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实施和评估质量4.0PMQ框架,用于汽车制造业过程监控
Fathy Alkhatib1, Mohamed Allam1, Vikas Swarnakar2
1Department of Management Science and Engineering, Khalifa University of Science and Technology, Abu Dhabi, 127781, United Arab Emirates.
Scientific reports
|July 9, 2025
概括
这项研究将机器学习 (ML) 整合到质量过程监测 (PMQ) 框架中,增强汽车制造业的缺陷检测. 增强的PMQ系统实现了高精度,改善了工业质量控制.
科学领域:
- 工业工程 工业工程 工业工程
- 人工智能的人工智能
- 制造系统制造系统的制造
背景情况:
- 传统的质量控制系统在与高维度,实时制造数据作斗争.
- 传统方法的局限性阻碍了有效的缺陷检测和流程优化.
- 集成先进的分析对于现代制造质量保证至关重要.
研究的目的:
- 通过机器学习 (ML) 增强质量过程监测 (PMQ) 框架,以改进缺陷检测.
- 引入一个人类在循环中的验证阶段,用于ML的解释性和监督.
- 在汽车制造环境中实施和评估人工智能驱动的质量控制系统.
主要方法:
- 应用机器学习算法,包括决策树,随机森林,梯度提升机,物流回归,支持向量机和人工神经网络.
- 开发并实施了一个增强的PMQ框架,并为人类-ML协作提供了一个新的验证阶段.
- 利用来自高精度汽车工厂的发动机制造数据进行缺陷分类和预测.
主要成果:
- 梯度提升机 (GBM) 和随机森林 (RF) 显示出卓越的性能,F1得分为0.98和曲线下面面积 (AUC) 为0.99.
- 功能重要性分析确定了座椅高度和底切口直径作为缺陷识别的关键预测指标.
- 实施的系统成功地对实时制造过程中的缺陷进行了分类和预测.
结论:
- 增强的PMQ框架与ML为管理复杂的制造数据和改善质量控制提供了强大的解决方案.
- 在验证阶段的结构化人机协作促进了对人工智能驱动的质量保证的信任和可解释性.
- 这项研究为在工业环境中采用人工智能和质量4.0原则提供了一个可扩展的蓝图.
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