Paper Title:
Quality Prediction of Laser Cladding Based on Evolutionary Neural Network
  Abstract

Artificial neural networks were introduced in the area of laser cladding forming. The prediction model of surface quality in laser cladding parts, including the width, depth of cladding layer and dilution rate, was proposed based on the improved learned arithmetic. The model combined the global optimization searching performance of the genetic algorithm and localization of the back propagation(BP) neural networks. Five technical parameters were selected to test the reliability of the mode. The simulation and experimental results show that the evolutionary neural network based on genetic algorithm can effectively overcome the problem of falling into local minimum point. This method can get higher accuracy of prediction. It improves the measurement precision with the maximum relative error 2.14% between the predicted content and the real value.

  Info
Periodical
Edited by
Ran Chen
Pages
1012-1017
DOI
10.4028/www.scientific.net/AMM.44-47.1012
Citation
Z. M. Xu, H. B. Wu, Z. H. Hong, "Quality Prediction of Laser Cladding Based on Evolutionary Neural Network", Applied Mechanics and Materials, Vols. 44-47, pp. 1012-1017, 2011
Online since
December 2010
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