Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition
Source abstract
Abstract
This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the Mg2SixSn1-x material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · High Temperature Alloys and Creep · X-ray Diffraction in Crystallography
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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