Author:
Liu Chunhua,Li Ming,Chen Peng,Zhang Chaoyun
Abstract
Purpose
This study aims to solve the problems of ambiguous localization, large calculation, poor real-time and limited applicability of bolt thread defect detection.
Design/methodology/approach
First, the acquired ultrasound image is used to acquire the larger area of the image, which is set as the compliant threaded area. Second, based on the determined coordinates of the center point in each selected region, the set of coordinates on the left and right sides of the bolts is acquired by DBSCAN method with parameters eps and MinPts, which is determined by data set dimension D and the k-distance curve. Finally, the defect detection boundary line fitting is completed using the acquired coordinate set, and the relationship between the distance from each detection point to the curve and d, which is obtained from the measurement of the standard bolt sample with known thread defect, is used to locate the bolt thread defect simultaneously.
Findings
In this paper, the bolt thread defect detection method with ultrasonic image is proposed; meanwhile, the ultrasonic image acquisition system is designed to complete the real-time localization of bolt thread defects.
Originality/value
The detection results show that the method can effectively detect bolt thread defects and locate the bolt thread defect location with wide applicability, small calculation and good real-time performance.
Subject
General Materials Science,General Chemical Engineering
Reference16 articles.
1. A physical investigation of dimensional and mechanical characteristics of 3D printed nut and bolt for industrial applications;Rapid Prototyping Journal,2022
2. Automatic defect identification in magnetic particle testing using a digital model aided De-noising method;Measurement,2022
3. An approach to boundary detection for 3D point clouds based on DBSCAN clustering;Pattern Recognition,2022
4. Review-material degradation assessed by digital image processing: fundamentals, progresses, and challenges;Journal of Materials Science & Technology,2020
5. Detection of thread defects on stainless steel bar surface based on machine vision;Journal of Chongqing University of Technology (Natural Science),2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献