积极检查与知识蒸用于制造过程中具有成本效益的故障预测
Junbong Heo1, Minhyeok Son2, Jaewoong Shim3,4
1Department of Data Science, Seoul National University of Science and Technology, 232 Gongneung-ro, Seoul, Nowon-gu, 01811, Republic of Korea.
Scientific reports
|February 13, 2026
概括
本研究引入了一种使用知识蒸来训练基本制造检查模型的新方法. 这种方法提高了预测准确度,同时降低了检查成本,提高了整体的成本效益.
科学领域:
- 制造过程优化制造过程优化
- 机器学习在质量控制中的应用.
背景情况:
- 制造检查对于故障检测至关重要,因成本而分为基本 (所有产品) 和高级 (采样产品).
- 在检查数据上训练的机器学习模型可以预测故障,但与高级模型相比,由于信息有限,基本模型表现不佳.
研究的目的:
- 开发一种具有成本效益的方法来培养高精度的基本检查模型.
- 通过将蒸的基本模型整合到主动检查框架中,提高制造检查的成本效益.
主要方法:
- 从先进模型提炼知识来训练基本模型.
- 将蒸的基本模型纳入积极的检查框架,以便在推断过程中选择性地使用模型.
- 使用现实世界半导体制造数据进行验证.
主要成果:
- 提出的知识蒸方法实现了基本模型的高预测准确性.
- 积极检查框架通过选择性地使用蒸的基本和高级模型来优化检查成本.
- 通过半导体制造案例研究证明了有效性.
结论:
- 知识蒸提供了一个可行的解决方案,可以在不增加成本的情况下增强基本的检查模型.
- 综合主动检查框架显著提高了制造质量控制的成本效益.
- 该方法为优化现实世界制造环境中的检查策略提供了一种实用的方法.
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