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Geometry Characteristics Prediction of Single Track Cladding Deposited by High Power Diode Laser Based on Genetic Algorithm and Neural Network

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Abstract

This paper aims to establish a correlation between the process parameters and geometrical characteristics of the sectional profile of the single track cladding deposited by high power diode laser with rectangle beam spot. By applying the genetic algorithm and back propagation neural network, a nonlinear model for predicting the geometry features of the single track cladding is developed. A full factorial design method is used to conduct the experiments, and the experimental results are chosen randomly as training dataset and testing dataset for the neural network. Three main input variables such as laser power, scanning speed, and powder thickness were considered. The performance of the genetic algorithm and back propagation artificial neural network was compared to that of the standard back propagation neural network. To improve the accuracy of the neural network, one-hidden-layer and double-hidden-layer neural network with different architectures were performed. Further, one-output and multi-output neural network are also trained and tested. The results indicate that, by using genetic algorithm, the prediction accuracy of the neural network is significantly improved. Meanwhile, the double-hidden-neural network has higher prediction accuracy than the one-hidden-layer-neural network, while the one-output-neural network has higher prediction accuracy than the multi-output-neural network.

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Abbreviations

STC:

single track cladding

GA:

genetic algorithm

BPNN:

back propagation neural network

ANN:

artificial neural network

FFD:

full factorial design

HPDL:

high power diode laser

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Authors and Affiliations

  1. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, China

    Huaming Liu, Xunpeng Qin, Song Huang, Lei Jin & Yongliang Wang

  2. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, China

    Huaming Liu, Xunpeng Qin, Lei Jin & Yongliang Wang

  3. School of Automotive Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, China

    Huaming Liu, Xunpeng Qin, Song Huang & Kaiyun Lei

Authors
  1. Huaming Liu
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  2. Xunpeng Qin
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  3. Song Huang
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  4. Lei Jin
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  5. Yongliang Wang
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  6. Kaiyun Lei
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Correspondence to Xunpeng Qin.

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Liu, H., Qin, X., Huang, S. et al. Geometry Characteristics Prediction of Single Track Cladding Deposited by High Power Diode Laser Based on Genetic Algorithm and Neural Network. Int. J. Precis. Eng. Manuf. 19, 1061–1070 (2018). https://doi.org/10.1007/s12541-018-0126-8

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  • DOI: https://doi.org/10.1007/s12541-018-0126-8

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