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: 20 dec. 2024. 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. 20 Dec. 2024
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)
Strojniški vestnik - Journal of Mechanical Engineering 62(2016)6, 340-350
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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 %.