Predicting material failure: Machine learning spots early abnormal grain growth signs for safer designs

Novel machine learning model can predict material failure before it happens

A team of 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, such as combustion engines.

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, such as combustion engines. A paper describing their novel machine learning method was recently published in Nature Computational Materials.

From Data Points to Decision Boundaries: A Hands-On Guide to Predictive Maintenance using PCA

Author(s): Luis Ramirez Originally published on Towards AI. For industrial equipment the default approach is preventive maintenance, which involves servicing equipment on a fixed schedule, such as monthly or semi-annually. While better than reactive maintenance (fixing equipment after failure), this one-size-fits-all strategy has significant drawbacks. Equipment units experience different operational conditions, loads, and degradation rates, yet…

Machine Learning Model Accurately Predicts Material Failure Before It Occurs

Lehigh University researchers developed a machine learning model that can predict abnormal grain growth in materials before failure, enabling early detection of structural weaknesses and paving the way for stronger, more reliable materials with potential applications across multiple scientific domains.