Performance of uncoated cutting tools when machining mild steel and aluminium alloy

This paper discuss of the performance of uncoated carbide cutting tools in milling by investigating through the surface roughness. Response Surface Methodology (RSM) is implemented to model the face milling process that are using four insert of uncoated carbide TiC as the cutting tool and mild steel...

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Main Author:	Mohd Fahmi, Md Yusuf
Format:	Undergraduates Project Papers
Language:	English
Published:	2010
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/1836/ http://umpir.ump.edu.my/id/eprint/1836/ http://umpir.ump.edu.my/id/eprint/1836/1/Mohd_Fahmi_Md_Yusuf_%28_CD_4948_%29.pdf

id	oai:umpir.ump.edu.my:1836
recordtype	eprints
spelling	oai:umpir.ump.edu.my:18362015-03-03T07:53:09Z http://umpir.ump.edu.my/id/eprint/1836/ Performance of uncoated cutting tools when machining mild steel and aluminium alloy Mohd Fahmi, Md Yusuf TJ Mechanical engineering and machinery This paper discuss of the performance of uncoated carbide cutting tools in milling by investigating through the surface roughness. Response Surface Methodology (RSM) is implemented to model the face milling process that are using four insert of uncoated carbide TiC as the cutting tool and mild steel AISI1020 and aluminium alloy AA6061 as materials due to predict the resulting of surface roughness. Data is collected from HAAS CNC milling machines were run by 15 samples of experiments for each material 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 surface roughness. Predictive value of surface roughness was analyzed by the method of RSM. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found where from the RSM approaches show the 76.51% accuracy for mild steel and 79.55% accuracy for aluminium alloy which reliable to be use in Ra prediction and state the feed parameter is the most significant parameter followed by depth of cut and cutting speed influence the surface roughness. 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/1836/1/Mohd_Fahmi_Md_Yusuf_%28_CD_4948_%29.pdf Mohd Fahmi, Md Yusuf (2010) Performance of uncoated cutting tools when machining mild steel and aluminium alloy. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:51499&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 Fahmi, Md Yusuf Performance of uncoated cutting tools when machining mild steel and aluminium alloy
description	This paper discuss of the performance of uncoated carbide cutting tools in milling by investigating through the surface roughness. Response Surface Methodology (RSM) is implemented to model the face milling process that are using four insert of uncoated carbide TiC as the cutting tool and mild steel AISI1020 and aluminium alloy AA6061 as materials due to predict the resulting of surface roughness. Data is collected from HAAS CNC milling machines were run by 15 samples of experiments for each material 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 surface roughness. Predictive value of surface roughness was analyzed by the method of RSM. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found where from the RSM approaches show the 76.51% accuracy for mild steel and 79.55% accuracy for aluminium alloy which reliable to be use in Ra prediction and state the feed parameter is the most significant parameter followed by depth of cut and cutting speed influence the surface roughness. 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 Fahmi, Md Yusuf
author_facet	Mohd Fahmi, Md Yusuf
author_sort	Mohd Fahmi, Md Yusuf
title	Performance of uncoated cutting tools when machining mild steel and aluminium alloy
title_short	Performance of uncoated cutting tools when machining mild steel and aluminium alloy
title_full	Performance of uncoated cutting tools when machining mild steel and aluminium alloy
title_fullStr	Performance of uncoated cutting tools when machining mild steel and aluminium alloy
title_full_unstemmed	Performance of uncoated cutting tools when machining mild steel and aluminium alloy
title_sort	performance of uncoated cutting tools when machining mild steel and aluminium alloy
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1836/ http://umpir.ump.edu.my/id/eprint/1836/ http://umpir.ump.edu.my/id/eprint/1836/1/Mohd_Fahmi_Md_Yusuf_%28_CD_4948_%29.pdf
first_indexed	2018-09-07T00:25:16Z
last_indexed	2018-09-07T00:25:16Z
_version_	1610906249796255744

Performance of uncoated cutting tools when machining mild steel and aluminium alloy

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