VAN, An-Le ;NGUYEN, Trung-Thanh . Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 68, n.6, p. 424-438, may 2022. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/optimization-of-friction-stir-welding-operation-using-optimal-taguchi-based-anfis-and-genetic-algorithm/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2022.111.
Van, A., & Nguyen, T. (2022). Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm. Strojniški vestnik - Journal of Mechanical Engineering, 68(6), 424-438. doi:http://dx.doi.org/10.5545/sv-jme.2022.111
@article{sv-jmesv-jme.2022.111,
author = {An-Le Van and Trung-Thanh Nguyen},
title = {Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {68},
number = {6},
year = {2022},
keywords = {Friction stir welding; Energy efficiency; Jointing efficiency; Micro-hardness; NCGA; },
abstract = {The friction stir welding (FSW) process is an effective approach to produce joints having superior quality. Unfortunately, most published investigations primarily addressed optimizing process parameters to boost product quality. In the current work, the FSW operation of the aluminum alloy has been considered and optimized to decrease the specific welding energy (SWE) and enhance the jointing efficiency (JE) as well as micro-hardness at the welded zone (MH). The parameter inputs are the rotational speed (S), welding speed (f), depth of penetration (D), and tool title angle (T). The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models were utilized to propose the welding responses in terms of the FSW parameters, while the Taguchi method was applied to optimize the ANFIS operating parameters. The neighborhood cultivation genetic algorithm (NCGA) was employed to determine the best solution. The obtained results indicated that the optimal values of the S, f, D, and T are 560 RPM, 90 mm/min, 0.9 mm, and 2 deg, respectively. The SWE is decreased by 17.0%, while the JE and MH are improved by 2.3% and 6.4%, respectively at the optimal solution. The optimal ANFIS models for the welding responses were adequate and reliably employed to forecast the response values. The proposed optimization approach comprising the orthogonal array-based ANFIS, Taguchi, and NCGA could be effectively and efficiently utilized to save experimental costs as well as human efforts, produce optimal predictive models, and select optimum outcomes. The observed findings contributed significant data to determine optimal FSW parameters and enhance welding responses. },
issn = {0039-2480}, pages = {424-438}, doi = {10.5545/sv-jme.2022.111},
url = {https://www.sv-jme.eu/sl/article/optimization-of-friction-stir-welding-operation-using-optimal-taguchi-based-anfis-and-genetic-algorithm/}
}
Van, A.,Nguyen, T. 2022 May 68. Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 68:6
%A Van, An-Le %A Nguyen, Trung-Thanh %D 2022 %T Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm %B 2022 %9 Friction stir welding; Energy efficiency; Jointing efficiency; Micro-hardness; NCGA; %! Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm %K Friction stir welding; Energy efficiency; Jointing efficiency; Micro-hardness; NCGA; %X The friction stir welding (FSW) process is an effective approach to produce joints having superior quality. Unfortunately, most published investigations primarily addressed optimizing process parameters to boost product quality. In the current work, the FSW operation of the aluminum alloy has been considered and optimized to decrease the specific welding energy (SWE) and enhance the jointing efficiency (JE) as well as micro-hardness at the welded zone (MH). The parameter inputs are the rotational speed (S), welding speed (f), depth of penetration (D), and tool title angle (T). The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models were utilized to propose the welding responses in terms of the FSW parameters, while the Taguchi method was applied to optimize the ANFIS operating parameters. The neighborhood cultivation genetic algorithm (NCGA) was employed to determine the best solution. The obtained results indicated that the optimal values of the S, f, D, and T are 560 RPM, 90 mm/min, 0.9 mm, and 2 deg, respectively. The SWE is decreased by 17.0%, while the JE and MH are improved by 2.3% and 6.4%, respectively at the optimal solution. The optimal ANFIS models for the welding responses were adequate and reliably employed to forecast the response values. The proposed optimization approach comprising the orthogonal array-based ANFIS, Taguchi, and NCGA could be effectively and efficiently utilized to save experimental costs as well as human efforts, produce optimal predictive models, and select optimum outcomes. The observed findings contributed significant data to determine optimal FSW parameters and enhance welding responses. %U https://www.sv-jme.eu/sl/article/optimization-of-friction-stir-welding-operation-using-optimal-taguchi-based-anfis-and-genetic-algorithm/ %0 Journal Article %R 10.5545/sv-jme.2022.111 %& 424 %P 15 %J Strojniški vestnik - Journal of Mechanical Engineering %V 68 %N 6 %@ 0039-2480 %8 2022-05-12 %7 2022-05-12
Van, An-Le, & Trung-Thanh Nguyen. "Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm." Strojniški vestnik - Journal of Mechanical Engineering [Online], 68.6 (2022): 424-438. Web. 20 Dec. 2024
TY - JOUR AU - Van, An-Le AU - Nguyen, Trung-Thanh PY - 2022 TI - Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2022.111 KW - Friction stir welding; Energy efficiency; Jointing efficiency; Micro-hardness; NCGA; N2 - The friction stir welding (FSW) process is an effective approach to produce joints having superior quality. Unfortunately, most published investigations primarily addressed optimizing process parameters to boost product quality. In the current work, the FSW operation of the aluminum alloy has been considered and optimized to decrease the specific welding energy (SWE) and enhance the jointing efficiency (JE) as well as micro-hardness at the welded zone (MH). The parameter inputs are the rotational speed (S), welding speed (f), depth of penetration (D), and tool title angle (T). The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models were utilized to propose the welding responses in terms of the FSW parameters, while the Taguchi method was applied to optimize the ANFIS operating parameters. The neighborhood cultivation genetic algorithm (NCGA) was employed to determine the best solution. The obtained results indicated that the optimal values of the S, f, D, and T are 560 RPM, 90 mm/min, 0.9 mm, and 2 deg, respectively. The SWE is decreased by 17.0%, while the JE and MH are improved by 2.3% and 6.4%, respectively at the optimal solution. The optimal ANFIS models for the welding responses were adequate and reliably employed to forecast the response values. The proposed optimization approach comprising the orthogonal array-based ANFIS, Taguchi, and NCGA could be effectively and efficiently utilized to save experimental costs as well as human efforts, produce optimal predictive models, and select optimum outcomes. The observed findings contributed significant data to determine optimal FSW parameters and enhance welding responses. UR - https://www.sv-jme.eu/sl/article/optimization-of-friction-stir-welding-operation-using-optimal-taguchi-based-anfis-and-genetic-algorithm/
@article{{sv-jme}{sv-jme.2022.111},
author = {Van, A., Nguyen, T.},
title = {Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {68},
number = {6},
year = {2022},
doi = {10.5545/sv-jme.2022.111},
url = {https://www.sv-jme.eu/sl/article/optimization-of-friction-stir-welding-operation-using-optimal-taguchi-based-anfis-and-genetic-algorithm/}
}
TY - JOUR AU - Van, An-Le AU - Nguyen, Trung-Thanh PY - 2022/05/12 TI - Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 68, No 6 (2022): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2022.111 KW - Friction stir welding, Energy efficiency, Jointing efficiency, Micro-hardness, NCGA, N2 - The friction stir welding (FSW) process is an effective approach to produce joints having superior quality. Unfortunately, most published investigations primarily addressed optimizing process parameters to boost product quality. In the current work, the FSW operation of the aluminum alloy has been considered and optimized to decrease the specific welding energy (SWE) and enhance the jointing efficiency (JE) as well as micro-hardness at the welded zone (MH). The parameter inputs are the rotational speed (S), welding speed (f), depth of penetration (D), and tool title angle (T). The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models were utilized to propose the welding responses in terms of the FSW parameters, while the Taguchi method was applied to optimize the ANFIS operating parameters. The neighborhood cultivation genetic algorithm (NCGA) was employed to determine the best solution. The obtained results indicated that the optimal values of the S, f, D, and T are 560 RPM, 90 mm/min, 0.9 mm, and 2 deg, respectively. The SWE is decreased by 17.0%, while the JE and MH are improved by 2.3% and 6.4%, respectively at the optimal solution. The optimal ANFIS models for the welding responses were adequate and reliably employed to forecast the response values. The proposed optimization approach comprising the orthogonal array-based ANFIS, Taguchi, and NCGA could be effectively and efficiently utilized to save experimental costs as well as human efforts, produce optimal predictive models, and select optimum outcomes. The observed findings contributed significant data to determine optimal FSW parameters and enhance welding responses. UR - https://www.sv-jme.eu/sl/article/optimization-of-friction-stir-welding-operation-using-optimal-taguchi-based-anfis-and-genetic-algorithm/
Van, An-Le, AND Nguyen, Trung-Thanh. "Optimization of Friction Stir Welding Operation using Optimal Taguchi-based ANFIS and Genetic Algorithm" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 68 Number 6 (12 May 2022)
Strojniški vestnik - Journal of Mechanical Engineering 68(2022)6, 424-438
© The Authors 2022. CC BY 4.0 Int.
The friction stir welding (FSW) process is an effective approach to produce joints having superior quality. Unfortunately, most published investigations primarily addressed optimizing process parameters to boost product quality. In the current work, the FSW operation of the aluminum alloy has been considered and optimized to decrease the specific welding energy (SWE) and enhance the jointing efficiency (JE) as well as micro-hardness at the welded zone (MH). The parameter inputs are the rotational speed (S), welding speed (f), depth of penetration (D), and tool title angle (T). The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models were utilized to propose the welding responses in terms of the FSW parameters, while the Taguchi method was applied to optimize the ANFIS operating parameters. The neighborhood cultivation genetic algorithm (NCGA) was employed to determine the best solution. The obtained results indicated that the optimal values of the S, f, D, and T are 560 RPM, 90 mm/min, 0.9 mm, and 2 deg, respectively. The SWE is decreased by 17.0%, while the JE and MH are improved by 2.3% and 6.4%, respectively at the optimal solution. The optimal ANFIS models for the welding responses were adequate and reliably employed to forecast the response values. The proposed optimization approach comprising the orthogonal array-based ANFIS, Taguchi, and NCGA could be effectively and efficiently utilized to save experimental costs as well as human efforts, produce optimal predictive models, and select optimum outcomes. The observed findings contributed significant data to determine optimal FSW parameters and enhance welding responses.