Drilling-force forecasting using neural networks

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Izvoz citacije: ABNT
POLANECKA, Ivana ;KOROŠEC, Marjan ;KOPAČ, Janez .
Drilling-force forecasting using neural networks. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 53, n.11, p. 771-783, august 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/drilling-force-forecasting-using-neural-networks/>. Date accessed: 24 aug. 2019. 
doi:http://dx.doi.org/.
Polanecka, I., Korošec, M., & Kopač, J.
(2007).
Drilling-force forecasting using neural networks.
Strojniški vestnik - Journal of Mechanical Engineering, 53(11), 771-783.
doi:http://dx.doi.org/
@article{.,
	author = {Ivana  Polanecka and Marjan  Korošec and Janez  Kopač},
	title = {Drilling-force forecasting using neural networks},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {53},
	number = {11},
	year = {2007},
	keywords = {neural networks; forecasting; drilling forces; databases; machinability; },
	abstract = {Neural networks are a forecasting tool that can be applied in many fields. Process sensing and data acquisition, for example, can be used to improve both the machinability and product properties during the manufacturing process. The time dynamics of these processes may be anywhere from highly dynamic to quasi-stationary. Our goal was to create a machinability database. The collected data will provide a basis for forecasting the cutting forces and cutting torque for new materials in the future. The force forecasts will also allow tool-wear monitoring and prediction.},
	issn = {0039-2480},	pages = {771-783},	doi = {},
	url = {https://www.sv-jme.eu/sl/article/drilling-force-forecasting-using-neural-networks/}
}
Polanecka, I.,Korošec, M.,Kopač, J.
2007 August 53. Drilling-force forecasting using neural networks. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 53:11
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%A Kopač, Janez 
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%K neural networks; forecasting; drilling forces; databases; machinability; 
%X Neural networks are a forecasting tool that can be applied in many fields. Process sensing and data acquisition, for example, can be used to improve both the machinability and product properties during the manufacturing process. The time dynamics of these processes may be anywhere from highly dynamic to quasi-stationary. Our goal was to create a machinability database. The collected data will provide a basis for forecasting the cutting forces and cutting torque for new materials in the future. The force forecasts will also allow tool-wear monitoring and prediction.
%U https://www.sv-jme.eu/sl/article/drilling-force-forecasting-using-neural-networks/
%0 Journal Article
%R 
%& 771
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%J Strojniški vestnik - Journal of Mechanical Engineering
%V 53
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%@ 0039-2480
%8 2017-08-18
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Polanecka, Ivana, Marjan  Korošec, & Janez  Kopač.
"Drilling-force forecasting using neural networks." Strojniški vestnik - Journal of Mechanical Engineering [Online], 53.11 (2007): 771-783. Web.  24 Aug. 2019
TY  - JOUR
AU  - Polanecka, Ivana 
AU  - Korošec, Marjan 
AU  - Kopač, Janez 
PY  - 2007
TI  - Drilling-force forecasting using neural networks
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - neural networks; forecasting; drilling forces; databases; machinability; 
N2  - Neural networks are a forecasting tool that can be applied in many fields. Process sensing and data acquisition, for example, can be used to improve both the machinability and product properties during the manufacturing process. The time dynamics of these processes may be anywhere from highly dynamic to quasi-stationary. Our goal was to create a machinability database. The collected data will provide a basis for forecasting the cutting forces and cutting torque for new materials in the future. The force forecasts will also allow tool-wear monitoring and prediction.
UR  - https://www.sv-jme.eu/sl/article/drilling-force-forecasting-using-neural-networks/
@article{{}{.},
	author = {Polanecka, I., Korošec, M., Kopač, J.},
	title = {Drilling-force forecasting using neural networks},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {53},
	number = {11},
	year = {2007},
	doi = {},
	url = {https://www.sv-jme.eu/sl/article/drilling-force-forecasting-using-neural-networks/}
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TY  - JOUR
AU  - Polanecka, Ivana 
AU  - Korošec, Marjan 
AU  - Kopač, Janez 
PY  - 2017/08/18
TI  - Drilling-force forecasting using neural networks
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 53, No 11 (2007): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - neural networks, forecasting, drilling forces, databases, machinability, 
N2  - Neural networks are a forecasting tool that can be applied in many fields. Process sensing and data acquisition, for example, can be used to improve both the machinability and product properties during the manufacturing process. The time dynamics of these processes may be anywhere from highly dynamic to quasi-stationary. Our goal was to create a machinability database. The collected data will provide a basis for forecasting the cutting forces and cutting torque for new materials in the future. The force forecasts will also allow tool-wear monitoring and prediction.
UR  - https://www.sv-jme.eu/sl/article/drilling-force-forecasting-using-neural-networks/
Polanecka, Ivana, Korošec, Marjan, AND Kopač, Janez.
"Drilling-force forecasting using neural networks" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 53 Number 11 (18 August 2017)

Avtorji

Inštitucije

  • Technical University Košice, Faculty of Mechanical Engineering, Slovakia
  • University of Ljubljana, Faculty of Mechanical Engineering, Slovenia
  • University of Ljubljana, Faculty of Mechanical Engineering, Slovenia

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 53(2007)11, 771-783

Neural networks are a forecasting tool that can be applied in many fields. Process sensing and data acquisition, for example, can be used to improve both the machinability and product properties during the manufacturing process. The time dynamics of these processes may be anywhere from highly dynamic to quasi-stationary. Our goal was to create a machinability database. The collected data will provide a basis for forecasting the cutting forces and cutting torque for new materials in the future. The force forecasts will also allow tool-wear monitoring and prediction.

neural networks; forecasting; drilling forces; databases; machinability;