Skip to main navigation Skip to search Skip to main content

Modeling and simulation of fuzzy neural network control system of penetration in titanium-alloy electron beam welding

  • Lanzhou University of Technology
  • Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

The control system modeling of penetration is studied in titanium-alloy electron beam welding. On the basis of analysing the characteristics of electron beam welding, the orthogonal test of three factors and five levels is designed, the test is done to obtain the values of molten width and penetration under different welding parameters, the values of molten width and penetration are used ??as the training sample to train the neural network, thus a BP neural network model between molten width and penetration is established, the molten width is input, the penetration is output. The model consists of a S-function hidden layer plus a linear output layer. Because the mathematical model for penetration is difficult to obtain, so the fuzzy controller is designed, its input variables are bias and change rate of bias, its output variables are the change of welding current, The controller has nine fuzzy-control rules. The BP neural network model and fuzzy controller are combined to establish fuzzy neural network control-system model of penetration of titanium-alloy electron beam welding, and unit step signal is used to carry out the simulation experiment of the model, the results show that the dynamic performance and steady-state performance of designed control system are eminent.

Original languageEnglish
Pages (from-to)28-32
Number of pages5
JournalJixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering
Volume48
Issue number10
DOIs
StatePublished - 20 May 2012

Keywords

  • Electron beam welding
  • Fuzzy control
  • Neural network

Fingerprint

Dive into the research topics of 'Modeling and simulation of fuzzy neural network control system of penetration in titanium-alloy electron beam welding'. Together they form a unique fingerprint.

Cite this