VAN, An-Le ;NGUYEN, Thai-Chung ;BUI, Huu-Toan ;NGUYEN, Trung-Thanh ;DANG, Xuan-Ba . Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality. Articles in Press, [S.l.], v. 0, n.0, p. , april 2024. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/>. Date accessed: 08 may. 2024. doi:http://dx.doi.org/.
Van, A., Nguyen, T., Bui, H., Nguyen, T., & Dang, X. (0). Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality. Articles in Press, 0(0), . doi:http://dx.doi.org/
@article{.,
author = {An-Le Van and Thai-Chung Nguyen and Huu-Toan Bui and Trung-Thanh Nguyen and Xuan-Ba Dang},
title = {Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality},
journal = {Articles in Press},
volume = {0},
number = {0},
year = {0},
keywords = {GTAW; Heat input; Ultimate tensile strength; Micro-hardness; radial basis function network; },
abstract = {Gas tungsten arc welding (GTAW) process is extensively applied to produce high-quality joints. Unfortunately, energy efficiency has not been considered in related publications. In this work, the current (I), voltage (V), flow rate (F), and arc gap (G) of the GTAW process of the Ti40A are optimized 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 the weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA II) were applied to compute the weights and produce feasible solutions. The evaluation by an area-based method of ranking (EAMR) approach was used to select the best optimality. The findings presented that the optimizing data of the 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 the prediction purpose. The ANSGA-II provides better optimality, as compared to the convetional NSGA-II. The method comprising the RBFN, ANSGAII, and EAMR was a prominent solution to deal with complicated optimization issues. The outcomes can be applied to improve energy efficiency and mechanical characteristics of the Ti40A joint. },
issn = {0039-2480}, pages = {}, doi = {},
url = {https://www.sv-jme.eu/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/}
}
Van, A.,Nguyen, T.,Bui, H.,Nguyen, T.,Dang, X. 0 April 0. Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality. Articles in Press. [Online] 0:0
%A Van, An-Le %A Nguyen, Thai-Chung %A Bui, Huu-Toan %A Nguyen, Trung-Thanh %A Dang, Xuan-Ba %D 0 %T Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality %B 0 %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 Gas tungsten arc welding (GTAW) process is extensively applied to produce high-quality joints. Unfortunately, energy efficiency has not been considered in related publications. In this work, the current (I), voltage (V), flow rate (F), and arc gap (G) of the GTAW process of the Ti40A are optimized 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 the weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA II) were applied to compute the weights and produce feasible solutions. The evaluation by an area-based method of ranking (EAMR) approach was used to select the best optimality. The findings presented that the optimizing data of the 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 the prediction purpose. The ANSGA-II provides better optimality, as compared to the convetional NSGA-II. The method comprising the RBFN, ANSGAII, and EAMR was a prominent solution to deal with complicated optimization issues. The outcomes can be applied to improve energy efficiency and mechanical characteristics of the Ti40A joint. %U https://www.sv-jme.eu/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/ %0 Journal Article %R %& %P 1 %J Articles in Press %V 0 %N 0 %@ 0039-2480 %8 2024-04-02 %7 2024-04-02
Van, An-Le, Thai-Chung Nguyen, Huu-Toan Bui, Trung-Thanh Nguyen, & Xuan-Ba Dang. "Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality." Articles in Press [Online], 0.0 (0): . Web. 08 May. 2024
TY - JOUR AU - Van, An-Le AU - Nguyen, Thai-Chung AU - Bui, Huu-Toan AU - Nguyen, Trung-Thanh AU - Dang, Xuan-Ba PY - 0 TI - Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality JF - Articles in Press DO - KW - GTAW; Heat input; Ultimate tensile strength; Micro-hardness; radial basis function network; N2 - Gas tungsten arc welding (GTAW) process is extensively applied to produce high-quality joints. Unfortunately, energy efficiency has not been considered in related publications. In this work, the current (I), voltage (V), flow rate (F), and arc gap (G) of the GTAW process of the Ti40A are optimized 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 the weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA II) were applied to compute the weights and produce feasible solutions. The evaluation by an area-based method of ranking (EAMR) approach was used to select the best optimality. The findings presented that the optimizing data of the 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 the prediction purpose. The ANSGA-II provides better optimality, as compared to the convetional NSGA-II. The method comprising the RBFN, ANSGAII, and EAMR was a prominent solution to deal with complicated optimization issues. The outcomes can be applied to improve energy efficiency and mechanical characteristics of the Ti40A joint. UR - https://www.sv-jme.eu/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/
@article{{}{.},
author = {Van, A., Nguyen, T., Bui, H., Nguyen, T., Dang, X.},
title = {Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality},
journal = {Articles in Press},
volume = {0},
number = {0},
year = {0},
doi = {},
url = {https://www.sv-jme.eu/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 - Nguyen, Trung-Thanh AU - Dang, Xuan-Ba PY - 2024/04/02 TI - Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality JF - Articles in Press; Vol 0, No 0 (0): Articles in Press DO - KW - GTAW, Heat input, Ultimate tensile strength, Micro-hardness, radial basis function network, N2 - Gas tungsten arc welding (GTAW) process is extensively applied to produce high-quality joints. Unfortunately, energy efficiency has not been considered in related publications. In this work, the current (I), voltage (V), flow rate (F), and arc gap (G) of the GTAW process of the Ti40A are optimized 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 the weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA II) were applied to compute the weights and produce feasible solutions. The evaluation by an area-based method of ranking (EAMR) approach was used to select the best optimality. The findings presented that the optimizing data of the 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 the prediction purpose. The ANSGA-II provides better optimality, as compared to the convetional NSGA-II. The method comprising the RBFN, ANSGAII, and EAMR was a prominent solution to deal with complicated optimization issues. The outcomes can be applied to improve energy efficiency and mechanical characteristics of the Ti40A joint. UR - https://www.sv-jme.eu/article/multi-response-optimization-of-gtaw-process-parameters-in-terms-of-energy-efficiency-and-quality/
Van, An-Le, Nguyen, Thai-Chung, Bui, Huu-Toan , Nguyen, Trung-Thanh, AND Dang, Xuan-Ba. "Multi-response Optimization of GTAW Process Parameters in terms of Energy Efficiency and Quality" Articles in Press [Online], Volume 0 Number 0 (02 April 2024)
Articles in Press
Gas tungsten arc welding (GTAW) process is extensively applied to produce high-quality joints. Unfortunately, energy efficiency has not been considered in related publications. In this work, the current (I), voltage (V), flow rate (F), and arc gap (G) of the GTAW process of the Ti40A are optimized 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 the weighted principal component analysis (WPCA) and an adaptive non-dominated sorting genetic algorithm II (ANSGA II) were applied to compute the weights and produce feasible solutions. The evaluation by an area-based method of ranking (EAMR) approach was used to select the best optimality. The findings presented that the optimizing data of the 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 the prediction purpose. The ANSGA-II provides better optimality, as compared to the convetional NSGA-II. The method comprising the RBFN, ANSGAII, and EAMR was a prominent solution to deal with complicated optimization issues. The outcomes can be applied to improve energy efficiency and mechanical characteristics of the Ti40A joint.