Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant

Grinding is often an important finishing process for many engineering components and for some components it is even a major production process. In this study, prediction model have been developed to find the effect of grinding condition in term of depth of cut and type of grinding coolant. Zinc Oxid...

Full description

Bibliographic Details
Main Authors:	K., Kadirgama, M., Yogeswaran, S. , Thiruchelvam, M. M., Rahman
Format:	Article
Language:	English
Published:	Iceland Journal of Life Sciences 2014
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/5271/ http://umpir.ump.edu.my/id/eprint/5271/ http://umpir.ump.edu.my/id/eprint/5271/1/paper.pdf

id	ump-5271
recordtype	eprints
spelling	ump-52712018-01-25T03:16:13Z http://umpir.ump.edu.my/id/eprint/5271/ Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant K., Kadirgama M., Yogeswaran S. , Thiruchelvam M. M., Rahman TJ Mechanical engineering and machinery Grinding is often an important finishing process for many engineering components and for some components it is even a major production process. In this study, prediction model have been developed to find the effect of grinding condition in term of depth of cut and type of grinding coolant. Zinc Oxide (ZnO) nano-coolant was used as a coolant with water as a based liquid. The experiments conducted with grinding depth in the range of 5 to 21μm. Silicon Carbide wheel are used to grind the AISI P20 tool work piece. Artificial intelligence model has been developed using Artificial Neural Network(ANN). Result shows that the lower surface roughness and wheel wear obtain at the lowest cutting depth which is 5 μm. Besides that, grind using ZnO nano-coolant gives best surface roughness and minimum wheel wears compared to grind using normal soluble coolant. The surface roughness have been reduced approximately 47.84% for single pass experiment and 126.1% for multi pass experiment. However, there is no wheel wheel wear obtain for grinding using ZnO nanocoolant. From the prediction of ANN, it can predict the surface roughness closely with the experimental value. Iceland Journal of Life Sciences 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5271/1/paper.pdf K., Kadirgama and M., Yogeswaran and S. , Thiruchelvam and M. M., Rahman (2014) Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant. Jokull Journal, 66 (5). pp. 1-15. ISSN 0449-0576 http://jokulljournal.com/index.html
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 K., Kadirgama M., Yogeswaran S. , Thiruchelvam M. M., Rahman Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant
description	Grinding is often an important finishing process for many engineering components and for some components it is even a major production process. In this study, prediction model have been developed to find the effect of grinding condition in term of depth of cut and type of grinding coolant. Zinc Oxide (ZnO) nano-coolant was used as a coolant with water as a based liquid. The experiments conducted with grinding depth in the range of 5 to 21μm. Silicon Carbide wheel are used to grind the AISI P20 tool work piece. Artificial intelligence model has been developed using Artificial Neural Network(ANN). Result shows that the lower surface roughness and wheel wear obtain at the lowest cutting depth which is 5 μm. Besides that, grind using ZnO nano-coolant gives best surface roughness and minimum wheel wears compared to grind using normal soluble coolant. The surface roughness have been reduced approximately 47.84% for single pass experiment and 126.1% for multi pass experiment. However, there is no wheel wheel wear obtain for grinding using ZnO nanocoolant. From the prediction of ANN, it can predict the surface roughness closely with the experimental value.
format	Article
author	K., Kadirgama M., Yogeswaran S. , Thiruchelvam M. M., Rahman
author_facet	K., Kadirgama M., Yogeswaran S. , Thiruchelvam M. M., Rahman
author_sort	K., Kadirgama
title	Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant
title_short	Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant
title_full	Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant
title_fullStr	Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant
title_full_unstemmed	Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant
title_sort	prediction of grinding machinability when grind p20 tool steel using water based zno nano-coolant
publisher	Iceland Journal of Life Sciences
publishDate	2014
url	http://umpir.ump.edu.my/id/eprint/5271/ http://umpir.ump.edu.my/id/eprint/5271/ http://umpir.ump.edu.my/id/eprint/5271/1/paper.pdf
first_indexed	2023-09-18T22:00:31Z
last_indexed	2023-09-18T22:00:31Z
_version_	1777414367145361408

Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant

Similar Items