CDS&E/Collaborative Research: Interpretable Machine Learning for Microstructure-Sensitive Fatigue Crack Initiation from Defects in Additive Manufactured Components

Purdue University, West Lafayette IN

Investigators

Abstract

Advancements in experimental and computational methods in recent decades have enabled production of a wealth of data for many engineering and science applications. However, these data do not readily translate into engineering knowledge. The objective of this project is to develop a machine-learning approach to facilitate this data-to-knowledge translation to better understand materials integrity. Such knowledge can help understand mechanical behaviors, such as fatigue, in additive manufactured components. The developed approach will improve the conventional process of materials certification, which is prohibitively expensive. The outcome of this project could potentially reduce consumer costs and increase adoption, which may ultimately advance the U.S. economy. To engage future generations and promote inclusion, K-12 students will interact with a user-friendly interface for hands-on demonstration of learning natural laws at the Utah Engineering Day and Purdue Space Day. Increasing the success and reliability of translating data into knowledge requires a shifted focus toward explainability and interpretability to perpetuate sound science and engineering principles. To this end, Genetic Programming based Symbolic Regression (GPSR) will be utilized to model fatigue damage in structural materials. GPSR models will then be trained using generated data sets from materials simulations, experiments, and guided by existing knowledge to discover new underlying mechanisms, i.e., knowledge. The research tasks will address a tractable means to model microstructure-dependent mechanisms into fatigue life predictions and supplant current practices. Specifically, GPSR models will be trained on a combination of high-energy X-ray diffraction microscopy and crystal plasticity finite element simulation data. The generated GPSR models will be a physics-regularized multiscale homogenization of pore-induced, microstructure-dependent fatigue crack initiation in an additive manufactured metal. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.