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Gao et al. J Mater Inf 2023;3:6 Journal of
DOI: 10.20517/jmi.2023.03
Materials Informatics

Research Article Open Access

Development of an accurate “composition-process-
properties” dataset for SLMed Al-Si-(Mg) alloys and

its application in alloy design

2
2
1
Tianchuang Gao , Jianbao Gao 1,* , Jinliang Zhang , Bo Song , Lijun Zhang 1,*
1
State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, Hunan, China.
2
State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology,
Wuhan 430074, Hubei, China.
* Correspondence to: Dr. Jianbao Gao, State Key Laboratory of Powder Metallurgy, Central South University, Lushannan Road No.
932, Changsha 410083, Hunan, China. E-mail: jianbao.gao@csu.edu.cn; Prof. Lijun Zhang, State Key Laboratory of Powder
Metallurgy, Central South University, Lushannan Road No. 932, Changsha 410083, Hunan, China. E-mail: lijun.zhang@csu.edu.cn
How to cite this article: Gao T, Gao J, Zhang J, Song B, Zhang L. Development of an accurate “composition-process-properties”
dataset for SLMed Al-Si-(Mg) alloys and its application in alloy design. J Mater Inf 2023;3:6.
https://dx.doi.org/10.20517/jmi.2023.03

Received: 18 Jan 2023 First Decision: 13 Feb 2023 Revised: 19 Feb 2023 Accepted: 17 Mar 2023 Published: 28 Mar 2023

Academic Editors: Xiao-Dong Xiang, Sergei V Kalinin, Xingjun Liu Copy Editor: Ke-Cui Yang Production Editor: Ke-Cui Yang

Abstract
Al-Si-Mg series alloys are the most common alloys available for additive manufacturing forming with low cracking
tendency. However, there is no systematic study on the computational design of SLMed Al-Si-(Mg) alloys due to
the huge parameter space of composition and processes. In this paper, a high-quality dataset of SLMed Al-Si-(Mg)
alloys containing 176 pieces of data from 50 publications was first established, which recorded the information,
including alloy compositions, process parameters, test conditions, and mechanical properties. A threshold value of
3
35 J/mm for energy density (Ed) was then proposed as a criterion to clean the data points with lower ultimate
tensile strength (UTS) and elongation (EL). The cleaned dataset consists of a first training/testing dataset with 142
data for model construction and a second testing dataset with 9 data for model verification. After that, four
machine learning models were applied to establish the quantitative relation of “composition-processes-properties”
in SLMed Al-Si-(Mg) alloys. The MLPReg model was chosen as the optimal one considering its best performance
and subsequently utilized to design novel compositions and process parameters for SLMed Al-Si-(Mg) alloys. The
UTS and EL of the designed alloy with a maximum comprehensive mechanical property are 549 MPa and 16%,
both of which are higher than all the available experimental data. It is anticipated that the present design strategy
based on the machine learning method should generally be applicable to other SLMed alloy systems.

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
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