A process-synergistic active learning framework for high-strength Al-Si alloys design

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 trial-and-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: 459.8 MPa for gravity casting with T6 heat treatment within three iterations and 220.5 MPa for gravity casting with hot extrusion in a single iteration. This framework handles sparse datasets effectively, capturing complex process-composition-property relationships and establishing a new paradigm for accelerated multi-objective material design.

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Publication Details

Journal
npj Computational Materials
Published
2025-07-14
DOI
https://doi.org/10.1038/s41524-025-01721-3
Citations
5
Primary Topic
Aluminum Alloy Microstructure Properties
Type
article
Field-Weighted Citation Impact
24.97

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article

A process-synergistic active learning framework for high-strength Al-Si alloys design

Xiangfa Liu, Xirui Yan, Daoxiu Li, Mengxia Han et al.
5 citations
npj Computational Materials
Aluminum Alloy Microstructure Properties
24.97
article

A process-synergistic active learning framework for high-strength Al-Si alloys design

Xiangfa Liu, Xirui Yan, Daoxiu Li, Mengxia Han, K.P. Hu, Hieng Kiat Jun, Heng Han Sua, Guiliang Liu, Yan Chen, Sida Liu, Dongqing Zhang, Kai Zhao, Jianming Cai, Kewei Xie
article en
5 citations

Abstract

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 trial-and-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: 459.8 MPa for gravity casting with T6 heat treatment within three iterations and 220.5 MPa for gravity casting with hot extrusion in a single iteration. This framework handles sparse datasets effectively, capturing complex process-composition-property relationships and establishing a new paradigm for accelerated multi-objective material design.

npj Computational MaterialsVol. 11(1)
Shandong University of Technology (CN), Schott (Germany) (DE), Crystal Research (United States) (US), Kementerian Pendidikan Malaysia (MY), Western Metal Materials (China) (CN), Xi'an Jiaotong University (CN), Universiti Tunku Abdul Rahman (MY)
Northwestern University, Shandong University, National Natural Science Foundation of China, Northwestern Polytechnical University, Central South University, China Postdoctoral Science Foundation, Ministry of Industry and Information Technology of the People's Republic of China, State Key Laboratory of Powder Metallurgy, State Key Laboratory of Solidification Processing, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 1%
Aluminum Alloy Microstructure Properties
24.97
DOIOpen Access PDFOpenAlex
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