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

  • Huaming Liu
  • Xunpeng QinEmail author
  • Song Huang
  • Lei Jin
  • Yongliang Wang
  • Kaiyun Lei
Regular Paper


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.


Single track cladding High power diode laser Genetic algorithm Back propagation artificial neural network Full factorial design 



single track cladding


genetic algorithm


back propagation neural network


artificial neural network


full factorial design


high power diode laser


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Copyright information

© Korean Society for Precision Engineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hubei Key Laboratory of Advanced Technology for Automotive ComponentsWuhan University of TechnologyWuhanChina
  2. 2.Hubei Collaborative Innovation Center for Automotive Components TechnologyWuhan University of TechnologyWuhanChina
  3. 3.School of Automotive EngineeringWuhan University of TechnologyWuhanChina

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