Thermal error modeling method based on gray linear regression

Abstract

The invention relates to a thermal error modeling method based on gray linear regression. The method comprises the following steps that first, on the basis of a gray thermal error model, a linear equation is introduced, a gray linear regression combination model is constructed; second, a least square method is used for solving a gray linear regression combination model parameter; third, the gray linear regression model is used for thermal error prediction; fourth, a BP nerve network is used for amending combination model residual errors, and prediction accuracy is improved. According to the method, the shortcoming that a linear regression model does not have exponential growth and cannot describe linear changing trend easily, and a gray thermal error model does not have a linear factor can be overcome, good capacity for solving linear and nonlinear problems is achieved, good effect is achieved for thermal error prediction on an accurate horizontal type machining center is achieved, linear factors and nonlinear factors of thermal error data are considered, the shortcoming of an original single gray model is overcome, and an accurate thermal error prediction value and high fitting degree are acquired.

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 modeling method of a gray linear regression combined model of thermal error of a precision horizontal machining center. Background technique [0002] Thermal error refers to the machining error caused by the thermal deformation of machine tool parts caused by the temperature rise of the machine tool, and the relative position change between the workpiece and the tool. Its main research contents include theoretical analysis of thermal deformation, measurement in thermal error compensation, key Point optimization, thermal error modeling and thermal error compensation are implemented in five parts. Thermal error is the largest error source of CNC machine tools and an important factor affecting machining accuracy, accounting for about 40% to 70% of the total error of machine tools. Therefore, it is necessary to control th...

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Problems solved by technology

However, these thermal error models are relatively simple and cannot fully express the overall picture of thermal error data. The linear regression model adopted has the disadvantages of no exponential growth and difficulty in describing the linear change trend. Linear and nonlinear factors, so the established thermal error model must have the ability to deal with linear and nonlinear problems

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