Open Access

REVIEW

Surface Characteristics Measurement Using Computer Vision: A Review

by Abdul Wahab Hashmi¹, Harlal Singh Mali¹, Anoj Meena¹, Mohammad Farukh Hashmi², Neeraj Dhanraj Bokde^3,4,*

1 Department of Mechanical Engineering, Advanced Manufacturing and Mechatronics Lab, Malaviya National Institute of Technology, Jaipur, 302017, India
2 Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, 506004, India
3 Department of Civil and Architectural Engineering, Aarhus University, Aarhus, 8000, Denmark
4 Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, 8000, Denmark

* Corresponding Author: Neeraj Dhanraj Bokde. Email:

Computer Modeling in Engineering & Sciences 2023, 135(2), 917-1005. https://doi.org/10.32604/cmes.2023.021223

Received 02 January 2022; Accepted 28 June 2022; Issue published 27 October 2022

Abstract

Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc., machine vision parameters. This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.

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Keywords

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