@ARTICLE{Gerçekcioğlu_E._Multi-Response_2023,
 author={Gerçekcioğlu, E. and Albaşkara, M.},
 volume={vol. 68},
 number={No 3},
 journal={Archives of Metallurgy and Materials},
 pages={861-868},
 howpublished={online},
 year={2023},
 publisher={Institute of Metallurgy and Materials Science of Polish Academy of Sciences},
 publisher={Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences},
 abstract={Multiple response optimization of the machining of 17-4 PH stainless steel material, which is difficult to process with traditional methods, with EDM was made by Taguchi-based grey relational analysis method. Surface roughness (Ra), material removal rate (MRR), and electrode wear rate (EWR) were the responses, while current, pulse-on time, pulse-off time, and voltage were chosen as process parameters. According to the multi-response optimization, the experiment level that gave the best result was A1B2C2D2. Optimum machining outputs were found as A1B3C1D1 using the Taguchi method. As a result of the Taguchi analysis and ANOVA, it was determined that the significant parameters according to multiple performance characteristics were current (56.22%) and voltage (22.40%). The surfaces of the best GRG and optimal sample were examined with XRD, SEM and EDX analysis and the effects on the surfaces were compared.},
 type={Article},
 title={Multi-Response Optimization of Electrical Discharge Machining of 17-4 PH SS Using Taguchi-Based Grey Relational Analysis},
 URL={http://www.czasopisma.pan.pl/Content/128323/PDF/AMM-2023-3-03-Gercekcioglu.pdf},
 doi={10.24425/amm.2023.145448},
 keywords={EDM, 17-4 PH SS, Taguchi-based Grey Relational Analysis, ANOVA, Surface Characterization},
}