Numerical control machine tool spindle thermal deformation prediction method and system

Abstract

The invention belongs to the field of machine tool thermal error measurement and specifically discloses a numerical control machine tool spindle thermal deformation prediction method and system. A modeling module is used for establishing a neural network model. The neural network model adopts motion state data and thermal deformation state data of a machine tool before the current moment as the input and adopts the spindle thermal deformation amount within a period of time after the current moment as the output. A data acquisition module is used for collecting the machine tool spindle current,the spindle speed, the environmental temperature and the spindle thermal deformation amount to establish a training set. A model training module is used for inputting data in the training set into the neural network model to implement training. A thermal deformation prediction module is used for acquiring the motion state and thermal deformation state data of a machine tool to be predicted in real time and inputting the motion state and thermal deformation state data to the trained neural network model to realize prediction on the spindle thermal deformation. Through the numerical control machine tool spindle thermal deformation prediction method and system, the prediction effect is good, and the influence of thermal errors of the machine tool in the machining process can be effectively reduced. The numerical control machine tool spindle thermal deformation prediction method and system are suitable for prediction on thermal deformation of a machine tool spindle without a built-in 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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