High-strength Al-Si alloys are important lightweight materials,but their optimal design is hindered by scarce-imbalance data,and complex compositional-process-property relationships.Traditional trialand-error experime...High-strength Al-Si alloys are important lightweight materials,but their optimal design is hindered by scarce-imbalance data,and complex compositional-process-property relationships.Traditional trialand-error experimentation fails to explore this multi-dimensional design space,where processing routes(PRs)and composition must be co-optimized to achieve superior strength.This study introduces a process-synergistic active learning(PSAL)framework leveraging a conditional Wasserstein autoencoder(c-WAE)to enable the data-efficient design.By encoding PRs as conditional variables,the PSAL framework reveals exceptional synergistic effects across diverse PRs,significantly outperforming single-process approaches.The process-aware latent representation facilitates the efficient exploration of potential compositions across multi-PRs simultaneously.Through iterative active learning cycles integrating machine learning predictions with experimental validations,ultimate tensile strength is greatly improved:459.8MPa for gravity casting with T6 heat treatment within three iterations and 220.5MPa for gravity casting with hot extrusion in a single iteration.This framework handles sparse datasets effectively,capturing complex processcomposition-property relationships and establishing a new paradigm for accelerated multi-objective material design.展开更多
基金supported by the open subject of the State Key Laboratory of Powder Metallurgy,Central South University(SKLPM-KF-003)a grant from the National Natural Science Foundation of China(52471142 and 52301167)+4 种基金the National Youth Talent Program,Ministry of Industry and Information Technology of China(GQQNKP005)the open subject of the State Key Laboratory of Solidification Processing,Northwestern Polytechnical University(SKLSP202403)the National Natural Science Foundation of China(12302140)the Fundamental Research Funds for the Central Universities of China(sxzy012023213)'China Postdoctoral Science Foundation(2023M732794)Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(GZB20230575).
文摘High-strength Al-Si alloys are important lightweight materials,but their optimal design is hindered by scarce-imbalance data,and complex compositional-process-property relationships.Traditional trialand-error experimentation fails to explore this multi-dimensional design space,where processing routes(PRs)and composition must be co-optimized to achieve superior strength.This study introduces a process-synergistic active learning(PSAL)framework leveraging a conditional Wasserstein autoencoder(c-WAE)to enable the data-efficient design.By encoding PRs as conditional variables,the PSAL framework reveals exceptional synergistic effects across diverse PRs,significantly outperforming single-process approaches.The process-aware latent representation facilitates the efficient exploration of potential compositions across multi-PRs simultaneously.Through iterative active learning cycles integrating machine learning predictions with experimental validations,ultimate tensile strength is greatly improved:459.8MPa for gravity casting with T6 heat treatment within three iterations and 220.5MPa for gravity casting with hot extrusion in a single iteration.This framework handles sparse datasets effectively,capturing complex processcomposition-property relationships and establishing a new paradigm for accelerated multi-objective material design.
