scholarly journals Design and Optimization of EDM using Metal Matrix Composite by Genetic algorithm and Jaya Algorithm

Aluminum Boron carbide (Al-B4C) is a form of metal matrix composite (MMC) belongs to advanced category of material which is gaining popularity now-a-days because of its excellent mechanical and physical properties. Unconventional machining processes (UMPs) are now day’s best options to machine such kinds of modern materials. Electro discharge machining (EDM) process now days the best UMP whichever utilizes thermic energy power of spark for material removal. In present research the EDM has been carried out in Al-B4C MMC by varying different EDM parameters to evaluate material removal rate (MRR) and tool wear rate (TWR). The response surface model (RSM) has been developed for both the MRR and TWR. The developed RSM has been utilized during optimization. Optimizations of responses the MRR and or TWR have been done by using genetic algorithm and jaya algorithm. Finally both the algorithms have been compared with respect to current manufacturing paradigm.

2020 ◽  
Vol 3 (1) ◽  
pp. 46-54
Author(s):  
Dwi Handoko

Pada penelitian ini dilakukan pembuatan metal matrix composite dari bahan serbuk tembaga murni yang akan dipadu dengan bahan penguat berupa serbuk grafit yang dilanjutkan dengan pengujian pada mesin EDM. Metode pencampuran kedua material ini dilakukan dengan proses Powder metalurgi melalui tahapan pencampuran (mixing), penekanan (compaction) dan dilanjutkan dengan proses pemanasan dengan suhu 800 oC(sintering). Pada penelitian ini ingin diketahui pengaruh tekanan akibat proses powder metalurgi terhadap laju keausan material (MRR) dan laju keausan elektroda (ERR) pada material baja ST.37 mesin EDM Chimer EZ Dengan parameter pemakan tetap, arus 2 Amper dan kedalaman 5 mm. Pengujian yang dilakukan yaitu kekerasan dan struktur mikro. Dari hasil penelitian ini menunjukkan dengan semakin meningkatnya tekanan kompaksi laju keausan material MRR dan kekerasan semakin meningkat, sementara laju keausan terendahi terjadi pada tekanan kompaksi 25.000 KN


2019 ◽  
Vol 33 ◽  
pp. 1-9
Author(s):  
P. Tripathy ◽  
K.P. Maity

The experimental investigation of process characteristics while performing micro-milling on hybrid aluminium metal matrix composite is discussed in this article. High Speed Steel micro end mill cutters are used for machining of micro-slots on Al6063 metal matrix composite reinforced with zirconia and silicon carbide. The tools are also treated cryogenically at -196°C using liquid nitrogen with a holding time of 24 hours. For this investigation, machining parameters like feed rate, cutting speed and depth of cut are considered as the process parameters. The effect of the process parameters on the material removal rate and surface roughness for hybrid metal matrix composite are analyzed. In addition, tools wear for untreated and cryo-treated single tempered tools are also investigated. The output responses i.e., material removal rate and surface roughness of cryo-treated tools exhibit better results than untreated tool due to increase in strength, hardness and wear resistance.


Author(s):  
Shrihar Pandey ◽  
Pankaj K Shrivastava

To shape advanced engineering materials, many unconventional machining processes have been developed. Electrical discharge machining is such an unconventional machining process which is very popular nowadays but it is limited by poor material removal efficiency. Electrical arc machining is another unconventional machining process which is quite similar to electrical discharge machining and is now gaining attention from research fraternity due to its high material removal efficiency. In the present research, an innovative unconventional machining process known as vibration-assisted electrical arc machining has been developed. The performance of vibration-assisted electrical arc machining has been evaluated during machining of Al–B4C metal matrix composite by considering peak current, flushing velocity of dielectric and tool vibrations as input control factors. The quality characteristics considered were material removal rate, tool wear rate, relative electrode wear rate and surface roughness. It has been observed that vibration-assisted electrical arc machining results in approximately 3000% more material removal rate as compared to conventional electrical discharge machining during machining of Al–B4C metal matrix composite. The effects of various input control factors on output parameters have also been discussed. Further modelling and optimization of the process parameters has also been done by artificial intelligence approach.


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