Quality prediction of laser cladding layer based on improved neural network

XU Da-peng; ZHOU Jian-zhong; GUO Hua-feng; JI Xia

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,was proposed based on the improved learned arithmetic.The model combined the global optimization searching performance of the genetic algorithm and the localization of the back propagation(BP) neural networks.Five technical parameters were selected to test the reliability of the model.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.

Corresponding author: ZHOU Jian-zhong, zhoujz@ujs.edu.cn;

1. School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China

Received Date: 2006-07-26

Accepted Date: 2006-09-06

Available Online: 2007-10-25

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,was proposed based on the improved learned arithmetic.The model combined the global optimization searching performance of the genetic algorithm and the localization of the back propagation(BP) neural networks.Five technical parameters were selected to test the reliability of the model.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.

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Reference (10)

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Quality prediction of laser cladding layer based on improved neural network

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