EFKOLIDIS, Nikolaos ;GARCÍA HERNÁNDEZ, César ;HUERTAS TALÓN, José Luis ;KYRATSIS, Panagiotis . Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 64, n.6, p. 351-361, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/modeling-and-prediction-of-thrust-force-and-torque-in-drilling-operations-of-al7075-using-ann-and-rsm-methodologies/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2017.5188.
Efkolidis, N., García Hernández, C., Huertas Talón, J., & Kyratsis, P. (2018). Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies. Strojniški vestnik - Journal of Mechanical Engineering, 64(6), 351-361. doi:http://dx.doi.org/10.5545/sv-jme.2017.5188
@article{sv-jmesv-jme.2017.5188,
author = {Nikolaos Efkolidis and César García Hernández and José Luis Huertas Talón and Panagiotis Kyratsis},
title = {Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {64},
number = {6},
year = {2018},
keywords = {sustainable manufacturing; Al7075; artificial neural networks; response surface methodology; thrust force; torque;},
abstract = {Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM- and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models.},
issn = {0039-2480}, pages = {351-361}, doi = {10.5545/sv-jme.2017.5188},
url = {https://www.sv-jme.eu/sl/article/modeling-and-prediction-of-thrust-force-and-torque-in-drilling-operations-of-al7075-using-ann-and-rsm-methodologies/}
}
Efkolidis, N.,García Hernández, C.,Huertas Talón, J.,Kyratsis, P. 2018 June 64. Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 64:6
%A Efkolidis, Nikolaos %A García Hernández, César %A Huertas Talón, José Luis %A Kyratsis, Panagiotis %D 2018 %T Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies %B 2018 %9 sustainable manufacturing; Al7075; artificial neural networks; response surface methodology; thrust force; torque; %! Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies %K sustainable manufacturing; Al7075; artificial neural networks; response surface methodology; thrust force; torque; %X Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM- and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models. %U https://www.sv-jme.eu/sl/article/modeling-and-prediction-of-thrust-force-and-torque-in-drilling-operations-of-al7075-using-ann-and-rsm-methodologies/ %0 Journal Article %R 10.5545/sv-jme.2017.5188 %& 351 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 64 %N 6 %@ 0039-2480 %8 2018-06-26 %7 2018-06-26
Efkolidis, Nikolaos, César García Hernández, José Luis Huertas Talón, & Panagiotis Kyratsis. "Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies." Strojniški vestnik - Journal of Mechanical Engineering [Online], 64.6 (2018): 351-361. Web. 20 Dec. 2024
TY - JOUR AU - Efkolidis, Nikolaos AU - García Hernández, César AU - Huertas Talón, José Luis AU - Kyratsis, Panagiotis PY - 2018 TI - Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2017.5188 KW - sustainable manufacturing; Al7075; artificial neural networks; response surface methodology; thrust force; torque; N2 - Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM- and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models. UR - https://www.sv-jme.eu/sl/article/modeling-and-prediction-of-thrust-force-and-torque-in-drilling-operations-of-al7075-using-ann-and-rsm-methodologies/
@article{{sv-jme}{sv-jme.2017.5188},
author = {Efkolidis, N., García Hernández, C., Huertas Talón, J., Kyratsis, P.},
title = {Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {64},
number = {6},
year = {2018},
doi = {10.5545/sv-jme.2017.5188},
url = {https://www.sv-jme.eu/sl/article/modeling-and-prediction-of-thrust-force-and-torque-in-drilling-operations-of-al7075-using-ann-and-rsm-methodologies/}
}
TY - JOUR AU - Efkolidis, Nikolaos AU - García Hernández, César AU - Huertas Talón, José Luis AU - Kyratsis, Panagiotis PY - 2018/06/26 TI - Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 64, No 6 (2018): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2017.5188 KW - sustainable manufacturing, Al7075, artificial neural networks, response surface methodology, thrust force, torque, N2 - Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM- and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models. UR - https://www.sv-jme.eu/sl/article/modeling-and-prediction-of-thrust-force-and-torque-in-drilling-operations-of-al7075-using-ann-and-rsm-methodologies/
Efkolidis, Nikolaos, García Hernández, César, Huertas Talón, José Luis, AND Kyratsis, Panagiotis. "Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 64 Number 6 (26 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 64(2018)6, 351-361
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM- and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models.