Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle

This project presents the prediction the grinding machining parameters for ductile cast iron using water based Zinc Oxide (ZnO) nanoparticles as a coolant. Studies were made to investigate the experimental performance of ductile cast iron during grinding process based on design of experiment. Respon...

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Main Author:	Mohd Sabarudin, Hj Sulong
Format:	Undergraduates Project Papers
Language:	English
Published:	2012
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/4913/ http://umpir.ump.edu.my/id/eprint/4913/ http://umpir.ump.edu.my/id/eprint/4913/1/cd7359_86.pdf

id	ump-4913
recordtype	eprints
spelling	ump-49132015-03-03T09:21:56Z http://umpir.ump.edu.my/id/eprint/4913/ Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle Mohd Sabarudin, Hj Sulong TJ Mechanical engineering and machinery This project presents the prediction the grinding machining parameters for ductile cast iron using water based Zinc Oxide (ZnO) nanoparticles as a coolant. Studies were made to investigate the experimental performance of ductile cast iron during grinding process based on design of experiment. Response surface modeling (RSM) is practical, economic and relatively easy for use. The experimental data was utilized to develop the mathematical model for first- and second order model by regression method. Contour plot is a helpful visualization of the surface when the factors are no more than three and in order to locate the optimum value. The quality of product was determined by output criteria that are minimum temperature rise, minimum surface roughness and maximum material removal rate. Based on prediction data, the second-order gives the good performance of the grinding machine with the significant p-value of analysis of variance that is below than 0.05 and support with R-square value nearly 0.99. From the model profiler and contour plot, the optimum parameter for grinding model is 20m/min table speed and 42.43μm depth of cut could for single pass grinding. For multiple pass grinding it optimized at the table of speed equal to 35.11m/min and 29.78μm depth of cut could for has best quality of product. As the conclusion, objectives were achieved where the grinding parameters were optimized, grinding performance was investigated and mathematical model for abrasive machining parameter was developed. The model was fit adequate and acceptable for sustainable grinding using 0.15% volume concentration of zinc oxide nanocoolant. 2012-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/4913/1/cd7359_86.pdf Mohd Sabarudin, Hj Sulong (2012) Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:75408&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 Sabarudin, Hj Sulong Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle
description	This project presents the prediction the grinding machining parameters for ductile cast iron using water based Zinc Oxide (ZnO) nanoparticles as a coolant. Studies were made to investigate the experimental performance of ductile cast iron during grinding process based on design of experiment. Response surface modeling (RSM) is practical, economic and relatively easy for use. The experimental data was utilized to develop the mathematical model for first- and second order model by regression method. Contour plot is a helpful visualization of the surface when the factors are no more than three and in order to locate the optimum value. The quality of product was determined by output criteria that are minimum temperature rise, minimum surface roughness and maximum material removal rate. Based on prediction data, the second-order gives the good performance of the grinding machine with the significant p-value of analysis of variance that is below than 0.05 and support with R-square value nearly 0.99. From the model profiler and contour plot, the optimum parameter for grinding model is 20m/min table speed and 42.43μm depth of cut could for single pass grinding. For multiple pass grinding it optimized at the table of speed equal to 35.11m/min and 29.78μm depth of cut could for has best quality of product. As the conclusion, objectives were achieved where the grinding parameters were optimized, grinding performance was investigated and mathematical model for abrasive machining parameter was developed. The model was fit adequate and acceptable for sustainable grinding using 0.15% volume concentration of zinc oxide nanocoolant.
format	Undergraduates Project Papers
author	Mohd Sabarudin, Hj Sulong
author_facet	Mohd Sabarudin, Hj Sulong
author_sort	Mohd Sabarudin, Hj Sulong
title	Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle
title_short	Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle
title_full	Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle
title_fullStr	Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle
title_full_unstemmed	Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle
title_sort	prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/4913/ http://umpir.ump.edu.my/id/eprint/4913/ http://umpir.ump.edu.my/id/eprint/4913/1/cd7359_86.pdf
first_indexed	2023-09-18T21:59:55Z
last_indexed	2023-09-18T21:59:55Z
_version_	1777414329505677312

Prediction of grinding machining parameters of ductile cast iron using water based zinc oxide nanoparticle

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