Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm

Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematica...

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Main Authors: Patwari, Muhammed Anayet Ullah, Amin, A. K. M. Nurul, Alam, Md. Shah
Format: Article
Language:English
Published: Trans Tech Publications, Switzerland 2011
Subjects:
TS Manufactures
Online Access:http://irep.iium.edu.my/17021/
http://irep.iium.edu.my/17021/
http://irep.iium.edu.my/17021/
http://irep.iium.edu.my/17021/1/2011_-AMR.264-265.1154.pdf
id iium-17021
recordtype eprints
spelling iium-170212012-03-07T03:03:06Z http://irep.iium.edu.my/17021/ Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Alam, Md. Shah TS Manufactures Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value. Trans Tech Publications, Switzerland 2011 Article PeerReviewed application/pdf en http://irep.iium.edu.my/17021/1/2011_-AMR.264-265.1154.pdf Patwari, Muhammed Anayet Ullah and Amin, A. K. M. Nurul and Alam, Md. Shah (2011) Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm. Advanced Materials Research, 264/65. pp. 1154-1159. ISSN 1022-6680 http://www.scientific.net/AMR.264-265.1154 10.4028/www.scientific.net/AMR.264-265.1154
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic TS Manufactures
spellingShingle TS Manufactures
Patwari, Muhammed Anayet Ullah
Amin, A. K. M. Nurul
Alam, Md. Shah
Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm
description Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.
format Article
author Patwari, Muhammed Anayet Ullah
Amin, A. K. M. Nurul
Alam, Md. Shah
author_facet Patwari, Muhammed Anayet Ullah
Amin, A. K. M. Nurul
Alam, Md. Shah
author_sort Patwari, Muhammed Anayet Ullah
title Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm
title_short Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm
title_full Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm
title_fullStr Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm
title_full_unstemmed Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm
title_sort improvement of surface roughness in end milling of ti6al4v by coupling rsm with genetic algorithm
publisher Trans Tech Publications, Switzerland
publishDate 2011
url http://irep.iium.edu.my/17021/
http://irep.iium.edu.my/17021/
http://irep.iium.edu.my/17021/
http://irep.iium.edu.my/17021/1/2011_-AMR.264-265.1154.pdf
first_indexed 2023-09-18T20:25:49Z
last_indexed 2023-09-18T20:25:49Z
_version_ 1777408408886968320

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