Determination of material properties of functionally graded hollow cylinders using artificial neural network
Using guided circumferential wave dispersion characteristics, an inverse method based on artificial neural network (ANN) is presented to determine the material properties of Functionally Graded Materials (FGM) pipes. The group velocities of several lowest modes at several lower frequencies are used as the inputs of the ANN model; the outputs of the ANN are the distribution function of the volume fraction of the FGM pipe. The Legendre polynomials method is used to calculate the dispersion curves for the FGM pipe. The internally recurrent neural network is used to improve the convergence speed.
circumferential wave neural network material properties Functionally Graded Materials hollow cylinder
Yu Jiangong
School of Mechanical and Power Engineering Henan Polytechnic University Jiaozuo 454003, P.R. China
国际会议
张家界
英文
202-205
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）