航空材料学报2026，Vol.46Issue(3)：47-55,9.DOI:10.11868/j.issn.1005-5053.2025.000023
基于数据驱动的Al-Cu合金多目标性能模型预测
Data-driven multi-objective property model prediction in Al-Cu alloys
摘要
Abstract
Cast aluminum alloys are widely used in aerospace,automotive and other industries due to their excellent mechanical properties.However,traditional alloy design faces challenges such as vast composition space,high costs of trial-and-error experiments and difficulty in predicting the nonlinear relationship between composition and properties.This paper proposes a machine learning model that combines backpropagation neural networks,principal component analysis,and genetic algorithms for multi-objective property prediction of cast aluminum alloys.The model establishes the relationship between alloy composition and properties through the nonlinear mapping of backpropagation neural networks,reduces dimensionality via principal component analysis,and optimizes network parameters using genetic algorithms-thereby improving prediction accuracy and training efficiency.The results show that the optimized model has mean squared error of 36.28,correlation coefficient of 0.91,and mean absolute error of 2.44.In the experimental verification of ultimate strength,yield strength,and elongation after fracture,the error between experimental values and predicted values is controlled within the range of±5%.This high prediction accuracy demonstrates the efficiency and reliability of the proposed model.关键词
铸造铝合金/主成分分析/反向传播神经网络/遗传算法/力学性能Key words
cast aluminum alloy/principal component analysis/backpropagation neural network/genetic algorithm/mechanical property分类
航空航天引用本文复制引用
廉红珍,陆春月..基于数据驱动的Al-Cu合金多目标性能模型预测[J].航空材料学报,2026,46(3):47-55,9.基金项目
国家科技部创新方法专项(2020IM020700) （2020IM020700）
