在精密电气元件的大规模生产过程中,预测缺陷率和故障原因诊断
1Department of Applied Chemistry School of Science and Technology Meiji University Kawasaki Japan.
Analytical science advances
|May 8, 2024
概括
通过分析组合工艺变量,预测精密电气元件制造中的缺陷率得到了改进. 与传统方法相比,这种多变量方法显著提高了预测准确性.
科学领域:
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
- 材料科学 材料科学 材料科学
背景情况:
- 精密电气元件的大规模生产面临的挑战是缺陷识别.
- 像温度和压力这样的过程变量 (PV) 被监控,但传统分析很难将它们与缺陷率 (DR) 联系起来.
研究的目的:
- 在精密电气元件制造中开发出缺陷率 (DR) 的预测模型.
- 通过结合领域知识和多变量分析,改善过程变量 (PV) 和 DR 之间的相关性.
主要方法:
- 利用来自实际大规模生产过程的时间序列数据.
- 应用集体机器学习方法,包括随机森林,梯度增强决策树,XGBoost和LightGBM.
- 纳入了用于适当转换PVs的域名知识,并包括了以前被忽视的变量.
主要成果:
- 实现了PV和DR之间的相关系数的显著改善,从0.17 (无变量) 增加到0.73 (多变量).
- 通过机器学习模型的特征重要性分析,确定了与DR密切相关的关键PV.
- 证明了多变量分析对传统单变量方法的有效性.
结论:
- 领域知识对于提高机器学习模型预测准确性和可解释性在制造缺陷分析中至关重要.
- 多变量分析包括不太明显的PV和特定领域的转换,大大改善了缺陷率预测.
- 集成机器学习模型有效地诊断影响产品缺陷的关键过程变量.
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