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/article/generation-of-a-model-for-cutting-forces-using-artificial-intelligence/>. Date accessed: 19 nov. 2024. 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/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/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. 19 Nov. 2024
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/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/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/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)
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.