NGUYEN, Trung-Thanh ;LE, Thai-Minh . Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 67, n.4, p. 167-179, may 2021. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/optimization-of-the-internal-burnishing-operation-for-energy-efficiency-and-surface-properties/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2021.7106.
Nguyen, T., & Le, T. (2021). Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties. Strojniški vestnik - Journal of Mechanical Engineering, 67(4), 167-179. doi:http://dx.doi.org/10.5545/sv-jme.2021.7106
@article{sv-jmesv-jme.2021.7106,
author = {Trung-Thanh Nguyen and Thai-Minh Le},
title = {Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {4},
year = {2021},
keywords = {roller burnishing; energy reduction; roughness; Rockwell hardness; optimization},
abstract = {Improving the surface quality after burnishing operation has been the subject of various published investigations. Unfortunately, the trade-off analysis between the energy consumption and surface characteristics of the internal burnishing has been not addressed due to the expensive cost and huge efforts required. The objective of the present work is to optimize burnishing factors, including the spindle speed, burnishing feed, depth of penetration, and the number of rollers for minimizing the energy consumed in the burnishing time, as well as surface roughness and maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used to develop burnishing objectives in terms of machining parameters. The optimization outcomes were selected using an evolution algorithm, specifically the non-dominated sorting particle swarm optimization (NSPSO). The results of the proposed ANFIS models are significant and can be employed to predict response values in industrial applications. The optimization technique comprising the ANFIS and NOPSO is a powerful approach to model burnishing performances and select optimal parameters as compared to the trial and error method as well as operator experience. Finally, the optimal solution can help to achieve the improvements in the energy consumed by 16.3 %, surface roughness by 24.3 %, and Rockwell hardness by 4.0 %, as compared to the common values .},
issn = {0039-2480}, pages = {167-179}, doi = {10.5545/sv-jme.2021.7106},
url = {https://www.sv-jme.eu/article/optimization-of-the-internal-burnishing-operation-for-energy-efficiency-and-surface-properties/}
}
Nguyen, T.,Le, T. 2021 May 67. Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 67:4
%A Nguyen, Trung-Thanh %A Le, Thai-Minh %D 2021 %T Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties %B 2021 %9 roller burnishing; energy reduction; roughness; Rockwell hardness; optimization %! Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties %K roller burnishing; energy reduction; roughness; Rockwell hardness; optimization %X Improving the surface quality after burnishing operation has been the subject of various published investigations. Unfortunately, the trade-off analysis between the energy consumption and surface characteristics of the internal burnishing has been not addressed due to the expensive cost and huge efforts required. The objective of the present work is to optimize burnishing factors, including the spindle speed, burnishing feed, depth of penetration, and the number of rollers for minimizing the energy consumed in the burnishing time, as well as surface roughness and maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used to develop burnishing objectives in terms of machining parameters. The optimization outcomes were selected using an evolution algorithm, specifically the non-dominated sorting particle swarm optimization (NSPSO). The results of the proposed ANFIS models are significant and can be employed to predict response values in industrial applications. The optimization technique comprising the ANFIS and NOPSO is a powerful approach to model burnishing performances and select optimal parameters as compared to the trial and error method as well as operator experience. Finally, the optimal solution can help to achieve the improvements in the energy consumed by 16.3 %, surface roughness by 24.3 %, and Rockwell hardness by 4.0 %, as compared to the common values . %U https://www.sv-jme.eu/article/optimization-of-the-internal-burnishing-operation-for-energy-efficiency-and-surface-properties/ %0 Journal Article %R 10.5545/sv-jme.2021.7106 %& 167 %P 13 %J Strojniški vestnik - Journal of Mechanical Engineering %V 67 %N 4 %@ 0039-2480 %8 2021-05-05 %7 2021-05-05
Nguyen, Trung-Thanh, & Thai-Minh Le. "Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties." Strojniški vestnik - Journal of Mechanical Engineering [Online], 67.4 (2021): 167-179. Web. 19 Nov. 2024
TY - JOUR AU - Nguyen, Trung-Thanh AU - Le, Thai-Minh PY - 2021 TI - Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7106 KW - roller burnishing; energy reduction; roughness; Rockwell hardness; optimization N2 - Improving the surface quality after burnishing operation has been the subject of various published investigations. Unfortunately, the trade-off analysis between the energy consumption and surface characteristics of the internal burnishing has been not addressed due to the expensive cost and huge efforts required. The objective of the present work is to optimize burnishing factors, including the spindle speed, burnishing feed, depth of penetration, and the number of rollers for minimizing the energy consumed in the burnishing time, as well as surface roughness and maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used to develop burnishing objectives in terms of machining parameters. The optimization outcomes were selected using an evolution algorithm, specifically the non-dominated sorting particle swarm optimization (NSPSO). The results of the proposed ANFIS models are significant and can be employed to predict response values in industrial applications. The optimization technique comprising the ANFIS and NOPSO is a powerful approach to model burnishing performances and select optimal parameters as compared to the trial and error method as well as operator experience. Finally, the optimal solution can help to achieve the improvements in the energy consumed by 16.3 %, surface roughness by 24.3 %, and Rockwell hardness by 4.0 %, as compared to the common values . UR - https://www.sv-jme.eu/article/optimization-of-the-internal-burnishing-operation-for-energy-efficiency-and-surface-properties/
@article{{sv-jme}{sv-jme.2021.7106},
author = {Nguyen, T., Le, T.},
title = {Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {4},
year = {2021},
doi = {10.5545/sv-jme.2021.7106},
url = {https://www.sv-jme.eu/article/optimization-of-the-internal-burnishing-operation-for-energy-efficiency-and-surface-properties/}
}
TY - JOUR AU - Nguyen, Trung-Thanh AU - Le, Thai-Minh PY - 2021/05/05 TI - Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 67, No 4 (2021): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7106 KW - roller burnishing, energy reduction, roughness, Rockwell hardness, optimization N2 - Improving the surface quality after burnishing operation has been the subject of various published investigations. Unfortunately, the trade-off analysis between the energy consumption and surface characteristics of the internal burnishing has been not addressed due to the expensive cost and huge efforts required. The objective of the present work is to optimize burnishing factors, including the spindle speed, burnishing feed, depth of penetration, and the number of rollers for minimizing the energy consumed in the burnishing time, as well as surface roughness and maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used to develop burnishing objectives in terms of machining parameters. The optimization outcomes were selected using an evolution algorithm, specifically the non-dominated sorting particle swarm optimization (NSPSO). The results of the proposed ANFIS models are significant and can be employed to predict response values in industrial applications. The optimization technique comprising the ANFIS and NOPSO is a powerful approach to model burnishing performances and select optimal parameters as compared to the trial and error method as well as operator experience. Finally, the optimal solution can help to achieve the improvements in the energy consumed by 16.3 %, surface roughness by 24.3 %, and Rockwell hardness by 4.0 %, as compared to the common values . UR - https://www.sv-jme.eu/article/optimization-of-the-internal-burnishing-operation-for-energy-efficiency-and-surface-properties/
Nguyen, Trung-Thanh, AND Le, Thai-Minh. "Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 67 Number 4 (05 May 2021)
Strojniški vestnik - Journal of Mechanical Engineering 67(2021)4, 167-179
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Improving the surface quality after burnishing operation has been the subject of various published investigations. Unfortunately, the trade-off analysis between the energy consumption and surface characteristics of the internal burnishing has been not addressed due to the expensive cost and huge efforts required. The objective of the present work is to optimize burnishing factors, including the spindle speed, burnishing feed, depth of penetration, and the number of rollers for minimizing the energy consumed in the burnishing time, as well as surface roughness and maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used to develop burnishing objectives in terms of machining parameters. The optimization outcomes were selected using an evolution algorithm, specifically the non-dominated sorting particle swarm optimization (NSPSO). The results of the proposed ANFIS models are significant and can be employed to predict response values in industrial applications. The optimization technique comprising the ANFIS and NOPSO is a powerful approach to model burnishing performances and select optimal parameters as compared to the trial and error method as well as operator experience. Finally, the optimal solution can help to achieve the improvements in the energy consumed by 16.3 %, surface roughness by 24.3 %, and Rockwell hardness by 4.0 %, as compared to the common values .