高强度铝硅合金设计的过程协同主动学习框架

IF 11.9 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Jianming Cai, Mengxia Han, Xirui Yan, Yan Chen, Daoxiu Li, Kai Zhao, Dongqing Zhang, Kaiqi Hu, Heng Han Sua, Hieng Kiat Jun, Kewei Xie, Guiliang Liu, Xiangfa Liu, Sida Liu
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引用次数: 0

摘要

高强度铝硅合金是重要的轻量化材料,但其优化设计受到稀缺数据不平衡和复杂的成分-工艺-性能关系的阻碍。传统的试错实验无法探索这种多维设计空间,在这种设计空间中,加工路线(pr)和成分必须共同优化,以达到更高的强度。本研究引入了一个过程协同主动学习(PSAL)框架,利用条件Wasserstein自动编码器(c-WAE)来实现数据高效设计。通过将pr编码为条件变量,PSAL框架揭示了跨不同pr的卓越协同效应,显著优于单流程方法。过程感知的潜在表示有助于同时有效地探索跨多个pr的潜在成分。通过将机器学习预测与实验验证相结合的迭代主动学习循环,极大地提高了极限抗拉强度:T6热处理重力铸件的极限抗拉强度在三次迭代中达到459.8 MPa,热挤压重力铸件的极限抗拉强度在一次迭代中达到220.5 MPa。该框架有效地处理稀疏数据集,捕获复杂的过程-组成-属性关系,并为加速多目标材料设计建立了新的范例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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