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Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application
Wei-Lung Mao1, Yu-Ying Chiu1, Bing-Hong Lin1
1Department of Electrical Engineering, Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan.
Sensors (Basel, Switzerland)
|May 28, 2022
Summary
This study introduces an automated system using deep learning for detecting defects in electric vehicle aluminum rims. The AI-powered inspection system achieves high accuracy and efficiency in identifying surface flaws, enhancing manufacturing quality control.
Area of Science:
- Industrial Manufacturing
- Artificial Intelligence
- Computer Vision
Background:
- Automated inspection is crucial for maintaining quality in large-scale manufacturing.
- Defect detection in automotive components like aluminum rims requires precise and efficient methods.
Purpose of the Study:
- To develop an automated system for detecting surface defects in forged aluminum rims for electric vehicles.
- To enhance the accuracy and speed of defect identification in industrial settings.
Main Methods:
- Utilized an eye-in-hand robot arm with a camera for 3D image acquisition.
- Employed deep learning, specifically convolutional neural networks (CNNs) and Generative Adversarial Networks (GANs), for defect detection and data augmentation.
- Developed a graphical user interface and a defect detection algorithm based on YOLO (You Only Look Once).
Main Results:
- Successfully generated additional training data using GANs and DCGANs.
- Achieved faster detection times and higher mean average precision (mAP) compared to existing methods.
- Demonstrated the system's accuracy and efficiency in identifying four types of defects: dirt spots, paint stains, scratches, and dents.
Conclusions:
- The developed AI system provides an effective solution for automated rim defect detection in industrial applications.
- The proposed method significantly improves the quality control process for electric vehicle components.
