复杂浓缩合金低模量和高屈服强度的多目标协同设计

IF 11.9 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Qingfeng Yin, Yuan Wu, Honghui Wu, Xiaobin Zhang, Suihe Jiang, Hui Wang, Xiongjun Liu, Zhaoping Lu
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引用次数: 0

摘要

低杨氏模量和高屈服强度同时满足金属植入材料的性能要求。以性能为导向的单一目标合金设计策略在有效解决杨氏模量与屈服强度之间的内在冲突方面面临挑战。在这项研究中,我们开发了一个用于模量和屈服强度多目标协同优化的机器学习模型,成功地同时预测了Ti-Zr-Hf-Nb-Ta-Mo-Sn合金体系的杨氏模量和屈服强度。系统地分析和识别了影响合金模量和强度的关键特征。在此基础上成功制备了一系列低杨氏模量、高屈服强度的复合浓缩合金(CCAs)。新开发的合金具有稳定的单相BCC(体心立方)结构,杨氏模量在40-50 GPa范围内,屈服强度为600-915 MPa,弹性允许应变约为1.5%。本研究建立的多目标机器学习模型可以协同优化复杂合金的低杨氏模量和高屈服强度,为先进生物医学合金的设计提供了一种新的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A multi-objective synergistic design for low modulus and high yield strength in complex concentrated alloys

A multi-objective synergistic design for low modulus and high yield strength in complex concentrated alloys

Low Young’s modulus and high yield strength are concurrently needed to meet the performance requirements of metallic implant materials. The single-objective performance-oriented alloy design strategies face challenges in effectively addressing the inherent conflict between Young’s modulus and yield strength. In this study, we developed a machine learning model for multi-objective synergistic optimization of modulus and yield strength, successfully enabling simultaneous prediction of Young’s modulus and yield strength in the Ti-Zr-Hf-Nb-Ta-Mo-Sn alloy system. The critical features influencing the modulus and strength of the alloys were systematically analyzed and identified. Moreover, a series of complex concentrated alloy (CCAs) with low Young’s modulus and high yield strength were successfully prepared based on this model. The newly developed alloys exhibited a stable single-phase BCC (body-centered-cubic) structure with Young’s modulus in the range of 40–50 GPa, yield strength of 600–915 MPa, and elastic admissible strain of approximately 1.5%. The multi-objective machine learning model developed in this study can synergistically optimize low Young’s modulus and high yield strength in complex alloys, providing a novel approach for the design of advanced biomedical alloys.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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