Composition optimization design and high temperature mechanical properties of cast heat-resistant aluminum alloy via machine learning

Abstract

Traditional trial-and-error methods for optimizing the composition of heat-resistant aluminum alloys often consume significant time and resources, making it difficult to achieve alloys with excellent mechanical properties. This study combines experimental and machine learning methods to predict the optimal alloy composition for maximum ultimate tensile strength(UTS) at 300 °C and 350 °C. The AdaBoost algorithm was chosen as the final model. Experimental results show that predictions of the machine learning model deviate by only 7.75 % from the actual results, with an R2 of 0.94. Furthermore, the study found that Al9FeNi and Al3Ni play key roles in enhancing the high-temperature mechanical properties of cast heat-resistant aluminum alloys. This model accurately predicts the high-temperature mechanical performance of heat-resistant aluminum alloys, providing effective guidance for their composition design.

Keywords

• Machine learning
• Heat-resistant aluminum alloy
• Mechanical properties
• Composition optimization design