You're invited to attend

"Opportunities for Data-Driven Modeling
in Metal Additive Manufacturing"

by

Amrita Basak

Assistant Professor | Department of Mechanical Engineering
Pennsylvania State University

Thursday, March 9
3 - 4 p.m.
Clary Theater, Student Success Center

About the Seminar: 
Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers toward the fabrication of complex objects through localized melting and re-solidification using high-power energy sources. The localized heating causes the formation of a melt pool that controls the microstructure and, therefore, the properties of the final part. Accurate prediction of the melt pool geometry is thus crucial. Melt pool surrogates created using numerical simulation data carry the signatures of modeling assumptions in their accuracy. On the contrary, surrogates developed using experimental data would require a significantly large amount of data for training purposes. To address these challenges, in this talk, I will illustrate how a multi-fidelity Gaussian process (MFGP) framework can be gainfully used to fuse experimental and synthetic data. Experimental melt pool dimensions are obtained from single-layer, single-track deposits fabricated via a popular metal AM process i.e., the powder-fed laser directed energy deposition. These constitute the high-fidelity/ground truth for the MFGP surrogate. An analytical Eagar-Tsai model is calibrated and queried at sampled input points within a predefined process parameter window to yield near-accurate estimates of low-fidelity melt pool data at a significantly lower computational budget. Thereafter, the MFGP surrogate is designed using an appropriate kernel and adequate calibration. An elaborate assessment of the trained surrogate on unseen experimental data yields results of high accuracy and low uncertainty. The proposed method of blending experimental data with synthetic data has the potential to reduce the experimental data required for creating data-driven surrogates without compromising accuracy. While the current work focuses on the L-DED of a popular metal alloy, SS316L, it can be easily extended to other properties of interest as well as other metal additive manufacturing processes as well.

About the Speaker: 
Dr. Amrita Basak is an Assistant Professor in the Department of Mechanical Engineering at the Pennsylvania State University – University Park. Dr. Basak's research group performs research at the intersection of additive manufacturing, materials characterization, computational modeling, and machine learning to enhance our fundamental understanding of the composition-process-structure-postprocess-property linkages in advanced manufacturing of metallic, ceramic, and polymeric materials. Dr. Basak received her Ph.D. in Mechanical Engineering from the Georgia Institute of Technology working under the supervision of Prof. Suman Das. She holds two master’s degrees – one in Aerospace Engineering from Georgia Tech and the second one in Chemical Engineering from the Indian Institute of Technology Kanpur. She received her undergraduate degree in Chemical Engineering from Jadavpur University, Kolkata, India. In between her academic stints, she spent approximately one year as a Process Engineer working for Intel Corporation, Portland, Oregon; one year as a Student Consultant in Department of Energy’s Industrial Assessment Center at Georgia Tech; four months as a computational fluid dynamics (CFD) research intern in Robert Bosch, Palo Alto, California; and six and half years as a Lead Engineer working for General Electric, Bangalore, India.