A method and system for predicting thermal deformation of a CNC machine tool spindle

Abstract

The invention belongs to the field of thermal error measurement of machine tools, and specifically discloses a method and system for predicting thermal deformation of a CNC machine tool spindle, which uses a model building module to construct a neural network model, and the neural network model is based on the motion state data and thermal parameters of the machine tool before the current moment. The deformation state data is used as input, and the thermal deformation of the spindle within a period of time after the current moment is output; the data acquisition module is used to collect the spindle current, spindle speed, ambient temperature and spindle thermal deformation of the machine tool to construct a training set; the model training module is used to The data in the training set is input into the neural network model for training; the thermal deformation prediction module is used to obtain the motion state and thermal deformation state data of the machine tool to be predicted in real time, and input it into the trained neural network model to realize the prediction of the thermal deformation of the spindle. The invention has good prediction effect, can effectively reduce the influence of thermal errors in the machining process of the machine tool, and is suitable for predicting the thermal deformation of the main shaft of the machine tool without a built-in temperature sensor.

Description

technical field [0001] The invention belongs to the field of thermal error measurement of machine tools, and more specifically relates to a method and system for predicting thermal deformation of a spindle of a numerically controlled machine tool. Background technique [0002] When CNC machine tools are performing high-speed and high-precision machining, there are many sources of error that affect the machining accuracy of the machine tool, such as geometric errors, clamping errors, thermal errors (ie, errors caused by thermal deformation), etc., and thermal errors account for 40% of the total error ~70% or so. Therefore, the impact of thermal errors on the machining accuracy of machine tools cannot be ignored. At present, the detection of thermal deformation mainly includes temperature field method, error prevention method and thermal deformation modeling method. [0003] The temperature field method refers to arranging a large number of temperature sensors near the heat-...

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Problems solved by technology

[0006] Aiming at the thermal error of the machining process caused by the large thermal deformation of the main shaft parts caused by the high-speed operation of the high-speed high-precision machining center due to the heating of the main shaft due to frictional forces, the present invention proposes a thermal deformation of the main shaft of a CNC machine tool The prediction method and system, which use the machine tool motion state and thermal deformation state data as input to train the neural network, so that the machine tool motion state data and thermal deformation state data obtained in real time are input into the trained neural network during the real-time operation of the machine tool. The predicted value of the thermal deformation of the spindle can be calculated in the network to achieve the purpose of real-time prediction and compensation. The prediction effect is good and can effectively reduce the influence of thermal errors in the machining process of the machine tool. It is suitable for the thermal deformation of the spindle of the machine tool without a built-in temperature sensor Prediction

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