Hybrid modeling method for feeding system based on dynamics and deep neural network

Abstract

The invention discloses a hybrid modeling method for a feeding system of a numerical control machine tool. The hybrid modeling method comprises a dynamic basic model and a neural network deviation model based on big data, wherein the dynamics basic model is obtained through dynamics theory analysis and parameter identification; the neural network deviation model is obtained by analyzing and training an instruction sequence, simulation prediction data of the dynamic basic model and actual response data; and the instruction sequence is input into a system mixing model, and the actual response sequence is predicted to obtain a mixed prediction sequence. Compared with a pure dynamic model, the technical scheme of the invention has the advantages that the simulation of a highly nonlinear process (such as a reverse process) is more accurate, and compared with a pure neural network model, the generalization ability under different processing technologies is stronger. Through a hybrid modelingmode, accurate simulation of a complex dynamic feeding system is realized.

Description

technical field [0001] The invention belongs to the field of numerical control, and in particular relates to a hybrid modeling method of a feed system based on dynamics and a deep neural network. Background technique [0002] The machining accuracy of CNC machine tools is closely related to the dynamic performance of the machine tool feed system. The modeling of the machine tool feed system is the basis for realizing control strategy optimization, parameter setting, prediction and compensation of following error and contour error, and improving the dynamic performance of the feed system. In the stage of machine tool design and debugging, the feed system model can be used to analyze the steady-state error and dynamic error to guide the control strategy optimization and parameter setting; in the stage of machine tool put into use, the feed system model can be used to analyze the following error and contour Error prediction and compensation. The current modeling of the feed s...

AI Technical Summary

Problems solved by technology

The technical solution of the present invention aims at the problem that the mathematical physics method adopted by the dynamic model is difficult to accurately simulate complex nonlinear elements, and the single neural network modeling method has weak generalization ability under different processing technologies, through the mixed modeling way to realize the accurate simulation of the complex dynamic feed system

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