NGUYEN, Trung-Thanh ;LE, Minh-Thai ;NGUYEN, Thai-Chung ;NGUYEN, Truong-An ;DANG, Xuan-Ba ;VAN, An-Le . Comparison and Optimization of Burnishing Parameters in Various Machining Conditions. Articles in Press, [S.l.], v. 0, n.0, p. , march 2025. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/>. Date accessed: 19 apr. 2025. doi:http://dx.doi.org/.
Nguyen, T., Le, M., Nguyen, T., Nguyen, T., Dang, X., & Van, A. (0). Comparison and Optimization of Burnishing Parameters in Various Machining Conditions. Articles in Press, 0(0), . doi:http://dx.doi.org/
@article{.,
author = {Trung-Thanh Nguyen and Minh-Thai Le and Thai-Chung Nguyen and Truong-An Nguyen and Xuan-Ba Dang and An-Le Van},
title = {Comparison and Optimization of Burnishing Parameters in Various Machining Conditions},
journal = {Articles in Press},
volume = {0},
number = {0},
year = {0},
keywords = {Cryogenic diamond burnishing; Energy consumption; Maximum roughness; Circularity; Kriging model; },
abstract = {This study proposes a cryogenic diamond burnishing process and optimizes cooling parameters, including the distance to nozzle (N), nozzle diameter (D), and CO2 flow rate (Q) to minimize the maximum roughness (R), energy consumption (E), and circularity (C). The Kriging and adaptive-network-based fuzzy inference system (ANFIS) approaches are used to develop the predictive models of the responses. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were utilized to compute the weights, generate feasible solutions, and determine the best optimality. The result presented that the optimal N, D, and Q were 15 mm, 9 mm, and 8 L/min, respectively. The reductions in the roughness, energy, and circularity were 15.5%, 2.0%, and 38.6%, respectively. The roughness and energy models were primarily affected by Q, D, and N, respectively, while circularity model was influenced by the N, D, and Q, respectively. The proposed process could be used to machine different holes with minimizing environmental impacts. Lower roughness and circularity could be obtained using the cryogenic diamond burnishing process. The Kriging-NSGA-II could be utilized to show non-linear data and produce the best results. },
issn = {0039-2480}, pages = {}, doi = {},
url = {https://www.sv-jme.eu/sl/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/}
}
Nguyen, T.,Le, M.,Nguyen, T.,Nguyen, T.,Dang, X.,Van, A. 0 March 0. Comparison and Optimization of Burnishing Parameters in Various Machining Conditions. Articles in Press. [Online] 0:0
%A Nguyen, Trung-Thanh %A Le, Minh-Thai %A Nguyen, Thai-Chung %A Nguyen, Truong-An %A Dang, Xuan-Ba %A Van, An-Le %D 0 %T Comparison and Optimization of Burnishing Parameters in Various Machining Conditions %B 0 %9 Cryogenic diamond burnishing; Energy consumption; Maximum roughness; Circularity; Kriging model; %! Comparison and Optimization of Burnishing Parameters in Various Machining Conditions %K Cryogenic diamond burnishing; Energy consumption; Maximum roughness; Circularity; Kriging model; %X This study proposes a cryogenic diamond burnishing process and optimizes cooling parameters, including the distance to nozzle (N), nozzle diameter (D), and CO2 flow rate (Q) to minimize the maximum roughness (R), energy consumption (E), and circularity (C). The Kriging and adaptive-network-based fuzzy inference system (ANFIS) approaches are used to develop the predictive models of the responses. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were utilized to compute the weights, generate feasible solutions, and determine the best optimality. The result presented that the optimal N, D, and Q were 15 mm, 9 mm, and 8 L/min, respectively. The reductions in the roughness, energy, and circularity were 15.5%, 2.0%, and 38.6%, respectively. The roughness and energy models were primarily affected by Q, D, and N, respectively, while circularity model was influenced by the N, D, and Q, respectively. The proposed process could be used to machine different holes with minimizing environmental impacts. Lower roughness and circularity could be obtained using the cryogenic diamond burnishing process. The Kriging-NSGA-II could be utilized to show non-linear data and produce the best results. %U https://www.sv-jme.eu/sl/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/ %0 Journal Article %R %& %P 1 %J Articles in Press %V 0 %N 0 %@ 0039-2480 %8 2025-03-21 %7 2025-03-21
Nguyen, Trung-Thanh, Minh-Thai Le, Thai-Chung Nguyen, Truong-An Nguyen, Xuan-Ba Dang, & An-Le Van. "Comparison and Optimization of Burnishing Parameters in Various Machining Conditions." Articles in Press [Online], 0.0 (0): . Web. 19 Apr. 2025
TY - JOUR AU - Nguyen, Trung-Thanh AU - Le, Minh-Thai AU - Nguyen, Thai-Chung AU - Nguyen, Truong-An AU - Dang, Xuan-Ba AU - Van, An-Le PY - 0 TI - Comparison and Optimization of Burnishing Parameters in Various Machining Conditions JF - Articles in Press DO - KW - Cryogenic diamond burnishing; Energy consumption; Maximum roughness; Circularity; Kriging model; N2 - This study proposes a cryogenic diamond burnishing process and optimizes cooling parameters, including the distance to nozzle (N), nozzle diameter (D), and CO2 flow rate (Q) to minimize the maximum roughness (R), energy consumption (E), and circularity (C). The Kriging and adaptive-network-based fuzzy inference system (ANFIS) approaches are used to develop the predictive models of the responses. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were utilized to compute the weights, generate feasible solutions, and determine the best optimality. The result presented that the optimal N, D, and Q were 15 mm, 9 mm, and 8 L/min, respectively. The reductions in the roughness, energy, and circularity were 15.5%, 2.0%, and 38.6%, respectively. The roughness and energy models were primarily affected by Q, D, and N, respectively, while circularity model was influenced by the N, D, and Q, respectively. The proposed process could be used to machine different holes with minimizing environmental impacts. Lower roughness and circularity could be obtained using the cryogenic diamond burnishing process. The Kriging-NSGA-II could be utilized to show non-linear data and produce the best results. UR - https://www.sv-jme.eu/sl/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/
@article{{}{.},
author = {Nguyen, T., Le, M., Nguyen, T., Nguyen, T., Dang, X., Van, A.},
title = {Comparison and Optimization of Burnishing Parameters in Various Machining Conditions},
journal = {Articles in Press},
volume = {0},
number = {0},
year = {0},
doi = {},
url = {https://www.sv-jme.eu/sl/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/}
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TY - JOUR AU - Nguyen, Trung-Thanh AU - Le, Minh-Thai AU - Nguyen, Thai-Chung AU - Nguyen, Truong-An AU - Dang, Xuan-Ba AU - Van, An-Le PY - 2025/03/21 TI - Comparison and Optimization of Burnishing Parameters in Various Machining Conditions JF - Articles in Press; Vol 0, No 0 (0): Articles in Press DO - KW - Cryogenic diamond burnishing, Energy consumption, Maximum roughness, Circularity, Kriging model, N2 - This study proposes a cryogenic diamond burnishing process and optimizes cooling parameters, including the distance to nozzle (N), nozzle diameter (D), and CO2 flow rate (Q) to minimize the maximum roughness (R), energy consumption (E), and circularity (C). The Kriging and adaptive-network-based fuzzy inference system (ANFIS) approaches are used to develop the predictive models of the responses. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were utilized to compute the weights, generate feasible solutions, and determine the best optimality. The result presented that the optimal N, D, and Q were 15 mm, 9 mm, and 8 L/min, respectively. The reductions in the roughness, energy, and circularity were 15.5%, 2.0%, and 38.6%, respectively. The roughness and energy models were primarily affected by Q, D, and N, respectively, while circularity model was influenced by the N, D, and Q, respectively. The proposed process could be used to machine different holes with minimizing environmental impacts. Lower roughness and circularity could be obtained using the cryogenic diamond burnishing process. The Kriging-NSGA-II could be utilized to show non-linear data and produce the best results. UR - https://www.sv-jme.eu/sl/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/
Nguyen, Trung-Thanh, Le, Minh-Thai, Nguyen, Thai-Chung, Nguyen, Truong-An, Dang, Xuan-Ba, AND Van, An-Le. "Comparison and Optimization of Burnishing Parameters in Various Machining Conditions" Articles in Press [Online], Volume 0 Number 0 (21 March 2025)
Articles in Press
This study proposes a cryogenic diamond burnishing process and optimizes cooling parameters, including the distance to nozzle (N), nozzle diameter (D), and CO2 flow rate (Q) to minimize the maximum roughness (R), energy consumption (E), and circularity (C). The Kriging and adaptive-network-based fuzzy inference system (ANFIS) approaches are used to develop the predictive models of the responses. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were utilized to compute the weights, generate feasible solutions, and determine the best optimality. The result presented that the optimal N, D, and Q were 15 mm, 9 mm, and 8 L/min, respectively. The reductions in the roughness, energy, and circularity were 15.5%, 2.0%, and 38.6%, respectively. The roughness and energy models were primarily affected by Q, D, and N, respectively, while circularity model was influenced by the N, D, and Q, respectively. The proposed process could be used to machine different holes with minimizing environmental impacts. Lower roughness and circularity could be obtained using the cryogenic diamond burnishing process. The Kriging-NSGA-II could be utilized to show non-linear data and produce the best results.