Recognition of 3D Surface Fractal Dimension Based on Convolutional Neural Network
Fractal dimension,as an important parameter for the surface morphology of mechanical machining,can be used to analyze the friction characteristics of contacting surfaces.However,most existing methods for calculating fractal di-mensions require selecting multiple scales to compute the corresponding measures,which not only affects the calculation speed and accuracy of fractal dimensions but also increases the complexity of computation.A recognition method of fractal dimension based on convolutional neural network(CNN)was proposed for the measurement of three-dimensional(3D)fractal dimension of machined surface.A 3D rough surface dataset containing different fractal dimensions was constructed using the Weierstrass-Mandelbrot fractal function.The single factor experiment method was used to analyze the influence of the network parameters(network depth,filter size,filter number)on the recognition accuracy of 3D fractal dimension,in order to find the optimal combination of neural network parameters.By comparing with the three methods of differential box dimension method,triangular prism surface area method and fractal Brownian motion method,the effectiveness of convolu-tional neural network method in identifying 3D fractal dimension was verified.The experimental results show that the aver-age absolute percentage error of the fractal dimension calculated by the convolutional neural network method can be con-trolled below 1.5％.The proposed method exhibits small errors across the entire dynamic range of fractal dimensions and can be used to calculate the fractal dimension of 3D surface profiles.
万方数据
维普
