A deep learning-based thermal error modeling method for machine tools

Abstract

The invention discloses a machine tool thermal error modeling method based on deep learning, which includes the following steps: first obtain the temperature data of the machine tool spindle, and perform normalization processing; The correlation of the thermal error in the direction, select m measuring points with higher correlation as the key temperature measuring points; establish the SAE network and initialize the network parameters, input the temperature data of the key temperature measuring points into the SAE network as an independent variable, Extract the temperature characteristics of the temperature data; use the temperature characteristics as independent variables, and input the corresponding thermal error data as dependent variables into the GA-BP neural network for training and thermal error prediction. The invention has the advantages of high prediction accuracy, good robustness, and the ability to effectively estimate the thermal error variation trend of the machine tool and the like.

Description

technical field [0001] The invention relates to the technical field of numerical control machine tools, in particular to a method for modeling thermal errors of machine tools based on deep learning. Background technique [0002] Among the various error sources of machine tools, thermal errors and geometric errors are the most important errors in machine tool error sources. 40% to 70% of the errors of high-end CNC machine tools are determined by thermal deformation. Thermal error has become the main source of error affecting the machining accuracy of parts. Establishing a model that can accurately describe the thermal deformation of the machine tool is the basis and decisive factor for thermal error compensation. Existing studies usually use multiple linear regression models, finite element models, least squares support vector machines, support vector machines, gray theory and neural network models and other modeling methods. Wang Xiushan and others established a multiple l...

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Problems solved by technology

High-dimensional data will produce dimensionality disaster, which will result in good modeling fitting effect but poor prediction effect; at the same time, the model established by traditional methods will show a strong phase effect due to environmental and seasonal changes, especially In the case of small sample size, it is difficult to meet the robustness requirements of the thermal error model, which restricts the implementation of thermal error compensation technology to a certain extent

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