Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 10; p. 3927
• Main Authors: Mao, Wei-Lung, Chiu, Yu-Ying, Lin, Bing-Hong, Wang, Chun-Chi, Wu, Yi-Ting, You, Cheng-Yu, Chien, Ying-Ren
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.05.2022; MDPI
• Subjects: Accuracy; Algorithms; Aluminum; Automation; Cameras; Datasets; deep convolutional generative adversarial networks (DCGAN); Deep Learning; Defects; Electric vehicles; Experiments; Industrial applications; Inspection; Light emitting diodes; Neural networks; Neural Networks, Computer; Normal distribution; Optimization; rim defect detection; Rims; Robot arms; robotic arm; Robotics; Robots; Software; YOLO algorithm; YOLO algorithm; rim defect detection; deep convolutional generative adversarial networks (DCGAN); robotic arm
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22103927

Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications.