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Page 2 of 15 Gao et al. J Mater Inf 2023;3:6 https://dx.doi.org/10.20517/jmi.2023.03

Keywords: Selective laser melting, Al-Si-Mg alloy, machine learning, alloy design, mechanical properties

INTRODUCTION
Due to their low density, high specific strength, high specific stiffness, and good plasticity, aluminum alloys
[1]
are the preferred lightweight structure materials in aerospace, automotive, ships, and other fields . The
rapid development of marine, aerospace, and automotive transportation has put forward strict requirements
for the performance of alloys. Selective laser melting (SLM) forming technology, as a promising additive
manufacturing (AM) technology , has the advantages of a fast cooling rate and grain refinement. It can
[2]
improve the mechanical properties of alloys and prepare parts with complex shapes, which greatly broadens
the application range of aluminum alloys. Among different types of aluminum alloys, the Al-Si-(Mg) (i.e.,
Al-Si and Al-Si-Mg) series alloys are one of the few aluminum alloy systems suitable for the additive
[3,4]
[5-7]
manufacturing process, including Al4Si , Al12Si , AlSi10Mg [8-10] , and AlSi7Mg [11,12] , etc. In particular,
alloys with near-eutectic compositions over a very narrow solidification temperature range of 40K~50K can
greatly reduce the risk of cracking during the laser additive manufacturing process and enable the
preparation of nearly fully dense products, thus receiving extensive research attention.

The mechanical properties of SLMed Al-Si-(Mg) alloys are closely related to their composition and complex
processing parameters. For instance, 0.2~0.4 wt.% Mg content may enhance the strength of alloys through
precipitation hardening due to the precipitation of the fine Mg Si phase during the aging heat treatment or
2
the AM process [9,11,13] . Furthermore, the specific process parameters of the SLM process significantly affect
the alloy properties, including laser power [14-16] , scanning speed [16,17] , powder layer thickness , scanning
[15]
spacing [18,19] , build direction [20-22] , scanning strategy [23,24] , and post heat-treatment processes [13,11,20] . Though a
number of experimental investigations have been devoted to different SLMed Al-Si-(Mg) alloys in the
literature, the factors that affect the properties and performance of alloys are too many, and thus there is still
no systematic study on the relation among the “composition-process-properties” of the SLMed Al-Si-(Mg)
alloys. Thus, there is an urgent need to solve this problem because it may block the efficient design of high-
performance SLMed Al-Si-(Mg) alloys over high-dimensional parameter space.

To solve this problem, a variety of computational methods at different scales, including CALculation of
PHAse Diagram (CALPHAD) [25-30] , phase-field modeling [31-33] , finite element simulation , and machine
[34]
learning (ML) [28,29,35,36] can be utilized. Among them, ML is one of the most efficient computational methods.
It can be utilized to establish the quantitative relation of “composition-processes-properties”, and even
accelerate the design of high-performance alloys over a high dimensional parameter space. Mondal et al.
[37]
employed a physical information-based machine learning method to systematically investigate the cracking
mechanism of SLMed 6061Al, 2024Al, and AlSi10Mg alloys. The decision trees, support vector machines,
and logistic regression techniques were used to predict crack formation conditions, and the cracking
[38]
susceptibility maps were established for optimizing the process parameters. Yu et al. used AdaBoost,
gradient tree boosting, K-nearest neighbors, decision tree, and Extra Trees regressors to successfully predict
the hardness and relative mass density of LPBFed CNTs/AlSi10Mg nanocomposites with cellular structure
features as input. The relative errors of the predicted hardness and relative mass density due to the optimal
model are as low as 3.61% and 1.42%, respectively. He et al. applied a gaussian process regression-based
[39]
machine learning approach to establish a processing window for a high-density additive manufactured 2-
vol% TiCN reinforced AlSi10Mg composite. Though the ML approach has been widely used to design
process parameters for SLMed Al alloys, there is still a lack of reports on the establishment of a high-quality
“composition-process-properties” dataset of the basic SLMed Al-Si-(Mg) alloys using the ML approach.
Such a situation urgently needs to be improved.