镀锌
条状物
生产线
过程(计算)
人工智能
带钢
工程类
自动化
工厂(面向对象编程)
机制(生物学)
计算机科学
冲压
模式识别(心理学)
机械工程
材料科学
哲学
认识论
图层(电子)
复合材料
程序设计语言
操作系统
作者
Hao Chen,Zhenguo Nie,Qiwei Xu,Jianghua Fei,Kang Yang,Yaguan Li,Hongbin Lin,Wenhui Fan,Xin-Jun Liu
出处
期刊:Journal of Computing and Information Science in Engineering
[ASME International]
日期:2022-12-27
卷期号:23 (4)
被引量:2
摘要
Abstract In the production of cold-rolled galvanized steel strips used for stamping car body parts, the in-situ and real-time defect detection is crucial for quality control, in which various types of defects inevitably occur. It is challenging to improve the accuracy of defect detection and classification by appropriate means to assist the manual screening process better. Defects under actual production conditions are often not prominent enough in defect characteristics, and there may be a significant similarity between different defect categories. To eliminate this weakness, we propose a data-driven deep learning approach named steel surface faulty detection attention net (SSFDANet) that uses images of the galvanized steel surfaces as input to identify whether the product is qualified and automatic classification of defect types instantaneously. This method can shorten product inspection time and improve the production line automation efficiency. In addition, the attention mechanism is utilized to enhance the performance of SSFDANet. Compared with the baseline ResNet, SSFDANet achieves a noticeable improvement in classification accuracy on test data. The well-trained model can successfully show an improved performance than the baseline models on the multiple types of faulty. Enhanced by SSFDANet with high classification accuracy, the defect rate of products is significantly reduced, and the production speed of the production line is significantly improved. Future prospective studies that are inspired by this article are also discussed.
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