Session: 03-08-01: Integrated Computational Materials Engineering

Paper Number: 152146

152146 - Physics-Based and Data-Driven Icme for Metal Additive Manufacturing: From Feedstock to Process Optimization 

In this presentation, we will present our recent progress in the physics-based and data-driven integrated computational materials and engineering (ICME) for metal additive manufacturing. Firstly, a lattice Boltzmann method(LBM)--based model for the ultrasound atomization process of powder generation is presented. The LBM model, which is accelerated by GPU-based computing, can correlate the powder diameter probability distribution with ultrasound parameters, such as vibration frequency and vibration magnitude. Then, we will present a multi physics processes model using a mixed sharp-diffusive interface approach. I will demonstrate how the developed model elucidates the fundamental metal AM physics (e.g., thermal history, energy absorption rate, melt pool dynamics, keyhole instability, and powder spattering) and predicts critical part quality-related quantities (e.g., defect and surface roughness). The proposed framework’s accuracy is assessed by thoroughly comparing the simulated results against experimental measurements from the National Institute of Standards and Technology AM benchmark tests and the Argonne National Laboratory using in-situ high-speed, high-energy x-ray imaging. I will also report other important quantities experiments cannot measure to show the framework's predictive capability. Finally, we integrate the multiphysics processing model with a machine learning-accelerated computer vision model to optimize cross gas flow parameters for powder spatter mitigation in laser powder bed fusion processes.

Presenting Author: Jinhui Yan University of Illinois At Urbana Champaign

Presenting Author Biography: Jinhui Yan is an associate professor in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign. He obtained his B.S., MS, and Ph.D. from Wuhan University (2009), Peking University (2012), and University of California, San Diego (2016), respectively. After a two-year postdoc at Northwestern University, he joined the faculty of CEE@UIUC. His research group broadly works on computational mechanics and its scientific and engineering applications. His honors include the ASME Robert M. and Mary Haythornthwaite Young Investigator Award in 2018 and the Gallagher Young Investigator Medal from the U.S. Association for Computational Mechanics (UASCM) in 2023. The AM model developed by his research group won two prizes in the 2022 NIST AM benchmark modeling contests. His work also won the Best Paper in Manufacturing Technology from the Vertical Flight Society in 2024. He is a Levenick Teaching Fellow and often enters the list of excellent teachers ranked by the students at UIUC. He currently serves as the vice-chair of the computational fluid dynamics (CFD) and fluid-structure interaction (FSI) technical thrust of USACM and the Computational FSI committee of ASME.