Prediction of Corrosion Resistance of B10 Copper-Nickel Alloy Based on Optimal Convolutional Neural Network

Abstract

Aiming at the defects of insufficient number of corrosion resistance prediction models and simple feature extraction of B10 copper-nickel alloy, a corrosion resistance prediction model of B10 copper-nickel alloy based on optimized convolutional neural network is proposed. The convolutional neural network architecture for grain boundary image characteristics is proposed to conclude a step-by-step convolution operation by analysing the traditional convolution operation process, and it proves theoretically that such a step-by-step convolution operation can reduce the consumption of additional parameters; learning pool proposed single-channel operation can reduce the loss of feature information; the multi-layer feature fusion learning strategy makes the expressive force of deep network extraction features diversified. The results of multiple experiments demonstrate that the improved algorithm introduced can improve the prediction accuracy of the model in image classification comparing with the traditional convolutional neural network model, and the improved convolutional neural network model can better achieve the prediction of corrosion resistance of B10 copper-nickel alloy.