Metal workpieces are indispensable in the manufacturing industry. Surface defects affect the appearance and efficiency of a workpiece and reduce the safety of manufactured products. Therefore, products must be inspected for surface defects, such as scratches, dirt, and chips. The traditional manual inspection method is time-consuming and labor-intensive, and human error is unavoidable when thousands of products require inspection. Therefore, an automated optical inspection method is often adopted. Traditional automated optical inspection algorithms are insufficient in the detection of defects on metal surfaces, but a convolutional neural network (CNN) may aid in the inspection. However, considerable time is required to select the optimal hyperparameters for a CNN through training and testing. First, we compared the ability of three CNNs, namely VGG-16, ResNet-50, and MobileNet v1, to detect defects on metal surfaces. These models were hypothetically implemented for transfer learning (TL). However, in deploying TL, the phenomenon of apparent convergence in prediction accuracy, followed by divergence in validation accuracy, may create a problem when the image pattern is not known in advance. Second, our developed automated machine-learning (AutoML) model was trained through a random search with the core layers of the network architecture of the three TL models. We developed a retraining criterion for scenarios in which the model exhibited poor training results such that a new neural network architecture and new hyperparameters could be selected for retraining when the defect accuracy criterion in the first TL was not met. Third, we used AutoKeras to execute AutoML and identify a model suitable for a metal-surface-defect dataset. The performance of TL, AutoKeras, and our designed AutoML model was compared. The results of this study were obtained using a small number of metal defect samples. Based on TL, the detection accuracy of VGG-16, ResNet-50, and MobileNet v1 was 91%, 59.00%, and 50%, respectively. Moreover, the AutoKeras model exhibited the highest accuracy of 99.83%. The accuracy of the self-designed AutoML model reached 95.50% when using a core layer module, obtained by combining the modules of VGG-16, ResNet-50, and MobileNet v1. The designed AutoML model effectively and accurately recognized defective and low-quality samples despite low training costs. The defect accuracy of the developed model was close to that of the existing AutoKeras model and thus can contribute to the development of new diagnostic technologies for smart manufacturing.

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.