Industrial and Systems Engineering Colloquium

October 6, 2020

October 6, 2020

Presenter: Arman Sabbaghi, PhD
                  Purdue University                    
Topic: "Distortion Model Transfer Between Materials in Laser Based Additive Manufacturing Systems"
Time: 5:00 pm ET
Password: 8o815n
Abstract: Distortion in laser-based additive manufacturing (AM) systems is a critical quality control issue that is known to be material-dependent. One key challenge in AM systems is learning distortion models for new materials given past experiments on distinct materials. We present a Bayesian methodology to transfer distortion models across different materials in an AM system, thereby leveraging past experiments to learn distortion models for new materials. We demonstrate the utility of this method with case studies on disks additively manufactured using Ti-6Al-4V and 316L stainless steel.
Biography: Dr. Arman Sabbaghi is an Associate Professor in the Department of Statistics, and an Associate Director of the Statistical Consulting Service, at Purdue University. He received his PhD in Statistics from Harvard University in 2014, his AM in Statistics from Harvard University in 2011, and his BS in Mathematics (with Honors) and BS in Mathematical Statistics from Purdue University in 2009. Dr. Sabbaghi's research interests are in Bayesian data analysis, experimental design, and causal inference. Specific major objectives of his current research are (1) the development of efficient and interpretable statistical frameworks and machine learning algorithms for modeling and quality control in additive manufacturing (AM) systems, (2) the creation of mathematical tools that facilitate the characterization of broad classes of experimental designs for the study and improvement of processes in engineering and the physical sciences, and (3) the development of new causal inference methods for the analysis of Big Observational Data and clinical trials plagued by nonadherence. He has published 17 peer-reviewed journal papers, 3 peer-reviewed conference proceedings papers, and 1 peer-reviewed book chapter. Dr. Sabbaghi has received funding from the National Science Foundation, the National Institutes of Health, and Sandia National Laboratories. Dr. Sabbaghi's publications have appeared in statistics and engineering journals, such as the Annals of Applied Statistics, Biometrika, Statistical Science, Technometrics, IIE Transactionson Quality and Reliability Engineering, IEEE Transactions on Automation Science and Engineering, and Nano Energy. He has served as a reviewer for the National Science Foundation and multiple statistics and engineering journals.
For additional information please contact Dr. Aziz Ezzat: