Roughness prediction of kerf cut with fiber laser based on BP artificial neural networks

GUO Huafeng; LI Juli; SUN Tao

doi:10.7510/jgjs.issn.1001-3806.2014.06.016

Volume 38 Issue 6

Sep. 2014

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Citation:

Roughness prediction of kerf cut with fiber laser based on BP artificial neural networks

1.
Department of Mechanical and Electronic Engineering, Xuzhou Institute of Technology, Xuzhou 221008, China

Received Date: 2013-11-29
Accepted Date: 2014-02-19

Abstract

In order to study effects of process parameters on kerf quality of fiber laser cutting, the relationship between process parameters and kerf quality was analyzed based on the test of laser cutting T4003 stainless steel. The prediction model between the main process parameters, such as laser power, cutting speed, assistant gas pressure and kerf roughness was established based on error back propagation artificial neural network. The samples collected by the cutting test was network trained and the training model was inspected by the test samples. The results show that, kerf roughness increases while laser power increases and kerf roughness decreases while cutting speed and assist gas pressure increase. The neural network prediction model has high precision and the network training has good effect. The maximum relative error between the predictive values and the test sample value is 2.4%. After training, the prediction model has high inspection precision, the maximum relative error of the test sample is only 6.23%. The model can predict the laser cutting kerf roughness effectively and can provide the experiment basis for selecting and optimizing process parameters and improving laser cutting quality.
- laser technique,
- kerf quality,
- back propagation artificial neural network,
- roughness,
- prediction

References

[1]	SCINTILLA L D, TRICARICO L. Estimating cutting front temperature difference in disk and CO2 laser beam fusion cutting[J]. Optics Laser Technology, 2012, 44(5):1468-1479.
[2]	PANDEY A K, DUBEY A K. Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet[J]. Optics Laser Technology, 2012, 44(6):1858-1865.
[3]	MUHAMMADA N, WHITEHEADA D, BOORB A, et al. Comparison of dry and wet fibre laser profile cutting of thin 316L stainless steel tubes for medical device applications[J]. Journal of Materials Processing Technology, 2010, 210(15): 2261-2267.
[4]	CHOUDHURY I A, SHIRLEY S. Laser cutting of polymeric materials: an experimental investigation [J]. Optics Laser Technology, 2010, 42(3):503-508.
[5]	WANG X H, YAO J H, ZHOU G B, et al. Numerical simulation and experiment of laser cutting liquid crystal display glass substrates[J]. Chinese Journal of Lasers, 2011, 38(6): 1-5 (in Chinese).
[6]	CHEN X Ch, JI L F, BAO Y, et al. Improving cutting quality by analysis of microstructure characteristics and solidification behaviour of recast layer formation on laser cut ceramic[J]. Journal of the European Ceramic Society, 2012, 32(10): 2203-2211.
[7]	JI L F, YAN Y Zh, BAO Y, et al. Crack-free cutting of thick and dense ceramics with CO2 laser by single-pass process[J]. Optics and Lasers in Engineering, 2008, 46(10): 785-790.
[8]	ELTAWAHNI H A, ROSSINI N S, DASSISTI M, et al. Evalaution and optimization of laser cutting parameters for plywood materials[J]. Optics and Lasers in Engineering, 2013, 51(9): 1029-1043.
[9]	RIVEIRO A, QUINTERO F, LUSQUIN~OS F, et al. Experimental study on the CO2 laser cutting of carbon fiber reinforced plastic composite[J]. Composites, 2012, A43(7):1400-1409.
[10]	LAMIKIZ A, LPEZ de LCALLE L N, SANCHEZ J A, et al. CO2 laser cutting of advanced high strength steels (AHSS)[J]. Applied Surface Science, 2005, 242(3/4): 362-368.
[11]	KIM M J. 3-D finite element analysis of evaporative laser cutting[J]. Applied Mathematical Modelling, 2005,29(10):938-954.
[12]	YILBAS B S. Laser cutting of thick sheet metals: Effects of cuttingparameters on kerf size variations[J]. Journal of Materials Processing Technology, 2008, 201(1/3):285-290.
[13]	YAN C, LI L J, LI J, et al. Review of surface quality study on laser sheets cutting[J]. Laser Technology, 2005, 29(3):270-274 (in Chinese).
[14]	TONG G, XU H, YU H Q. Control model of laser cutting quality based on simulated annealing and BP neural network [J]. Machinery Design Manufacture, 2012(6):85-87 (in Chinese).
[15]	LIU J, GUO Q D,WANG Y H. Optimizing the laser-cutting parameters by using improved artificial neural network [J]. Laser Journal,2007, 28(6): 80-81 (in Chinese).
[16]	WU Y H, ZHANG Y Q, CHEN W Zh, et al. Application of RBF Neural Network in three dimensional laser cutting[J].China Mechanical Engineering, 2006, 17(12): 1234-1237(in Chinese).
[17]	LIU Y, XU D, TAN M. A genetic algorithm based artificial neural network approach for parameter selection of laser cutting [J]. Manufacturing Automation, 2006, 28(12): 20-22(in Chinese).
[18]	CHEN J M, ZUO T Ch. Analysis of artificial neural network on 3-D orientation CO2 laser cutting [J]. Chinese Journal of Lasers, 2004, 31(3): 245-248 (in Chinese).
[19]	FENG W J, QIN F D, CHEN Y Y, et al. Analysis of technological parameter for laser cutting stainless steel sheet[J]. Machinery Design Manufacture, 2011(11):191-192(in Chinese).
[20]	RIVEIRO A, QUINTERO F, LUSQUIN~OS F, et al. Influence of assist gas nature on the surfaces obtained by laser cutting of Al-Cu alloys [J]. Surface Coatings Technology,2010, 205(7): 1878-1885.
[21]	YANG D H, MA L, HUANG W D. Component's surface quality predicitons by laser rapid forming based on artifical neural networks[J].Chinese Journal of Lasers, 2011, 38(8): 1-5 (in Chinese).
[22]	YU J H, LI X N, ZHAI Y, et al. BP ANN model for annealing parameter and hardness of zirconium base alloy[J]. Rare Metal Materials and Engineering, 2012, 41(8): 1346-1350 (in Chinese).

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Roughness prediction of kerf cut with fiber laser based on BP artificial neural networks

1. Department of Mechanical and Electronic Engineering, Xuzhou Institute of Technology, Xuzhou 221008, China

Received Date: 2013-11-29

Accepted Date: 2014-02-19

Available Online: 2014-11-25

Abstract: In order to study effects of process parameters on kerf quality of fiber laser cutting, the relationship between process parameters and kerf quality was analyzed based on the test of laser cutting T4003 stainless steel. The prediction model between the main process parameters, such as laser power, cutting speed, assistant gas pressure and kerf roughness was established based on error back propagation artificial neural network. The samples collected by the cutting test was network trained and the training model was inspected by the test samples. The results show that, kerf roughness increases while laser power increases and kerf roughness decreases while cutting speed and assist gas pressure increase. The neural network prediction model has high precision and the network training has good effect. The maximum relative error between the predictive values and the test sample value is 2.4%. After training, the prediction model has high inspection precision, the maximum relative error of the test sample is only 6.23%. The model can predict the laser cutting kerf roughness effectively and can provide the experiment basis for selecting and optimizing process parameters and improving laser cutting quality.

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Reference (22)

[1]	SCINTILLA L D, TRICARICO L. Estimating cutting front temperature difference in disk and CO2 laser beam fusion cutting[J]. Optics Laser Technology, 2012, 44(5):1468-1479.
[2]	PANDEY A K, DUBEY A K. Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet[J]. Optics Laser Technology, 2012, 44(6):1858-1865.
[3]	MUHAMMADA N, WHITEHEADA D, BOORB A, et al. Comparison of dry and wet fibre laser profile cutting of thin 316L stainless steel tubes for medical device applications[J]. Journal of Materials Processing Technology, 2010, 210(15): 2261-2267.
[4]	CHOUDHURY I A, SHIRLEY S. Laser cutting of polymeric materials: an experimental investigation [J]. Optics Laser Technology, 2010, 42(3):503-508.
[5]	WANG X H, YAO J H, ZHOU G B, et al. Numerical simulation and experiment of laser cutting liquid crystal display glass substrates[J]. Chinese Journal of Lasers, 2011, 38(6): 1-5 (in Chinese).
[6]	CHEN X Ch, JI L F, BAO Y, et al. Improving cutting quality by analysis of microstructure characteristics and solidification behaviour of recast layer formation on laser cut ceramic[J]. Journal of the European Ceramic Society, 2012, 32(10): 2203-2211.
[7]	JI L F, YAN Y Zh, BAO Y, et al. Crack-free cutting of thick and dense ceramics with CO2 laser by single-pass process[J]. Optics and Lasers in Engineering, 2008, 46(10): 785-790.
[8]	ELTAWAHNI H A, ROSSINI N S, DASSISTI M, et al. Evalaution and optimization of laser cutting parameters for plywood materials[J]. Optics and Lasers in Engineering, 2013, 51(9): 1029-1043.
[9]	RIVEIRO A, QUINTERO F, LUSQUIN~OS F, et al. Experimental study on the CO2 laser cutting of carbon fiber reinforced plastic composite[J]. Composites, 2012, A43(7):1400-1409.
[10]	LAMIKIZ A, LPEZ de LCALLE L N, SANCHEZ J A, et al. CO2 laser cutting of advanced high strength steels (AHSS)[J]. Applied Surface Science, 2005, 242(3/4): 362-368.
[11]	KIM M J. 3-D finite element analysis of evaporative laser cutting[J]. Applied Mathematical Modelling, 2005,29(10):938-954.
[12]	YILBAS B S. Laser cutting of thick sheet metals: Effects of cuttingparameters on kerf size variations[J]. Journal of Materials Processing Technology, 2008, 201(1/3):285-290.
[13]	YAN C, LI L J, LI J, et al. Review of surface quality study on laser sheets cutting[J]. Laser Technology, 2005, 29(3):270-274 (in Chinese).
[14]	TONG G, XU H, YU H Q. Control model of laser cutting quality based on simulated annealing and BP neural network [J]. Machinery Design Manufacture, 2012(6):85-87 (in Chinese).
[15]	LIU J, GUO Q D,WANG Y H. Optimizing the laser-cutting parameters by using improved artificial neural network [J]. Laser Journal,2007, 28(6): 80-81 (in Chinese).
[16]	WU Y H, ZHANG Y Q, CHEN W Zh, et al. Application of RBF Neural Network in three dimensional laser cutting[J].China Mechanical Engineering, 2006, 17(12): 1234-1237(in Chinese).
[17]	LIU Y, XU D, TAN M. A genetic algorithm based artificial neural network approach for parameter selection of laser cutting [J]. Manufacturing Automation, 2006, 28(12): 20-22(in Chinese).
[18]	CHEN J M, ZUO T Ch. Analysis of artificial neural network on 3-D orientation CO2 laser cutting [J]. Chinese Journal of Lasers, 2004, 31(3): 245-248 (in Chinese).
[19]	FENG W J, QIN F D, CHEN Y Y, et al. Analysis of technological parameter for laser cutting stainless steel sheet[J]. Machinery Design Manufacture, 2011(11):191-192(in Chinese).
[20]	RIVEIRO A, QUINTERO F, LUSQUIN~OS F, et al. Influence of assist gas nature on the surfaces obtained by laser cutting of Al-Cu alloys [J]. Surface Coatings Technology,2010, 205(7): 1878-1885.
[21]	YANG D H, MA L, HUANG W D. Component's surface quality predicitons by laser rapid forming based on artifical neural networks[J].Chinese Journal of Lasers, 2011, 38(8): 1-5 (in Chinese).
[22]	YU J H, LI X N, ZHAI Y, et al. BP ANN model for annealing parameter and hardness of zirconium base alloy[J]. Rare Metal Materials and Engineering, 2012, 41(8): 1346-1350 (in Chinese).

Roughness prediction of kerf cut with fiber laser based on BP artificial neural networks

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通讯作者: 陈斌, bchen63@163.com

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Roughness prediction of kerf cut with fiber laser based on BP artificial neural networks

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