Physics-based and data-driven modeling approaches for additive and subtractive manufacturing processes
Prof. Tony Schmitz
Department of Mechanical, Aerospace, and Biomedical Engineering
University of Tennessee, Knoxville
Faculty host: Sougata Roy
Seminar on March 4, 2025 at 11:00 AM in 2004 Black Engr.
Abstract
Dr. Schmitz will describe two recent research efforts in support of advanced manufacturing. These research efforts demonstrate the combination of physics-based and data-driven modeling approaches for additive and subtractive manufacturing processes.
- Spindle speed selection in additive friction stir deposition for desired temperature – The research objective is to derive and demonstrate a physics-based, analytical model for additive friction stir deposition (AFSD) spindle speed selection to achieve a desired deposition temperature.
A sample partitioning approach for milling stability modeling with chatter frequency test point selection – The research objective is to increase milling stability model accuracy when the inputs are initially uncertain using limited tests and sample acceptance or rejection
Tony Schmitz received his BS in Mechanical Engineering from Temple University in 1993, his MS in Mechanical Engineering from the University of Florida (UF) in 1996, and his PhD in Mechanical Engineering from UF in 1999. Schmitz completed a post-doctoral appointment at the National Institute of Standards and Technology and was then employed as a Mechanical Engineer from 1999-2002. Schmitz accepted an appointment in the UF Department of Mechanical and Aerospace Engineering in 2002 and joined the Mechanical Engineering Department at UNC Charlotte in 2011.
Dr. Schmitz joined the Mechanical, Aerospace, and Biomedical Engineering department at the University of Tennessee, Knoxville in 2019 with a Joint Faculty position at Oak Ridge National Laboratory. His most recent appointment is Director of the Southeastern Advanced Machine Tools Network, a consortium of companies, colleges and universities, national laboratories, and non-profit organizations that seeks to strengthen the US industrial base by investing in machine tool research and development, education, workforce development, and supply chain support. He continues his manufacturing research in support of the US machine tool industry with an emphasis on machining dynamics, metrology, machine learning, and additive manufacturing.
This seminar counts towards the ME 6000 seminar requirement for Mechanical Engineering graduate students.