Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

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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/article/modeling-and-prediction-of-thrust-force-and-torque-in-drilling-operations-of-al7075-using-ann-and-rsm-methodologies/>. Date accessed: 14 oct. 2019. 
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/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/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.  14 Oct. 2019
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/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/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/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)

Authors

Affiliations

  • University of Zaragoza, Department of Design and Manufacturing Engineering, Spain
  • University of Zaragoza, Department of Design and Manufacturing Engineering, Spain
  • University of Zaragoza, Department of Design and Manufacturing Engineering, Spain
  • Western Macedonia University of Applied Sciences, Department of Mechanical Engineering & Industrial Design, Greece

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 64(2018)6, 351-361

10.5545/sv-jme.2017.5188

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.

sustainable manufacturing; Al7075; artificial neural networks; response surface methodology; thrust force; torque;