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 […]
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 […]
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) […]
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 […]
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. […]
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 […]
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. […]
Our paper titled “Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts” has been published on Additive Manufacturing. The paper was co-authored by Yuhang Yang and Prof. Chenhui Shao. Industrial-scale production applications of additive manufacturing (AM) are growing rapidly, and scalable AM production requires quality systems that monitor and control […]
Our paper titled “Efficient Online Tracking-by-Detection With Kalman Filter” has been published on IEEE Access. The paper was co-authored by Siyuan Chen and Prof. Chenhui Shao. Visual Multi-Object Tracking (MOT) has a promisingly broad application in manufacturing, construction, traffic, logistics, etc., especially in large-scale applications where it is not feasible to attach markers to many […]
Our paper titled “Adaptive sampling design for multi-task learning of Gaussian processes in manufacturing” has been published in the Journal of Manufacturing Systems. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Multi-task learning (MTL) is a machine learning technique used to enhance learning performance in similar-but-not-identical tasks. However, the accuracy of MTL […]