14 citations · 32 across the 7 of their papers we have counts for
8 papers
Temperature-Dependent Multiphysics Modeling of Additive Friction Stir Deposition Using Multi-Task Coupled Physics-Informed Neural Networks
Dhrubajyoti Gupta, Nikhil Gotawala, Raghav Gnanasambandam +4
Additive friction stir deposition (AFSD) involves coupled thermal and material-flow fields governed by frictional heating, plastic deformation, and tool-imposed boundary conditions…
A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems
Jihoon Chung, Zhenyu Kong
Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue c…
Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network
Jihoon Chung, Bo Shen, Zhenyu +1
Supervised classification methods have been widely utilized for the quality assurance of the advanced manufacturing process, such as additive manufacturing (AM) for anomaly (defect…
Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing
Jihoon Chung, Bo Shen, Andrew Chung Chee Law +2
Additive Manufacturing (AM) is a powerful technology that produces complex 3D geometries using various materials in a layer-by-layer fashion. However, quality assurance is the main…
A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems
Jihoon Chung, Bo Shen, Zhenyu +1
This paper addresses the problem of fault diagnosis in multistation assembly systems. Fault diagnosis is to identify process faults that cause the excessive dimensional variation o…
Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks
Raghav Gnanasambandam, Bo Shen, Jihoon Chung +3
Physics-informed Neural Networks (PINNs) are gaining attention in the engineering and scientific literature for solving a range of differential equations with applications in weath…
Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee
Bo Shen, Weijun Xie, Zhenyu Kong
The objective of this study is to address the problem of background/foreground separation with missing pixels by combining the video acquisition, video recovery, background/foregro…
WOOD: Wasserstein-based Out-of-Distribution Detection
Yinan Wang, Wenbo Sun, Jionghua "Judy" Jin +2
The training and test data for deep-neural-network-based classifiers are usually assumed to be sampled from the same distribution. When part of the test samples are drawn from a di…