High-order multi-stage auto-regressive distributed lag modeling method of thermal error compensation of numerical control machine

Abstract

The invention discloses a high-order multi-stage auto-regressive distributed lag modeling method of thermal error compensation of a numerical control machine. The method is characterized by comprising the following steps of: providing a high-order multi-stage auto-regressive distributed lag modeling formula of a thermal error of a numerical control machine containing a coefficient to be solved, selecting numerical control machine thermal error lag phases and numerical control machine temperature lag phases as 1, 2, 3 and 4 respectively, and substituting experimental data to fit the coefficient to be solved in the formula according to a least square method so as to determine high-order multi-stage auto-regressive distributed lag models of thermal error compensation of the numerical control machine in different lag phases; substituting the experimental data into each model to obtain a residual sum of squares of each model; and substituting the residual sum of squares of each model into an akaike information criterion to determine an optimal lag phase to determine a high-order multi-stage auto-regressive distributed lag model of a thermal error of the numerical control machine. The invention discloses a modeling method of thermal error compensation of the numerical control machine, which has the advantages of convenience in application, easiness in modeling and high stability, and has higher accuracy than a traditional ADL (automatic data logger) model.

Description

technical field [0001] The invention belongs to the application field of error compensation of numerical control machine tools, and in particular relates to a compensation modeling method for machine tool thermal errors. Background technique [0002] In mechanical processing, due to the thermal deformation caused by the temperature rise of various parts of the machine tool, the original relatively correct position between the tool and the workpiece on the machine tool has changed, resulting in machining errors. A large number of studies have shown that the thermal error is the largest error source of the machine tool, accounting for 30% to 70% of the total error of the machine tool. Therefore, establishing a high-precision mathematical model to model the thermal error of the machine tool to realize the compensation of the thermal error of the machine tool is the key to improving the machining accuracy. key technologies. At present, the thermal error modeling methods of CNC ...

AI Technical Summary

Problems solved by technology

Among them, the traditional multiple regression algorithm is simple and convenient in modeling, but it has low precision and poor stability, and it is difficult to realize high-precision compensation for thermal errors of CNC machine tools

Compared with multiple regression, the neural network model has higher accuracy, but requires a large number of samples for training, complex modeling, and relatively difficult application

The ADL model can improve the compensation accuracy by several times compared with the multiple regression model, but the compensation accuracy is still not enough to realize the thermal error compensation of precision CNC machine tools

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