3.25.25 Dr. Peng (Edward) Wang – Case Western Reserve University
Applicable and Generalizable Machine Learning for Intelligent Welding, from Quality Prediction to Robotic Automation
Date: March 28, 2025; Time: 2:30 PM Location: PWEB 175
Abstract: In the last decade, the manufacturing sector has adopted Industry 4.0 innovations, including edge and cloud computing, Artificial Intelligence (AI), and Machine Learning (ML), enhancing production visibility, quality, automation, productivity, and safety. This presentation highlights novel ML applications in welding processes, through case studies in Resistance Spot Welding (RSW), laser welding, and arc welding.
The case study of RSW focuses on process sensing and modeling for quality prediction and defect detection. This study not only employs data-driven modeling but also utilizes ML to uncover physical insights into the RSW process, enhancing feature extraction and developing a more generalizable model for predicting quality and defects. It also introduces a new ML approach to create virtual signals for force and displacement using dynamic resistance measurements, addressing the lack of novel process sensing in facilities due to high costs. The case study of laser welding tackles feature engineering, i.e., from sensing data characterization to feature selection, to improve the model generalizability and decision-making efficiency in a plant production scenario. Transfer learning is also investigated to enable the ML models to adapt to dynamically changing welding conditions. The third case study targets the robotic automation of arc welding. To enable robotic operational adaptivity, a hybrid ML-based process characterization, and online adaptive control framework are developed for robotic arc welding to automatically and efficiently achieve the desired weld pool condition, given any initial conditions. These case studies showcase significant potential for advancing welding processes to new levels of efficiency and effectiveness.
Biographical Sketch: Dr. Peng (Edward) Wang is currently an Associate Professor in the Department of Mechanical and Aerospace Engineering at Case Western Reserve University (CWRU). Dr. Wang has extensive experience in developing novel ML methodologies for machine condition monitoring and diagnosis, process modeling and quality prediction, and collaborative robots. Dr. Wang is the recipient of the CAREER award from the US National Science Foundation in 2023, Young Investigator Award from the International Symposium of Flexible Automation in 2024, Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers (SME) in 2022, the Best Paper Award from the 2023 Manufacturing Science and Research Conference (MSEC), Outstanding Technical Paper Award from the SME North American Manufacturing Research Conference (NAMRC) in 2017, 2020, and 2021, and other best paper awards. Dr. Wang is an Associate Editor of the IEEE Sensors Journal and Journal of Intelligent Manufacturing.
4.04.25 Dr. Ilya Kovalenko – Penn State University
Developing Intelligent Automation for Smart and Sustainable Manufacturing Systems
Date: April 4, 2025; Time: 2:30 PM Location: PWEB 175
Abstract: The current manufacturing paradigm is shifting toward the development of production systems that require greater flexibility and adaptability. To achieve this objective, new system-level control strategies must be developed to control and coordinate different components on the shop floor. This talk will focus on our recent approaches to improving the flexibility and adaptability of manufacturing systems across different levels of automation. First, I will introduce some of our recent work in leveraging artificial intelligence technology to enhance automation-operator interactions on the shop floor. Then, we will generalize these results to the system level and discuss how models and controllers can be developed to improve manufacturing system cooperation, coordination, and performance. Case studies from both simulations and real-world environments will be provided to showcase the exciting possibilities for the future of manufacturing systems.
Biographical Sketch: Ilya Kovalenko is currently an Assistant Professor in the Department of Mechanical Engineering and the Department of Industrial & Manufacturing Engineering at Penn State University. He received both his PhD in Mechanical Engineering (2020) and his MS degree in Mechanical Engineering from the University of Michigan (2018), and his BS degree in Mechanical Engineering from the Georgia Institute of Technology (2015). He was awarded the NSF Graduate Research Fellowship in 2016, the University of Michigan’s College of Engineering Distinguished Leadership Award in 2020, and the NSF CAREER in 2025. His current research interests lie in the areas of control theory, artificial intelligence, and smart manufacturing, with a focus on cooperative control, cyber-physical systems, and robotics.