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A Smart Monitoring System for Automatic Welding Defect Detection
IEEE Transactions on Industrial Electronics ( IF 7.7 ) Pub Date : 2019-12-01 , DOI: 10.1109/tie.2019.2896165
Paolo Sassi , Paolo Tripicchio , Carlo Alberto Avizzano

This paper introduces an intelligent system able to perform quality control assessment in an industrial production line. Deep learning techniques are employed and proved successful in a real application for the inspection of welding defects on an assembly line of fuel injectors. Starting from state-of-the-art deep architectures and using the transfer learning technique, it is possible to train a network with about 7 million parameters using a reduced number of injector's images, obtaining an accuracy of 97.22%. The system is also configured in order to exploit new data, collected during operation, to extend the existing dataset and to improve further its performance. The developed system shows that deep neural networks can successfully perform quality inspection tasks that are usually demanded to humans.

中文翻译:

一种焊接缺陷自动检测智能监控系统

本文介绍了一种能够在工业生产线上进行质量控制评估的智能系统。采用深度学习技术并在实际应用中证明是成功的,用于检查燃油喷射器装配线上的焊接缺陷。从最先进的深度架构开始,使用迁移学习技术,可以使用减少数量的注射器图像训练一个具有大约 700 万个参数的网络,获得 97.22% 的准确率。该系统还被配置为利用在操作期间收集的新数据来扩展现有数据集并进一步提高其性能。开发的系统表明,深度神经网络可以成功执行人类通常需要的质量检测任务。
更新日期:2019-12-01
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