Session: Rising Stars of Mechanical Engineering Celebration & Showcase

Paper Number: 147872

147872 - Career: Manufacturing Usa: Deep Learning to Understand Fatigue Performance and Processing Relationship of Complex Parts by Additive Manufacturing for High-Consequence Applications 

Metal additive manufacturing (AM) such as laser powder-bed fusion (LPBF) has been increasingly explored not only for product innovation, but also shop-floor production, demonstrated by growing success from a variety of industries. However, the lack of knowledge in both fatigue failure and the performance uncertainty of LPBF parts poses a significant challenge and undermines the potential of deploying LPBF for high-consequence applications. This Faculty Early Career Development (CAREER) award supports fundamental research to understand the effects of LPBF processing on defects and subsequent fatigue behavior, advance the knowledge of fatigue scattering of LPBF parts that are complex in geometry and subject to multiaxial loading. The effort will establish a physics-centric, machine learning framework for fatigue life predictions, serving as a technological foundation for future metal AM production of dynamic load-bearing applications, and thus, enhance the competitiveness of U.S. industry. This CAREER project will also integrate education and outreach programs designed to broaden the participation from underrepresented groups through actively engaging K-12 students for STEM education and recruiting women and minorities into research, priming future generations of diverse engineers with the knowledge and skills indispensable in the age of manufacturing innovation and big data.

The ultimate goal of this early career effort is to understand fatigue failures of complex LPBF parts under multiaxial loading for data-driven fatigue life predictions. The research will investigate the nature of fatigue failures from plastic deformation and crack initiation at the highest stress concentrations and translate fatigue life predictions into evaluating the crack growth at the vulnerable zones using a multiscale approach. On the micro-scale, critical defects with crack-initiating features (by x-ray computed tomography or optical profilometry) will be identified based on the correlation with fatigue failures; both the effects of critical defects and their spatial interactions on crack growth will be examined using fracture mechanics and data-intense statistics. On the part scale, the weak regions of the highest stress concentrations will be examined by finite element modeling of stress and strain behaviors through decoupling multiaxial loading. The effects of critical defects and the principal stresses at vulnerable localities will then be incorporated into a hierarchical graph convolutional network of deep learning to model their synergistic impacts on crack growth and calculate the fatigue life of LPBF parts with advanced data analytics. The findings are expected to generate new knowledge of defect formation relevant to fatigue performance of LPBF parts, uncover the synergistic impacts of multiscale factors on fatigue fractures, and further LPBF adoption for high-consequence applications.

Presenting Author: Jia Liu Auburn University

Presenting Author Biography: Dr. Jia (Peter) Liu is an Assistant Professor in the Department of Industrial and Systems Engineering at Auburn University. He graduated from Virginia Tech with a Ph.D. in Industrial and Systems Engineering and an M.S. in Statistics in 2017. He also received a B.S. and an M.S. in Electrical Engineering from Zhejiang University, China, in 2005 and 2007, respectively. He focuses on interpretable data-driven modeling for complex manufacturing processes with heterogeneous sensor data to achieve online process monitoring, product quality prediction, and control, such as additive manufacturing, semiconductor manufacturing, and electronics packaging. He has been working in this area for seven years, and published over 40 journal and conference papers. His research contribution has been featured in IISE magazine. He has been honored with several awards, including the 2023 NSF Career Award, the 2023 Ginn Achievement Faculty Fellow at Auburn University, and the 2020 Early Career Development Grant at Auburn University.

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