A process-synergistic active learning framework for high-strength Al-Si alloys design
Jianming Cai, Mengxia Han, Xirui Yan, Yan Chen, Daoxiu Li, Kai Zhao, Dongqing Zhang, K.P. Hu, Heng Han Sua, Hieng Kiat Jun, Kewei Xie, Guiliang Liu, Xiangfa Liu, Sida Liu
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.