UMP Institutional Repository

Prediction of grinding machanability when grinds Haynes 242 using water based coolant

Hazwani, Sidik (2012) Prediction of grinding machanability when grinds Haynes 242 using water based coolant. Faculty of Mechanical Engineering, Universiti Malaysia Pahang.

[img]
Preview
PDF (Table of content)
Prediction of grinding machanability when grinds Haynes 242 using water based coolant (Table of content).pdf - Accepted Version

Download (92kB) | Preview
[img]
Preview
PDF (Abstract)
Prediction of grinding machanability when grinds Haynes 242 using water based coolant (Abstract).pdf - Accepted Version

Download (36kB) | Preview
[img]
Preview
PDF (Chapter 1)
Prediction of grinding machanability when grinds Haynes 242 using water based coolant (Chapter 1).pdf - Accepted Version

Download (36kB) | Preview
[img]
Preview
PDF (References)
Prediction of grinding machanability when grinds Haynes 242 using water based coolant (References).pdf - Accepted Version

Download (55kB) | Preview

Abstract

Grinding is one of the important finishing machining operations. It is applied at the last stage of manufacturing process so that the high dimensional accuracy and desirable surface finish product can be achieved. However, there is a lot of problem occur in order to achieve the high dimensional accuracy and desirable surface finish. For examples, surface burning due to excessive heat produce and undesirable surface finish outcomes. This report deals with the prediction of grinding machanability when grind Haynes 242 using water based coolant. The aims of this report are to find the optimum parameter of the grinding process which is depth of cut (�m) where the type of surface roughness produces will be investigate and to develop prediction model of surface roughness by using an artificial neural network analysis. This report describes the experimental setups and procedures in finding the optimum value of the depth of cut and thus leading with the development of the prediction model. The material used is Haynes 242 and the depth of cut is independent variables. Calculations of the data were done which had obtained by using Perthometer and analysis was made by using ANN. The grindability results have shown optimum parameter which is gives the lowest surface roughness value. The results from the experiment show that the Al2O3 wheel gives a good surface roughness compare to the SiC at the depth of cut 5μm. By the end of the projects, prediction model had been developed by using ANN and was compared in the graph. The ANN model obtained from predicted value is accurate and effective in predicting the correlation and R-squared which is give 1% of minimum error and 2% of maximum error when using the Al2O3 grinding wheel.

Item Type: Undergraduates Project Papers
Additional Information: Project paper (Bachelor of Mechanical Engineering (Manufacturing Engineering)) -- Universiti Malaysia Pahang – 2012, SV: Dr Kumaran A/L Kadirgama
Uncontrolled Keywords: Grinding machines; Surface roughness
Subjects: T Technology > TP Chemical technology
Faculty/Division: Faculty of Mechanical Engineering
Depositing User: Nik Ahmad Nasyrun Nik Abd Malik
Date Deposited: 03 Sep 2014 02:13
Last Modified: 07 Apr 2017 01:58
URI: http://umpir.ump.edu.my/id/eprint/4943
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item