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/article/drilling-force-forecasting-using-neural-networks/>. Date accessed: 19 dec. 2024. 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/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
%A Polanecka, Ivana %A Korošec, Marjan %A Kopač, Janez %D 2007 %T Drilling-force forecasting using neural networks %B 2007 %9 neural networks; forecasting; drilling forces; databases; machinability; %! Drilling-force forecasting using neural networks %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/article/drilling-force-forecasting-using-neural-networks/ %0 Journal Article %R %& 771 %P 13 %J Strojniški vestnik - Journal of Mechanical Engineering %V 53 %N 11 %@ 0039-2480 %8 2017-08-18 %7 2017-08-18
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. 19 Dec. 2024
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/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/article/drilling-force-forecasting-using-neural-networks/} }
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/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)
Strojniški vestnik - Journal of Mechanical Engineering 53(2007)11, 771-783
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.