Development of Automatic Inspection and Optimization Platform for Computer Numerical Control Machining Using Automatic Optical Inspection and Artificial Intelligence - Qi-Ren Lin; Bo-Cing Hu; Liang-Yin Kuo; Ting-Yi Shen (Engineering Proceedings)
We developed an automatic optical inspection (AOI) system for detecting defects in finished workpieces and determining the parameters for CNC machining. The system addresses quality control issues in CNC machining using image processing, machine learning, and G-code analysis techniques. The accuracy and efficiency of CNC machining were improved by reducing manual inspection tasks, minimizing production downtime, and achieving higher precision in defect detection and correction. Experiments were conducted in a pre-planned CNC machining environment to validate the effectiveness of the proposed AOI system. The system was tested on metals and composites and CNC lathes and milling machines. The AOI system significantly improved defect detection accuracy, exceeding 95% across different defect types. The proposed machining parameters enabled a reduction in the recurrence rate of defects by approximately 80%, demonstrating the potential to enhance overall machining quality. By developing AOI recognition and optimizing CNC machining parameters, an automated and intelligent defect detection and correction solution was realized. The reliability and accuracy of CNC processes were improved, and data-driven automated manufacturing and process optimization were achieved, meeting the goals of intelligent manufacturing and Industry 4.0.