Customized Convolutional Neural Networks Technology for Machined Product Inspection

• Published in: Applied sciences 2022-03, Vol.12 (6), p.3014
• Main Authors: Huang, Yi-Cheng, Hung, Kuo-Chun, Liu, Chun-Chang, Chuang, Ting-Hsueh, Chiou, Shean-Juinn
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Cameras; Chipping; Computer architecture; custom-designed models; Customization; Deep learning; Defects; Flaw detection; Human error; Inspection; Light; Manufacturing; Manufacturing industry; metal workpieces; Neural networks; smart manufacturing; Workpieces
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12063014

Metal workpieces are an indispensable and important part of the manufacturing industry. Surface flaws not only affect the appearance, but also affect the efficiency of the workpiece and reduce the safety of the product. Therefore, the appearance of the product needs to be inspected to determine if there are surface defects, such as scratches, dirt, chipped objects, etc., after production is completed. The traditional manual comparison inspection method is not only time-consuming and labor-intensive, but human error is also unavoidable when inspecting thousands or tens of thousands of products. Therefore, Automated Optical Inspection (AOI) is often used today. The traditional AOI algorithm does not fully meet the subtle detection requirements and needs to import a Convolutional Neural Network (CNN), but the common deep residual networks are too large, such as ResNet-101, ResNet-152, DarkNet-19, and DarkNet-53. Therefore, this research proposes an improved customized convolutional neural network. We used a self-built convolutional neural network model to detect the defects on the metal’s surface. Grad–CAM was used to display the result of the last layer of convolution as the basis for judging whether it was OK or NG. The self-designed CNN network architecture could be customized and adjusted without using a large network model. The customized network model designed in this study was compared with LeNet, VGG-19, ResNet-34, DarkNet-19, and DarkNet-53 after training five times each. The experimental results show that the self-built customized deep learning model avoiding the use of pooling and fully connected layers can effectively improve the recognition rate of defective samples and unqualified samples, and reduce the training cost. Our custom-designed models have great advantages over other models. The results of this paper contribute to the development of new diagnostic technologies for smart manufacturing.