Mechanical and Civil Engineering Seminar

Mechanical and Civil Engineering Seminar Series

Title: Toward a Model-Based Qualification Paradigm for Metal Additive Manufacturing

Abstract:

This seminar will present recent advances in models and methods developed toward achieving a model-based qualification paradigm for laser powder bed fusion (L-PBF) additive manufacturing (AM). First, we will present the modified inherent strain model, which is proposed to predict the residual stress and deformation in as-built L-PBF parts in an accurate and efficient manner. In the proposed model, the definition of inherent strains is modified to capture the effects of changing boundary conditions on the conversion of thermal energy into strain energy. This modified definition allows for the extraction of inherent strains from a detailed process simulation based on moving point heat source model in a representative domain. The extracted inherent strains can then be applied to a layerwise quasi-static finite element model to simulate the part-scale residual stress and deformation field. Both numerical and experimental studies are conducted to demonstrate the accuracy and efficiency of the modified model. Several applications of the proposed method will be highlighted which include support structure design, build orientation optimization, cracking prediction, and scanning path optimization.

Next, we will discuss a new L-PBF process modeling approach which combines matrix-free finite element method (FEM), adaptive remeshing, and graphical processing unit (GPU) parallelization. This approach allows for scanwise process simulation of L-PBF process with temperature-dependent thermal properties at the part scale. Compared to the conventional FEM using the global stiffness approach and a uniform mesh running on 10 CPU cores, L-PBF process simulation based on the proposed methodology running on a Titan V GPU card enables a speedup of over 10,000x. This significant speedup facilitates detailed thermal history and melt pool geometry predictions at high resolution for centimeter-scale parts within days of computation time. Two parts consisting of various geometric features are simulated to reveal the effects of scan strategy and local geometry on melt pool size variation, which correlate well with melt pool and lack-of-fusion porosity measurements obtained via experiment.

Bio:

Dr. Albert To is currently a 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 30+ companies and national labs to address the most pressing needs in modeling and simulation for additive manufacturing. Dr. To received his B.S. degree from University of California, Berkeley and M.S. degree from Massachusetts Institute of Technology. He received 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 additive manufacturing have been adopted and commercialized by engineering simulation software companies such as Ansys. Professor To has over 130 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 responsible for the modeling and simulation area. 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.

NOTE: At this time, in-person Mechanical and Civil Engineering Lectures are open to all Caltech students/staff/faculty/visitors.