Yunxuan Zhou , Zihao Wang , Wenhui Tao , Yongkang Sun , Junjie Wu , Gang Wang , Yu Xiu , Huiyu Ji , Yulin Liu , Anping Dong , Jie Wang , Jun Wang , Mengmeng Wang , Qi Liu
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引用次数: 0
Abstract
This study proposes a systematic inverse-design methodology for aluminum alloys, integrating machine learning (ML) with multi-objective optimization. Based on an industrial dataset comprising 3790 alloy records, a database was constructed, incorporating the mass fractions of 14 elements along with 160 weighted atomic descriptors. Forward feature selection identified optimal descriptor subsets-19 features for Brinell hardness(HB) and 14 for electrical conductivity(EC). A comparative assessment of several regression algorithms identified Extreme Gradient Boosting (XGBoost) as the most accurate predictor. The optimized XGBoost models were coupled with the expected-improvement (EI) criterion to construct a multi-objective expected-improvement (MOEI) function, which was subsequently maximized using particle swarm optimization (PSO). This iterative procedure converged on an as-cast alloy composition of Al-5.86Si-1.93Cu-0.56Mn-0.65Mg-0.28Cr-1.67Ni-1.36Zn-0.10Ti-0.92Fe-0.049Sr (wt%), striking an optimal balance between HB and EC. CALPHAD-based thermodynamic calculations and microstructural validation confirmed that the alloy achieves 96.1 HB and 24.4 % IACS. Experimental measurements deviated by less than 3.5 HB and 2.2 % IACS from the predictions, demonstrating that the inverse design workflow can reproduce target properties within experimental uncertainty.
期刊介绍:
The design of industrial processes requires reliable thermodynamic data. CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) aims to promote computational thermodynamics through development of models to represent thermodynamic properties for various phases which permit prediction of properties of multicomponent systems from those of binary and ternary subsystems, critical assessment of data and their incorporation into self-consistent databases, development of software to optimize and derive thermodynamic parameters and the development and use of databanks for calculations to improve understanding of various industrial and technological processes. This work is disseminated through the CALPHAD journal and its annual conference.