在使用CTGAN和CatBoost的HSLA钢的连续造中用于内部裂纹预测的数据驱动方法
Mengying Geng1, Haonan Ma2, Shuangli Liu1,3
1National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel, Switzerland)
|August 14, 2025
概括
本研究引入了一种用于检测高强度低合金钢 (HSLA) 内裂的新方法. 通过使用生成对抗网络 (GAN) 来平衡数据,CatBoost模型显著提高了缺陷预测的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
背景情况:
- 高强度低合金钢 (HSLA) 的内部裂是连续造中的关键问题,影响产品的可靠性.
- 工业缺陷数据集经常表现出类不平衡,阻碍了传统机器学习模型在识别罕见缺陷方面的性能.
- 准确的缺陷检测对于优化钢铁制造工艺和确保产品质量至关重要.
研究的目的:
- 开发一个强大的预测框架,用于在连续造过程中识别HSLA钢的内部裂纹缺陷.
- 通过使用先进的数据增强技术,解决缺陷数据集中的类失衡的挑战.
- 评估条件表式生成对抗网络 (CTGAN) 和 CatBoost 模型用于缺陷预测的有效性.
主要方法:
- 收集并预处理了来自连续的733个过程记录的数据集.
- 相互信息被用来从数据集中选择25个信息特征.
- 条件表式生成对抗网络 (CTGAN) 用于生成合成少数类样本 (裂) 用于数据增强.
- 增强数据集被用于训练和评估CatBoost分类模型,比较其与KNN,SVM和MLP的性能.
主要成果:
- 特性分布分析和主要组件可视化证实,CTGAN有效地保留了原来的少数类数据的统计结构.
- 基于CTGAN的数据增强显著改善了所有测试模型的分类性能.
- 拟议的CTGAN-CatBoost模型实现了卓越的性能,准确度为0.9239,精度为0.9041,回忆率为0.9018,F1得分为0.9022,优于其他机器学习方法.
结论:
- 基于条件表式生成对抗网络 (CTGAN) 的增强对于解决工业缺陷数据集中的类不平衡非常有效.
- 集成的CTGAN-CatBoost模型为钢铁制造业内部裂纹缺陷的在线预测提供了强大而准确的解决方案.
- 这种方法提高了HSLA钢的连续造质量控制的可靠性和效率.
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