Beyond small data limitations: Transfer learning-enabled framework for predicting mechanical properties of aluminum matrix composites
A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS), has developed an innovative machine learning framework (PAMCs-MP) for predicting the mechanical properties of particle-reinforced aluminum matrix composites (PAMCs). Despite limited existing datasets, the approach uses extensive pre-training on larger aluminium alloy datasets to guide multi-objective optimization tasks effectively. The model achieves high predictive accuracy, R² values of over 92% for ultimate tensile strength and over 90% for elongation, demonstrating ...
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