BAJIĆ, Dražen ;CELENT, Luka ;JOZIĆ, Sonja . Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 58, n.11, p. 673-682, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/modeling-of-the-influence-of-cutting-parameters-on-the-surface-roughness-tool-wear-and-the-cutting-force-in-face-milling-in-off-line-process-control/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2012.456.
Bajić, D., Celent, L., & Jozić, S. (2012). Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control. Strojniški vestnik - Journal of Mechanical Engineering, 58(11), 673-682. doi:http://dx.doi.org/10.5545/sv-jme.2012.456
@article{sv-jmesv-jme.2012.456,
author = {Dražen Bajić and Luka Celent and Sonja Jozić},
title = {Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {58},
number = {11},
year = {2012},
keywords = {Off-line process control; Surface roughness; Cutting force; Tool wear, Regression Analysis; Radial basis function neural network},
abstract = {Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on the surface roughness, tool wear and the cutting force components in face milling as part of the off-line process control. The experiments were carried out in order to define model for process planning. Cutting speed, feed per tooth and depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have the relative prediction error below 10%. The research has shown that when training dataset is small neural networks modeling methodologies are comparable with regression analysis methodology and furthermore it can even offer better result, in this case average relative error of 3,35%. Advantages of off-line process control which utilizes process models by using this two modeling methodologies were explained in theory.},
issn = {0039-2480}, pages = {673-682}, doi = {10.5545/sv-jme.2012.456},
url = {https://www.sv-jme.eu/sl/article/modeling-of-the-influence-of-cutting-parameters-on-the-surface-roughness-tool-wear-and-the-cutting-force-in-face-milling-in-off-line-process-control/}
}
Bajić, D.,Celent, L.,Jozić, S. 2012 June 58. Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 58:11
%A Bajić, Dražen %A Celent, Luka %A Jozić, Sonja %D 2012 %T Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control %B 2012 %9 Off-line process control; Surface roughness; Cutting force; Tool wear, Regression Analysis; Radial basis function neural network %! Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control %K Off-line process control; Surface roughness; Cutting force; Tool wear, Regression Analysis; Radial basis function neural network %X Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on the surface roughness, tool wear and the cutting force components in face milling as part of the off-line process control. The experiments were carried out in order to define model for process planning. Cutting speed, feed per tooth and depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have the relative prediction error below 10%. The research has shown that when training dataset is small neural networks modeling methodologies are comparable with regression analysis methodology and furthermore it can even offer better result, in this case average relative error of 3,35%. Advantages of off-line process control which utilizes process models by using this two modeling methodologies were explained in theory. %U https://www.sv-jme.eu/sl/article/modeling-of-the-influence-of-cutting-parameters-on-the-surface-roughness-tool-wear-and-the-cutting-force-in-face-milling-in-off-line-process-control/ %0 Journal Article %R 10.5545/sv-jme.2012.456 %& 673 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 58 %N 11 %@ 0039-2480 %8 2018-06-28 %7 2018-06-28
Bajić, Dražen, Luka Celent, & Sonja Jozić. "Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control." Strojniški vestnik - Journal of Mechanical Engineering [Online], 58.11 (2012): 673-682. Web. 20 Dec. 2024
TY - JOUR AU - Bajić, Dražen AU - Celent, Luka AU - Jozić, Sonja PY - 2012 TI - Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2012.456 KW - Off-line process control; Surface roughness; Cutting force; Tool wear, Regression Analysis; Radial basis function neural network N2 - Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on the surface roughness, tool wear and the cutting force components in face milling as part of the off-line process control. The experiments were carried out in order to define model for process planning. Cutting speed, feed per tooth and depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have the relative prediction error below 10%. The research has shown that when training dataset is small neural networks modeling methodologies are comparable with regression analysis methodology and furthermore it can even offer better result, in this case average relative error of 3,35%. Advantages of off-line process control which utilizes process models by using this two modeling methodologies were explained in theory. UR - https://www.sv-jme.eu/sl/article/modeling-of-the-influence-of-cutting-parameters-on-the-surface-roughness-tool-wear-and-the-cutting-force-in-face-milling-in-off-line-process-control/
@article{{sv-jme}{sv-jme.2012.456},
author = {Bajić, D., Celent, L., Jozić, S.},
title = {Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {58},
number = {11},
year = {2012},
doi = {10.5545/sv-jme.2012.456},
url = {https://www.sv-jme.eu/sl/article/modeling-of-the-influence-of-cutting-parameters-on-the-surface-roughness-tool-wear-and-the-cutting-force-in-face-milling-in-off-line-process-control/}
}
TY - JOUR AU - Bajić, Dražen AU - Celent, Luka AU - Jozić, Sonja PY - 2018/06/28 TI - Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 58, No 11 (2012): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2012.456 KW - Off-line process control, Surface roughness, Cutting force, Tool wear, Regression Analysis, Radial basis function neural network N2 - Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on the surface roughness, tool wear and the cutting force components in face milling as part of the off-line process control. The experiments were carried out in order to define model for process planning. Cutting speed, feed per tooth and depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have the relative prediction error below 10%. The research has shown that when training dataset is small neural networks modeling methodologies are comparable with regression analysis methodology and furthermore it can even offer better result, in this case average relative error of 3,35%. Advantages of off-line process control which utilizes process models by using this two modeling methodologies were explained in theory. UR - https://www.sv-jme.eu/sl/article/modeling-of-the-influence-of-cutting-parameters-on-the-surface-roughness-tool-wear-and-the-cutting-force-in-face-milling-in-off-line-process-control/
Bajić, Dražen, Celent, Luka, AND Jozić, Sonja. "Modeling of the influence of cutting parameters on the surface roughness, tool wear and the cutting force in face milling in off-line process control" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 58 Number 11 (28 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 58(2012)11, 673-682
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on the surface roughness, tool wear and the cutting force components in face milling as part of the off-line process control. The experiments were carried out in order to define model for process planning. Cutting speed, feed per tooth and depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have the relative prediction error below 10%. The research has shown that when training dataset is small neural networks modeling methodologies are comparable with regression analysis methodology and furthermore it can even offer better result, in this case average relative error of 3,35%. Advantages of off-line process control which utilizes process models by using this two modeling methodologies were explained in theory.