Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning

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Izvoz citacije: ABNT
BAJIĆ, Dražen ;LELA, Branimir ;CUKOR, Goran .
Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 54, n.5, p. 322-333, august 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/examination-and-modelling-of-the-influence-of-cutting-parameters-on-the-cutting-force-and-the-surface-roughness-in-longitudinal-turning/>. Date accessed: 17 jun. 2024. 
doi:http://dx.doi.org/.
Bajić, D., Lela, B., & Cukor, G.
(2008).
Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning.
Strojniški vestnik - Journal of Mechanical Engineering, 54(5), 322-333.
doi:http://dx.doi.org/
@article{.,
	author = {Dražen  Bajić and Branimir  Lela and Goran  Cukor},
	title = {Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {54},
	number = {5},
	year = {2008},
	keywords = {longitudinal turning; cutting forces; surface roughness; neural networks; },
	abstract = {This paper examines the influence of three cutting parameters on the surface roughness and the cutting force components in longitudinal turning. The cutting speed, the feed rate and the depth of cut have been taken as influential factors. Two modelling methodologies, namely regression analysis and neural networks, have been applied to experimentally determined data. Also, for both methodologies the ability of interpolation and extrapolation has been tested. Results obtained by neural network models have been compared to those obtained by regression models. Both methodologies give nearly similar results when interpolation is observed. However, regarding extrapolation neural network models give better results. In order to find the optimum values of the cutting parameters an optimization has been carried out.},
	issn = {0039-2480},	pages = {322-333},	doi = {},
	url = {https://www.sv-jme.eu/sl/article/examination-and-modelling-of-the-influence-of-cutting-parameters-on-the-cutting-force-and-the-surface-roughness-in-longitudinal-turning/}
}
Bajić, D.,Lela, B.,Cukor, G.
2008 August 54. Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 54:5
%A Bajić, Dražen 
%A Lela, Branimir 
%A Cukor, Goran 
%D 2008
%T Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning
%B 2008
%9 longitudinal turning; cutting forces; surface roughness; neural networks; 
%! Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning
%K longitudinal turning; cutting forces; surface roughness; neural networks; 
%X This paper examines the influence of three cutting parameters on the surface roughness and the cutting force components in longitudinal turning. The cutting speed, the feed rate and the depth of cut have been taken as influential factors. Two modelling methodologies, namely regression analysis and neural networks, have been applied to experimentally determined data. Also, for both methodologies the ability of interpolation and extrapolation has been tested. Results obtained by neural network models have been compared to those obtained by regression models. Both methodologies give nearly similar results when interpolation is observed. However, regarding extrapolation neural network models give better results. In order to find the optimum values of the cutting parameters an optimization has been carried out.
%U https://www.sv-jme.eu/sl/article/examination-and-modelling-of-the-influence-of-cutting-parameters-on-the-cutting-force-and-the-surface-roughness-in-longitudinal-turning/
%0 Journal Article
%R 
%& 322
%P 12
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 54
%N 5
%@ 0039-2480
%8 2017-08-21
%7 2017-08-21
Bajić, Dražen, Branimir  Lela, & Goran  Cukor.
"Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning." Strojniški vestnik - Journal of Mechanical Engineering [Online], 54.5 (2008): 322-333. Web.  17 Jun. 2024
TY  - JOUR
AU  - Bajić, Dražen 
AU  - Lela, Branimir 
AU  - Cukor, Goran 
PY  - 2008
TI  - Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - longitudinal turning; cutting forces; surface roughness; neural networks; 
N2  - This paper examines the influence of three cutting parameters on the surface roughness and the cutting force components in longitudinal turning. The cutting speed, the feed rate and the depth of cut have been taken as influential factors. Two modelling methodologies, namely regression analysis and neural networks, have been applied to experimentally determined data. Also, for both methodologies the ability of interpolation and extrapolation has been tested. Results obtained by neural network models have been compared to those obtained by regression models. Both methodologies give nearly similar results when interpolation is observed. However, regarding extrapolation neural network models give better results. In order to find the optimum values of the cutting parameters an optimization has been carried out.
UR  - https://www.sv-jme.eu/sl/article/examination-and-modelling-of-the-influence-of-cutting-parameters-on-the-cutting-force-and-the-surface-roughness-in-longitudinal-turning/
@article{{}{.},
	author = {Bajić, D., Lela, B., Cukor, G.},
	title = {Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {54},
	number = {5},
	year = {2008},
	doi = {},
	url = {https://www.sv-jme.eu/sl/article/examination-and-modelling-of-the-influence-of-cutting-parameters-on-the-cutting-force-and-the-surface-roughness-in-longitudinal-turning/}
}
TY  - JOUR
AU  - Bajić, Dražen 
AU  - Lela, Branimir 
AU  - Cukor, Goran 
PY  - 2017/08/21
TI  - Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 54, No 5 (2008): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - longitudinal turning, cutting forces, surface roughness, neural networks, 
N2  - This paper examines the influence of three cutting parameters on the surface roughness and the cutting force components in longitudinal turning. The cutting speed, the feed rate and the depth of cut have been taken as influential factors. Two modelling methodologies, namely regression analysis and neural networks, have been applied to experimentally determined data. Also, for both methodologies the ability of interpolation and extrapolation has been tested. Results obtained by neural network models have been compared to those obtained by regression models. Both methodologies give nearly similar results when interpolation is observed. However, regarding extrapolation neural network models give better results. In order to find the optimum values of the cutting parameters an optimization has been carried out.
UR  - https://www.sv-jme.eu/sl/article/examination-and-modelling-of-the-influence-of-cutting-parameters-on-the-cutting-force-and-the-surface-roughness-in-longitudinal-turning/
Bajić, Dražen, Lela, Branimir, AND Cukor, Goran.
"Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 54 Number 5 (21 August 2017)

Avtorji

Inštitucije

  • University of Split, Faculty of Electrical Eng., Mechanical eng. and Naval Architecture, Croatia
  • University of Split, Faculty of Electrical Eng., Mechanical eng. and Naval Architecture, Croatia
  • University of Rijeka, Faculty of Engineering, Croatia

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 54(2008)5, 322-333
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.

This paper examines the influence of three cutting parameters on the surface roughness and the cutting force components in longitudinal turning. The cutting speed, the feed rate and the depth of cut have been taken as influential factors. Two modelling methodologies, namely regression analysis and neural networks, have been applied to experimentally determined data. Also, for both methodologies the ability of interpolation and extrapolation has been tested. Results obtained by neural network models have been compared to those obtained by regression models. Both methodologies give nearly similar results when interpolation is observed. However, regarding extrapolation neural network models give better results. In order to find the optimum values of the cutting parameters an optimization has been carried out.

longitudinal turning; cutting forces; surface roughness; neural networks;