Thermal error prediction model method for electric spindle with variable bearing pre-tightening force

Abstract

The invention discloses a thermal error prediction model method for an electric spindle with variable bearing pre-tightening force, which comprises the following steps: constructing an electric spindle temperature field model, and analyzing the temperature of a heat source and the temperature of a key component; establishing a motorized spindle statics finite element model by using different pretightening force conditions and spindle component parameters changed due to temperature change under the conditions, and analyzing the relationship between the thermal error of the motorized spindle and the pretightening force and the temperature; establishing a grey wolf optimization algorithm (GWO) model, adopting a mode of randomly generating a grey wolf population, initializing positions of alpha, beta and delta wolf of a grey wolf pack, globally searching a fitness optimal value of each body of the wolf pack, and searching a penalty factor (C) and a kernel function width (g) of a support vector regression (SVM) model; building a thermal error prediction model of the SVM variable pre-tightening force motorized spindle, and training the model to enable the model to reach training precision; and finally, through comparison between a BP neural network thermal error prediction model and a DE-GWO-SVM thermal error prediction model, showing that the method has better performance compared with a traditional model.

Description

technical field [0001] The invention relates to the field of thermal error analysis of a high-speed electric spindle, in particular to a modeling method for a thermal error prediction model of an electric spindle based on support vector regression optimized by the Gray Wolf algorithm. Background technique [0002] The bearing in the internal structure of the electric spindle is an important element to support the rotating parts. The bearing provides the necessary support stiffness, load tolerance and rotation accuracy for the electric spindle. Bearing pre-tightening enables the bearing to obtain sufficient rigidity, reduces the vibration of the spindle, and provides the necessary conditions for the stable operation of the electric spindle. [0003] The pretightening force of commonly used spindle bearing pretightening devices is determined based on comprehensive consideration of low-speed heavy cutting, high-speed light cutting and tool change force, and the value of the pre...

AI Technical Summary

Problems solved by technology

[0005] At this stage, most of the electric spindle thermal error prediction models are neural network models, which have high precision and strong linear ability. However, the neural network system cannot predict the results well for new samples, and the practical application will be limited to a certain extent.

Optimizing the neural network model with an optimization algorithm will increase the computational workload, reduce the efficiency of the model, and make it difficult to feed back thermal errors in time

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