RODIĆ, Dragan ;GOSTIMIROVIĆ, Marin ;SEKULIĆ, Milenko ;SAVKOVIĆ, Borislav ;ALEKSIĆ, Andjelko . Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 69, n.9-10, p. 376-387, april 2023. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/fuzzy-logic-approach-to-predict-surface-roughness-in-powder-mixed-electric-discharge-machining-of-titanium-alloy/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2023.561.
Rodić, D., Gostimirović, M., Sekulić, M., Savković, B., & Aleksić, A. (2023). Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy. Strojniški vestnik - Journal of Mechanical Engineering, 69(9-10), 376-387. doi:http://dx.doi.org/10.5545/sv-jme.2023.561
@article{sv-jmesv-jme.2023.561,
author = {Dragan Rodić and Marin Gostimirović and Milenko Sekulić and Borislav Savković and Andjelko Aleksić},
title = {Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {69},
number = {9-10},
year = {2023},
keywords = {ANFIS; discharge current; pulse duration; duty cycle; graphite powder; },
abstract = {This study deals with fuzzy logic based modeling and parametric analysis in powder mixed electrical discharge machining of titanium alloys. The central composition plan was used to design the experiments considering four parameters, namely discharge current, pulse duration, duty cycle as well as graphite powder concentration. All experiments were performed with different parameter combinations and the performance, i.e., surface roughness, was evaluated. The adaptive neuro-fuzzy inference system was used to understand and define the input-output relationship. The experimental results and the model results were compared and it was found that the results accurately predicted the reactions in the erosion of titanium alloys. In addition, the model was verified using data that had not participated in the training of the model, with an error of about 10%. In addition, a fuzzy plot was used to analyze the influence of input parameters on surface roughness. It was found that the discharge current was the most important influencing parameter. Additional experiments proved the positive effect of graphite powder, which reduced the surface roughness by 27 %.},
issn = {0039-2480}, pages = {376-387}, doi = {10.5545/sv-jme.2023.561},
url = {https://www.sv-jme.eu/sl/article/fuzzy-logic-approach-to-predict-surface-roughness-in-powder-mixed-electric-discharge-machining-of-titanium-alloy/}
}
Rodić, D.,Gostimirović, M.,Sekulić, M.,Savković, B.,Aleksić, A. 2023 April 69. Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 69:9-10
%A Rodić, Dragan %A Gostimirović, Marin %A Sekulić, Milenko %A Savković, Borislav %A Aleksić, Andjelko %D 2023 %T Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy %B 2023 %9 ANFIS; discharge current; pulse duration; duty cycle; graphite powder; %! Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy %K ANFIS; discharge current; pulse duration; duty cycle; graphite powder; %X This study deals with fuzzy logic based modeling and parametric analysis in powder mixed electrical discharge machining of titanium alloys. The central composition plan was used to design the experiments considering four parameters, namely discharge current, pulse duration, duty cycle as well as graphite powder concentration. All experiments were performed with different parameter combinations and the performance, i.e., surface roughness, was evaluated. The adaptive neuro-fuzzy inference system was used to understand and define the input-output relationship. The experimental results and the model results were compared and it was found that the results accurately predicted the reactions in the erosion of titanium alloys. In addition, the model was verified using data that had not participated in the training of the model, with an error of about 10%. In addition, a fuzzy plot was used to analyze the influence of input parameters on surface roughness. It was found that the discharge current was the most important influencing parameter. Additional experiments proved the positive effect of graphite powder, which reduced the surface roughness by 27 %. %U https://www.sv-jme.eu/sl/article/fuzzy-logic-approach-to-predict-surface-roughness-in-powder-mixed-electric-discharge-machining-of-titanium-alloy/ %0 Journal Article %R 10.5545/sv-jme.2023.561 %& 376 %P 12 %J Strojniški vestnik - Journal of Mechanical Engineering %V 69 %N 9-10 %@ 0039-2480 %8 2023-04-04 %7 2023-04-04
Rodić, Dragan, Marin Gostimirović, Milenko Sekulić, Borislav Savković, & Andjelko Aleksić. "Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy." Strojniški vestnik - Journal of Mechanical Engineering [Online], 69.9-10 (2023): 376-387. Web. 20 Dec. 2024
TY - JOUR AU - Rodić, Dragan AU - Gostimirović, Marin AU - Sekulić, Milenko AU - Savković, Borislav AU - Aleksić, Andjelko PY - 2023 TI - Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2023.561 KW - ANFIS; discharge current; pulse duration; duty cycle; graphite powder; N2 - This study deals with fuzzy logic based modeling and parametric analysis in powder mixed electrical discharge machining of titanium alloys. The central composition plan was used to design the experiments considering four parameters, namely discharge current, pulse duration, duty cycle as well as graphite powder concentration. All experiments were performed with different parameter combinations and the performance, i.e., surface roughness, was evaluated. The adaptive neuro-fuzzy inference system was used to understand and define the input-output relationship. The experimental results and the model results were compared and it was found that the results accurately predicted the reactions in the erosion of titanium alloys. In addition, the model was verified using data that had not participated in the training of the model, with an error of about 10%. In addition, a fuzzy plot was used to analyze the influence of input parameters on surface roughness. It was found that the discharge current was the most important influencing parameter. Additional experiments proved the positive effect of graphite powder, which reduced the surface roughness by 27 %. UR - https://www.sv-jme.eu/sl/article/fuzzy-logic-approach-to-predict-surface-roughness-in-powder-mixed-electric-discharge-machining-of-titanium-alloy/
@article{{sv-jme}{sv-jme.2023.561},
author = {Rodić, D., Gostimirović, M., Sekulić, M., Savković, B., Aleksić, A.},
title = {Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {69},
number = {9-10},
year = {2023},
doi = {10.5545/sv-jme.2023.561},
url = {https://www.sv-jme.eu/sl/article/fuzzy-logic-approach-to-predict-surface-roughness-in-powder-mixed-electric-discharge-machining-of-titanium-alloy/}
}
TY - JOUR AU - Rodić, Dragan AU - Gostimirović, Marin AU - Sekulić, Milenko AU - Savković, Borislav AU - Aleksić, Andjelko PY - 2023/04/04 TI - Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 69, No 9-10 (2023): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2023.561 KW - ANFIS, discharge current, pulse duration, duty cycle, graphite powder, N2 - This study deals with fuzzy logic based modeling and parametric analysis in powder mixed electrical discharge machining of titanium alloys. The central composition plan was used to design the experiments considering four parameters, namely discharge current, pulse duration, duty cycle as well as graphite powder concentration. All experiments were performed with different parameter combinations and the performance, i.e., surface roughness, was evaluated. The adaptive neuro-fuzzy inference system was used to understand and define the input-output relationship. The experimental results and the model results were compared and it was found that the results accurately predicted the reactions in the erosion of titanium alloys. In addition, the model was verified using data that had not participated in the training of the model, with an error of about 10%. In addition, a fuzzy plot was used to analyze the influence of input parameters on surface roughness. It was found that the discharge current was the most important influencing parameter. Additional experiments proved the positive effect of graphite powder, which reduced the surface roughness by 27 %. UR - https://www.sv-jme.eu/sl/article/fuzzy-logic-approach-to-predict-surface-roughness-in-powder-mixed-electric-discharge-machining-of-titanium-alloy/
Rodić, Dragan, Gostimirović, Marin, Sekulić, Milenko, Savković, Borislav, AND Aleksić, Andjelko. "Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 69 Number 9-10 (04 April 2023)
Strojniški vestnik - Journal of Mechanical Engineering 69(2023)9-10, 376-387
© The Authors 2023. CC BY 4.0 Int.
This study deals with fuzzy logic based modeling and parametric analysis in powder mixed electrical discharge machining of titanium alloys. The central composition plan was used to design the experiments considering four parameters, namely discharge current, pulse duration, duty cycle as well as graphite powder concentration. All experiments were performed with different parameter combinations and the performance, i.e., surface roughness, was evaluated. The adaptive neuro-fuzzy inference system was used to understand and define the input-output relationship. The experimental results and the model results were compared and it was found that the results accurately predicted the reactions in the erosion of titanium alloys. In addition, the model was verified using data that had not participated in the training of the model, with an error of about 10%. In addition, a fuzzy plot was used to analyze the influence of input parameters on surface roughness. It was found that the discharge current was the most important influencing parameter. Additional experiments proved the positive effect of graphite powder, which reduced the surface roughness by 27 %.