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 […]
Publication
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. […]
Paper on Hierarchical Data Models for Additive Manufacturing Published in AM
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 […]
Paper on Multi-Object Tracking in Videos published on IEEE Access
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 […]
Paper on adaptive sampling for multi-task Gaussian processes published in JMS
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 […]
Paper on Hybrid Multi-Task Learning-Based Response Surface Modeling Published in JMS
A paper entitled “hybrid multi-task learning-based response surface modeling in manufacturing” is recently published in the Journal of Manufacturing Systems. The paper was co-authored by Yuhang Yang and Chenhui Shao. This paper developed a hybrid multi-task learning-based method tocost-effectively model the response surfaces of multiple similar-but-not-identical manufacturing processes. The method was evaluated using a simulation-based numerical […]
Review Paper on Data-Driven Intelligent 3D Surface Measurement Published in Machines
A feature article titled “data-driven intelligent 3D surface measurement in smart manufacturing: review and outlook” has been published in Machines recently. The paper was co-authored by Yuhang Yang, Zhiqiao Dong, Yuquan Meng and Chenhui Shao. This paper reviewed and summarized existing research in interpolation and sampling design techniques in various manufacturing scenarios, which can potentially […]
Paper on Multi-Task Learning of Spatiotemporal Processes Published in JMS
A paper entitled “multi-task learning for data-efficient spatiotemporal modeling of tool surface progression in ultrasonic welding” is recently published in the Journal of Manufacturing Systems. The paper was co-authored by Haotian Chen, Yuhang Yang, and Chenhui Shao. This paper developed a multi-task learning method to enable data-efficient spatiotemporal modeling. The method was evaluated using tool […]