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Optimization of electrical discharge machining parameters of SISIC through response surface methodology
Abdul Azeez Abdu Aliyu1, Jafri Mohd Rohani2, Ahmad Majdi Abdul Rani3, Hamidon Musa4.
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.
Affiliation:
- Universiti Teknologi PETRONAS, Malaysia
- Universiti Teknologi PETRONAS, Malaysia
- Universiti Teknologi Malaysia, Malaysia
- Universiti Teknologi Malaysia, Malaysia
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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