中文

Journal of Shanghai University >

2022 , Vol. 28 >Issue 3: 512 - 522

DOI: https://doi.org/10.12066/j.issn.1007-2861.2386

Machine Learning

Data-driven based properties prediction and reverse design of aluminum matrix composites

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  • 1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    2. State Key Laboratory of Metal Matrix Composites, School of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    3. Center of Materials Informatics and Data Science, Materials Genome Institute, Shanghai University, Shanghai 200444, China
    4. Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China

Received date: 2022-03-30

  Online published: 2022-05-27

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Abstract

A data-driven approach was used to analyze the chemical composition and preparation process of aluminum matrix composites SiCp(0.5CNT)/7075Al, and analyze the tensile strength and elongation. An integrated framework comprising eight machine learning algorithms was constructed to automatically perform parameters tuning and optimal model selection. Subsequently, an inverse design of the material was conducted. Experimental results showed that under the heat treatment of a solid solution at 470 ${^\circ}$C for 40 min and aging at 120 ${^\circ}$C for 15 h, the predicted tensile strength and elongation of SiCp(0.5CNT)/7075Al-1.0Mg were 617.48 MPa and 2.98%, respectively, whereas the real experimental values were 647.0 MPa and 3.31%, respectively. The mean absolute percentage errors (MAPE) of the two mechanical properties between the predicted and experimental results were 4.56% and 9.97%, respectively. It indicated the effectiveness of the data-driven method for the process optimization and property improvement of aluminum matrix composites.

Key words： machine learning; feature analysis; aluminum matrix composite; inverse design

Cite this article

CHEN Shuizhou, WANG Xiaoshu, OUYANG Qiubao, ZHANG Rui . Data-driven based properties prediction and reverse design of aluminum matrix composites[J]. Journal of Shanghai University, 2022 , 28(3) : 512 -522 . DOI: 10.12066/j.issn.1007-2861.2386

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