Using Neural Networks to Follow the Wear of a S390 Twist Drill

KRIVOKAPIĆ, Zdravko ;ZOGOVIĆ, Vukasin ;SPAIĆ, Obrad .
Using Neural Networks to Follow the Wear of a S390 Twist Drill. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 52, n.7-8, p. 437-442, august 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/using-neural-networks-to-follow-the-wear-of-a-s390-twist-drill/>. Date accessed: 19 nov. 2024. 
doi:http://dx.doi.org/.

Krivokapić, Z., Zogović, V., & Spaić, O.
(2006).
Using Neural Networks to Follow the Wear of a S390 Twist Drill.
Strojniški vestnik - Journal of Mechanical Engineering, 52(7-8), 437-442.
doi:http://dx.doi.org/

@article{.,
	author = {Zdravko  Krivokapić and Vukasin  Zogović and Obrad  Spaić},
	title = {Using Neural Networks to Follow the Wear of a S390 Twist Drill},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {52},
	number = {7-8},
	year = {2006},
	keywords = {neural networks; drilling; twist drill; wear processes; },
	abstract = {This paper deals with the use of neural networks for the integration of information as well as the parameters of the cutting process (speed, feed and diameter). Two sharpening methods and different working times related to the wear parameters are studied. The material used for the twist drill (S390) is obtained with power technology. Experimental results are used to train the neural networks, as one approach to the modeling of this process. The back-propagation algorithm is used as a model for neural networks. The neural networks with test shapes are trained (offline). The obtained results are presented.},
	issn = {0039-2480},	pages = {437-442},	doi = {},
	url = {https://www.sv-jme.eu/sl/article/using-neural-networks-to-follow-the-wear-of-a-s390-twist-drill/}
}

Krivokapić, Z.,Zogović, V.,Spaić, O.
2006 August 52. Using Neural Networks to Follow the Wear of a S390 Twist Drill. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 52:7-8

%A Krivokapić, Zdravko 
%A Zogović, Vukasin 
%A Spaić, Obrad 
%D 2006
%T Using Neural Networks to Follow the Wear of a S390 Twist Drill
%B 2006
%9 neural networks; drilling; twist drill; wear processes; 
%! Using Neural Networks to Follow the Wear of a S390 Twist Drill
%K neural networks; drilling; twist drill; wear processes; 
%X This paper deals with the use of neural networks for the integration of information as well as the parameters of the cutting process (speed, feed and diameter). Two sharpening methods and different working times related to the wear parameters are studied. The material used for the twist drill (S390) is obtained with power technology. Experimental results are used to train the neural networks, as one approach to the modeling of this process. The back-propagation algorithm is used as a model for neural networks. The neural networks with test shapes are trained (offline). The obtained results are presented.
%U https://www.sv-jme.eu/sl/article/using-neural-networks-to-follow-the-wear-of-a-s390-twist-drill/
%0 Journal Article
%R 
%& 437
%P 6
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 52
%N 7-8
%@ 0039-2480
%8 2017-08-18
%7 2017-08-18

Krivokapić, Zdravko, Vukasin  Zogović, & Obrad  Spaić.
"Using Neural Networks to Follow the Wear of a S390 Twist Drill." Strojniški vestnik - Journal of Mechanical Engineering [Online], 52.7-8 (2006): 437-442. Web.  19 Nov. 2024

TY  - JOUR
AU  - Krivokapić, Zdravko 
AU  - Zogović, Vukasin 
AU  - Spaić, Obrad 
PY  - 2006
TI  - Using Neural Networks to Follow the Wear of a S390 Twist Drill
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - neural networks; drilling; twist drill; wear processes; 
N2  - This paper deals with the use of neural networks for the integration of information as well as the parameters of the cutting process (speed, feed and diameter). Two sharpening methods and different working times related to the wear parameters are studied. The material used for the twist drill (S390) is obtained with power technology. Experimental results are used to train the neural networks, as one approach to the modeling of this process. The back-propagation algorithm is used as a model for neural networks. The neural networks with test shapes are trained (offline). The obtained results are presented.
UR  - https://www.sv-jme.eu/sl/article/using-neural-networks-to-follow-the-wear-of-a-s390-twist-drill/

@article{{}{.},
	author = {Krivokapić, Z., Zogović, V., Spaić, O.},
	title = {Using Neural Networks to Follow the Wear of a S390 Twist Drill},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {52},
	number = {7-8},
	year = {2006},
	doi = {},
	url = {https://www.sv-jme.eu/sl/article/using-neural-networks-to-follow-the-wear-of-a-s390-twist-drill/}
}

TY  - JOUR
AU  - Krivokapić, Zdravko 
AU  - Zogović, Vukasin 
AU  - Spaić, Obrad 
PY  - 2017/08/18
TI  - Using Neural Networks to Follow the Wear of a S390 Twist Drill
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 52, No 7-8 (2006): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - neural networks, drilling, twist drill, wear processes, 
N2  - This paper deals with the use of neural networks for the integration of information as well as the parameters of the cutting process (speed, feed and diameter). Two sharpening methods and different working times related to the wear parameters are studied. The material used for the twist drill (S390) is obtained with power technology. Experimental results are used to train the neural networks, as one approach to the modeling of this process. The back-propagation algorithm is used as a model for neural networks. The neural networks with test shapes are trained (offline). The obtained results are presented.
UR  - https://www.sv-jme.eu/sl/article/using-neural-networks-to-follow-the-wear-of-a-s390-twist-drill/

Krivokapić, Zdravko, Zogović, Vukasin, AND Spaić, Obrad.
"Using Neural Networks to Follow the Wear of a S390 Twist Drill" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 52 Number 7-8 (18 August 2017)