JOURNAL ARTICLE

Surface roughness measurement with laser triangulation

Fuzhong BaiXiao Yan ZhangChaoping Tian

Year: 2016 Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Vol: 9684 Pages: 96843A-96843A   Publisher: SPIE

Abstract

A surface roughness measurement method is introduced in the paper, which is based on laser triangulation and digital image processing technique. In the measuring system, we use the line-structured light as light source, microscope lens and high-accuracy CCD sensor as displacement sensor as well. In addition, the working angle corresponding to the optimal sensitivity is considered in the optical structure design to improve the measuring accuracy. Through necessary image processing operation for the light strip image, such as center-line extraction with the barycenter algorithm, Gaussian filtering, the value of roughness is calculated. A standard planing surface is measured experimentally with the proposed method and the stylus method (Mitutoyo SJ-410) respectively. The profilograms of surface appearance are greatly similar in the shape and the amplitude to two methods. Also, the roughness statistics values are close. The results indicate that the laser triangulation with the line-structured light can be applied to measure the surface roughness with the advantages of rapid measurement and visualized display of surface roughness profile.

Keywords:
Stylus Surface roughness Optics Structured light Triangulation Surface finish Laser Materials science Lens (geology) Profilometer Computer vision Line (geometry) Computer science Mathematics Geometry Physics

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Cited By
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FWCI (Field Weighted Citation Impact)
10
Refs
0.09
Citation Normalized Percentile
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Citation History

Topics

Surface Roughness and Optical Measurements
Physical Sciences →  Engineering →  Computational Mechanics
Advanced Measurement and Metrology Techniques
Physical Sciences →  Engineering →  Mechanical Engineering
Optical measurement and interference techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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