Peng Wang, assistant professor in the Department of Electrical and Computer Engineering and Department of Mechanical Engineering, has received a prestigious award from National Science Foundation for his project “Understanding Manufacturing Process Dynamics and Machine Tool Anomaly Detection Through Process Sensing and Machine Learning”.  I. S. Jawahir in the Institute for Sustainable Manufacturing and the Department of Mechanical Engineering is Co-PI.

This $444,000 grant will go toward researching a next-generation process sensing-machine learning architecture for machine tool anomaly detection.

The abstract for the project is below.

"Effective and efficient machine tool maintenance plays a significant role in ensuring manufacturing productivity, product quality, operational safety, and profitability. With the advancement of process sensing, the Internet of Things, data analytics, and cloud computing, more manufacturing plants are favoring predictive maintenance over traditional preventive maintenance. Predictive maintenance involves monitoring and predicting machine tool condition and performance. It avoids unnecessary maintenance and prevents catastrophic failure of machine tools, thereby saving operational costs and improving production reliability. However, there are barriers to fully implement predictive maintenance such as insufficient accuracy and reliability of machine tool anomaly or fault detection by existing techniques. This award supports fundamental research on designing a next-generation process sensing-machine learning architecture for capturing manufacturing process dynamics that reveals the underlying dependency of product quality on process settings and machine conditions. This research engages industry in assessing the performance and scalability of this novel machine tool health-monitoring technique at actual manufacturing plants, with the outcomes offering a competitive edge to the U.S. manufacturing sector in the global market."

Wang joined the UK College of Engineering faculty in 2019.

Peng Wang, assistant professor in the Department of Electrical and Computer Engineering and Department of Mechanical Engineering, has received a prestigious award from National Science Foundation for his project “Understanding Manufacturing Process Dynamics and Machine Tool Anomaly Detection Through Process Sensing and Machine Learning”.  I. S. Jawahir in the Institute for Sustainable Manufacturing and the Department of Mechanical Engineering is Co-PI.

This $444,000 grant will go toward researching a next-generation process sensing-machine learning architecture for machine tool anomaly detection.

The abstract for the project is below.

"Effective and efficient machine tool maintenance plays a significant role in ensuring manufacturing productivity, product quality, operational safety, and profitability. With the advancement of process sensing, the Internet of Things, data analytics, and cloud computing, more manufacturing plants are favoring predictive maintenance over traditional preventive maintenance. Predictive maintenance involves monitoring and predicting machine tool condition and performance. It avoids unnecessary maintenance and prevents catastrophic failure of machine tools, thereby saving operational costs and improving production reliability. However, there are barriers to fully implement predictive maintenance such as insufficient accuracy and reliability of machine tool anomaly or fault detection by existing techniques. This award supports fundamental research on designing a next-generation process sensing-machine learning architecture for capturing manufacturing process dynamics that reveals the underlying dependency of product quality on process settings and machine conditions. This research engages industry in assessing the performance and scalability of this novel machine tool health-monitoring technique at actual manufacturing plants, with the outcomes offering a competitive edge to the U.S. manufacturing sector in the global market."

Wang joined the UK College of Engineering faculty in 2019.