Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

IF 4.2 2区工程技术 Q2 ENGINEERING, MANUFACTURING

Advances in Manufacturing Pub Date : 2024-03-17 DOI:10.1007/s40436-024-00485-1

Yu-Tong Yang, Zhong-Yuan Qiu, Zhen Zheng, Liang-Xi Pu, Ding-Ding Chen, Jiang Zheng, Rui-Jie Zhang, Bo Zhang, Shi-Yao Huang

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Abstract

High-pressure die casting (HPDC) is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation. However, the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation. Therefore, a methodology for property prediction must be developed. Material characterization, simulation technologies, and artificial intelligence (AI) algorithms were employed. Firstly, an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg alloy. Moreover, a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results. Additionally, the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model, allowing accurate prediction of the property distribution of the HPDC Al-Si alloy. The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.

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高压压铸铝硅合金的铝化性能分布预测

高压压铸（HPDC）是汽车行业中最受欢迎的大规模生产工艺之一，因为它能够实现零件整合。然而，大型 HPDC 产品机械性能的不均匀分布增加了零件性能评估的复杂性。因此，必须开发一种性能预测方法。我们采用了材料表征、模拟技术和人工智能（AI）算法。首先，采用图像识别技术为典型的 HPDC Al7Si0.2Mg 合金构建温度-微结构特征模型。此外，基于有限元方法和代表性体积元素模型结果，采用机器学习方法建立了孔隙率/微结构-力学性能模型。此外，还将铸造模拟软件的计算结果与孔隙率/微结构-力学性能模型进行了映射，从而准确预测了 HPDC Al-Si 合金的性能分布。本研究开发的人工智能属性分布模型有望为汽车行业的智能 HPDC 零件设计平台奠定基础。

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来源期刊

Advances in Manufacturing Materials Science-Polymers and Plastics

CiteScore

9.10

自引率

3.80%

发文量

274

期刊介绍： As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.