Laser cladding height prediction based on neural network

Shujuan Jiang*, Weijun Liu, Liangliang Nan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Real-time detection and closed-loop control of laser cladding height is necessary for forming high quality parts. Technological parameters are coupled and the forming process is a non-linear process. A large number of laser parameters affect the quality of the laser cladding surface. Based on the analysis of the influence of laser parameters on cladding height, the BP (Back propagation) neural network prediction model of cladding height is build. The neural network arithmetic is designed and the samples are acquired by laser forming experiment. The training samples are used to train the network to accomplish the mapping relation between input and output of the network. The test samples are used to verify the performance of the trained network. Simulation results indicate that the prediction model has sufficient accuracy. The BP neural network prediction model of cladding height is feasible and valid in theory and in practice. The laser cladding height BP neural network prediction model lays the foundation for real-time height prediction and closed-loop control in laser forming process, and it has great significance for improving the quality of formed parts.

Original languageEnglish (US)
JournalJixie Gongcheng Xuebao/Journal of Mechanical Engineering
Volume45
Issue number3
DOIs
StatePublished - Mar 2009
Externally publishedYes

Keywords

  • Laser cladding height
  • Laser parameters
  • Neural network
  • Prediction model

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Science Applications
  • Applied Mathematics

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