Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis

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ÇIÇEK, Adem ;KIVAK, Turgay ;SAMTAŞ, Gürcan ;ÇAY, Yusuf .
Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 58, n.7-8, p. 492-498, october 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/modeling-of-thrust-forces-in-drilling-of-aisi-316-stainless-steel-using-artificial-neural-network-and-multiple-regression-analysis/>. Date accessed: 22 sep. 2021. 
doi:http://dx.doi.org/10.5545/sv-jme.2011.297.
Çiçek, A., Kıvak, T., Samtaş, G., & Çay, Y.
(2012).
Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis.
Strojniški vestnik - Journal of Mechanical Engineering, 58(7-8), 492-498.
doi:http://dx.doi.org/10.5545/sv-jme.2011.297
@article{sv-jmesv-jme.2011.297,
	author = {Adem  Çiçek and Turgay  Kıvak and Gürcan  Samtaş and Yusuf  Çay},
	title = {Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {58},
	number = {7-8},
	year = {2012},
	keywords = {Artificial neural networks, regression analysis, cryogenic treatment, machining, thrust force, predictive modeling},
	abstract = {In this study, the effects of cutting parameters (cutting speed, feed rate) and deep cryogenic treatment on thrust force in drilling of AISI 316 stainless steel have been investigated. To observe the effects of deep cryogenic treatment, M35 HSS twist drills were cryogenically treated at -196 °C for 24h and tempered at 200 °C for 2h after conventional heat treatment. Experimental results showed that the lowest thrust forces were measured with cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. Scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. ANNs results showed that the SCG model with five neurons in the hidden layer produced absolute fraction of variance (R2) of 0,999907 and 0.999871 for training data and test data respectively. Root mean square error (RMSE) was 0.00769 and 0.009066, and mean error percentage (MEP) was 0.725947 and 0.930127 for training and test data, respectively.},
	issn = {0039-2480},	pages = {492-498},	doi = {10.5545/sv-jme.2011.297},
	url = {https://www.sv-jme.eu/article/modeling-of-thrust-forces-in-drilling-of-aisi-316-stainless-steel-using-artificial-neural-network-and-multiple-regression-analysis/}
}
Çiçek, A.,Kıvak, T.,Samtaş, G.,Çay, Y.
2012 October 58. Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 58:7-8
%A Çiçek, Adem 
%A Kıvak, Turgay 
%A Samtaş, Gürcan 
%A Çay, Yusuf 
%D 2012
%T Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis
%B 2012
%9 Artificial neural networks, regression analysis, cryogenic treatment, machining, thrust force, predictive modeling
%! Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis
%K Artificial neural networks, regression analysis, cryogenic treatment, machining, thrust force, predictive modeling
%X In this study, the effects of cutting parameters (cutting speed, feed rate) and deep cryogenic treatment on thrust force in drilling of AISI 316 stainless steel have been investigated. To observe the effects of deep cryogenic treatment, M35 HSS twist drills were cryogenically treated at -196 °C for 24h and tempered at 200 °C for 2h after conventional heat treatment. Experimental results showed that the lowest thrust forces were measured with cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. Scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. ANNs results showed that the SCG model with five neurons in the hidden layer produced absolute fraction of variance (R2) of 0,999907 and 0.999871 for training data and test data respectively. Root mean square error (RMSE) was 0.00769 and 0.009066, and mean error percentage (MEP) was 0.725947 and 0.930127 for training and test data, respectively.
%U https://www.sv-jme.eu/article/modeling-of-thrust-forces-in-drilling-of-aisi-316-stainless-steel-using-artificial-neural-network-and-multiple-regression-analysis/
%0 Journal Article
%R 10.5545/sv-jme.2011.297
%& 492
%P 7
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 58
%N 7-8
%@ 0039-2480
%8 2018-10-11
%7 2018-10-11
Çiçek, Adem, Turgay  Kıvak, Gürcan  Samtaş, & Yusuf  Çay.
"Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis." Strojniški vestnik - Journal of Mechanical Engineering [Online], 58.7-8 (2012): 492-498. Web.  22 Sep. 2021
TY  - JOUR
AU  - Çiçek, Adem 
AU  - Kıvak, Turgay 
AU  - Samtaş, Gürcan 
AU  - Çay, Yusuf 
PY  - 2012
TI  - Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2011.297
KW  - Artificial neural networks, regression analysis, cryogenic treatment, machining, thrust force, predictive modeling
N2  - In this study, the effects of cutting parameters (cutting speed, feed rate) and deep cryogenic treatment on thrust force in drilling of AISI 316 stainless steel have been investigated. To observe the effects of deep cryogenic treatment, M35 HSS twist drills were cryogenically treated at -196 °C for 24h and tempered at 200 °C for 2h after conventional heat treatment. Experimental results showed that the lowest thrust forces were measured with cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. Scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. ANNs results showed that the SCG model with five neurons in the hidden layer produced absolute fraction of variance (R2) of 0,999907 and 0.999871 for training data and test data respectively. Root mean square error (RMSE) was 0.00769 and 0.009066, and mean error percentage (MEP) was 0.725947 and 0.930127 for training and test data, respectively.
UR  - https://www.sv-jme.eu/article/modeling-of-thrust-forces-in-drilling-of-aisi-316-stainless-steel-using-artificial-neural-network-and-multiple-regression-analysis/
@article{{sv-jme}{sv-jme.2011.297},
	author = {Çiçek, A., Kıvak, T., Samtaş, G., Çay, Y.},
	title = {Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {58},
	number = {7-8},
	year = {2012},
	doi = {10.5545/sv-jme.2011.297},
	url = {https://www.sv-jme.eu/article/modeling-of-thrust-forces-in-drilling-of-aisi-316-stainless-steel-using-artificial-neural-network-and-multiple-regression-analysis/}
}
TY  - JOUR
AU  - Çiçek, Adem 
AU  - Kıvak, Turgay 
AU  - Samtaş, Gürcan 
AU  - Çay, Yusuf 
PY  - 2018/10/11
TI  - Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 58, No 7-8 (2012): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2011.297
KW  - Artificial neural networks, regression analysis, cryogenic treatment, machining, thrust force, predictive modeling
N2  - In this study, the effects of cutting parameters (cutting speed, feed rate) and deep cryogenic treatment on thrust force in drilling of AISI 316 stainless steel have been investigated. To observe the effects of deep cryogenic treatment, M35 HSS twist drills were cryogenically treated at -196 °C for 24h and tempered at 200 °C for 2h after conventional heat treatment. Experimental results showed that the lowest thrust forces were measured with cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. Scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. ANNs results showed that the SCG model with five neurons in the hidden layer produced absolute fraction of variance (R2) of 0,999907 and 0.999871 for training data and test data respectively. Root mean square error (RMSE) was 0.00769 and 0.009066, and mean error percentage (MEP) was 0.725947 and 0.930127 for training and test data, respectively.
UR  - https://www.sv-jme.eu/article/modeling-of-thrust-forces-in-drilling-of-aisi-316-stainless-steel-using-artificial-neural-network-and-multiple-regression-analysis/
Çiçek, Adem, Kıvak, Turgay, Samtaş, Gürcan, AND Çay, Yusuf.
"Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 58 Number 7-8 (11 October 2018)

Authors

Affiliations

  • Düzce University, Faculty of Technology, Turkey 1
  • Düzce University, Cumayeri Vocational School of Higher Education, Turkey 2
  • 3
  • Karabük University, Faculty of Engineering, Turkey 4

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 58(2012)7-8, 492-498

https://doi.org/10.5545/sv-jme.2011.297

In this study, the effects of cutting parameters (cutting speed, feed rate) and deep cryogenic treatment on thrust force in drilling of AISI 316 stainless steel have been investigated. To observe the effects of deep cryogenic treatment, M35 HSS twist drills were cryogenically treated at -196 °C for 24h and tempered at 200 °C for 2h after conventional heat treatment. Experimental results showed that the lowest thrust forces were measured with cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. Scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. ANNs results showed that the SCG model with five neurons in the hidden layer produced absolute fraction of variance (R2) of 0,999907 and 0.999871 for training data and test data respectively. Root mean square error (RMSE) was 0.00769 and 0.009066, and mean error percentage (MEP) was 0.725947 and 0.930127 for training and test data, respectively.

Artificial neural networks, regression analysis, cryogenic treatment, machining, thrust force, predictive modeling