Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application
Wei-Lung Mao,
Yu-Ying Chiu,
Bing-Hong Lin,
Chun-Chi Wang,
Yi-Ting Wu,
Cheng-Yu You,
Ying-Ren Chien
Affiliations
Wei-Lung Mao
Department of Electrical Engineering, Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan
Yu-Ying Chiu
Department of Electrical Engineering, Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan
Bing-Hong Lin
Department of Electrical Engineering, Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan
Chun-Chi Wang
Department of Electrical Engineering, Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan
Yi-Ting Wu
Department of Electrical Engineering, Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan
Cheng-Yu You
Department of Electrical Engineering, Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan
Ying-Ren Chien
Department of Electrical Engineering, National Ilan University, Yilan 260007, Taiwan
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.
