Please use this identifier to cite or link to this item:
http://hdl.handle.net/11452/29787
Title: | Evaluation of surface roughness and material removal rate in the wire electrical discharge machining of Al/B4C composites via the Taguchi method |
Authors: | Ekici, Ergün Motorcu, Ali Rıza Uludağ Üniversitesi/Teknik Bilimler Meslek Yüksekokulu/Makine Programı. Kuş, Abdil AAG-9412-2021 57196667786 |
Keywords: | 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 |
Issue Date: | Aug-2016 |
Publisher: | Sage Publications |
Citation: | 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. |
Abstract: | 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 |
Appears in Collections: | Scopus Web of Science |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.