LE, Minh-Thai ;LE VAN, An ;NGUYEN, Trung-Thanh . Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 69, n.3-4, p. 155-168, february 2023. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/impacts-of-burnishing-variables-on-the-quality-indicators-in-a-single-diamond-burnishing-operation/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2022.303.
Le, M., Le Van, A., & Nguyen, T. (2023). Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation. Strojniški vestnik - Journal of Mechanical Engineering, 69(3-4), 155-168. doi:http://dx.doi.org/10.5545/sv-jme.2022.303
@article{sv-jmesv-jme.2022.303,
author = {Minh-Thai Le and An Le Van and Trung-Thanh Nguyen},
title = {Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {69},
number = {3-4},
year = {2023},
keywords = {Single diamond burnishing; Average roughness; Vickers hardness; Bayesian regularization; NSGA-G; },
abstract = {Diamond burnishing is an effective solution to finish a surface. The purpose of the current work is to optimize parameter inputs, including the spindle speed (S), depth of penetration (D), feed rate (f), and diameter of tool-tip (DT) for improving the Vickers hardness (VH) and decreasing the average roughness (Ra) of a new diamond burnishing process. A set of burnishing experiments is executed under a new cooling lubrication system comprising the minimum quantity lubrication and double vortex tubes. The Bayesian regularized feed-forward neural network (BRFFNN) models of the performances are proposed in terms of the inputs. The criteria importance through the inter-criteria correlation (CRITIC) method and non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) are applied to compute the weights of responses and find optimality. The optimal outcomes of the S, D, f, and DT were 370 rpm, 0.10 mm, 0.04 mm/rev, and 8 mm, respectively. The improvements in the Ra and VH were 40.7 % and 7.6 %, respectively, as compared to the original parameters. An effective approach combining the BRFFNN, CRITIC, and NSGA-G can be widely utilized to deal with complicated optimization problems. The optimizing results can be employed to enhance the surface properties of the burnished surface.},
issn = {0039-2480}, pages = {155-168}, doi = {10.5545/sv-jme.2022.303},
url = {https://www.sv-jme.eu/sl/article/impacts-of-burnishing-variables-on-the-quality-indicators-in-a-single-diamond-burnishing-operation/}
}
Le, M.,Le Van, A.,Nguyen, T. 2023 February 69. Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 69:3-4
%A Le, Minh-Thai %A Le Van, An %A Nguyen, Trung-Thanh %D 2023 %T Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation %B 2023 %9 Single diamond burnishing; Average roughness; Vickers hardness; Bayesian regularization; NSGA-G; %! Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation %K Single diamond burnishing; Average roughness; Vickers hardness; Bayesian regularization; NSGA-G; %X Diamond burnishing is an effective solution to finish a surface. The purpose of the current work is to optimize parameter inputs, including the spindle speed (S), depth of penetration (D), feed rate (f), and diameter of tool-tip (DT) for improving the Vickers hardness (VH) and decreasing the average roughness (Ra) of a new diamond burnishing process. A set of burnishing experiments is executed under a new cooling lubrication system comprising the minimum quantity lubrication and double vortex tubes. The Bayesian regularized feed-forward neural network (BRFFNN) models of the performances are proposed in terms of the inputs. The criteria importance through the inter-criteria correlation (CRITIC) method and non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) are applied to compute the weights of responses and find optimality. The optimal outcomes of the S, D, f, and DT were 370 rpm, 0.10 mm, 0.04 mm/rev, and 8 mm, respectively. The improvements in the Ra and VH were 40.7 % and 7.6 %, respectively, as compared to the original parameters. An effective approach combining the BRFFNN, CRITIC, and NSGA-G can be widely utilized to deal with complicated optimization problems. The optimizing results can be employed to enhance the surface properties of the burnished surface. %U https://www.sv-jme.eu/sl/article/impacts-of-burnishing-variables-on-the-quality-indicators-in-a-single-diamond-burnishing-operation/ %0 Journal Article %R 10.5545/sv-jme.2022.303 %& 155 %P 14 %J Strojniški vestnik - Journal of Mechanical Engineering %V 69 %N 3-4 %@ 0039-2480 %8 2023-02-22 %7 2023-02-22
Le, Minh-Thai, An Le Van, & Trung-Thanh Nguyen. "Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation." Strojniški vestnik - Journal of Mechanical Engineering [Online], 69.3-4 (2023): 155-168. Web. 20 Dec. 2024
TY - JOUR AU - Le, Minh-Thai AU - Le Van, An AU - Nguyen, Trung-Thanh PY - 2023 TI - Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2022.303 KW - Single diamond burnishing; Average roughness; Vickers hardness; Bayesian regularization; NSGA-G; N2 - Diamond burnishing is an effective solution to finish a surface. The purpose of the current work is to optimize parameter inputs, including the spindle speed (S), depth of penetration (D), feed rate (f), and diameter of tool-tip (DT) for improving the Vickers hardness (VH) and decreasing the average roughness (Ra) of a new diamond burnishing process. A set of burnishing experiments is executed under a new cooling lubrication system comprising the minimum quantity lubrication and double vortex tubes. The Bayesian regularized feed-forward neural network (BRFFNN) models of the performances are proposed in terms of the inputs. The criteria importance through the inter-criteria correlation (CRITIC) method and non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) are applied to compute the weights of responses and find optimality. The optimal outcomes of the S, D, f, and DT were 370 rpm, 0.10 mm, 0.04 mm/rev, and 8 mm, respectively. The improvements in the Ra and VH were 40.7 % and 7.6 %, respectively, as compared to the original parameters. An effective approach combining the BRFFNN, CRITIC, and NSGA-G can be widely utilized to deal with complicated optimization problems. The optimizing results can be employed to enhance the surface properties of the burnished surface. UR - https://www.sv-jme.eu/sl/article/impacts-of-burnishing-variables-on-the-quality-indicators-in-a-single-diamond-burnishing-operation/
@article{{sv-jme}{sv-jme.2022.303},
author = {Le, M., Le Van, A., Nguyen, T.},
title = {Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {69},
number = {3-4},
year = {2023},
doi = {10.5545/sv-jme.2022.303},
url = {https://www.sv-jme.eu/sl/article/impacts-of-burnishing-variables-on-the-quality-indicators-in-a-single-diamond-burnishing-operation/}
}
TY - JOUR AU - Le, Minh-Thai AU - Le Van, An AU - Nguyen, Trung-Thanh PY - 2023/02/22 TI - Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 69, No 3-4 (2023): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2022.303 KW - Single diamond burnishing, Average roughness, Vickers hardness, Bayesian regularization, NSGA-G, N2 - Diamond burnishing is an effective solution to finish a surface. The purpose of the current work is to optimize parameter inputs, including the spindle speed (S), depth of penetration (D), feed rate (f), and diameter of tool-tip (DT) for improving the Vickers hardness (VH) and decreasing the average roughness (Ra) of a new diamond burnishing process. A set of burnishing experiments is executed under a new cooling lubrication system comprising the minimum quantity lubrication and double vortex tubes. The Bayesian regularized feed-forward neural network (BRFFNN) models of the performances are proposed in terms of the inputs. The criteria importance through the inter-criteria correlation (CRITIC) method and non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) are applied to compute the weights of responses and find optimality. The optimal outcomes of the S, D, f, and DT were 370 rpm, 0.10 mm, 0.04 mm/rev, and 8 mm, respectively. The improvements in the Ra and VH were 40.7 % and 7.6 %, respectively, as compared to the original parameters. An effective approach combining the BRFFNN, CRITIC, and NSGA-G can be widely utilized to deal with complicated optimization problems. The optimizing results can be employed to enhance the surface properties of the burnished surface. UR - https://www.sv-jme.eu/sl/article/impacts-of-burnishing-variables-on-the-quality-indicators-in-a-single-diamond-burnishing-operation/
Le, Minh-Thai, Le Van, An, AND Nguyen, Trung-Thanh. "Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 69 Number 3-4 (22 February 2023)
Strojniški vestnik - Journal of Mechanical Engineering 69(2023)3-4, 155-168
© The Authors 2023. CC BY 4.0 Int.
Diamond burnishing is an effective solution to finish a surface. The purpose of the current work is to optimize parameter inputs, including the spindle speed (S), depth of penetration (D), feed rate (f), and diameter of tool-tip (DT) for improving the Vickers hardness (VH) and decreasing the average roughness (Ra) of a new diamond burnishing process. A set of burnishing experiments is executed under a new cooling lubrication system comprising the minimum quantity lubrication and double vortex tubes. The Bayesian regularized feed-forward neural network (BRFFNN) models of the performances are proposed in terms of the inputs. The criteria importance through the inter-criteria correlation (CRITIC) method and non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) are applied to compute the weights of responses and find optimality. The optimal outcomes of the S, D, f, and DT were 370 rpm, 0.10 mm, 0.04 mm/rev, and 8 mm, respectively. The improvements in the Ra and VH were 40.7 % and 7.6 %, respectively, as compared to the original parameters. An effective approach combining the BRFFNN, CRITIC, and NSGA-G can be widely utilized to deal with complicated optimization problems. The optimizing results can be employed to enhance the surface properties of the burnished surface.