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NSF: Integrative Manufacturing and Production Engineering Education Leveraging Data Science Program (IMPEL)

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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

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

6

Permanent URL

https://peer.asee.org/37530

Download Count

51

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Paper Authors

biography

Mohsen Moghaddam Northeastern University

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Mohsen Moghaddam, Ph.D., is an Assistant Professor of Mechanical and Industrial Engineering at Northeastern University. Prior to joining Northeastern, he was with the GE-Purdue Partnership in Research and Innovation in Advanced Manufacturing as a Postdoctoral Associate. He received his PhD from Purdue University in 2016. His areas of research interest include cyber-physical manufacturing, human-technology collaboration, user-centered design, artificial intelligence, and machine learning. He is coauthor of over 40 refereed journal articles and two books including Revolutionizing Collaboration Through e-Work, e-Business, and e-Service (Springer, 2015), and Best Matching Theory & Applications (Springer, 2017). He also served as a reviewer for several international journals such as Int. J. Production Economics, Int. J. Production Research, J. Intelligent Manufacturing, Computers & Industrial Engineering, Decision Support Systems, Computers in Industry, IEEE Trans. on Industrial Informatics, IEEE Trans. on Systems, Man, & Cybernetics, and IEEE Trans. on Automation Science and Engineering. His scholarly research is supported by the U.S. National Science Foundation (NSF) and industry.

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Jacqueline A. Isaacs Northeastern University Orcid 16x16 orcid.org/0000-0001-8406-0083

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Dr. Jacqueline Isaacs joined Northeastern in 1995 and has focused her research pursuits on assessment of the regulatory, economic, environmental and ethical issues facing the development of nanomanufacturing and other emerging technologies. Her 1998 NSF Career Award is one of the first that focused on environmentally benign manufacturing. She also guides research on development and assessment of educational computer games where students explore environmentally benign processes and supply chains in manufacturing. She has been recognized by Northeastern University, receiving a University-wide Excellence in Teaching Award in 2000, the President’s Aspiration Award in 2005, and a College of Engineering Excellence in Mentoring Award in 2015. An ELATE Fellow, Dr. Isaacs has served in numerous administrative leadership roles at Northeastern.

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Sagar Kamarthi Northeastern University Orcid 16x16 orcid.org/0000-0001-5845-7714

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Sagar Kamarthi is a Professor of Mechanical and Industrial Engineering and Director of Data Analytics Engineering Program at the Northeastern University, Boston, MA. He received his MS and PhD degrees from the Pennsylvania State University. He teaches courses in data analytics and visualization. His research interests are in machine learning applications in smart manufacturing and personalized healthcare. His published over 200 peer-reviewed research papers. He received multiple best paper awards. He secured over $11 Million worth of research funding from various funding agencies. He received the 2020 University Excellence in Teaching Award, 2019 College of Engineering Martin W. Essigmann Outstanding Teaching Award and the 2016 College of Engineering Outstanding Faculty Service Award.

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Martin Storksdieck Oregon State University

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I am Professor of Education and director of Oregon State University’s STEM Research Center. The Center consists of a team of dedicated professionals of various disciplinary backgrounds who conduct applied research on STEM education and science engagement at the intersection of research, policy and practice, with a strong focus on equity and social justice. Prior to joining OSU, I directed the Board on Science Education and the Roundtable on Climate Change Education at the U.S. National Academy of Sciences. Currently, I serve on the Science Advisory Boards for the National Oceanic and Atmospheric Administration (NOAA) and the Leibniz Institute for Science and Mathematics Education in Kiel (Germany). I am also the Chair of Trustees for TERC, a nonprofit R&D, and serve on the board of the Tree Media Foundation. Previously, I served on the boards of the Citizen Science Association and the Visitor Studies Association.

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Xiaoning Jin Northeastern University

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Prof. Xiaoning (Sarah) Jin’s research focus is in the area of modeling and analysis for intelligent and advanced manufacturing processes and systems, with a specialization in diagnostics and prognostics (D&P), control and predictive decision making. Her works have been applied to a variety of industry applications ranging from automotive manufacturing, roll-to-roll printing process monitoring, precision manufacturing processes, smart operations and maintenance strategy for maritime equipment, etc. Prof. Jin’s research has been sponsored through multiple federal agencies, including NSF, Manufacturing USA Institutes and industry.

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Abstract

IMPEL is a transformative workforce education and training program that addresses the current and projected skills gaps and requirements in the area of data science in the U.S. manufacturing sector. The mission of IMPEL is to facilitate lifelong learning for the production engineering STEM workforce through designing sustainable, pedagogically-proven data science curricula via modular courses with interactive online learning labs and experiential project-based learning.

The project team has accomplished three main tasks towards the goals of the project in Year 1.

(1) Developed a data-driven skills gap and requirement analysis that utilizes the Emsi Labor Market Analytics data to understand the supply-demand tradeoff of critical skills and domain knowledge perceived in the U.S. manufacturing industry. This study also identifies the critical skills and domain knowledge required for data science related jobs that are highly in-demand in today’s and future advanced manufacturing industry.

(2) Conducted a comprehensive review of emerging research topics and trends in the broader area of manufacturing/production science and engineering that leverage data science. The review also includes a comprehensive analysis of the current state of data science and techniques employed for tackling challenging problems in the Industry 4.0 environment. By performing keyword co-occurrence network analysis, the research team discovers the structure and pattern of knowledge components and their trajectories in the field of manufacturing.

(3) Conducted interviews and consultations with industry experts from diverse manufacturing companies to capture, analyze, and incorporate their experiences associated with acquiring and using relevant on-the-job skills in the design of course modules that serve practical needs and learning preferences.

The findings from these activities are collectively used to tailor the curricula and courses, and in turn address the skills gaps of individuals in the current and future workforce through optimized modularization and customization of learning materials. The IMPEL team has completed the design and development of the first course, “Data Analytics”, and would be completing the second and third courses, “Sensor Analytics” and “Algorithms for Engineering Applications” in next two quarters.

Moghaddam, M., & Isaacs, J. A., & Kamarthi, S., & Storksdieck, M., & Jin, X. (2021, July), NSF: Integrative Manufacturing and Production Engineering Education Leveraging Data Science Program (IMPEL) Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37530

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