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Page 12 of 15 Gao et al. J Mater Inf 2023;3:6 https://dx.doi.org/10.20517/jmi.2023.03

established. The dataset records the information on alloy composition, process parameters, test conditions,
and mechanical properties in detail. The effect of three main variables (i.e., Ed, layer rotation, and test
direction) on the mechanical properties was systematically analyzed in the three representative alloys
3
(Al12Si, AlSi10Mg, and Al7SiMg). A threshold value of 35 J/mm for Ed was used as a criterion to clean the
data points with lower UTS and 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.

● Four ML models were employed to establish the quantitative relation of “composition-processes-
properties” in SLMed Al-Si-(Mg) alloys. After a comprehensive comparison, the MLPReg model was
selected due to its best performance on both training and testing sets. The selected MLPReg model was then
utilized to design novel compositions and process parameters for SLMed Al-Si-(Mg) alloys with maximum
UTS, YS, EL, and comprehensive mechanical property index QI. For the alloy with maximum QI, its UTS
can reach 549.4MPa, which is ~40MPa higher than the best one over the available experimental data, while
its elongation can still retain 16%.

● The successful demonstration in this paper indicates that the present design strategy driven by the ML
technique should generally be applicable to other SLMed alloy systems.

DECLARATIONS
Authors’ contributions
Establishment, selection, and verification of machine learning model: Gao T
Made contributions to the conception and design of the study: Gao T, Gao J, Zhang L
Literatures data collation, interpretation, analysis, cleaning, and construction of data set, drafted the
manuscript: Gao T, Gao J, Zhang J
Supervised, revised, and finalized the manuscript: Gao T, Gao J, Zhang J, Song B, Zhang L

Availability of data and materials
The using data set and the trained machine learning model can be seen in Supplementary Materials.

Financial support and sponsorship
The financial support from the Science and Technology Program of Guangxi province, China (Grant No.
AB21220028), the Natural Science Foundation of Hunan Province for Distinguished Young Scholars (Grant
No. 2021JJ10062), and the Lvyangjinfeng Talent program of Yangzhou is acknowledged.

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2023.