Optimised Tool Life by Partial Swarm Optimisation

The aim of the this paper is to develop the tool life prediction model for P20 tool steel with aid of statistical method and find the optimisation values with partial swarm optimisation (PSO), using coated carbide cutting tool. By using Response Surface Method (RSM), first and second order models we...

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Main Authors: K., Kadirgama, M. M., Rahman, M. M., Noor, M. S. M., Sani
Format: Article
Language:English
Published: 2010
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/2233/
http://umpir.ump.edu.my/id/eprint/2233/1/Optimised_Tool_Life_By_Partial_Swarm_Optimisation.pdf
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author K., Kadirgama
M. M., Rahman
M. M., Noor
M. S. M., Sani
author_facet K., Kadirgama
M. M., Rahman
M. M., Noor
M. S. M., Sani
author_sort K., Kadirgama
building UMP Institutional Repository
collection Online Access
description The aim of the this paper is to develop the tool life prediction model for P20 tool steel with aid of statistical method and find the optimisation values with partial swarm optimisation (PSO), using coated carbide cutting tool. By using Response Surface Method (RSM), first and second order models were developed with 95% confidence level. The tool life model was developed in terms of cutting speed, feed rate, axial depth and radial depth, using RSM. It was found that the feedrate, cutting speed, axial depth and radial depth played a major role. Tool life increases with a reduction in cutting speed and feedrate. For end-milling of P20 tool steel, the optimum conditions obtained from PSO are: cutting speed of 100 m/s, federate of 0.1 mm/tooth, axial depth of 1.9596 mm and radial depth of 2 mm. Using these parameters, a tool life of 40.52 min was obtained.
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institution Universiti Malaysia Pahang
institution_category Local University
language English
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publishDate 2010
recordtype eprints
repository_type Digital Repository
spelling ump-22332018-01-23T04:16:57Z http://umpir.ump.edu.my/id/eprint/2233/ Optimised Tool Life by Partial Swarm Optimisation K., Kadirgama M. M., Rahman M. M., Noor M. S. M., Sani TJ Mechanical engineering and machinery The aim of the this paper is to develop the tool life prediction model for P20 tool steel with aid of statistical method and find the optimisation values with partial swarm optimisation (PSO), using coated carbide cutting tool. By using Response Surface Method (RSM), first and second order models were developed with 95% confidence level. The tool life model was developed in terms of cutting speed, feed rate, axial depth and radial depth, using RSM. It was found that the feedrate, cutting speed, axial depth and radial depth played a major role. Tool life increases with a reduction in cutting speed and feedrate. For end-milling of P20 tool steel, the optimum conditions obtained from PSO are: cutting speed of 100 m/s, federate of 0.1 mm/tooth, axial depth of 1.9596 mm and radial depth of 2 mm. Using these parameters, a tool life of 40.52 min was obtained. 2010 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2233/1/Optimised_Tool_Life_By_Partial_Swarm_Optimisation.pdf K., Kadirgama and M. M., Rahman and M. M., Noor and M. S. M., Sani (2010) Optimised Tool Life by Partial Swarm Optimisation. International Journal of Material Forming, 3 (1). pp. 479-482. (Published) http://www.springerlink.com/content/8727173k5373161p/
spellingShingle TJ Mechanical engineering and machinery
K., Kadirgama
M. M., Rahman
M. M., Noor
M. S. M., Sani
Optimised Tool Life by Partial Swarm Optimisation
title Optimised Tool Life by Partial Swarm Optimisation
title_full Optimised Tool Life by Partial Swarm Optimisation
title_fullStr Optimised Tool Life by Partial Swarm Optimisation
title_full_unstemmed Optimised Tool Life by Partial Swarm Optimisation
title_short Optimised Tool Life by Partial Swarm Optimisation
title_sort optimised tool life by partial swarm optimisation
topic TJ Mechanical engineering and machinery
url http://umpir.ump.edu.my/id/eprint/2233/
http://umpir.ump.edu.my/id/eprint/2233/
http://umpir.ump.edu.my/id/eprint/2233/1/Optimised_Tool_Life_By_Partial_Swarm_Optimisation.pdf