Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication

This paper presents the optimization of the grinding parameters of ductile cast iron in wet conditions and with the minimum quantity lubrication (MQL) technique. The objective of this project is to investigate the performance of ductile cast iron during the grinding process using the MQL technique...

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Main Authors:	N. S. M., Sahid, M. M., Rahman, K., Kadirgama
Format:	Article
Language:	English
Published:	Universiti Malaysia Pahang 2015
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/9876/ http://umpir.ump.edu.my/id/eprint/9876/ http://umpir.ump.edu.my/id/eprint/9876/ http://umpir.ump.edu.my/id/eprint/9876/1/Neural%20Network%20Modeling%20Of%20Grinding%20Parameters%20Of%20Ductile%20Cast%20Iron%20Using%20Minimum%20Quantity%20Lubrication.pdf

id	ump-9876
recordtype	eprints
spelling	ump-98762018-01-30T00:53:35Z http://umpir.ump.edu.my/id/eprint/9876/ Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication N. S. M., Sahid M. M., Rahman K., Kadirgama TJ Mechanical engineering and machinery This paper presents the optimization of the grinding parameters of ductile cast iron in wet conditions and with the minimum quantity lubrication (MQL) technique. The objective of this project is to investigate the performance of ductile cast iron during the grinding process using the MQL technique and to develop artificial neural network modeling. In this project we used the DOE method to perform the experiments. Analysis of variance with the artificial neural network method is used to investigate significant effects on the performance characteristics and the optimal cutting parameters of the grinding process. Ductile cast iron was used in this experiment and the ethanol glycol was applied in the conventional method and compared with the MQL method. During conventional grinding, a dense and hard slurry layer was formed on the wheel surface and the performance of the ductile cast iron was very low, threatening the ecology and health of the workers. In order to combat the negative effects of conventional cutting fluids, the MQL method was used in the process to formulate modern cutting fluids endowed with user- and eco-friendly properties. Aluminum oxide was used as the grinding wheel (PSA-60JBV). This model has been validated by the experimental results of ductile cast iron grinding. Each method uses two passes - single-pass and multiple-pass. The prediction model shows that depth of cut and table speed have the greatest effect on the surface roughness and material removal rate for the MQL technique with multiple-passes by showing improved surface roughness, preventing workpiece burning and enabling a more friendly environment. Thus, various other parameters need to be added for further experiments, such as the wheel speed, distance from the wheel to the workpiece zone contact, and the geometry of the nozzle. Universiti Malaysia Pahang 2015 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/9876/1/Neural%20Network%20Modeling%20Of%20Grinding%20Parameters%20Of%20Ductile%20Cast%20Iron%20Using%20Minimum%20Quantity%20Lubrication.pdf N. S. M., Sahid and M. M., Rahman and K., Kadirgama (2015) Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication. International Journal of Automotive and Mechanical Engineering (IJAME), 11. pp. 2608-2621. ISSN 1985-9325(Print); 2180-1606 (Online) http://dx.doi.org/10.15282/ijame.11.2015.39.0220 10.15282/ijame.11.2015.39.0220
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 N. S. M., Sahid M. M., Rahman K., Kadirgama Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication
description	This paper presents the optimization of the grinding parameters of ductile cast iron in wet conditions and with the minimum quantity lubrication (MQL) technique. The objective of this project is to investigate the performance of ductile cast iron during the grinding process using the MQL technique and to develop artificial neural network modeling. In this project we used the DOE method to perform the experiments. Analysis of variance with the artificial neural network method is used to investigate significant effects on the performance characteristics and the optimal cutting parameters of the grinding process. Ductile cast iron was used in this experiment and the ethanol glycol was applied in the conventional method and compared with the MQL method. During conventional grinding, a dense and hard slurry layer was formed on the wheel surface and the performance of the ductile cast iron was very low, threatening the ecology and health of the workers. In order to combat the negative effects of conventional cutting fluids, the MQL method was used in the process to formulate modern cutting fluids endowed with user- and eco-friendly properties. Aluminum oxide was used as the grinding wheel (PSA-60JBV). This model has been validated by the experimental results of ductile cast iron grinding. Each method uses two passes - single-pass and multiple-pass. The prediction model shows that depth of cut and table speed have the greatest effect on the surface roughness and material removal rate for the MQL technique with multiple-passes by showing improved surface roughness, preventing workpiece burning and enabling a more friendly environment. Thus, various other parameters need to be added for further experiments, such as the wheel speed, distance from the wheel to the workpiece zone contact, and the geometry of the nozzle.
format	Article
author	N. S. M., Sahid M. M., Rahman K., Kadirgama
author_facet	N. S. M., Sahid M. M., Rahman K., Kadirgama
author_sort	N. S. M., Sahid
title	Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication
title_short	Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication
title_full	Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication
title_fullStr	Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication
title_full_unstemmed	Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication
title_sort	neural network modeling of grinding parameters of ductile cast iron using minimum quantity lubrication
publisher	Universiti Malaysia Pahang
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/9876/ http://umpir.ump.edu.my/id/eprint/9876/ http://umpir.ump.edu.my/id/eprint/9876/ http://umpir.ump.edu.my/id/eprint/9876/1/Neural%20Network%20Modeling%20Of%20Grinding%20Parameters%20Of%20Ductile%20Cast%20Iron%20Using%20Minimum%20Quantity%20Lubrication.pdf
first_indexed	2023-09-18T22:08:54Z
last_indexed	2023-09-18T22:08:54Z
_version_	1777414894344208384

Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication

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