VAN, An-Le ;NGUYEN, Thai-Chung ;BUI, Huu-Toan ;DANG, Xuan-Ba ;NGUYEN, Trung-Thanh . Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 70, n.5-6, p. 259-269, april 2024. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2023.890.
Van, A., Nguyen, T., Bui, H., Dang, X., & Nguyen, T. (2024). Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality. Strojniški vestnik - Journal of Mechanical Engineering, 70(5-6), 259-269. doi:http://dx.doi.org/10.5545/sv-jme.2023.890
@article{sv-jmesv-jme.2023.890,
author = {An-Le Van and Thai-Chung Nguyen and Huu-Toan Bui and Xuan-Ba Dang and Trung-Thanh Nguyen},
title = {Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {70},
number = {5-6},
year = {2024},
keywords = {GTAW; Heat input; Ultimate tensile strength; Micro-hardness; radial basis function network; },
abstract = {This work optimizes the current (I), voltage (V), flow rate (F), and arc gap (G) of the gas tungsten arc welding (GTAW) of the Ti40A titanium alloy to decrease the heat input (HI) and improve the ultimate tensile strength (TS) and micro-hardness (MH). The radial basis function network (RBFN) was utilized to present performance measures, while weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA-II) were applied to estimate the weights and generate optimal points. The evaluation via an area-based method of ranking (EAMR) was employed to determine the best solution. The results indicated that the optimal I, V, F, and G are 89 A, 23 V, 20 L/min, and 1.5 mm, respectively. The improvements in the TS and MH were 1.2 % and 19.8 %, respectively, while the HI was saved by 18.4 %. The RBFN models provided acceptable accuracy for prediction purposes. The ANSGA-II provides better optimality than the conventional NSGA-II. The HI, TS, and MH of the practical GTAW Ti40A could be enhanced using optimality. The optimization method could be utilized to deal with optimization problems for not only other GTAW operations but also other machining processes.},
issn = {0039-2480}, pages = {259-269}, doi = {10.5545/sv-jme.2023.890},
url = {https://www.sv-jme.eu/sl/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/}
}
Van, A.,Nguyen, T.,Bui, H.,Dang, X.,Nguyen, T. 2024 April 70. Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 70:5-6
%A Van, An-Le %A Nguyen, Thai-Chung %A Bui, Huu-Toan %A Dang, Xuan-Ba %A Nguyen, Trung-Thanh %D 2024 %T Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality %B 2024 %9 GTAW; Heat input; Ultimate tensile strength; Micro-hardness; radial basis function network; %! Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality %K GTAW; Heat input; Ultimate tensile strength; Micro-hardness; radial basis function network; %X This work optimizes the current (I), voltage (V), flow rate (F), and arc gap (G) of the gas tungsten arc welding (GTAW) of the Ti40A titanium alloy to decrease the heat input (HI) and improve the ultimate tensile strength (TS) and micro-hardness (MH). The radial basis function network (RBFN) was utilized to present performance measures, while weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA-II) were applied to estimate the weights and generate optimal points. The evaluation via an area-based method of ranking (EAMR) was employed to determine the best solution. The results indicated that the optimal I, V, F, and G are 89 A, 23 V, 20 L/min, and 1.5 mm, respectively. The improvements in the TS and MH were 1.2 % and 19.8 %, respectively, while the HI was saved by 18.4 %. The RBFN models provided acceptable accuracy for prediction purposes. The ANSGA-II provides better optimality than the conventional NSGA-II. The HI, TS, and MH of the practical GTAW Ti40A could be enhanced using optimality. The optimization method could be utilized to deal with optimization problems for not only other GTAW operations but also other machining processes. %U https://www.sv-jme.eu/sl/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/ %0 Journal Article %R 10.5545/sv-jme.2023.890 %& 259 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 70 %N 5-6 %@ 0039-2480 %8 2024-04-02 %7 2024-04-02
Van, An-Le, Thai-Chung Nguyen, Huu-Toan Bui, Xuan-Ba Dang, & Trung-Thanh Nguyen. "Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality." Strojniški vestnik - Journal of Mechanical Engineering [Online], 70.5-6 (2024): 259-269. Web. 19 Nov. 2024
TY - JOUR AU - Van, An-Le AU - Nguyen, Thai-Chung AU - Bui, Huu-Toan AU - Dang, Xuan-Ba AU - Nguyen, Trung-Thanh PY - 2024 TI - Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2023.890 KW - GTAW; Heat input; Ultimate tensile strength; Micro-hardness; radial basis function network; N2 - This work optimizes the current (I), voltage (V), flow rate (F), and arc gap (G) of the gas tungsten arc welding (GTAW) of the Ti40A titanium alloy to decrease the heat input (HI) and improve the ultimate tensile strength (TS) and micro-hardness (MH). The radial basis function network (RBFN) was utilized to present performance measures, while weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA-II) were applied to estimate the weights and generate optimal points. The evaluation via an area-based method of ranking (EAMR) was employed to determine the best solution. The results indicated that the optimal I, V, F, and G are 89 A, 23 V, 20 L/min, and 1.5 mm, respectively. The improvements in the TS and MH were 1.2 % and 19.8 %, respectively, while the HI was saved by 18.4 %. The RBFN models provided acceptable accuracy for prediction purposes. The ANSGA-II provides better optimality than the conventional NSGA-II. The HI, TS, and MH of the practical GTAW Ti40A could be enhanced using optimality. The optimization method could be utilized to deal with optimization problems for not only other GTAW operations but also other machining processes. UR - https://www.sv-jme.eu/sl/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/
@article{{sv-jme}{sv-jme.2023.890},
author = {Van, A., Nguyen, T., Bui, H., Dang, X., Nguyen, T.},
title = {Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {70},
number = {5-6},
year = {2024},
doi = {10.5545/sv-jme.2023.890},
url = {https://www.sv-jme.eu/sl/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/}
}
TY - JOUR AU - Van, An-Le AU - Nguyen, Thai-Chung AU - Bui, Huu-Toan AU - Dang, Xuan-Ba AU - Nguyen, Trung-Thanh PY - 2024/04/02 TI - Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 70, No 5-6 (2024): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2023.890 KW - GTAW, Heat input, Ultimate tensile strength, Micro-hardness, radial basis function network, N2 - This work optimizes the current (I), voltage (V), flow rate (F), and arc gap (G) of the gas tungsten arc welding (GTAW) of the Ti40A titanium alloy to decrease the heat input (HI) and improve the ultimate tensile strength (TS) and micro-hardness (MH). The radial basis function network (RBFN) was utilized to present performance measures, while weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA-II) were applied to estimate the weights and generate optimal points. The evaluation via an area-based method of ranking (EAMR) was employed to determine the best solution. The results indicated that the optimal I, V, F, and G are 89 A, 23 V, 20 L/min, and 1.5 mm, respectively. The improvements in the TS and MH were 1.2 % and 19.8 %, respectively, while the HI was saved by 18.4 %. The RBFN models provided acceptable accuracy for prediction purposes. The ANSGA-II provides better optimality than the conventional NSGA-II. The HI, TS, and MH of the practical GTAW Ti40A could be enhanced using optimality. The optimization method could be utilized to deal with optimization problems for not only other GTAW operations but also other machining processes. UR - https://www.sv-jme.eu/sl/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/
Van, An-Le, Nguyen, Thai-Chung, Bui, Huu-Toan , Dang, Xuan-Ba, AND Nguyen, Trung-Thanh. "Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 70 Number 5-6 (02 April 2024)
Strojniški vestnik - Journal of Mechanical Engineering 70(2024)5-6, 259-269
© The Authors 2024. CC BY 4.0 Int.
This work optimizes the current (I), voltage (V), flow rate (F), and arc gap (G) of the gas tungsten arc welding (GTAW) of the Ti40A titanium alloy to decrease the heat input (HI) and improve the ultimate tensile strength (TS) and micro-hardness (MH). The radial basis function network (RBFN) was utilized to present performance measures, while weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA-II) were applied to estimate the weights and generate optimal points. The evaluation via an area-based method of ranking (EAMR) was employed to determine the best solution. The results indicated that the optimal I, V, F, and G are 89 A, 23 V, 20 L/min, and 1.5 mm, respectively. The improvements in the TS and MH were 1.2 % and 19.8 %, respectively, while the HI was saved by 18.4 %. The RBFN models provided acceptable accuracy for prediction purposes. The ANSGA-II provides better optimality than the conventional NSGA-II. The HI, TS, and MH of the practical GTAW Ti40A could be enhanced using optimality. The optimization method could be utilized to deal with optimization problems for not only other GTAW operations but also other machining processes.