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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 ...
A recent NSR paper measures mechanical properties of solids as a function of both crystalline grain size and grain-boundary thickness The world of solid materials is not composed solely of perfectly ...
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