Optimization of tool life using in milling using radial basis function network

This paper discuss of the Optimization of tool life in milling using Radial basis Function Network (RBFN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using high speed steel coated HS-Co as the cutting tool and aluminium alloy T6061 as m...

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Main Author:	Mohd Faizal, Aziz
Format:	Undergraduates Project Papers
Language:	English
Published:	2010
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/1459/ http://umpir.ump.edu.my/id/eprint/1459/ http://umpir.ump.edu.my/id/eprint/1459/1/Mohd_Faizal_Aziz_%28_CD_5048_%29.pdf

id	ump-1459
recordtype	eprints
spelling	ump-14592015-03-03T07:51:12Z http://umpir.ump.edu.my/id/eprint/1459/ Optimization of tool life using in milling using radial basis function network Mohd Faizal, Aziz TJ Mechanical engineering and machinery This paper discuss of the Optimization of tool life in milling using Radial basis Function Network (RBFN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using high speed steel coated HS-Co as the cutting tool and aluminium alloy T6061 as material due to predict the resulting of flank wear. Data is collected from RoboDrill T14i CNC milling machines were run by 15 samples of experiments using DOE approach that generate by Box-Behnkin method due to table design in MINITAB packages. The inputs of the model consist of feed, cutting speed and depth of cut while the output from the model is Flank wear occur on the tool surface. The model is validated through a comparison of the experimental values with their predicted counterparts. The analysis of the flank wear is using IM1700 Inverted Metallograph microscope for examine the minimum size of the flank wear within 0.3mm. The optimization of the tool life is studied to compare the relationship of the parameters involve. Cutting speed is the greater influence to the tool fatigue criterion which is result the performance of the cutting tool. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining. 2010-12 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1459/1/Mohd_Faizal_Aziz_%28_CD_5048_%29.pdf Mohd Faizal, Aziz (2010) Optimization of tool life using in milling using radial basis function network. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:52379&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Mohd Faizal, Aziz Optimization of tool life using in milling using radial basis function network
description	This paper discuss of the Optimization of tool life in milling using Radial basis Function Network (RBFN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using high speed steel coated HS-Co as the cutting tool and aluminium alloy T6061 as material due to predict the resulting of flank wear. Data is collected from RoboDrill T14i CNC milling machines were run by 15 samples of experiments using DOE approach that generate by Box-Behnkin method due to table design in MINITAB packages. The inputs of the model consist of feed, cutting speed and depth of cut while the output from the model is Flank wear occur on the tool surface. The model is validated through a comparison of the experimental values with their predicted counterparts. The analysis of the flank wear is using IM1700 Inverted Metallograph microscope for examine the minimum size of the flank wear within 0.3mm. The optimization of the tool life is studied to compare the relationship of the parameters involve. Cutting speed is the greater influence to the tool fatigue criterion which is result the performance of the cutting tool. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining.
format	Undergraduates Project Papers
author	Mohd Faizal, Aziz
author_facet	Mohd Faizal, Aziz
author_sort	Mohd Faizal, Aziz
title	Optimization of tool life using in milling using radial basis function network
title_short	Optimization of tool life using in milling using radial basis function network
title_full	Optimization of tool life using in milling using radial basis function network
title_fullStr	Optimization of tool life using in milling using radial basis function network
title_full_unstemmed	Optimization of tool life using in milling using radial basis function network
title_sort	optimization of tool life using in milling using radial basis function network
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1459/ http://umpir.ump.edu.my/id/eprint/1459/ http://umpir.ump.edu.my/id/eprint/1459/1/Mohd_Faizal_Aziz_%28_CD_5048_%29.pdf
first_indexed	2023-09-18T21:54:37Z
last_indexed	2023-09-18T21:54:37Z
_version_	1777413995473403904

Optimization of tool life using in milling using radial basis function network

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