Tan, X.; Lu, Q.; Chen, D.; Wang, Z.; Chen, H.; Peng, X.; Zhang, W.; Xiao, H.; Liu, Z.; Guo, L.; Zhang, Q. Machine Learning Prediction of Phase and Tensile Properties of High Entropy Alloys Manufactured by Selective Laser Melting. Preprints2024, 2024020793. https://doi.org/10.20944/preprints202402.0793.v1
APA Style
Tan, X., Lu, Q., Chen, D., Wang, Z., Chen, H., Peng, X., Zhang, W., Xiao, H., Liu, Z., Guo, L., & Zhang, Q. (2024). Machine Learning Prediction of Phase and Tensile Properties of High Entropy Alloys Manufactured by Selective Laser Melting. Preprints. https://doi.org/10.20944/preprints202402.0793.v1
Chicago/Turabian Style
Tan, X., Liang Guo and Qingmao Zhang. 2024 "Machine Learning Prediction of Phase and Tensile Properties of High Entropy Alloys Manufactured by Selective Laser Melting" Preprints. https://doi.org/10.20944/preprints202402.0793.v1
Abstract
The utilization of selective laser melting (SLM) for high entropy alloys (HEAs) holds significant promise in commercial applications, and substantial experimental research efforts have been directed toward this domain. To take advantage of the reported experimental data of SLM manufactured (SLM-ed) HEAs and reduce unnecessary experimentation, this study incorporates machine learning (ML) techniques for the phase and tensile properties prediction of SLM-ed HEAs, thus presenting a novel avenue for accelerating the discovery of new SLM-ed HEAs. Through the adjustment of material descriptors and machine learning models, a model has been developed with an impressive accuracy of 93.8% in distinguishing between face-centered cubic (FCC), body-centered cubic (BCC), and dual-phase (FCC+BCC). Additionally, optimized models have been devised for the prediction of tensile properties, namely ultimate tensile strength (UTS), yield strength (YS), and elongation (δ), achieving noteworthy MAPEs (mean absolute percentage errors) of 10.33%, 8.55%, and 28.48%, respectively. Furthermore, several HEAs were fabricated using SLM, and the experimental outcomes exhibited a favorable alignment with the predicted results. These efforts carry significant implications in advancing the utilization of SLM for HEAs.
Chemistry and Materials Science, Materials Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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