Prediction of contact fatigue life of AT40 ceramic coating based on neural network

Abstract

Purpose With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets, Design/methodology/approach A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability. Findings A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case. Practical implications The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited. Originality/value A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.

Fatigue modeling using neural networks: A comprehensive review

“…Besides the FNN, RNN, RBFNN, and MNN, other NN models also appeared in the literature and are reviewed as follows. Zhou et al 92 developed a multiple NN ensemble to predict the contact fatigue life of a ceramic coating. A schematic of NN ensembles is shown in Figure 10.…”

Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

“…It is explored that the self-encoder structure can be used as a feasible method for image end-to-end compression [5,6], but the deep learning-based compression method has not surpassed the traditional compression efficiency and the compression performance of the traditional encoding method. A variable bitrate compression method based on cyclic convolution deep learning (RNN) is proposed [7]. Based on the autoencoder structure, the method encodes images through multiple iterations of the network, so as to achieve variable bitrate coding and progressive coding.…”

Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

“…The principle of the self-encoder is not complicated. It can be understood as a system that attempts to restore the original input, so the self-encoder is often used to automatically learn features and data compression [45] .…”