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 Continue Reading »
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 Continue Reading »
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 Continue Reading »
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 Continue Reading »
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, Continue Reading »
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 Continue Reading »
Ultrasonic metal welding (UMW) is a solid-state joining method with various industrial applications including battery assembly, automotive body construction, and electronic packaging. Among the advantages of UMW over conventional fusion welding are the ability to join dissimilar metals, reduced energy consumption and short welding time. Despite of its numerous advantages, this technique Continue Reading »
Fault diagnosis for rolling elements in rotating machinery persistently receives high research interest due to the said machinery’s prevalence in a broad range of applications. State-of-the-art methods in such setups focus on effective identification of faults that usually involve a single component while rejecting noise from limited sources. This paper studies the Continue Reading »
This paper presents a new approach for temporal detection of short human activities in untrimmed videos. Most present methods for temporal action detection, to our best knowledge, are trained on public action datasets that feature actions spanning up to tens and hundreds of seconds. However, it is often desired in manufacturing, transportation, Continue Reading »
High-resolution spatiotemporal data is crucial for characterizing, modeling, and monitoring the space-time dynamics of complex systems in manufacturing. However, the acquisition of such data is generally expensive and time-consuming. Spatiotemporal interpolation aims to predict the values at unmeasured locations using measured data, and emerges as a promising solution to cost-effectively characterizing spatiotemporal Continue Reading »