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Sintering Process Analysis of Aluminum Matrix Composites Using Machine Learning

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Abstract:

The sintering behavior of spark plasma sintering was analyzed by extracting features from process data obtained during the fabrication of aluminum matrix composites and machine learning using the obtained features were performed to predict relative density of composites. Seventy-five samples were sintered with different types of reinforcement, and different temperature and pressure conditions. Regression methods include linear regression such as Ridge, Lasso and Elastic Net, and nonlinear regression such as random forest, gradient boosting and XGBoost were tested. XGBoost had the highest prediction accuracy and the trained model was used for Shapley additive explanations value analysis and inverse analysis.

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Periodical:

Defect and Diffusion Forum (Volume 446)

Pages:

77-82

DOI:

https://doi.org/10.4028/p-a6BA7w

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Online since:

January 2026

Authors:

Kenjiro Sugio*, Yuuki Shinohara, Yoshikazu Hayashi, Gen Sasaki

Keywords:

Aluminum Matrix Composites, Inverse Analysis, Machine Learning, SHAP, Spark Plasma Sintering

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© 2026 Trans Tech Publications Ltd. All Rights Reserved

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