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

Paper Number: 187734

187734 - Computational Multi-Physics for Metal Additive Manufacturing: From Feedstock to Process 

This presentation will highlight our recent progress in multi-physics modeling for metal additive manufacturing (AM), with a focus on linking process physics to material quality and manufacturability. We begin by introducing a GPU-accelerated Volume-of-Fluid Lattice Boltzmann framework developed for metal ultrasonic atomization. This model resolves the complex free-surface dynamics governing ligament breakup and droplet formation, enabling quantitative correlations between powder diameter probability distributions and key atomization parameters such as vibration frequency and amplitude. These insights provide a physics-based foundation for tailoring powder characteristics for AM applications.

We then present a sharp-interface, diffusive multi-physics process model for laser-based AM that captures melt pool dynamics, thermal history, phase evolution, void formation, and surface roughness. By explicitly resolving coupled heat transfer, fluid flow, and interfacial phenomena, the model directly connects process parameters to part-scale quality metrics.

Finally, we integrate multi-physics process modeling with machine learning–accelerated computer vision to optimize cross-gas flow conditions for powder spatter mitigation. The proposed modeling framework is rigorously validated against experimental measurements from UIUC, the National Institute of Standards and Technology (NIST), and Argonne National Laboratory using in-situ high-speed, high-resolution X-ray imaging. In addition to measurable quantities, we report internal process variables inaccessible to experiments, demonstrating the predictive capability and design utility of the proposed approach.

Presenting Author: Jinhui Yan University of Illinois 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 the first place in the 2025 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 chair of the computational fluid dynamics (CFD) and fluid-structure interaction (FSI) technical thrust of USACM and the Computational FSI committee of ASME.