Presented by: Dr. Nancy Diaz-Elsayed, Sohan Nagaraj, and Donald McCleeary

Thursday, January 20, 2022 – 11:00am-12:00pm

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Abstract: The COVID-19 pandemic has brought to light many challenges in the resilience of our manufacturing systems and, consequently, highlighted opportunities for Industry 4.0 technologies to fill gaps on the production floor. Considering part quality, for example, it is often an indicator for manufacturing competitiveness. In this Tech Talk, researchers at the University of South Florida’s Smart and Sustainable Systems Laboratory (S3 Lab) will demonstrate tool condition monitoring (TCM) on the CNC machining process with low-cost, indirect sensing methods. TCM offers a method to detect signs of tool wear in real time. The technique often employs direct or indirect sensing methods to monitor tool health. Applications include analyses of vibrations, electrical power demand, acoustic emissions, and cutting forces. TCM will be presented in the context of a multi-criteria decision-making framework for technology selection in manufacturing. Realizing the vision for sustainable and resilient manufacturing will inevitably require greater availability and transparency of key production data, to create a future in which our response to changes in production requirements can be realized swiftly and efficiently. 

Biography: Dr. Nancy Diaz-Elsayed is an Assistant Professor at the University of South Florida (USF) in the Department of Mechanical Engineering (ME). She holds a M.S. and Ph.D. in Mechanical Engineering from UC Berkeley and a B.S. in Mechanical Engineering from MIT. Her projects have spanned discrete and continuous processes, including the development of a building intelligence application that combined real-time data with Building Information Models to improve the performance of factories and commercial buildings, and the sustainable design of integrated wastewater management systems. Her research interests include system and process modeling, technology development for smart and sustainable systems, and the role of industrial symbiosis in the design and growth of urban environments. Prior to joining USF, she was the Sustainable Manufacturing Specialist at Autodesk. Dr. Diaz-Elsayed is currently a member of the American Society of Mechanical Engineers (ASME) and the Society of Manufacturing Engineers (SME), and leads the Women in Advanced Manufacturing committee for ASME’s Manufacturing Engineering Division (MED).

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Dr. Dazhong Wu
Assistant Professor, Department of Mechanical and Aerospace Engineering
University of Central Florida

Friday, February 18, 2022 10AM – 11AM EST

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Abstract

Over the past few years, machine learning has been transforming the manufacturing industry. Machine learning enables manufacturers practice predictive maintenance to reduce unexpected equipment downtime as well as improve product quality by extracting data and information from manufacturing data. This presentation will discuss two applications of machine learning in smart manufacturing. One application is to predict melt pool temperature in directed energy deposition for 3-D printing of metal. The second case study tells the story about how machine learning is applied to predicting the wear of cutting tools used in machining processes. Share these applications of machine learning to help students understand the role of “big data” in smart manufacturing.

Biosketch

Dr. Dazhong Wu is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Central Florida. He received his Ph.D. in Mechanical Engineering from Georgia Tech. His research is focused on data-driven smart manufacturing, additive manufacturing, prognostics and health management. He has published over 40 journal articles. He is one of the highly cited researchers in the field of smart manufacturing. He was selected as one of the Most Influential Professors in Smart Manufacturing by the Smart Manufacturing Magazine. He serves as an Associate Editor for the Journal of Manufacturing Systems and Journal of Intelligent Manufacturing.

Register Now

Dr. Dazhong Wu
Assistant Professor, Department of Mechanical and Aerospace Engineering
University of Central Florida

Friday, February 18, 2022 10AM – 11AM EST

Register Now

Abstract

Over the past few years, machine learning has been transforming the manufacturing industry. Machine learning enables manufacturers practice predictive maintenance to reduce unexpected equipment downtime as well as improve product quality by extracting data and information from manufacturing data. This presentation will discuss two applications of machine learning in smart manufacturing. One application is to predict melt pool temperature in directed energy deposition for 3-D printing of metal. The second case study tells the story about how machine learning is applied to predicting the wear of cutting tools used in machining processes. Share these applications of machine learning to help students understand the role of “big data” in smart manufacturing.

Biosketch

Dr. Dazhong Wu is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Central Florida. He received his Ph.D. in Mechanical Engineering from Georgia Tech. His research is focused on data-driven smart manufacturing, additive manufacturing, prognostics and health management. He has published over 40 journal articles. He is one of the highly cited researchers in the field of smart manufacturing. He was selected as one of the Most Influential Professors in Smart Manufacturing by the Smart Manufacturing Magazine. He serves as an Associate Editor for the Journal of Manufacturing Systems and Journal of Intelligent Manufacturing.

Register Now