Ball Screw Surface Defect Impact Level Categorization Using the Collective Constraint Intelligent Prediction Technique
نویسندگان
1 Associate Professor, Department of Computer Science and Engineering, National Institute of Technology, Trichy, Tamil Nadu, India.
2 Research Scholar, Department of Computer Science and Engineering, National Institute of Technology, Trichy, Tamil Nadu, India.
doi
10.22059/jitm.2026.106253چکیده
Ball screw failures often exhibit early signs of surface defects, which can arise from contamination and indicate deterioration. Identifying these surface defects is crucial for minimizing repair costs and maximizing machinery uptime. This study presents a prediction algorithm based on transfer learning methods to enhance the accuracy of ball screw surface defect detection throughout its lifecycle. We investigate four transfer learning models: CCIP-V, CCIP-D, VGG16, and DenseNet, utilizing image data mining techniques for defect classification. These models are validated using a specialized dataset of ball screw surface defects, employing Region of Interest (ROI) masking techniques to enhance image classification for each model. Our findings reveal that the proposed hybrid approach, combining CCIP and ROI, demonstrates superior performance, with the best classifier achieving an accuracy of 0.983. Notably, the CCIP classifier, when enhanced with ROI techniques, achieves an impressive accuracy of 0.985, effectively predicting defect impact levels. This research underscores the potential of transfer learning and the integration of CCIP and ROI in improving classifier performance and identifying surface defect severity in ball screws.
