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 | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 126.42 kB | Adobe PDF | View/Open |
02_certificate.pdf | 206.81 kB | Adobe PDF | View/Open | |
04_declaration.pdf | 206.48 kB | Adobe PDF | View/Open | |
05_ackowledgement.pdf | 5.85 kB | Adobe PDF | View/Open | |
06_contents.pdf | 5.34 kB | Adobe PDF | View/Open | |
07_list_of_tables.pdf | 15.22 kB | Adobe PDF | View/Open | |
08_list_of_figures.pdf | 228.26 kB | Adobe PDF | View/Open | |
09_abbreviations.pdf | 6.53 kB | Adobe PDF | View/Open | |
10_chapter1.pdf | 363.21 kB | Adobe PDF | View/Open | |
11_chapter2.pdf | 411.1 kB | Adobe PDF | View/Open | |
12_chapter3.pdf | 1.31 MB | Adobe PDF | View/Open | |
13_chapter4.pdf | 727.51 kB | Adobe PDF | View/Open | |
14_chapter5.pdf | 1.71 MB | Adobe PDF | View/Open | |
15_conclusion.pdf | 350.34 kB | Adobe PDF | View/Open | |
16_biblography.pdf | 369.22 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 350.34 kB | Adobe PDF | View/Open |
Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
Altmetric Badge: