Abstract

The machine-learning approach was investigated to predict the mechanical properties of Cu—Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new materials and provide physical insights into their properties. Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors. The results show that the sequential minimal optimization algorithm for support vector regression with a puk kernel (SMOreg/puk) model demonstrated the best prediction ability. Specifically, its predictions exhibited the highest correlation coefficient and lowest error among the predictions of the six models. The SMOreg/puk model was subsequently applied to predict the tensile strength and hardness of Cu—Al alloys and provide guidance for composition design to achieve the expected values. With the guidance of the SMOreg/puk model, Cu—12Al—6Ni alloy with a tensile strength (390 MPa) and hardness (HB 139) that reached the expected values was developed.

中文翻译：

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机器学习辅助的Cu-Al合金力学性能预测

摘要

对机器学习方法进行了研究，以预测使用粉末冶金技术制造的Cu-Al合金的机械性能，以提高新材料的制造和表征速度，并提供有关其性能的物理见解。使用六种算法构建预测模型，并以压块的化学成分和孔隙率作为描述子。结果表明，采用puk核（SMOreg / puk）模型的支持向量回归的顺序最小优化算法表现出最佳的预测能力。具体而言，在六个模型的预测中，其预测显示出最高的相关系数和最低的误差。随后将SMOreg / puk模型应用于预测Cu-Al合金的拉伸强度和硬度，并为成分设计提供指导以达到预期值。在SMOreg / puk模型的指导下，开发了抗拉强度（390 MPa）和硬度（HB 139）达到预期值的Cu-12Al-6Ni合金。