Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/344940
Title: Experimental investigation and Process Parameters optimization in Wire Electrical Discharge Machining of Aluminium Hybrid Metal Matrix Composite
Researcher: A Muniappan
Guide(s): C Thiagarajan
Keywords: Engineering
Engineering and Technology
Engineering Mechanical
University: Saveetha University
Completed Date: 2020
Abstract: The objective of the present work was to investigate the effects of the various WEDM process parameters on the machining quality and to obtain the optimal sets of process parameters so that the quality of machined parts can be optimized. The working ranges and levels of the WEDM process parameters are found using one factor at a time approach. Pilot experiments were conducted to selected the process parameters. Based on the pilot experiment result Pulse on time, Pulse off Time, Peak current, Gapset voltage, wire feed and Wire tension parameters were selected for this study. newlineThe consistent quality of parts being machined in electrical discharge machining and wire electrical discharge machining is difficult because the process parameters cannot be controlled effectively. These are the biggest challenges for the researchers and practicing engineers. Experiments are designed by Taguchi orthogonal array and Response Surface Methodology. 27 experiments are conducted by applying the combination of different process parameters, developed by Taguchi orthogonal array. Similarly, 54 experiments are conducted by executing the various combinations of process parameters, developed by Response surface methodology. Experimentally observed and theoretically predicted responses for the experiments conducted in WEDM are analysed and the experiment that gives optimum response is found out. Taguchi technique and Grey Relational analysis have been used for multi- response optimization. Confirmation experiments are further conducted to validate the results. ANN has been trained and implemented using a fully developed feed forward back propagation neural network to evaluate the error profile of responses in WEDM. newline
Pagination: 
URI: http://hdl.handle.net/10603/344940
Appears in Departments:Department of Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File126.42 kBAdobe PDFView/Open
02_certificate.pdf206.81 kBAdobe PDFView/Open
04_declaration.pdf206.48 kBAdobe PDFView/Open
05_ackowledgement.pdf5.85 kBAdobe PDFView/Open
06_contents.pdf5.34 kBAdobe PDFView/Open
07_list_of_tables.pdf15.22 kBAdobe PDFView/Open
08_list_of_figures.pdf228.26 kBAdobe PDFView/Open
09_abbreviations.pdf6.53 kBAdobe PDFView/Open
10_chapter1.pdf363.21 kBAdobe PDFView/Open
11_chapter2.pdf411.1 kBAdobe PDFView/Open
12_chapter3.pdf1.31 MBAdobe PDFView/Open
13_chapter4.pdf727.51 kBAdobe PDFView/Open
14_chapter5.pdf1.71 MBAdobe PDFView/Open
15_conclusion.pdf350.34 kBAdobe PDFView/Open
16_biblography.pdf369.22 kBAdobe PDFView/Open
80_recommendation.pdf350.34 kBAdobe PDFView/Open
Show full item record


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: