基于微观组织三维表征的铸造铝合金一体化成形工艺优化

苗以升; 侯清怀; 王俊升; 李钟尧; 吴雪龙; 王诗豪; 郎玉玲; 孔德才; 马小英; 乔海波

doi:10.16080/j.issn1671-833x.2024.23/24.072

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基于微观组织三维表征的铸造铝合金一体化成形工艺优化

论坛——精密铸造 | 更新时间：2025-10-30

- 基于微观组织三维表征的铸造铝合金一体化成形工艺优化
- 航空制造技术 2024年67卷第23/24期
- 作者机构：
  
  1. 北京理工大学材料学院,北京,100081
  2. 北京理工大学前沿交叉科学研究院,北京,100081
  3. 北京理工大学机械与车辆学院,北京,100081
  4. 中信戴卡股份有限公司,秦皇岛,066010
- 作者简介：
- 基金信息：
- DOI：10.16080/j.issn1671-833x.2024.23/24.072
  中图分类号：
- 纸质出版：2024
- 稿件说明：
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苗以升，侯清怀，王俊升，李钟尧，吴雪龙，王诗豪，郎玉玲，孔德才，马小英，乔海波. 基于微观组织三维表征的铸造铝合金一体化成形工艺优化[J]. 航空制造技术, 2024, 67(23/24): 72-79. MIAO Yisheng, HOU Qinghuai, WANG Junsheng, LI Zhongyao, WU Xuelong, WANG Shihao, LANG Yuling, KONG Decai, MA Xiaoying, QIAO Haibo. Optimization of Integrated Forming Process for Cast Aluminum Alloys Based on 3D Microstructure Characterization[J]. Aeronautical Manufacturing Technology, 2024, 67(23/24): 72-79.

MIAO Yisheng, HOU Qinghuai, WANG Junsheng, et al. Optimization of Integrated Forming Process for Cast Aluminum Alloys Based on 3D Microstructure Characterization[J]. Aeronautical Manufacturing Technology, 2024, 67(23/24).
苗以升，侯清怀，王俊升，李钟尧，吴雪龙，王诗豪，郎玉玲，孔德才，马小英，乔海波. 基于微观组织三维表征的铸造铝合金一体化成形工艺优化[J]. 航空制造技术, 2024, 67(23/24): 72-79. MIAO Yisheng, HOU Qinghuai, WANG Junsheng, LI Zhongyao, WU Xuelong, WANG Shihao, LANG Yuling, KONG Decai, MA Xiaoying, QIAO Haibo. Optimization of Integrated Forming Process for Cast Aluminum Alloys Based on 3D Microstructure Characterization[J]. Aeronautical Manufacturing Technology, 2024, 67(23/24): 72-79. DOI： 10.16080/j.issn1671-833x.2024.23/24.072.

MIAO Yisheng, HOU Qinghuai, WANG Junsheng, et al. Optimization of Integrated Forming Process for Cast Aluminum Alloys Based on 3D Microstructure Characterization[J]. Aeronautical Manufacturing Technology, 2024, 67(23/24). DOI： 10.16080/j.issn1671-833x.2024.23/24.072.

摘要

铝合金中普遍存在气孔、缩孔等铸造缺陷，严重影响其使用性能。本研究自主开发了一种基于元胞自动机的铸造铝合金缺陷多尺度预测模型。为了验证该模型的准确性，采用X 射线计算机断层扫描技术（X-CT）三维表征了零部件不同部位的孔洞尺寸和形貌特征，预测和试验结果的对比分析表明，该模型能够有效地预测零部件不同部位的孔洞尺寸，其中孔洞当量直径的预测效果最佳，平均相对误差在25% 左右。该多尺度预测模型可以有效加速车轮铸造工艺优化，提高车轮生产效率，降低生产成本。

Abstract

Casting defects such as gas and shrinkage porosity are common in aluminum alloys

which seriously affect their performance. In this study

a multi-scale prediction model for defects in cast aluminum alloys based on cellular automaton models was developed independently. To verify the accuracy of the model

X-ray computed tomography (XCT) was utilized to characterize the size and morphology of the microporosities in different parts of the components in three dimensions. A comparative analysis of the predicted and experimental results revealed that the model was capable of efficiently predicting the microporosities sizes in various parts of the components

with the most accurate predictions for the equivalent diameter of the microporosities

exhibiting an average relative error of approximately 25%. This multiscale prediction model can effectively accelerate the optimization of the wheel casting process

improve wheel production efficiency and reduce production costs.

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