Aliyu, A. A. A. and Rohani, J. M. and Rani, A. M. A. and Musa, H. (2017) Optimization of electrical discharge machining parameters of SiSiC through response surface methodology. Jurnal Teknologi, 79 (1). pp. 119-129. ISSN 0127-9696
|
PDF
717kB |
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....
Abstract
In recent years, researchers have demonstrated increases interest in studies involving silicon carbide (SiC) materials due to several industrial applications. Extreme hardness and high brittleness properties of SiC make the machining of such material very difficult, time consuming and costly. Electrical discharge machining (EDM) has been regarded as the most viable method for the machining of SiC. The mechanism of EDM process is complex. Researchers have acknowledged a challenge in generating a model that accurately describes the correlation between the input parameters and the responses. This paper reports the study on parametric optimization of siliconized silicon carbide (SiSiC) for the following quality responses; material removal rate (MRR), tool wear ratio (TWR) and surface roughness (Ra). The experiments were planned using Face centered central composite design. The models which related MRR, TWR and Ra with the most significant factors such as discharge current (Ip), pulse-on time (Ton), and servo voltage (Sv) were developed. In order to develop, improve and optimize the models response surface methodology (RSM) was used. Non-linear models were proposed for MRR and Ra while linear model was proposed for TWR. The margin of error between predicted and experimental values of MRR, TWR and Ra are found within 6.7, 5.6 and 2.5% respectively. Thus, the excellent reproducibility of this experimental study is confirmed, and the models developed for MRR, TWR and Ra are justified to be valid by the confirmation tests.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Modeling, Optimization |
Subjects: | T Technology > TJ Mechanical engineering and machinery |
Divisions: | Mechanical Engineering |
ID Code: | 76764 |
Deposited By: | Fazli Masari |
Deposited On: | 31 May 2018 09:28 |
Last Modified: | 31 May 2018 09:28 |
Repository Staff Only: item control page