Quality Optimization of Laser-Cutted Ni-Based Alloys Based on Genetic Algorithm

Yiying Zhang; Yan Cao; Yuxiang Chen; Xiangwei Mu

doi:10.3788/LOP55.111404

[1] Feng W Q, Zhang Y F, Tao W et al. Dissimilar metals combination of superalloy/stainless steel in T-shape by laser penetration welding[J]. Chinese Journal of Lasers, 39, 1003003(2012).

[2] Luo D, Lu Y Y, Guo X X et al. Laser repairing process of V-groove in single-crystal superalloy[J]. Chinese Journal of Lasers, 43, 0503001(2016).

[3] Ming X L, Chen J, Tan H et al. Research on persistent fracture mechanism of laser forming repaired GH4169 superalloy[J]. Chinese Journal of Lasers, 42, 0403005(2015).

[4] Chen Y X, Gao L. Analysis of processing parameters for laser cutting high-temperature alloy steel by orthogonal method[J]. Laser & Optoelectronics Progress, 53, 111403(2016).

[5] Yu T C, Chen Y X, Yue X et al. Parameters of laser cutting Ni-based alloy steel with factors interaction[J]. Laser & Optoelectronics Progress, 55, 021413(2018).

[6] Zhao J P, Peng Y H, Nan Y B et al. Analysis and prediction of 2Al2 aluminum alloy cutting process parameter[J]. Journal of Shaanxi University of Technology (Natural Science Edition), 33, 28-33(2017).

[7] Chen J M, Zuo T C. Analysis of artificial neural network on 3D orientation CO2 laser cutting[J]. Chinese Journal of Lasers, 31, 245-248(2004).

[8] Guo H F, Li J L, Sun T. Roughness prediction of kerf cut with fiber laser based on BP artificial neural networks[J]. Laser Technology, 38, 798-803(2014).

[9] Li J M, Li G Z, Wang C M et al. Design of quality prediction system of laser cutting based on MATLAB neural network[J]. Applied Laser, 34, 122-125(2014).

[10] Ding H, Li Y W, Yuan D Q. Roughness prediction of laser drilling pore surface based on back-propagation artificial neural networks[J]. Laser & Optoelectronics Progress, 54, 011407(2017).

[11] Tong G, Xu H, Yu H Q[J]. Control model of laser cutting quality based on simulated annealing and BP neural network Machinery Design & Manufacture, 2012, 85-87.

[12] Badkar D S, Pandey K S, Buvanashekaran G. Development of RSM-and ANN-based models to predict and analyze the effects of process parameters of laser-hardened commercially pure titanium on heat input and tensile strength[J]. The International Journal of Advanced Manufacturing Technology, 65, 1319-1338(2013). http://link.springer.com/article/10.1007/s00170-012-4259-0

[13] Chaki S, Ghosal S. Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel[J]. Production Engineering, 5, 251-262(2011). http://link.springer.com/article/10.1007/s11740-011-0298-x

[14] Cao F G[M]. Laser beam machining, 67(2015).

[15] Han L Q, Shi Y[M]. Artificial neural network theory and application, 51-52(2016).

[16] Wang X C, Shi F, Yu L et al[M]. Matlab neural networks 43 cases analysis, 36-38(2013).

[17] Yu S G, Zhang L, Xie W L et al. Fast and high-resolution spectrum analysis of optical frequency domain reflectometry based on genetic algorithm and Zoom FFT[J]. Acta Optica Sinica, 37, 0406003(2017).

[18] Zhou X Y, Zhang C, Kuang S Q et al. Analysis of optical thin film structure based on quantum-inspired genetic algorithm[J]. Chinese Journal of Lasers, 44, 1203002(2017).

[19] Gao H, Xue L Y. Back propagation neural network based on improved genetic algorithm fitting LED spectral model[J]. Laser & Optoelectronics Progress, 54, 072302(2017).