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| dc.contributor.author | YARED, ATNAFU WUBIE | |
| dc.date.accessioned | 2022-12-31T08:23:13Z | |
| dc.date.available | 2022-12-31T08:23:13Z | |
| dc.date.issued | 2022-09-29 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14820 | |
| dc.description.abstract | Electrical Discharge Machining (EDM) is a Nontraditional machining Process used to produce complex and irregular shapes and cut hard materials that are difficult to machine by conventional machining processes easily and with high accuracy. In Amhara Metal and Machine Industry technology Development Enterprise (AMMITDE) shaping a fixed guide is difficult because of its high roughness. The objective of this paper was to optimize the process parameters of EDM on mild steel AISI 1020 workpieces using copper as an electrode. The input parameters like current, pulse off time, pulse on time, and voltage affect the responses like surface roughness and material removal rate. The L9 orthogonal array was used to find a set of parameters to experiment. The experiment was conducted on AMMIMTDE by using ZNC 540 EDM machine. This study investigates the impact of EDM parameters by utilizing an Artificial Neural Network (ANN) and a Multi-Objective Genetic Algorithm technique done by MATLAB software. Based on the input and the output result value ANN model was developed and trained with a Genetic Algorithm (GA) for getting optimal weight. This objective function was further optimized by GA for obtaining an optimum result. From the analysis of the results Electro-Discharge Machining parameters like a current of 7.1664 Amp, Pulse off time of 60.24404 μs, Pulse on time of 80.0209 μs, and voltage of 60.1729 Volt with their response value of 29.43022 mm 3 /sec material removal rate and 3.63945 µm surface roughness were obtained. Finally, a confirmation test was conducted by utilizing the optimal process parameters for three specimens then measured responses showed that 29.1004 mm 3 /sec material removal rate and 3.5642 µm surface roughness with an error of 0.33 and 0.075 respectively from the predicted optimal value. Keywords: Artificial Neural Network, Genetic Algorithm, EDM Parameters and Responses, AMMIMTDE, Electro Discharge Machining | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | MECHANICAL AND INDUSTRIAL ENGINEERING | en_US |
| dc.title | Process parameter optimization of Electro Discharge Machining on mild steel fixed guide for Dishka gun using Artificial Neural Network based on Genetic Algorithm (Case Study on Producing Fixed Guide in Amhara Metal Industry and Machine Technology Development Enterprise) | en_US |
| dc.type | Thesis | en_US |
