A Neural Network Error Compensation Technology for Precision Grinding Based on Genetic Algorithm

By comprehensively analyzing the error information in the grinding process and using the basic method of artificial neural network, a precision grinding error compensation model based on neural network theory is established, and its structure and algorithm are described in detail. The hardware implementation method of real-time error compensation for grinding is given, and the compensation ability of the error compensation system is improved through the reasonable selection of samples and the learning process of the system. Considering the influence of signal transmission delay, voltage signal fluctuation and mechanical system inertia in the actual machining process, an adaptive control strategy is proposed to make the compensation process transition smoothly and reduce the influence of unknown conditions.