Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials

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Izvoz citacije: ABNT
ŽUPERL, Uroš ;ČUŠ, Franci ;IRGOLIČ, Tomaž .
Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 62, n.6, p. 340-350, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/prediction-of-cutting-forces-in-ball-end-milling-of-multi-layered-metal-materials/>. Date accessed: 09 dec. 2021. 
doi:http://dx.doi.org/10.5545/sv-jme.2015.3289.
Župerl, U., Čuš, F., & Irgolič, T.
(2016).
Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials.
Strojniški vestnik - Journal of Mechanical Engineering, 62(6), 340-350.
doi:http://dx.doi.org/10.5545/sv-jme.2015.3289
@article{sv-jmesv-jme.2015.3289,
	author = {Uroš  Župerl and Franci  Čuš and Tomaž  Irgolič},
	title = {Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {62},
	number = {6},
	year = {2016},
	keywords = {End milling; Cutting forces; functionally graded material; LENS; layer thickness; ANN.},
	abstract = {This paper outlines the experimental exploration of cutting forces produced during ball-end milling of multi-layered metal materials manufactured by the laser engineered net shaping (LENS) process. The research employs an artificial neural network (ANN) technique for predicting the cutting forces during the machining of 16MnCr5/316L four-layered metal material with a solid carbide ball-end mill. Hardness and thickness of the particular manufactured layer in above mentioned advanced material have been considered during training of the ANN model. Model predictions were compared with experimental data and were found to be in good agreement. Experimental results demonstrate that this method can accurately predict cutting force within a maximum prediction error of 4.8 %.},
	issn = {0039-2480},	pages = {340-350},	doi = {10.5545/sv-jme.2015.3289},
	url = {https://www.sv-jme.eu/sl/article/prediction-of-cutting-forces-in-ball-end-milling-of-multi-layered-metal-materials/}
}
Župerl, U.,Čuš, F.,Irgolič, T.
2016 June 62. Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 62:6
%A Župerl, Uroš 
%A Čuš, Franci 
%A Irgolič, Tomaž 
%D 2016
%T Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials
%B 2016
%9 End milling; Cutting forces; functionally graded material; LENS; layer thickness; ANN.
%! Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials
%K End milling; Cutting forces; functionally graded material; LENS; layer thickness; ANN.
%X This paper outlines the experimental exploration of cutting forces produced during ball-end milling of multi-layered metal materials manufactured by the laser engineered net shaping (LENS) process. The research employs an artificial neural network (ANN) technique for predicting the cutting forces during the machining of 16MnCr5/316L four-layered metal material with a solid carbide ball-end mill. Hardness and thickness of the particular manufactured layer in above mentioned advanced material have been considered during training of the ANN model. Model predictions were compared with experimental data and were found to be in good agreement. Experimental results demonstrate that this method can accurately predict cutting force within a maximum prediction error of 4.8 %.
%U https://www.sv-jme.eu/sl/article/prediction-of-cutting-forces-in-ball-end-milling-of-multi-layered-metal-materials/
%0 Journal Article
%R 10.5545/sv-jme.2015.3289
%& 340
%P 11
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 62
%N 6
%@ 0039-2480
%8 2018-06-27
%7 2018-06-27
Župerl, Uroš, Franci  Čuš, & Tomaž  Irgolič.
"Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials." Strojniški vestnik - Journal of Mechanical Engineering [Online], 62.6 (2016): 340-350. Web.  09 Dec. 2021
TY  - JOUR
AU  - Župerl, Uroš 
AU  - Čuš, Franci 
AU  - Irgolič, Tomaž 
PY  - 2016
TI  - Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2015.3289
KW  - End milling; Cutting forces; functionally graded material; LENS; layer thickness; ANN.
N2  - This paper outlines the experimental exploration of cutting forces produced during ball-end milling of multi-layered metal materials manufactured by the laser engineered net shaping (LENS) process. The research employs an artificial neural network (ANN) technique for predicting the cutting forces during the machining of 16MnCr5/316L four-layered metal material with a solid carbide ball-end mill. Hardness and thickness of the particular manufactured layer in above mentioned advanced material have been considered during training of the ANN model. Model predictions were compared with experimental data and were found to be in good agreement. Experimental results demonstrate that this method can accurately predict cutting force within a maximum prediction error of 4.8 %.
UR  - https://www.sv-jme.eu/sl/article/prediction-of-cutting-forces-in-ball-end-milling-of-multi-layered-metal-materials/
@article{{sv-jme}{sv-jme.2015.3289},
	author = {Župerl, U., Čuš, F., Irgolič, T.},
	title = {Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {62},
	number = {6},
	year = {2016},
	doi = {10.5545/sv-jme.2015.3289},
	url = {https://www.sv-jme.eu/sl/article/prediction-of-cutting-forces-in-ball-end-milling-of-multi-layered-metal-materials/}
}
TY  - JOUR
AU  - Župerl, Uroš 
AU  - Čuš, Franci 
AU  - Irgolič, Tomaž 
PY  - 2018/06/27
TI  - Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 62, No 6 (2016): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2015.3289
KW  - End milling, Cutting forces, functionally graded material, LENS, layer thickness, ANN.
N2  - This paper outlines the experimental exploration of cutting forces produced during ball-end milling of multi-layered metal materials manufactured by the laser engineered net shaping (LENS) process. The research employs an artificial neural network (ANN) technique for predicting the cutting forces during the machining of 16MnCr5/316L four-layered metal material with a solid carbide ball-end mill. Hardness and thickness of the particular manufactured layer in above mentioned advanced material have been considered during training of the ANN model. Model predictions were compared with experimental data and were found to be in good agreement. Experimental results demonstrate that this method can accurately predict cutting force within a maximum prediction error of 4.8 %.
UR  - https://www.sv-jme.eu/sl/article/prediction-of-cutting-forces-in-ball-end-milling-of-multi-layered-metal-materials/
Župerl, Uroš, Čuš, Franci, AND Irgolič, Tomaž.
"Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 62 Number 6 (27 June 2018)

Avtorji

Inštitucije

  • University of Maribor, Faculty of Mechanical Engineering, Slovenia 1

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 62(2016)6, 340-350

https://doi.org/10.5545/sv-jme.2015.3289

This paper outlines the experimental exploration of cutting forces produced during ball-end milling of multi-layered metal materials manufactured by the laser engineered net shaping (LENS) process. The research employs an artificial neural network (ANN) technique for predicting the cutting forces during the machining of 16MnCr5/316L four-layered metal material with a solid carbide ball-end mill. Hardness and thickness of the particular manufactured layer in above mentioned advanced material have been considered during training of the ANN model. Model predictions were compared with experimental data and were found to be in good agreement. Experimental results demonstrate that this method can accurately predict cutting force within a maximum prediction error of 4.8 %.

End milling; Cutting forces; functionally graded material; LENS; layer thickness; ANN.