Materials and Manufacturing Processes, pages 1-11

Estimation of surface roughness of direct metal laser sintered AlSi10Mg using artificial neural networks and response surface methodology

Uma Maheshwera Reddy Paturi ¹

Dheeraj Goud Vanga ¹

Rennie Bowen Duggem ¹

Nitin Kotkunde ²

N. Venkata Reddy ³

Sunil Dutta ⁴

Hide authors affiliations

Department of Mechanical Engineering, CVR College of Engineering, Hyderabad, Telangana, India |

Department of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Hyderabad, India |

School of Materials Science and Engineering, Gyeongsang National University, Jinju, Republic of Korea |

⁴

Techedge Unique Solutions Pvt Ltd, Secunderabad, Telangana, India |

Publication type: Journal Article

Publication date: 2023-05-27

Taylor & Francis

Journal: Materials and Manufacturing Processes

scimago Q1

SJR: 1.058

CiteScore: 9.0

Impact factor: 4.1

ISSN: 10426914, 15322475

DOI: 10.1080/10426914.2023.2217890

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General Materials Science

Mechanical Engineering

Industrial and Manufacturing Engineering

Mechanics of Materials

Found

	1 2 3 4 5
Materials and Manufacturing Processes	Materials and Manufacturing Processes, 5, 71.43% Materials and Manufacturing Processes 5 publications, 71.43%
International Journal of Metalcasting	International Journal of Metalcasting, 1, 14.29% International Journal of Metalcasting 1 publication, 14.29%
Optics and Laser Technology	Optics and Laser Technology, 1, 14.29% Optics and Laser Technology 1 publication, 14.29%
	1 2 3 4 5

	1 2 3 4 5
Taylor & Francis	Taylor & Francis, 5, 71.43% Taylor & Francis 5 publications, 71.43%
Springer Nature	Springer Nature, 1, 14.29% Springer Nature 1 publication, 14.29%
Elsevier	Elsevier, 1, 14.29% Elsevier 1 publication, 14.29%
	1 2 3 4 5

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Publisher

Taylor & Francis

Journal

Materials and Manufacturing Processes

scimago Q1

SJR

1.058

CiteScore

9.0

Impact factor

4.1

ISSN

10426914 (Print)

15322475 (Electronic)

Estimation of surface roughness of direct metal laser sintered AlSi10Mg using artificial neural networks and response surface methodology

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