOI: 10.18260/1-2--42428
Permanent URL: https://peer.asee.org/42428
Download Count: 113

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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.

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Lucas Wiese Purdue University at West Lafayette (COE) orcid.org/0009-0008-3620-0035

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I am a PhD student at Purdue University in the Computer & Information Technology department. I work in the Research On Computing in Engineering and Technology Education lab under Prof. Alejandra J. Magana and the Governance and Responsible AI Lab under Prof. Daniel S. Schiff.

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Martin Jun Purdue University

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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.

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Alejandra J. Magana Purdue University at West Lafayette (COE) orcid.org/0000-0001-6117-7502

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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

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Ali Shakouri

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Ali Shakouri is the Mary Jo and Robert L. Kirk Director of the Birck Nanotechnology Center and a Professor of Electrical and Computer Engineering at Purdue University in Indiana. He received his Engineering degree from Telecom Paris, France in 1990 and Ph

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Abstract

Data analytics and Artificial Intelligence (AI) have transformed many industries in the last decade. In tandem, a skilled workforce needs to understand how to gather/access data to extract trends and optimize operations, and how to label the key events and develop training data sets which can be used by machine learning (ML) experts for advanced analytics. The power of ML and AI has not been fully realized in the manufacturing sector. One of the major challenges is that the small and medium manufacturers which account for 98% of industry lack the dedicated data analytic workforce. This is combined with aging workers and significant challenges in hiring factory floor workers. To address this need, partnerships have been established between industry and academia through Wabash Heartland Innovation Network (WHIN) at Purdue University. In collaboration with Ivy Tech Community College, a series of workshops were developed to introduce data analytics, internet of things and basic machine learning concepts to local small and large manufacturing companies. This study will describe three short courses geared toward industry workers and professionals. The first short course is on the topic of energy savings and data analytics for Variable Frequency Drives (VFDs). The main goal of this workshop was to introduce electric motor data and VFDs for motor control to industry partners. Motors are a major source of energy consumption in manufacturing and other industries. In right applications, VFDs can reduce energy usage with relatively short return on investments. Using VFD or utilizing SMART motor overload devices, it is possible to gather data for motor diagnostics, predictive maintenance, and process monitoring. The second course is on the topic of recording and managing data from factory workers’ observations. A key requirement for some machine learning application is the training data set and labeling of key events to complement automatically recorded machine data. In this workshop, AirTable will be introduced as a method to reduce paperwork and help capture key events and observations. Examples from electric motor maintenance and computer numerical control (CNC) machining are provided. The third short course will be on the topic of industrial internet of thing (IIoT) sensors. The course objective is to introduce IIoT device installation, accessing the data (e.g. power consumption, vibration, temperature) and use dashboards and alarms to monitor the operations. To provide formative feedback on the impact of the three short courses for industry on data in manufacturing, we used an adapted framework of the Kirkpatrick Training Evaluation Model (Kirkpatrick, 1996), combining elements of Guskey's Evaluation Model of faculty development (Guskey & Sparks, 1991). Kirkpatrick's and Guskey's models have the first two levels (Level 1 and Level 2) in common, namely, assess of participant's reactions and learning. The third level (Level 3) focuses on organizational support for promoting change, accounting for contextual effects. The fourth and fifth levels of the model (Level 4 and Level 5) focus on the use of the new knowledge and skills as applied in practice and the overall impact on the organization. This study reports on participants' perceptions regarding Levels 1, 2, and 3 of our evaluation model. Levels 1 and 3 were assessed immediately after the workshop through a survey, and level 2 required applying a form of assessment immediately after the workshop (i.e., performance task). In our future work, we will assess Levels 4 and 5 annually, starting a year after the implementation of the short courses, using objective metrics and interviews with managers. This assessment strategy will start over for each cohort of new participants entering the professional development program.

Citation
Format

Fiock, T. J., & Mohn, J., & Mack, J., & Mousoulis, C., & Kim, E., & Wiese, L., & Jun, M., & Magana, A. J., & Shakouri, A. (2023, June), Data Analytics Short Courses for Reskilling and Upskilling Indiana's Manufacturing Workforce Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42428

TY - CPAPER
AB - Data analytics and Artificial Intelligence (AI) have transformed many industries in the last decade. In tandem, a skilled workforce needs to understand how to gather/access data to extract trends and optimize operations, and how to label the key events and develop training data sets which can be used by machine learning (ML) experts for advanced analytics. The power of ML and AI has not been fully realized in the manufacturing sector. One of the major challenges is that the small and medium manufacturers which account for 98% of industry lack the dedicated data analytic workforce. This is combined with aging workers and significant challenges in hiring factory floor workers.
To address this need, partnerships have been established between industry and academia through Wabash Heartland Innovation Network (WHIN) at Purdue University. In collaboration with Ivy Tech Community College, a series of workshops were developed to introduce data analytics, internet of things and basic machine learning concepts to local small and large manufacturing companies. This study will describe three short courses geared toward industry workers and professionals. The first short course is on the topic of energy savings and data analytics for Variable Frequency Drives (VFDs). The main goal of this workshop was to introduce electric motor data and VFDs for motor control to industry partners. Motors are a major source of energy consumption in manufacturing and other industries. In right applications, VFDs can reduce energy usage with relatively short return on investments. Using VFD or utilizing SMART motor overload devices, it is possible to gather data for motor diagnostics, predictive maintenance, and process monitoring. The second course is on the topic of recording and managing data from factory workers’ observations. A key requirement for some machine learning application is the training data set and labeling of key events to complement automatically recorded machine data. In this workshop, AirTable will be introduced as a method to reduce paperwork and help capture key events and observations. Examples from electric motor maintenance and computer numerical control (CNC) machining are provided. The third short course will be on the topic of industrial internet of thing (IIoT) sensors. The course objective is to introduce IIoT device installation, accessing the data (e.g. power consumption, vibration, temperature) and use dashboards and alarms to monitor the operations.
To provide formative feedback on the impact of the three short courses for industry on data in manufacturing, we used an adapted framework of the Kirkpatrick Training Evaluation Model (Kirkpatrick, 1996), combining elements of Guskey&#39;s Evaluation Model of faculty development (Guskey &amp;amp; Sparks, 1991). Kirkpatrick&#39;s and Guskey&#39;s models have the first two levels (Level 1 and Level 2) in common, namely, assess of participant&#39;s reactions and learning. The third level (Level 3) focuses on organizational support for promoting change, accounting for contextual effects. The fourth and fifth levels of the model (Level 4 and Level 5) focus on the use of the new knowledge and skills as applied in practice and the overall impact on the organization.
This study reports on participants&#39; perceptions regarding Levels 1, 2, and 3 of our evaluation model. Levels 1 and 3 were assessed immediately after the workshop through a survey, and level 2 required applying a form of assessment immediately after the workshop (i.e., performance task). In our future work, we will assess Levels 4 and 5 annually, starting a year after the implementation of the short courses, using objective metrics and interviews with managers. This assessment strategy will start over for each cohort of new participants entering the professional development program.

AU - Ted J. Fiock
AU - Jonathan Mohn
AU - John Mack
AU - Charilaos Mousoulis
AU - Eunseob Kim
AU - Lucas Wiese
AU - Martin Jun
AU - Alejandra J. Magana
AU - Ali Shakouri
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Data Analytics Short Courses for Reskilling and Upskilling Indiana's Manufacturing Workforce
UR - https://peer.asee.org/42428
DO - 10.18260/1-2--42428
ER -