电阻点焊中的故障预测:机器学习方法比较

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Gabriele Ciravegna, Franco Galante, Danilo Giordano, Tania Cerquitelli, Marco Mellia
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引用次数: 0

摘要

电阻点焊在制造业中被广泛采用,其特点是可靠性高、生产线自动化简单。检测焊接缺陷是一项艰巨的任务,需要进行破坏性检测或昂贵而缓慢的非破坏性检测(如超声波)。进行焊接的机器人会自动收集上下文和特定过程的数据。在本文中,我们将测试这些数据是否可用于预测缺陷焊缝。为此,我们使用了在实际工业工厂中收集的数据集,该数据集描述了标有超声波质量检查的焊接相关数据。我们利用这些数据开发了几种基于浅层和深度学习机器学习算法的管道,并测试了这些管道在预测缺陷焊缝方面的性能。结果表明,尽管开发了不同的管道和复杂的模型,但基于机器学习的缺陷检测算法性能有限。通过对模型预测的定性分析,我们发现正确的预测往往是数据固有偏差和内在局限性的结果。因此,我们得出结论,自动收集的数据存在局限性,妨碍了运行中的生产工厂的故障检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Prediction in Resistance Spot Welding: A Comparison of Machine Learning Approaches
Resistance spot welding is widely adopted in manufacturing and is characterized by high reliability and simple automation in the production line. The detection of defective welds is a difficult task that requires either destructive or expensive and slow non-destructive testing (e.g., ultrasound). The robots performing the welding automatically collect contextual and process-specific data. In this paper, we test whether these data can be used to predict defective welds. To do so, we use a dataset collected in a real industrial plant that describes welding-related data labeled with ultrasonic quality checks. We use these data to develop several pipelines based on shallow and deep learning machine learning algorithms and test the performance of these pipelines in predicting defective welds. Our results show that, despite the development of different pipelines and complex models, the machine-learning-based defect detection algorithms achieve limited performance. Using a qualitative analysis of model predictions, we show that correct predictions are often a consequence of inherent biases and intrinsic limitations in the data. We therefore conclude that the automatically collected data have limitations that hamper fault detection in a running production plant.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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