Path Synthesis of Crank-Rocker Mechanism Using Fourier Descriptors Based Neural Network

In this paper, the first five harmonics components of Fourier descriptors are used to describe the desired coupler curve with a high precision. Neural network is trained with the normalized Fourier descriptors of the coupler angle functions of relative length four-bar mechanisms and corresponding linkages lengths in the established database. Trained neural network is used to predict the relative linkage lengths of desired crank-rocker mechanism with the normalized Fourier descriptors of the desired coupler curve. With the inner connection between harmonics components of the desired coupler curve and the coupler angle function of predicted relative lengths of four-bar mechanism, the linkage lengths, coupler and configuration parameters of the desired mechanism are calculated. A group of desired coupler trajectories with known mechanism configurations are generated randomly and are used to test the proposed method. The results show that with generated neural network, a high precision four-bar mechanism can be obtained without searching the whole database.

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