Our paper titled “Hybrid physics-guided data-driven modeling for generalizable geometric accuracy prediction and improvement in two-photon lithography” has been published on Journal of Manufacturing Processes. The paper was co-authored by Sixian Jia, Jieliyue Sun, Andrew Howes, Prof. Michhelle R. Dawson, Prof. Kimani C. Toussaint Jr. , and Prof. Chenhui Shao. Two-photon lithography (TPL) is an additive […]
News
Paper on Explainable Few-shot Learning for Ultrasonic Metal Welding Published in Journal of Manufacturing Processes
Recently, our paper titled “Explainable few-shot learning for online anomaly detection in ultrasonic metal welding with varying configurations” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yuquan Meng, Kuan-Chieh Lu, Zhiqiao Dong, Shichen Li, and Prof. Chenhui Shao. Modern manufacturing is featured by rapid reconfiguration and agile adaptation that necessitate varying process configurations. In ultrasonic metal welding, a process […]
Papers published at MSEC 2023 and NAMRC 51 conferences
Our PhD students Kuan-Chieh, Zhiqiao, and Manan published papers and present their research at MSEC 2023 & NAMRC 51 at Rutgers University this summer. Kuan-Chieh’s paper titled “Online Cost-Effective Classification of Mixed Tool and Material Conditions in Ultrasonic Metal Welding: Towards Integrated Monitoring and Control” has been published at MSEC 2023. Ultrasonic metal welding (UMW) […]
Yuquan has defended his doctoral dissertation successfully
Yuquan Meng has successfully defended his doctoral dissertation titled “Physics-Informed Machine Learning for Smart Decision-Making in Ultrasonic Metal Welding.” The defense was conducted before an esteemed Examination Committee consisting of Prof. Chenhui Shao, Prof. Placid Ferreira, Prof. Srinivasa Salapaka, and Prof. Pingfeng Wang. In his research, Yuquan Meng has made significant contributions to the field […]
Paper on End-to-end Online Quality Prediction for Ultrasonic Metal Welding published in JMP
Our paper titled “End-to-end online quality prediction for ultrasonic metal welding using sensor fusion and deep learning” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yulun Wu, Yuquan Meng and Prof. Chenhui Shao. In industrial-scale production applications of ultrasonic metal welding (UMW), there is a strong need for predicting joint […]
Kuan-Chieh will start his internship at Intel this summer
Kuan-Chieh will start an internship position at Intel in summer 2023. He will work with the Logic Technology Development Group to analyze epitaxially grown films and develop an AI algorithm to predict the quality based on process conditions. His Ph.D. research focuses on data efficiency in manufacturing quality monitoring and real-time parameter adjustment for disturbance […]
Manan will start his internship at Seagate this Summer
Manan has accepted an internship offer from Seagate Technology for Summer 2023 where he will work as an AI/Machine Learning Intern. He will work in Seagate’s highly dynamic Global Wafer Systems (GWS) Team and collaborate with a global team of Data Scientists and Machine Learning Engineers to shape the future of new data products in […]
Paper on Clustered Federated Learning published in IEEE Transactions on Industrial Informatics
Our paper titled “A greedy agglomerative framework for clustered federated learning” has been published in the IEEE Transactions on Industrial Informatics. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Federated learning (FL) has received widespread attention for supporting the training of deep learning models across multiple IoT devices while preserving data privacy. […]
Paper on Federated Learning-based Defect Detection for Additive Manufacturing published in JMS
Our paper titled “Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing” has been published in the Journal of Manufacturing Systems. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Federated learning (FL) is an emerging machine learning (ML) paradigm which allows several participants (manufacturers) to collaboratively train a model while […]
Paper on ML-Enabled Geometric Compliance Improvement for Two-Photon Lithography Published in JMP
Our paper titled “Machine-learning-enabled geometric compliance improvement in two-photon lithography without hardware modifications” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yuhang Yang and Prof. Chenhui Shao. In recent years, two-photon lithography (TPL) has emerged as a practical and promising micro- and nano-fabrication technique for a wide range of applications. […]