Artificial Intelligence for Manufacturing Quality Modeling
Mar 19, 2020
Overall Information
This project uses artificial intelligence and machine learning methods to develop algorithms for anomaly detection and quality prediciton in the manufacturing systems considering the heterogeneous data types in manufacturing systems (e.g., images, signals).
Hao Yan
Assistant Professor in School of Computing, Informatics, and Decision Systems Engineering
My research interests include Data Science for Complex Systems.
Publications
In multistage manufacturing systems, modeling multiple quality indices based on the process sensing variables is important. However, …
Hao Yan,
Nurretin D. Sergin,
William A. Brenneman,
Stephen J. Lange,
Shan Ba
This paper develops a unified framework for training and deploying deep neural networks on the edge computing framework for image …
Jiayu Huang,
Nurretin Sergin,
Akshay Dua,
Erfan Bank Tavakoli,
Hao Yan,
Fengbo Ren,
Feng Ju
Variational autoencoders have been recently proposed for the problem of process monitoring. While these works show impressive results …
Nurettin Dorukhan Sergin,
Hao Yan
Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, …
Hao Yan,
Huai Ming Yeh,
Nurettin Sergin