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Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation
Yuan Yu1, Jinsheng Ren1, Qi Zhang1
1College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Sensors (Basel, Switzerland)
|August 23, 2020
Summary
This study introduces K-means clustering for accurate tire marking point evaluation in smart factories. The method achieves high accuracy in rating and evaluating tire marking point completeness for quality inspection.
Area of Science:
- Automotive Engineering
- Computer Vision
- Image Processing
Background:
- Tire marking points are crucial for dynamic balance and uniformity, impacting tire installation.
- Incomplete marking points hinder recognition and affect tire installation processes.
- Accurate evaluation of marking point completeness is essential for finished tire quality inspection.
Purpose of the Study:
- To develop a high-precision method for evaluating tire marking point completeness in smart factories.
- To introduce the K-means clustering algorithm for image segmentation of tire marking points.
Main Methods:
- Image segmentation of tire marking points using the K-means clustering algorithm.
- Weighted pixel calculation within marking point contours to determine completeness.
- Rating and evaluation of marking point completeness based on calculated values.
Main Results:
- The K-means clustering algorithm effectively segments tire marking point images.
- The proposed method achieved 95% accuracy in rating marking point completeness.
- The method demonstrated 99% accuracy in evaluating marking point completeness.
Conclusions:
- The developed machine vision-based method provides a practical and accurate approach for tire marking point completeness evaluation.
- This technique is significant for improving quality inspection processes in smart tire manufacturing.
- The K-means clustering algorithm offers a robust solution for automated tire inspection.
