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. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 71, n.3-4, p. 127-135, march 2025. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/>. Date accessed: 29 may. 2025. doi:http://dx.doi.org/10.5545/sv-jme.2024.1248.
Nguyen, T., Le, M., Nguyen, T., Nguyen, T., Dang, X., & Van, A. (2025). Comparison and Optimization of Burnishing Parameters in Various Machining Conditions. Strojniški vestnik - Journal of Mechanical Engineering, 71(3-4), 127-135. doi:http://dx.doi.org/10.5545/sv-jme.2024.1248
@article{sv-jmesv-jme.2024.1248,
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 = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {71},
number = {3-4},
year = {2025},
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) methods were ultilized to propose the response models. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were applied to calculate the weights, generate feasible solutions, and select the best optimal data. The result indicated 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 were achieved 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 = {127-135}, doi = {10.5545/sv-jme.2024.1248},
url = {https://www.sv-jme.eu/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/}
}
Nguyen, T.,Le, M.,Nguyen, T.,Nguyen, T.,Dang, X.,Van, A. 2025 March 71. Comparison and Optimization of Burnishing Parameters in Various Machining Conditions. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 71:3-4
%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 2025 %T Comparison and Optimization of Burnishing Parameters in Various Machining Conditions %B 2025 %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) methods were ultilized to propose the response models. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were applied to calculate the weights, generate feasible solutions, and select the best optimal data. The result indicated 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 were achieved 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/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/ %0 Journal Article %R 10.5545/sv-jme.2024.1248 %& 127 %P 9 %J Strojniški vestnik - Journal of Mechanical Engineering %V 71 %N 3-4 %@ 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." Strojniški vestnik - Journal of Mechanical Engineering [Online], 71.3-4 (2025): 127-135. Web. 29 May. 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 - 2025 TI - Comparison and Optimization of Burnishing Parameters in Various Machining Conditions JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2024.1248 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) methods were ultilized to propose the response models. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were applied to calculate the weights, generate feasible solutions, and select the best optimal data. The result indicated 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 were achieved 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/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/
@article{{sv-jme}{sv-jme.2024.1248},
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 = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {71},
number = {3-4},
year = {2025},
doi = {10.5545/sv-jme.2024.1248},
url = {https://www.sv-jme.eu/article/comparison-and-optimization-of-burnishing-parameters-in-various-machining-conditions/}
}
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 - Strojniški vestnik - Journal of Mechanical Engineering; Vol 71, No 3-4 (2025): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2024.1248 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) methods were ultilized to propose the response models. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were applied to calculate the weights, generate feasible solutions, and select the best optimal data. The result indicated 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 were achieved 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/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" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 71 Number 3-4 (21 March 2025)
Strojniški vestnik - Journal of Mechanical Engineering 71(2025)3-4, 127-135
© The Authors 2025. CC BY 4.0 Int.
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) methods were ultilized to propose the response models. The CRITIC, non-dominated sorting genetic algorithm-II (NSGA-II), and MABAC were applied to calculate the weights, generate feasible solutions, and select the best optimal data. The result indicated 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 were achieved using the cryogenic diamond burnishing process. The Kriging-NSGA-II could be utilized to show non-linear data and produce the best results.