MAE Seminar – Data-driven modeling to understand fatigue performance of additively manufactured metals

Details

Dear Undergraduate and Graduate Students, Faculty, and Staff,
You are invited! UF Department of Mechanical and Aerospace Engineering’s Seminar Series
This is a perfect opportunity to enjoy some coffee, cookies, and captivating talks! These sessions feature amazing guest speakers, from academic trailblazers and industry movers to our very own faculty candidates showing off their expertise and fresh perspectives.
Come for the treats, stay for the engaging discussions, and connect with fellow MAE enthusiasts. Everyone is welcome!

Data-driven modeling to understand fatigue performance of additively manufactured metals

September 30, 2025, at 12:50pm
Location: MAE-A 303

Jia “Peter” Liu
Associate Professor
Trey Lauderdale Industrial and Systems Engineering Faculty Fellow
University of Florida
Dept. of Industrial & Systems Engineering

Abstract
Emerging technologies, such as AI, advanced sensing, and big data, provide tools to revolutionize the manufacturing industry, help advance the understanding of manufacturing processes, and improve productivity and quality. Also, many manufacturers are transforming digitally and forming an adequate infrastructure for collaborative AI applications, potentially advancing the national manufacturing capability and enhancing robust supply chains.

In this talk, I will demonstrate an efficient way of incorporating physics knowledge and using or developing data-driven models for manufacturing processes to achieve accurate prediction and an interpretable understanding. I will introduce our work in data-driven modeling to advance the understanding of fatigue failure in laser beam powder bed fusion (L-PBF). Fatigue failure is usually attributed to crack initiation and propagation by fracture mechanics in microstructural traits, such as volumetric defects and surface roughness. Our work not only utilizes physics knowledge in L-PBF but also leverages the power of data-driven methods to address the issues of complex physics and data sparsity to advance non-destructive fatigue life prediction for L-PBF parts and their potential adoption to more fatigue-critical applications. I will also introduce our progress in novel federated learning to enable privacy-preserving information sharing for machine learning modeling among distributed manufacturers.

Biography
Jia “Peter” Liu is an associate professor and the Trey Lauderdale Industrial and Systems Engineering fellow at the University of Florida. His research interests encompass statistical learning, deep learning, and LLM with applications in advanced manufacturing. He works to integrate physics knowledge and interpretable data-driven modeling for complex manufacturing processes with heterogeneous sensors, mainly focusing on understanding the fatigue performance of powder bed fusion with applications in the aerospace, defense, and automotive sectors. His research has been funded by NSF, DoD, FAA, and NIST, and he has been honored with several awards, including the 2024 ASME Rising Star of Mechanical Engineering and the 2023 NSF CAREER Award. He is a senior member of INFORMS and a member of IISE, ASME, and SME. He holds a Ph.D. in Industrial and Systems Engineering, an M.S. in Statistics from Virginia Tech, and a B.S. and M.S. in Electrical Engineering from Zhejiang University, China.

MAE Faculty Host: Dr. Hitomi Greenslet