Employing deep learning in non‐parametric inverse visualization of elastic–plastic mechanisms in dual‐phase steels
Siyu Han,
Chenchong Wang,
Yu Zhang
et al.
Abstract:Enhancing the interpretability of machine learning methods for predicting material properties is a key, yet complex topic in materials science. This study proposes an interpretable convolutional neural network (CNN) to establish the relationship between the microstructural evolution and mechanical properties of non‐uniform and nonlinear multisystem dual‐phase steel materials and achieve an inverse analysis of the elastic‐plastic mechanism. This study demonstrates that the developed CNN model achieves an accura… Show more
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