<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In the non-contact detection of thread profile boundary correction, it remains challenging to ensure that the thread axis intersects the CCD camera axis perpendicularly. Here, we addressed this issue using modified algorithms. We established the Cartesian coordinate system according to the spatial geometric relationship of the thread. We used the center of the bottom of the thread as the origin, and the image of the extreme position image was replaced by the image of the approximate extreme position. In addition, we analyzed the relationship between the boundary of the theoretical thread image and the theoretical profile. We calculated the coordinate transformation of the point on the theoretical tooth profile and the coordinate function of the point on the boundary of the theoretical image. At the same time, the extreme value of the function was obtained, and the boundary equation of the theoretical thread image was deduced. The difference equation between the two functions was used to correct the boundary point of the actual thread image, and the fitting results were used to detect the key parameters of the external thread of the cylinder. Further experiment proves that the above algorithm effectively improves the detection accuracy of thread quality, and the detection error of main geometric parameters is reduced by more than 50 %.</p></abstract>

In the non-contact detection of thread profile boundary correction, it remains challenging to ensure that the thread axis intersects the CCD camera axis perpendicularly. Here, we addressed this issue using modified algorithms. We established the Cartesian coordinate system according to the spatial geometric relationship of the thread. We used the center of the bottom of the thread as the origin, and the image of the extreme position image was replaced by the image of the approximate extreme position. In addition, we analyzed the relationship between the boundary of the theoretical thread image and the theoretical profile. We calculated the coordinate transformation of the point on the theoretical tooth profile and the coordinate function of the point on the boundary of the theoretical image. At the same time, the extreme value of the function was obtained, and the boundary equation of the theoretical thread image was deduced. The difference equation between the two functions was used to correct the boundary point of the actual thread image, and the fitting results were used to detect the key parameters of the external thread of the cylinder. Further experiment proves that the above algorithm effectively improves the detection accuracy of thread quality, and the detection error of main geometric parameters is reduced by more than 50 %.

Research on Modified Algorithms of Cylindrical External Thread Profile Based on Machine Vision

Institute of Mechanical Engineering of Zhejiang University

Ningbo Institute of Technology of Zhejiang University

Institute of Engineering of Zhejiang University

Institute of Mechanical Engineering and Mechanics of Ningbo University

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In the non-contact detection of thread profile boundary correction, it remains challenging to ensure that the thread axis intersects the CCD camera axis...

Research on Modified Algorithms of Cylindrical External Thread Profile Based on Machine Vision

Published Online: Feb 24, 2020

Page range: 15 - 21

Received: Jun 27, 2019

Accepted: Jan 25, 2020

DOI: https://doi.org/10.2478/msr-2020-0003

Keywords
Theoretical tooth profile, theoretical image boundary, actual tooth profile, actual image boundary, machine vision, difference equation, screw thread image processing

© 2020 Chen JH et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Research on Modified Algorithms of Cylindrical External Thread Profile Based on Machine Vision

Published Online: Feb 24, 2020

Page range: 15 - 21

Received: Jun 27, 2019

Accepted: Jan 25, 2020

DOI: https://doi.org/10.2478/msr-2020-0003

KeywordsTheoretical tooth profile, theoretical image boundary, actual tooth profile, actual image boundary, machine vision, difference equation, screw thread image processing

© 2020 Chen JH et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Theoretical tooth profile, theoretical image boundary, actual tooth profile, actual image boundary, machine vision, difference equation, screw thread image processing