...@virginia.edu 25 06 2024 28 12 2024 28 12 2024 11 02 2025 microgrid battery degradation manufacturing scheduling deep learning multiagent control and automation modeling and simulation sustainable manufacturing National Science Foundation 10.13039/100000001...

... variation including part deformation. This study proposes a methodology for physics-informed sensor fusion and deep learning feature extraction to predict geometric variation that leads to deformation within WA-DED parts. Using thermal sensors from multiple vantage points combined with physics-based...

... computer vision deep learning Hong Kong Polytechnic University 10.13039/501100004377 Laboratory for Artificial Intelligence in Design (Project Code: RP2-1) Research Grants Council, University Grants Committee 10.13039/501100002920 General Research Fund (PolyU15210222...

.... Each multi-spectral sensor monitors spectral emissions at two material-dependent wavelengths. The time series data generated from the multiple multi-spectral sensors are converted into a rasterized image per layer to be fed into a supervised deep learning (DL)-based semantic segmentation pipeline...

... and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based...

... and FSW; and (9) efforts in advanced sensing, data fusion/sensor fusion, and process control using machine learning/deep learning, model predictive control (MPC), and adaptive control. Email: yuming.zhang@uky.edu Email: yyang@ewi.org Email: zhang.3978@osu.edu Email: sjoona@kaist.ac.kr...

... on Deep Learning for Safety-Critical Applications in Engineering at ICML-AAMAS-IJCAI , Stockholm, Sweden , July 13–19 . [8] Wang , T. , Yu , J. , Siegel , D. , and Lee , J. , 2008 , “ A Similarity-Based Prognostics Approach for Remaining Useful Life Estimation of Engineered...