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125054

Prediction of Surface Roughness of Turning Operations Using Computer Vision.

Article

Last updated: 22 Jan 2023

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Subjects

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Tags

Mechanical Power Engineering

Abstract

It is well known that measuring surface roughness is vital to quality control of the machined work piece. Recently, vision systems have been applied in industries for quality control and online inspection. Thus, measuring surface roughness using computer vision became easier and more flexible. Texture features are one of the most important techniques that have been utilized in industries in many applications. In this paper, the texture features of the gray level co-occurrence matrix (GLCM) have been utilized to predict surface roughness of specimens machined by tuning operations. The relationship between GLCM texture features and surface roughness has been investigated to discover which texture features can be used to predict surface roughness. The correlation coefficient between each texture feature and the arithinetic average height (Ra) was calculated and discussed. The investigation showed that six texture features are highly correlated with Ra. Therefore, a software has been developed to predict surface roughness for specimens machined by turning operations using these texture features. The results showed that the maximum percentage of error between the actual Ra and the predicted Ra was about ±7%. 

DOI

10.21608/bfemu.2020.125054

Keywords

Surface roughness, Computer Vision, Image processing, Texture Features

Authors

First Name

Elamir

Last Name

Gadelmawla

MiddleName

-

Affiliation

Associate Professor., Production Engineering and Mechanical Design Department., Faculty of Engineering., El-Mansoura University., 35516 Mansoura., Egypt.

Email

esamy@mans.edu.eg

City

Mansoura

Orcid

-

Volume

35

Article Issue

3

Related Issue

18592

Issue Date

2010-09-01

Receive Date

2010-07-11

Publish Date

2020-11-23

Page Start

1

Page End

13

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 35_Issue 3_Pages 1-13.pdf

PDF . 245.3kB

Link

https://bfemu.journals.ekb.eg/article_125054.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=125054

Order

4

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023