A Method for Measuring Shaft Diameter Based on Light Stripe Image Enhancement

Author:

Li Chunfeng12,Xu Xiping1,Liu Siyuan3,Ren Zhen4

Affiliation:

1. College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China

2. School of Electronic Information Engineering, Changchun University, Changchun 130022, China

3. School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

4. College of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, China

Abstract

When the workpiece surface exhibits strong reflectivity, it becomes challenging to obtain accurate key measurements using non-contact, visual measurement techniques due to poor image quality. In this paper, we propose a high-precision measurement method shaft diameter based on an enhanced quality stripe image. By capturing two stripe images with different exposure times, we leverage their different characteristics. The results extracted from the low-exposure image are used to perform grayscale correction on the high-exposure image, improving the distribution of stripe grayscale and resulting in more accurate extraction results for the center points. The incorporation of different measurement positions and angles further enhanced measurement precision and robustness. Additionally, ellipse fitting is employed to derive shaft diameter. This method was applied to the profiles of different cross-sections and angles within the same shaft segment. To reduce the shape error of the shaft measurement, the average of these measurements was taken as the estimate of the average diameter for the shaft segment. In the experiments, the average shaft diameters determined by averaging elliptical estimations were compared with shaft diameters obtained using a coordinate measuring machine (CMM) the maximum error and the minimum error were respectively 18 μm and 7 μm; the average error was 11 μm; and the root mean squared error of the multiple measurement results was 10.98 μm. The measurement accuracy achieved is six times higher than that obtained from the unprocessed stripe images.

Funder

National Natural Science Foundation of China

Jilin Province Science and Technology Development Plan Project

Publisher

MDPI AG

Reference37 articles.

1. A machine vision–based radial circular runout measurement method;Li;Int. J. Adv. Manuf. Technol.,2023

2. Miao, J., Yuan, H., Li, L., and Liu, S. (2019). Advances in Intelligent Systems and Computing, Springer.

3. Bai, R., Jiang, N., Yu, L., and Zhao, J. (2021). Journal of Physics: Conference Series, IOP Publishing.

4. Chen, S., Tao, W., Zhao, H., and Lv, N. (2021). Transactions on Intelligent Welding Manufacturing, Springer.

5. Li, X., Wang, S., and Xu, K. (2022). Automatic Measurement of External Thread at the End of Sucker Rod Based on Machine Vision. Sensors, 22.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3