DİNLE

SEMINAR:Modified Inherent Strain Method for Predicting Residual...03-05-2021

Speaker: Albert To

Title: Modified Inherent Strain Method for Predicting Residual Distortion and Stress in Laser Powder Bed Fusion Parts

Date/Time: 5 May 2021 / 16:00 - 17:00

Zoom: Meeting IDhttps://sabanciuniv.zoom.us/webinar/register/WN_zoc9iP0VSvuo7ieTDUPi2Q

Passcode: Webinar registration is required.

Abstract: Laser powder bed fusion (L-PBF) additive manufacturing (AM) has been widely employed to produce various metal parts with complex geometries. However, the large thermal gradient caused by the fast, intensive and repeated laser scanning brings significant residual deformation and stress to the as-built metal parts, increasing manufacturing difficulty and geometrical inaccuracy as a result. This talk presents a modified inherent strain method recently proposed that is capable of efficient prediction of the residual stress and deformation in as-built L-PBF parts. The proposed model defines the inherent strains in a way that allows for the extraction of inherent strains from a detailed process simulation based on moving point heat source model in a representative domain. Then the extracted inherent strains are applied to a part-scale quasi-static finite element model in a layer-by-layer manner to simulate the part-scale residual stress and deformation field. Both numerical and experimental studies are conducted to validate the proposed method, which show that the modified inherent strain method can provide accurate prediction of residual stress without losing accuracy.  Several applications of the proposed method will be highlighted in this talk including support structure design, build orientation optimization, cracking prediction, and scanning path optimization.

Bio:Dr. Albert To is currently William Kepler Whiteford Professor in the Department of Mechanical Engineering and Materials Science at University of Pittsburgh, where he also serves as the Director of the ANSYS Additive Manufacturing Research Laboratory.  He is also the Founding Director of the MOST-AM Consortium, which is a public-private partnership of 31 organizations formed to address the most pressing needs in modeling and simulation for additive manufacturing.  Dr. To received his B.S. degree from U.C. Berkeley and M.S. degree from Massachusetts Institute of Technology.  He obtained his Ph.D. from U.C. Berkeley in 2005 and conducted postdoctoral research at Northwestern University from 2005-2008.  He joined University of Pittsburgh as Assistant Professor in 2008 and was promoted to Associate Professor in 2014 and to Endowed Professor in 2019.  His current research interests lie in design optimization, fast process modeling, and process-microstructure-property relationship for metal additive manufacturing.  The computational methods his group developed for AM have been adopted and commercialized by engineering simulation software companies such as ANSYS.  Dr. To has over 110 peer-reviewed journal publications in journals such as Additive Manufacturing, Computer Methods in Applied Mechanics and Engineering, Journal of Mechanics and Physics of Materials, and Scripta Materialia.  He is currently an associate editor for Additive Manufacturing in charge of modeling and simulation.  He has been a recipient of the NSF BRIGE Award in 2009, the Board of Visitors Faculty Award from the School of Engineering in 2016, and the Carnegie Science Award in the Advanced Manufacturing and Materials Category in 2018.