Design and Development of Machine Learning Projects for Engineering Students
- Conference
- Location
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Marietta, Georgia
- Publication Date
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March 10, 2024
- Start Date
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March 10, 2024
- End Date
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March 12, 2024
- Page Count
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6
- DOI
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10.18260/1-2--45514
- Permanent URL
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https://strategy.asee.org/45514
- Download Count
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22
Paper Authors
Arash Afshar
Dr. Arash Afshar is currently an associate professor in the School of Engineering at Mercer University. He earned his M.S in systems and design and Ph.D. in solid mechanics from the State University of New York at Stony Brook. He also received his B.S and M.S in Solid Mechanics from Amirkabir University of Technology in Tehran, Iran. His teaching and research interests are in the areas of composite materials, finite element analysis, mechanical design and machine learning. Prior to joining Mercer University, he taught at Saginaw Valley State University and worked as a design engineer in oil and gas industry.
Dorina Marta Mihut
Dr. Dorina Marta Mihut is an Associate Professor in the Mechanical Engineering Department at Mercer University School of Engineering. She graduated with Ph.D. in Materials Science at University of Nebraska-Lincoln; Ph.D. in Technical Physics at Babes-Bolyai University, Romania; M.S. in Mechanical Engineering, University of Nebraska-Lincoln; and B.S. in Mechanical Engineering at Technical University Cluj-Napoca, Romania. Her teaching and research interests are in the area of materials science and engineering, thin films and coatings depositions using physical vapor deposition systems and related analysis, coatings for wear and corrosion resistance improvement, environmental protection, protection against electromagnetic interference, and antibacterial coatings. Before joining Mercer University, Dr. Dorina Mihut worked as Associate Professor at The University of Texas Pan American, TX, USA, and as Process Engineer at Ion Bond, IHI Group, USA.
Education
Ph.D Materials Science, University of Nebraska, Lincoln
Ph.D Technical Physics, Babes-Balyai University, Cluj-Napoca, Romania
M.S. Mechanical Engineering, University of Nebraska, Lincoln
B.S. Mechanical Engineering, Technical University of Cluj – Napoca, Romania
Michael Ryan Sweeney
Abstract
This research project presents a valuable educational opportunity for engineering students to gain hands-on experience in the application of machine learning algorithms to real-world engineering challenges. The study focuses on the development of a predictive model for Young's modulus and Poisson's ratio of Auxetic materials, known for their unique negative Poisson’s ratio property, using the Python programming language in conjunction with Ansys Workbench. The project leveraged finite element simulations conducted on unit cells with hollow inclusions. The geometric parameters served as input features for the subsequent machine-learning model. Through the direct optimization feature in Ansys Workbench, a dataset comprising over 100 design points with randomized geometric parameter values was generated. The calculated Young's modulus and Poisson's ratio, obtained from the finite element simulations, were utilized as labels in the machine learning algorithm. The research culminated in the development of a linear regression model, trained on Ansys-generated data, with the primary objective of predicting the mechanical properties of each unit cell based on their geometric parameters. This project exemplifies a practical and interdisciplinary approach to teaching machine learning to engineering students, bridging the gap between theoretical knowledge and real-world applications in materials science and simulation technology.
Afshar, A., & Mihut, D. M., & Sweeney, M. R. (2024, March), Design and Development of Machine Learning Projects for Engineering Students Paper presented at 2024 South East Section Meeting, Marietta, Georgia. 10.18260/1-2--45514
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