Fuzzy Logic Approach to Predict Surface Roughness in Powder Mixed Electric Discharge Machining of Titanium Alloy

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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/article/fuzzy-logic-approach-to-predict-surface-roughness-in-powder-mixed-electric-discharge-machining-of-titanium-alloy/>. Date accessed: 24 apr. 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/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/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.  24 Apr. 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/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/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/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)

Authors

Affiliations

  • University of Novi Sad, Faculty of Technical Sciences, Serbia 1

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)9-10, 376-387
© The Authors 2023. CC BY 4.0 Int.

https://doi.org/10.5545/sv-jme.2023.561

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 %.

ANFIS; discharge current; pulse duration; duty cycle; graphite powder;