Classification and correlation of surface roughness in metallic parts using texture descriptors

Sir Suarez; Enrique Alegre; Joaquin Barreiro; Patricia Morala-Arguello; Victor Gonzalez-Castro

Classification and correlation of surface roughness in metallic parts using texture descriptors

Sir Suarez, Enrique Alegre, Joaquin Barreiro, Patricia Morala-Arguello, Victor Gonzalez-Castro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

In this paper we present a method to classify the surface roughness in metallic part after machining processes using an artificial vision system. Two texture analysis methods are used: Co-occurrence matrix (GLCM) and the energy of the texture obtained by Laws' method. These descriptors are classified with Lineal and Quadratic Discriminant Analysis (LDA and QDA) and Artificial Neural Networks (ANN.) The best results have been achieved using the Laws mask R5R5 (94.03%) and the combined correlation descriptor extracted from the GLCM (94.23%), both classified using Neural Networks. These results show the success of the method and the possibility to correlate these descriptors with the average roughness (Ra).

Original language	English
Title of host publication	Annals of DAAAM and Proceedings of the International DAAAM Symposium
Publisher	Danube Adria Association for Automation and Manufacturing, DAAAM
Pages	1293-1294
Number of pages	2
ISBN (Print)	9783901509704
Publication status	Published - 1 Jan 2009
Externally published	Yes
Event	Annals of DAAAM for 2009 and 20th International DAAAM Symposium "Intelligent Manufacturing and Automation: Focus on Theory, Practice and Education" - Vienna, United Kingdom Duration: 25 Nov 2009 → 28 Nov 2009

Conference

Conference	Annals of DAAAM for 2009 and 20th International DAAAM Symposium "Intelligent Manufacturing and Automation: Focus on Theory, Practice and Education"
Country/Territory	United Kingdom
City	Vienna
Period	25/11/09 → 28/11/09

Keywords / Materials (for Non-textual outputs)

Co-occurrence matrix
Laws
Roughness
Surface texture

Cite this

@inproceedings{f6cfea37e9c84415ab9487d20df4cbdf,

title = "Classification and correlation of surface roughness in metallic parts using texture descriptors",

abstract = "In this paper we present a method to classify the surface roughness in metallic part after machining processes using an artificial vision system. Two texture analysis methods are used: Co-occurrence matrix (GLCM) and the energy of the texture obtained by Laws' method. These descriptors are classified with Lineal and Quadratic Discriminant Analysis (LDA and QDA) and Artificial Neural Networks (ANN.) The best results have been achieved using the Laws mask R5R5 (94.03%) and the combined correlation descriptor extracted from the GLCM (94.23%), both classified using Neural Networks. These results show the success of the method and the possibility to correlate these descriptors with the average roughness (Ra).",

keywords = "Co-occurrence matrix, Laws, Roughness, Surface texture",

author = "Sir Suarez and Enrique Alegre and Joaquin Barreiro and Patricia Morala-Arguello and Victor Gonzalez-Castro",

year = "2009",

month = jan,

day = "1",

language = "English",

isbn = "9783901509704",

pages = "1293--1294",

booktitle = "Annals of DAAAM and Proceedings of the International DAAAM Symposium",

publisher = "Danube Adria Association for Automation and Manufacturing, DAAAM",

note = "Annals of DAAAM for 2009 and 20th International DAAAM Symposium {"}Intelligent Manufacturing and Automation: Focus on Theory, Practice and Education{"} ; Conference date: 25-11-2009 Through 28-11-2009",

}

Suarez, S, Alegre, E, Barreiro, J, Morala-Arguello, P & Gonzalez-Castro, V 2009, Classification and correlation of surface roughness in metallic parts using texture descriptors. in Annals of DAAAM and Proceedings of the International DAAAM Symposium. Danube Adria Association for Automation and Manufacturing, DAAAM, pp. 1293-1294, Annals of DAAAM for 2009 and 20th International DAAAM Symposium "Intelligent Manufacturing and Automation: Focus on Theory, Practice and Education", Vienna, United Kingdom, 25/11/09.

Classification and correlation of surface roughness in metallic parts using texture descriptors. / Suarez, Sir; Alegre, Enrique; Barreiro, Joaquin et al.
Annals of DAAAM and Proceedings of the International DAAAM Symposium. Danube Adria Association for Automation and Manufacturing, DAAAM, 2009. p. 1293-1294.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Classification and correlation of surface roughness in metallic parts using texture descriptors

AU - Suarez, Sir

AU - Alegre, Enrique

AU - Barreiro, Joaquin

AU - Morala-Arguello, Patricia

AU - Gonzalez-Castro, Victor

PY - 2009/1/1

Y1 - 2009/1/1

N2 - In this paper we present a method to classify the surface roughness in metallic part after machining processes using an artificial vision system. Two texture analysis methods are used: Co-occurrence matrix (GLCM) and the energy of the texture obtained by Laws' method. These descriptors are classified with Lineal and Quadratic Discriminant Analysis (LDA and QDA) and Artificial Neural Networks (ANN.) The best results have been achieved using the Laws mask R5R5 (94.03%) and the combined correlation descriptor extracted from the GLCM (94.23%), both classified using Neural Networks. These results show the success of the method and the possibility to correlate these descriptors with the average roughness (Ra).

AB - In this paper we present a method to classify the surface roughness in metallic part after machining processes using an artificial vision system. Two texture analysis methods are used: Co-occurrence matrix (GLCM) and the energy of the texture obtained by Laws' method. These descriptors are classified with Lineal and Quadratic Discriminant Analysis (LDA and QDA) and Artificial Neural Networks (ANN.) The best results have been achieved using the Laws mask R5R5 (94.03%) and the combined correlation descriptor extracted from the GLCM (94.23%), both classified using Neural Networks. These results show the success of the method and the possibility to correlate these descriptors with the average roughness (Ra).

KW - Co-occurrence matrix

KW - Laws

KW - Roughness

KW - Surface texture

UR - http://www.scopus.com/inward/record.url?scp=84867571895&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84867571895

SN - 9783901509704

SP - 1293

EP - 1294

BT - Annals of DAAAM and Proceedings of the International DAAAM Symposium

PB - Danube Adria Association for Automation and Manufacturing, DAAAM

T2 - Annals of DAAAM for 2009 and 20th International DAAAM Symposium "Intelligent Manufacturing and Automation: Focus on Theory, Practice and Education"

Y2 - 25 November 2009 through 28 November 2009

ER -

Classification and correlation of surface roughness in metallic parts using texture descriptors

Abstract / Description of output

Conference

Keywords / Materials (for Non-textual outputs)

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Cite this