Artificial neural network base on grey relational analysis estimate displacement of bridge-type amplifier

Abstract

Abstract The power of complaint mechanisms is able for a dramatic reduction in the number of parts required to compass a specified task. It may reduce manufacturing and assembly time and cost. Reduce the number of joints can also increase mechanism precision. The output displacement must be larger than input displacement with lower stress. Thus, Optimize geometry-shape for besting parameter design of the structure was investigated in this paper. The part was designed by Autodesk Inventor and determined displacement amplifier and stress by FEM in ANSYS. We estimate the displacement of a bridge-type amplifier using Artificial Neural Network (ANN) and regression method (RM) base on Grey relational analysis (GRA). The variability of the thickness, the incline angle, the length and the width of the flexure hinges (FH) affects the variability of target displacement (DI) and stress (ST). The influence of the width and the area of the location force is very small and not clear. The result of the simulation is verified by ANOVA and also compared with the predicted value of ANN, GRA, and RM. The displacement of a bridge-type amplifier is reached 0.95 mm with displacement input 0.01 mm. The ratio amplifier is got 95 times.