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Başlık: Evaluation of surface roughness and material removal rate in the wire electrical discharge machining of Al/B4C composites via the Taguchi method
Yazarlar: Ekici, Ergün
Motorcu, Ali Rıza
Uludağ Üniversitesi/Teknik Bilimler Meslek Yüksekokulu/Makine Programı.
Kuş, Abdil
AAG-9412-2021
57196667786
Anahtar kelimeler: Materials science
Metal matrix composite (MMC)
Wire electrical discharge machining (WEDM)
Material removal rate
Kerf
Taguchi method
Surface roughness
Metal-matrix composites
Parametric optimization
Mechanical-properties
Cutting speed
Wedm process
Cut edm
Microstructure
Steel
Wear
Tool
Aluminum
Electric discharge machining
Electric discharges
Hot pressing
Metal cutting
Metal pressing
Metallic matrix composites
Regression analysis
Taguchi methods
Wire
Correlation coefficient
Effective parameters
Electrical discharges
Evaluation of surface roughness
Predictive equations
Wire electrical discharge machining
Yayın Tarihi: Ağu-2016
Yayıncı: Sage Publications
Atıf: Ekici, E. vd. (2016). "Evaluation of surface roughness and material removal rate in the wire electrical discharge machining of Al/B4C composites via the Taguchi method". Journal of Composite Materials, 50(18), 2575-2586.
Özet: This study researched the effects of machining parameters on surface roughness and material removal rate in the wire electrical discharge cutting of high-density Al/B4C metal matrix composites produced via the hot pressing method. Wire tension, reinforcement percentage, wire speed, pulse-on time and pulse-off time were set as the control factors. The Taguchi L-18 (2(1)x3(4)) orthogonal array was used in the experiment design and determination of the optimum control factors. Variance analysis was applied to determine the effects of the control factors on the surface roughness and material removal rate. The results showed the most effective parameters to be pulse-on time (30.22%) for surface roughness and wire speed (83.20%) for material removal rate, and the optimum levels of the control factors to be A(2)B(1)C(2)D(1)E(1) and A(2)B(2)C(3)D(2)E(2), respectively. Predictive equations were then developed by applying linear regression analysis, and the adjusted correlation coefficients were calculated as 0.61 for surface roughness and 0.785 for material removal rate.
URI: https://doi.org/10.1177/0021998315609788
https://journals.sagepub.com/doi/10.1177/0021998315609788
http://hdl.handle.net/11452/29787
ISSN: 0021-9983
1530-793X
Koleksiyonlarda Görünür:Scopus
Web of Science

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