Surface Defect Recognition of Injection Molded Products Based on Deep Learning

As the manufacturing industry has higher requirements for product quality, traditional methods for surface defect recognition of injection molded products face many limitations, especially in the feature design and selection of complex surface defects, which are difficult to fully capture information. To this end, this study proposes a new method based on deep convolutional neural network (CNN), which achieves efficient recognition and classification of surface defects ofinjection molded products through automatic learning and feature extraction. Experimental results show that this method is significantly better than traditional methods in terms of robustness, and its robustness of surface defect recognition reaches 89.5%. The deep learning model can capture image information more comprehensively, thereby improving the accuracy and reliability of recognition. This study provides a more efficient and accurate quality inspection solution for the manufacturing industry, which is expected to improve the production quality of injection molded products and reduce human errors. The research results have opened up a new direction for the application of deep learning technology in industrial production, which is of great significance to the future development of the manufacturing industry.