Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method

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KLANCNIK, Simon ;BEGIC-HAJDAREVIC, Derzija ;PAULIC, Matej ;FICKO, Mirko ;CEKIC, Ahmet ;COHODAR HUSIC, Maida .
Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 61, n.12, p. 714-720, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/prediction-of-laser-cut-quality-for-tungsten-alloy-using-the-neural-network-method/>. Date accessed: 29 sep. 2021. 
doi:http://dx.doi.org/10.5545/sv-jme.2015.2717.
Klancnik, S., Begic-Hajdarevic, D., Paulic, M., Ficko, M., Cekic, A., & Cohodar Husic, M.
(2015).
Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method.
Strojniški vestnik - Journal of Mechanical Engineering, 61(12), 714-720.
doi:http://dx.doi.org/10.5545/sv-jme.2015.2717
@article{sv-jmesv-jme.2015.2717,
	author = {Simon  Klancnik and Derzija  Begic-Hajdarevic and Matej  Paulic and Mirko  Ficko and Ahmet  Cekic and Maida  Cohodar Husic},
	title = {Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {61},
	number = {12},
	year = {2015},
	keywords = {laser cutting; cut quality; artificial neural network; tungsten alloy},
	abstract = {The cut quality is of great importance during the laser cutting process. The quality of laser cut mainly depends on an appropriate selection of process parameters. In this paper, the effect of process parameters was analysed on the laser cut quality of an uncommon alloy, the tungsten alloy (W ≈ 92.5 % and the remainder Fe and Ni) sheet with thickness of 1 mm. This alloy has a wide application in different industrial areas, e.g. in medical applications, the automobile sectors, and the aircraft industry. This paper introduces a developed back-propagation artificial neural network (BP- ANN) model for the analysis and prediction of cut quality during the CO2 laser cutting process. In the presented study, three input process parameters were considered such as laser power, cutting speed and assist gas type, and two output parameters such as kerf width and average surface roughness. Amongst the 42 experimental results, 34 data sets were chosen for training the network, whilst the remaining 8 results were used as test data. The average prediction error was found to be 5.5 % for kerf width and 9.5 % for surface roughness. The results of the predicted kerf width and surface roughness by the BP-ANN model were compared with experimental data. Based on the results of the study, it was shown that the proposed artificial neural network model could be a useful tool for analysing and predicting surface roughness and kerf width during CO2 laser cutting processes.},
	issn = {0039-2480},	pages = {714-720},	doi = {10.5545/sv-jme.2015.2717},
	url = {https://www.sv-jme.eu/sl/article/prediction-of-laser-cut-quality-for-tungsten-alloy-using-the-neural-network-method/}
}
Klancnik, S.,Begic-Hajdarevic, D.,Paulic, M.,Ficko, M.,Cekic, A.,Cohodar Husic, M.
2015 June 61. Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 61:12
%A Klancnik, Simon 
%A Begic-Hajdarevic, Derzija 
%A Paulic, Matej 
%A Ficko, Mirko 
%A Cekic, Ahmet 
%A Cohodar Husic, Maida 
%D 2015
%T Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method
%B 2015
%9 laser cutting; cut quality; artificial neural network; tungsten alloy
%! Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method
%K laser cutting; cut quality; artificial neural network; tungsten alloy
%X The cut quality is of great importance during the laser cutting process. The quality of laser cut mainly depends on an appropriate selection of process parameters. In this paper, the effect of process parameters was analysed on the laser cut quality of an uncommon alloy, the tungsten alloy (W ≈ 92.5 % and the remainder Fe and Ni) sheet with thickness of 1 mm. This alloy has a wide application in different industrial areas, e.g. in medical applications, the automobile sectors, and the aircraft industry. This paper introduces a developed back-propagation artificial neural network (BP- ANN) model for the analysis and prediction of cut quality during the CO2 laser cutting process. In the presented study, three input process parameters were considered such as laser power, cutting speed and assist gas type, and two output parameters such as kerf width and average surface roughness. Amongst the 42 experimental results, 34 data sets were chosen for training the network, whilst the remaining 8 results were used as test data. The average prediction error was found to be 5.5 % for kerf width and 9.5 % for surface roughness. The results of the predicted kerf width and surface roughness by the BP-ANN model were compared with experimental data. Based on the results of the study, it was shown that the proposed artificial neural network model could be a useful tool for analysing and predicting surface roughness and kerf width during CO2 laser cutting processes.
%U https://www.sv-jme.eu/sl/article/prediction-of-laser-cut-quality-for-tungsten-alloy-using-the-neural-network-method/
%0 Journal Article
%R 10.5545/sv-jme.2015.2717
%& 714
%P 7
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 61
%N 12
%@ 0039-2480
%8 2018-06-27
%7 2018-06-27
Klancnik, Simon, Derzija  Begic-Hajdarevic, Matej  Paulic, Mirko  Ficko, Ahmet  Cekic, & Maida  Cohodar Husic.
"Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method." Strojniški vestnik - Journal of Mechanical Engineering [Online], 61.12 (2015): 714-720. Web.  29 Sep. 2021
TY  - JOUR
AU  - Klancnik, Simon 
AU  - Begic-Hajdarevic, Derzija 
AU  - Paulic, Matej 
AU  - Ficko, Mirko 
AU  - Cekic, Ahmet 
AU  - Cohodar Husic, Maida 
PY  - 2015
TI  - Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2015.2717
KW  - laser cutting; cut quality; artificial neural network; tungsten alloy
N2  - The cut quality is of great importance during the laser cutting process. The quality of laser cut mainly depends on an appropriate selection of process parameters. In this paper, the effect of process parameters was analysed on the laser cut quality of an uncommon alloy, the tungsten alloy (W ≈ 92.5 % and the remainder Fe and Ni) sheet with thickness of 1 mm. This alloy has a wide application in different industrial areas, e.g. in medical applications, the automobile sectors, and the aircraft industry. This paper introduces a developed back-propagation artificial neural network (BP- ANN) model for the analysis and prediction of cut quality during the CO2 laser cutting process. In the presented study, three input process parameters were considered such as laser power, cutting speed and assist gas type, and two output parameters such as kerf width and average surface roughness. Amongst the 42 experimental results, 34 data sets were chosen for training the network, whilst the remaining 8 results were used as test data. The average prediction error was found to be 5.5 % for kerf width and 9.5 % for surface roughness. The results of the predicted kerf width and surface roughness by the BP-ANN model were compared with experimental data. Based on the results of the study, it was shown that the proposed artificial neural network model could be a useful tool for analysing and predicting surface roughness and kerf width during CO2 laser cutting processes.
UR  - https://www.sv-jme.eu/sl/article/prediction-of-laser-cut-quality-for-tungsten-alloy-using-the-neural-network-method/
@article{{sv-jme}{sv-jme.2015.2717},
	author = {Klancnik, S., Begic-Hajdarevic, D., Paulic, M., Ficko, M., Cekic, A., Cohodar Husic, M.},
	title = {Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {61},
	number = {12},
	year = {2015},
	doi = {10.5545/sv-jme.2015.2717},
	url = {https://www.sv-jme.eu/sl/article/prediction-of-laser-cut-quality-for-tungsten-alloy-using-the-neural-network-method/}
}
TY  - JOUR
AU  - Klancnik, Simon 
AU  - Begic-Hajdarevic, Derzija 
AU  - Paulic, Matej 
AU  - Ficko, Mirko 
AU  - Cekic, Ahmet 
AU  - Cohodar Husic, Maida 
PY  - 2018/06/27
TI  - Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 61, No 12 (2015): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2015.2717
KW  - laser cutting, cut quality, artificial neural network, tungsten alloy
N2  - The cut quality is of great importance during the laser cutting process. The quality of laser cut mainly depends on an appropriate selection of process parameters. In this paper, the effect of process parameters was analysed on the laser cut quality of an uncommon alloy, the tungsten alloy (W ≈ 92.5 % and the remainder Fe and Ni) sheet with thickness of 1 mm. This alloy has a wide application in different industrial areas, e.g. in medical applications, the automobile sectors, and the aircraft industry. This paper introduces a developed back-propagation artificial neural network (BP- ANN) model for the analysis and prediction of cut quality during the CO2 laser cutting process. In the presented study, three input process parameters were considered such as laser power, cutting speed and assist gas type, and two output parameters such as kerf width and average surface roughness. Amongst the 42 experimental results, 34 data sets were chosen for training the network, whilst the remaining 8 results were used as test data. The average prediction error was found to be 5.5 % for kerf width and 9.5 % for surface roughness. The results of the predicted kerf width and surface roughness by the BP-ANN model were compared with experimental data. Based on the results of the study, it was shown that the proposed artificial neural network model could be a useful tool for analysing and predicting surface roughness and kerf width during CO2 laser cutting processes.
UR  - https://www.sv-jme.eu/sl/article/prediction-of-laser-cut-quality-for-tungsten-alloy-using-the-neural-network-method/
Klancnik, Simon, Begic-Hajdarevic, Derzija, Paulic, Matej, Ficko, Mirko, Cekic, Ahmet, AND Cohodar Husic, Maida.
"Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 61 Number 12 (27 June 2018)

Avtorji

Inštitucije

  • University of Maribor, Faculty of Mechanical Engineering, Slovenia 1
  • University of Sarajevo, Faculty of Mechanical Engineering, Bosnia and Herzegovina 2

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 61(2015)12, 714-720

https://doi.org/10.5545/sv-jme.2015.2717

The cut quality is of great importance during the laser cutting process. The quality of laser cut mainly depends on an appropriate selection of process parameters. In this paper, the effect of process parameters was analysed on the laser cut quality of an uncommon alloy, the tungsten alloy (W ≈ 92.5 % and the remainder Fe and Ni) sheet with thickness of 1 mm. This alloy has a wide application in different industrial areas, e.g. in medical applications, the automobile sectors, and the aircraft industry. This paper introduces a developed back-propagation artificial neural network (BP- ANN) model for the analysis and prediction of cut quality during the CO2 laser cutting process. In the presented study, three input process parameters were considered such as laser power, cutting speed and assist gas type, and two output parameters such as kerf width and average surface roughness. Amongst the 42 experimental results, 34 data sets were chosen for training the network, whilst the remaining 8 results were used as test data. The average prediction error was found to be 5.5 % for kerf width and 9.5 % for surface roughness. The results of the predicted kerf width and surface roughness by the BP-ANN model were compared with experimental data. Based on the results of the study, it was shown that the proposed artificial neural network model could be a useful tool for analysing and predicting surface roughness and kerf width during CO2 laser cutting processes.

laser cutting; cut quality; artificial neural network; tungsten alloy