Prediction of grinding machinability when grind aluminium alloy using water based coolant

Optimization of parameters for the surface quality of material is very important for this research because of higher demands for surface finishing products especially in the manufacturing process. More researchers have tried various methods in order to reduce production cost and to produce very econ...

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Main Author:	Jamilah, Mustafha
Format:	Undergraduates Project Papers
Language:	English
Published:	2012
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/4555/ http://umpir.ump.edu.my/id/eprint/4555/ http://umpir.ump.edu.my/id/eprint/4555/1/cd6857_80.pdf

id	ump-4555
recordtype	eprints
spelling	ump-45552015-03-03T09:19:44Z http://umpir.ump.edu.my/id/eprint/4555/ Prediction of grinding machinability when grind aluminium alloy using water based coolant Jamilah, Mustafha TJ Mechanical engineering and machinery Optimization of parameters for the surface quality of material is very important for this research because of higher demands for surface finishing products especially in the manufacturing process. More researchers have tried various methods in order to reduce production cost and to produce very economical machining process. One of the most common machines in the finishing process of the product is grinding machine. For this thesis, the present study involves prediction of grinding machine when grinds aluminium using water based coolant. This thesis has been run to find optimum parameters such as wheel wear and depth of cut. Different number of passes which are single pass and multi pass with different parameters will be studied and compared. Another objective of this thesis is to investigate surface roughness produced during grinding process. Prediction model of surface roughness was developed to present accurate data. The selected material for this study was Aluminium Alloy 6061 T6 and was used water based coolant as cooling lubrication. Experiments were conducted based on Design of Experiment (DOE) and the Neural Network is employed to find optimum parameters and predicted of surface roughness and wheel wear for the selected material. These experiments were divided into two by using two different grinders which are aluminium oxide and silicon carbide. The surface roughness was measured at every increment of 2µm depth of cut. The results have found that the surface roughness increased when the depth of cut increased while the surface roughness decreased when number of passes increased. Besides, the surface quality becomes smoother when using Aluminium Carbide as grinder compared to Silicon Carbide. 2012-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/4555/1/cd6857_80.pdf Jamilah, Mustafha (2012) Prediction of grinding machinability when grind aluminium alloy using water based coolant. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:73076&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 Jamilah, Mustafha Prediction of grinding machinability when grind aluminium alloy using water based coolant
description	Optimization of parameters for the surface quality of material is very important for this research because of higher demands for surface finishing products especially in the manufacturing process. More researchers have tried various methods in order to reduce production cost and to produce very economical machining process. One of the most common machines in the finishing process of the product is grinding machine. For this thesis, the present study involves prediction of grinding machine when grinds aluminium using water based coolant. This thesis has been run to find optimum parameters such as wheel wear and depth of cut. Different number of passes which are single pass and multi pass with different parameters will be studied and compared. Another objective of this thesis is to investigate surface roughness produced during grinding process. Prediction model of surface roughness was developed to present accurate data. The selected material for this study was Aluminium Alloy 6061 T6 and was used water based coolant as cooling lubrication. Experiments were conducted based on Design of Experiment (DOE) and the Neural Network is employed to find optimum parameters and predicted of surface roughness and wheel wear for the selected material. These experiments were divided into two by using two different grinders which are aluminium oxide and silicon carbide. The surface roughness was measured at every increment of 2µm depth of cut. The results have found that the surface roughness increased when the depth of cut increased while the surface roughness decreased when number of passes increased. Besides, the surface quality becomes smoother when using Aluminium Carbide as grinder compared to Silicon Carbide.
format	Undergraduates Project Papers
author	Jamilah, Mustafha
author_facet	Jamilah, Mustafha
author_sort	Jamilah, Mustafha
title	Prediction of grinding machinability when grind aluminium alloy using water based coolant
title_short	Prediction of grinding machinability when grind aluminium alloy using water based coolant
title_full	Prediction of grinding machinability when grind aluminium alloy using water based coolant
title_fullStr	Prediction of grinding machinability when grind aluminium alloy using water based coolant
title_full_unstemmed	Prediction of grinding machinability when grind aluminium alloy using water based coolant
title_sort	prediction of grinding machinability when grind aluminium alloy using water based coolant
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/4555/ http://umpir.ump.edu.my/id/eprint/4555/ http://umpir.ump.edu.my/id/eprint/4555/1/cd6857_80.pdf
first_indexed	2023-09-18T21:59:14Z
last_indexed	2023-09-18T21:59:14Z
_version_	1777414286520352768

Prediction of grinding machinability when grind aluminium alloy using water based coolant

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