Electrical discharge machining of hybrid material using copper electrode
In recent years, there has been a great of interest in copper alloy (FeCuSn) hybrid metal material due to several industrial applications. Extreme hardness and high brittleness properties of FeCuSn makes the machining of such material very difficult and time consuming, especially using traditional m...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Online Access: | http://eprints.utm.my/id/eprint/53561/1/AmirulAkmalMohammad%20YazidMFKM2015.pdf http://eprints.utm.my/id/eprint/53561/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85658 |
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| Summary: | In recent years, there has been a great of interest in copper alloy (FeCuSn) hybrid metal material due to several industrial applications. Extreme hardness and high brittleness properties of FeCuSn makes the machining of such material very difficult and time consuming, especially using traditional machining methods such as grinding and lapping techniques. Due to this, the cost of machining FeCuSn is very high. Despite high demand in electrical discharge machining (EDM) process by modern manufacturing industries, the mechanism of the process is quite complex. It is difficult to generate a model that can accurately correlate the input parameters with the responses. Optimum parameters play a significant role in increasing production rate and reducing the machining time. In this work, study on parametric optimization of surface roughness (Ra) , material removal rate (MRR) and tool wear ratio (TWR) on die-sinking EDM of copper alloy (FeCuSn) was carried out. This study also establishes the models that relate the responses and the most significant design parameters like pulse-on time (Ton), discharge current (Ip) and servo voltage (SV) will be achieved . Full factorial design was applied to select the most influential design parameters. The experimental data was analyzed using the analysis of variance (ANOVA). The ANOVA results revealed that Ton, Ip and SV were the most influential parameters which affect the Ra, MRR and TWR. The optimum responses (Ra, MRR and TWR) were achieved through the optimum parameters setting predicted by the design expert software. The developed models were validated through confirmation runs, and the error between the experimental and predicted values of the responses lies within the acceptable limit. |
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