
Predicting material failure: Machine learning spots early abnormal grain growth signs for safer designs
A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of stronger, more reliable materials for high-stress environments, […]