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.

• Periodical: Applied Mechanics and Materials (Volumes 44-47)
• Pages: 1012-1017
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.44-47.1012
• Online since: December 2010
• Authors: Zhao Mei Xu, Hai Bing Wu, Zong Hai Hong
• Keywords: Artificial Neural Network (ANN), Genetic Algorithm (GA), Laser Cladding Forming (LCF), Laser Technique, Quality of Cladding Layer

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