Nonlinear Error Compensation of Digital Dial Indicator based on SSA-GA-BP Neural Network
When the digital dial gauge is used for precision measurement,the inherent error and elastic error caused by the production,assembly and use of wear,extrusion and collision of parts seriously reduce the measurement accuracy.Aiming at this problem,using the advantages of genetic algorithm(GA)with fast optimization speed,high precision and parallel search ability,and the global optimization performance of sparrow search algorithm(SSA),the initial weights,thresholds and network structure of back propagation(BP)neural network are optimized,and a SSA-GA-BP neural network model based on nonlinear error compensation of digital dial gauge measurement data is proposed.It is compared with the evaluation indexes of traditional BP neural network and GA-BP neural network optimized by genetic algorithm.The results show that the proposed SSA-GA-BP neural network can reduce the nonlinear error of the digital dial gauge from the maximum error of 5.504 μm before compensation to 0.883 μm within the measurement range.The evaluation index data has certain advantages,and the feasibility of using the neural network model for fitting compensation is verified.
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