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在智能制造中可解释的AI驱动的质量和状态监测
M Nadeem Ahangar1, Z A Farhat1, Aparajithan Sivanathan1
1AMRC North West, University of Sheffield, Blackburn BB2 7HN, UK.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
可解释的人工智能 (XAI) 技术打开了人工智能在制造业中的黑子. 这项研究为可信的人工智能提供了一个框架,确保工业应用中的透明度和人类可解释性.
科学领域:
- 工业人工智能 工业人工智能
- 可解释的人工智能 (XAI)
- 制造业 制造技术 制造技术
背景情况:
- 在制造业中采用人工智能 (AI) 面临着信任和监管障碍,原因是不透明的"黑子"模型.
- 可解释性AI (XAI) 对于理解工业环境中的AI决策至关重要.
研究的目的:
- 调查用于在制造业中解释AI系统的XAI技术,重点是可解释性而不是性能优化.
- 在不同的制造用例中提出和应用统一的,以解释为中心的框架.
主要方法:
- 应用已建立的XAI技术 (Grad-CAM,集成梯度,SHAP等) 对异质数据模式 (视觉,声学) 进行调整.
- 在不同的AI模型,任务和监督策略中评估了XAI的一致性.
- 利用了三个制造用例:造缺陷分类,金属表面缺陷定位和声学异常检测.
主要成果:
- XAI技术成功地暴露了模型的注意力,特征相关性和决策驱动因素.
- 人工智能模型行为与可物理解释的缺陷/故障机制之间证明了对齐.
- XAI促进了透明,可审计和人类可解释的AI决策.
结论:
- 对于制造业中可靠的人工智能来说,XAI是必不可少的,它使人能在循环中进行监督和问责.
- 拟议的跨模式框架支持工业5.0原则,以实现透明的工业人工智能.
- 将可解释性定位为核心要求提高了人工智能接受度和监管合规性.
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