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Clayton Cooper, Ph.D.

Education

• Ph.D., Mechanical Engineering, Case Western Reserve University, 2024
• M.S., Mechanical Engineering, Case Western Reserve University, 2020
• B.S.E., Mechanical Engineering, Case Western Reserve University, 2020

Research Interests

• Physics-informed machine learning
• Manufacturing process modeling
• Digital twinning
• Nonintrusive sensing
• Explainable artificial intelligence

Research Bio

Dr. Cooper is an emerging leader in the domestic and international manufacturing research communities. His research interests are in the areas of physics-informed machine learning (PIML) and explainable artificial intelligence for digital twin enablement via improving the observability of manufacturing processes and product quality. His research has led to the development of a novel PIML framework and the invention of several novel machine learning methods and unobtrusive sensing techniques for pre-, in-, and post-process prediction of part quality in various manufacturing fields, including machining, directed energy deposition, human-robot collaborative assembly, and casting.

Courses Taught

MME 231: Manufacturing Processes

Current Projects

Current projects include: machine learning-driven prediction of deformation in sheet metal forming processes, computer vision-enabled digital twins in manufacturing, and development of novel physics-informed machine learning methods for manufacturing applications.

Publications

Journal Articles

• C. Cooper, J. Zhang, I. Ragai, and R. X. Gao, “Multi-sensor fusion and machine learning-driven sequence-to-sequence translation for interpretable process signature prediction in machining,” Journal of Manufacturing Systems, p. S0278612524000761, May 2024, doi: 10.1016/j.jmsy.2024.04.010. (won Outstanding Paper Award at NAMRC 52)
• L. Hu, H. Phan, S. Srinivasan, C. Cooper, J. Zhang, B. Yuan, R. X. Gao, and Y. B. Guo, “Multimodal Data-Driven Machine Learning for the Prediction of Surface ¾Ã¾ÃÈÈÊÓƵography in End Milling,” Production Engineering, Jan. 2024, doi: 10.1007/s11740-023-01253-z.
• C. Cooper, J. Zhang, and R. X. Gao, “Error Homogenization in Physics-Informed Neural Networks for Modeling in Manufacturing,” Journal of Manufacturing Systems, vol. 71, pp. 298–308, Dec. 2023, doi: 10.1016/j.jmsy.2023.09.013.
• C. Cooper, J. Zhang, Y. B. Guo, and R. X. Gao, “Surface roughness prediction through GAN synthesized power signal as a process signature,” Journal of Manufacturing Systems, vol. 62, pp. 660–669, Jun. 2023, doi: 10.1016/j.jmsy.2023.05.016. (won Student Research Presentation Award at NAMRC 51)
• C. Cooper, J. Zhang, J. Huang, J. Bennett, J. Cao, and R. X. Gao, “Tensile strength prediction in directed energy deposition through physics-informed machine learning and Shapley additive explanations,” Journal of Materials Processing Technology, vol. 315, p. 117908, Jun. 2023, doi: 10.1016/j.jmatprotec.2023.117908.
• C. Cooper, J. Zhang, L. Hu, Y. Guo, and R. X. Gao, “Texture-Aware Ridgelet Transform and Machine Learning for Surface Roughness Prediction,” IEEE Trans. Instrum. Meas., vol. 71, pp. 1–10, Oct. 2022, doi: 10.1109/TIM.2022.3214630.
• S. Guo, M. Agarwal, C. Cooper, Q. Tian, R. X. Gao, W. Guo, and Y. Guo, “Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm,” Journal of Manufacturing Systems, vol. 62, pp. 145–163, Jan. 2022, doi: 10.1016/j.jmsy.2021.11.003.
• L. Wang, S. Liu, C. Cooper, X. V. Wang, and R. X. Gao, “Function block-based human-robot collaborative assembly driven by brainwaves,” CIRP Annals, pp. 5-8, May 2021, doi: 10.1016/j.cirp.2021.04.091.

Book Chapters

• J. Zhang, C. Cooper, and R. X. Gao, “Federated Learning for Privacy-Preserving Collaboration in Smart Manufacturing,” in Manufacturing Driving Circular Economy, H. Kohl, G. Seliger, and F. Dietrich, Eds., in Lecture Notes in Mechanical Engineering. Cham: Springer International Publishing, 2023, pp. 845–853. doi: 10.1007/978-3-031-28839-5_94.

Conference Publications

• C. Cooper, J. Zhang, R. X. Gao, “Deformation prediction in English wheeling through physics-informed machine learning,” to be published in proceedings of the 2024 International Conference on Precision Engineering, Sendai, Japan (keynote paper)
  
• S. Liu, L. Wang, X. V. Wang, C. Cooper, and R. X. Gao, “Leveraging multimodal data for intuitive robot control towards human-robot collaborative assembly,” in Procedia CIRP, 2021, pp. 206–211. doi: 10.1016/j.procir.2021.11.035.
  
• C. Cooper, D. Liu, J. Zhang, and R. X. Gao, “Feature-Based Transfer Learning for Bearing Fault Recognition Without Available Fault Data,” in Proceedings of the 2020 International Symposium on Flexible Automation, Virtual, Online: American Society of Mechanical Engineers, Jul. 2020. doi: 10.1115/ISFA2020-9636.
  
• C. Cooper, J. Zhang, R. X. Gao, P. Wang, and I. Ragai, “Anomaly detection in milling tools using acoustic signals and generative adversarial networks,” in Procedia Manufacturing, 2020, pp. 372–378. doi: 10.1016/j.promfg.2020.05.059.
  
• C. Cooper, P. Wang, J. Zheng, R. X. Gao, T. Roney, I. Ragai, and D. Shaffer, “Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals,” in Procedia Manufacturing, 2020, pp. 105–111. doi: 10.1016/j.promfg.2020.07.004.