Generation of a Model for Cutting Forces Using Artificial Intelligence

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Izvoz citacije: ABNT
MILFELNER, Matjaž ;ŽUPERL, Uroš ;ČUŠ, Franci .
Generation of a Model for Cutting Forces Using Artificial Intelligence. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 51, n.1, p. 41-54, august 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/generation-of-a-model-for-cutting-forces-using-artificial-intelligence/>. Date accessed: 02 dec. 2022. 
doi:http://dx.doi.org/.
Milfelner, M., Župerl, U., & Čuš, F.
(2005).
Generation of a Model for Cutting Forces Using Artificial Intelligence.
Strojniški vestnik - Journal of Mechanical Engineering, 51(1), 41-54.
doi:http://dx.doi.org/
@article{.,
	author = {Matjaž  Milfelner and Uroš  Župerl and Franci  Čuš},
	title = {Generation of a Model for Cutting Forces Using Artificial Intelligence},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {51},
	number = {1},
	year = {2005},
	keywords = {genetic models; cutting forces; milling; ball-end mill; },
	abstract = {Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligence (genetic programming). In the genetic model, all the parameters influencing the size of the cutting forces during the milling process are considered. The presented model is generated from experimental data for Ck45 steel with different cutting parameters. The results indicate that the genetic model of the cutting force agrees with the experimental data.},
	issn = {0039-2480},	pages = {41-54},	doi = {},
	url = {https://www.sv-jme.eu/sl/article/generation-of-a-model-for-cutting-forces-using-artificial-intelligence/}
}
Milfelner, M.,Župerl, U.,Čuš, F.
2005 August 51. Generation of a Model for Cutting Forces Using Artificial Intelligence. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 51:1
%A Milfelner, Matjaž 
%A Župerl, Uroš 
%A Čuš, Franci 
%D 2005
%T Generation of a Model for Cutting Forces Using Artificial Intelligence
%B 2005
%9 genetic models; cutting forces; milling; ball-end mill; 
%! Generation of a Model for Cutting Forces Using Artificial Intelligence
%K genetic models; cutting forces; milling; ball-end mill; 
%X Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligence (genetic programming). In the genetic model, all the parameters influencing the size of the cutting forces during the milling process are considered. The presented model is generated from experimental data for Ck45 steel with different cutting parameters. The results indicate that the genetic model of the cutting force agrees with the experimental data.
%U https://www.sv-jme.eu/sl/article/generation-of-a-model-for-cutting-forces-using-artificial-intelligence/
%0 Journal Article
%R 
%& 41
%P 14
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 51
%N 1
%@ 0039-2480
%8 2017-08-18
%7 2017-08-18
Milfelner, Matjaž, Uroš  Župerl, & Franci  Čuš.
"Generation of a Model for Cutting Forces Using Artificial Intelligence." Strojniški vestnik - Journal of Mechanical Engineering [Online], 51.1 (2005): 41-54. Web.  02 Dec. 2022
TY  - JOUR
AU  - Milfelner, Matjaž 
AU  - Župerl, Uroš 
AU  - Čuš, Franci 
PY  - 2005
TI  - Generation of a Model for Cutting Forces Using Artificial Intelligence
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - genetic models; cutting forces; milling; ball-end mill; 
N2  - Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligence (genetic programming). In the genetic model, all the parameters influencing the size of the cutting forces during the milling process are considered. The presented model is generated from experimental data for Ck45 steel with different cutting parameters. The results indicate that the genetic model of the cutting force agrees with the experimental data.
UR  - https://www.sv-jme.eu/sl/article/generation-of-a-model-for-cutting-forces-using-artificial-intelligence/
@article{{}{.},
	author = {Milfelner, M., Župerl, U., Čuš, F.},
	title = {Generation of a Model for Cutting Forces Using Artificial Intelligence},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {51},
	number = {1},
	year = {2005},
	doi = {},
	url = {https://www.sv-jme.eu/sl/article/generation-of-a-model-for-cutting-forces-using-artificial-intelligence/}
}
TY  - JOUR
AU  - Milfelner, Matjaž 
AU  - Župerl, Uroš 
AU  - Čuš, Franci 
PY  - 2017/08/18
TI  - Generation of a Model for Cutting Forces Using Artificial Intelligence
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 51, No 1 (2005): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - genetic models, cutting forces, milling, ball-end mill, 
N2  - Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligence (genetic programming). In the genetic model, all the parameters influencing the size of the cutting forces during the milling process are considered. The presented model is generated from experimental data for Ck45 steel with different cutting parameters. The results indicate that the genetic model of the cutting force agrees with the experimental data.
UR  - https://www.sv-jme.eu/sl/article/generation-of-a-model-for-cutting-forces-using-artificial-intelligence/
Milfelner, Matjaž, Župerl, Uroš, AND Čuš, Franci.
"Generation of a Model for Cutting Forces Using Artificial Intelligence" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 51 Number 1 (18 August 2017)

Avtorji

Inštitucije

  • University of Maribor, Faculty of Mechanical Engineering, Slovenia
  • University of Maribor, Faculty of Mechanical Engineering, Slovenia
  • University of Maribor, Faculty of Mechanical Engineering, Slovenia

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 51(2005)1, 41-54
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.

Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligence (genetic programming). In the genetic model, all the parameters influencing the size of the cutting forces during the milling process are considered. The presented model is generated from experimental data for Ck45 steel with different cutting parameters. The results indicate that the genetic model of the cutting force agrees with the experimental data.

genetic models; cutting forces; milling; ball-end mill;