王硕,王俊升,梁婷婷,薛程鹏,杨兴海,田光元,苏辉,李全,吴雪龙

• 1:北京理工大学材料学院
• 2:北京理工大学前沿交叉科学研究院
• 3:北京理工大学机械与车辆学院

摘要(Abstract)：

利用机器学习框架搭建材料研究设计平台对材料性能进行分析与预测，成为开发新型材料的重要手段。铝合金的导电率和强度往往是互斥的，导电率的提高，伴随着强度的降低。使用SVM、RF、ELM、BP和DNN五种机器学习方法建立6000系铝合金的导电率和强度的机器学习预测模型。发现以热力学数据和加工工艺为特征输入，在合金性能预测模型的构建方面表现出巨大潜力。并最终筛选出精确度高，泛化能力好的深度神经网络预测模型。经过与实验数据验证，证明了所提模型对于铝合金导电率、强度预报的可靠性。

关键词(KeyWords)： Al-Mg-Si合金;机器学习;导电率;力学性能

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金面上项目(52073030)，国家自然科学基金区域创新联合基金重点项目(U20A20276)

作者(Author): 王硕,王俊升,梁婷婷,薛程鹏,杨兴海,田光元,苏辉,李全,吴雪龙

DOI: 10.13289/j.issn.1009-6264.2023-zt07

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