Spindle thermal elongation prediction method based on load current, rotational speed and thermal elongation history

Abstract

The application discloses a main shaft thermal elongation prediction method based on load current, rotating speed and thermal elongation history, comprising the following steps: 1) obtaining the main shaft rotating speed and the main shaft load at the current time; 2) based on a preset prediction model, calculating the thermal elongation prediction value at the current time according to the main shaft rotating speed and the main shaft load at the current time and the thermal elongation at the previous time; and 3) completing the prediction and compensation of the main shaft thermal elongation according to the obtained thermal elongation prediction value. The prediction model of the method fuses the rotating speed, the load and the thermal elongation history information, has clear physical mechanism, can accurately describe the thermal dynamic process under the complex working conditions of variable rotating speed and variable load, significantly improves the prediction accuracy and the robustness of the model, overcomes the shortcomings of poor adaptability of the traditional model and difficulty in deploying the intelligent model, and the prediction model coefficients can be determined by designing limited group typical working condition experiments and utilizing conventional regression analysis, so that the modeling difficulty and cost are greatly reduced without the need of massive data training.

Description

Technical Field

[0001] This invention belongs to the field of CNC machine tool precision control technology, specifically relating to a method for predicting and compensating for axial thermal elongation of an electric spindle caused by thermal effects during operation. Background Technology

[0002] The machining accuracy of high-precision machine tools is significantly affected by spindle thermal errors. When the spindle rotates at high speed, the heat generated by the motor and bearings causes the spindle to thermally expand, resulting in axial thermal elongation, which directly affects machining accuracy.

[0003] The existing technology has the following main limitations: 1. Temperature sensor-dependent model: This model requires a temperature sensor to detect temperature data. The sensor needs to be installed inside the spindle, which is difficult to implement. In addition, the actual temperature response lags behind thermal deformation, and the prediction effect is poor, especially in the case of large changes in speed and load.

[0004] 2. Constant speed or simple dynamic model: such as the natural exponential model, is only applicable to constant speed conditions and cannot adapt to variable speed or load processes during processing.

[0005] 3. Pure speed-driven model: such as the compensation model based on speed and cumulative thermal error. This model improves speed adaptability, but does not take into account the impact of load changes on heat dissipation, and the prediction accuracy is limited when the load fluctuates greatly.

[0006] 4. Complex data-driven models: such as support vector machines and neural networks. These models are complex, computationally intensive, require a large amount of high-quality experimental data, and have high requirements for data dimensionality, making them difficult to integrate into CNC systems to achieve real-time online compensation.

[0007] Therefore, there is an urgent need for a method for predicting and compensating spindle thermal elongation that can adapt to complex variable speed and load conditions, does not rely on internal temperature sensors, and meets the requirements of real-time performance and ease of implementation. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of existing technologies by providing a method for predicting and compensating for spindle thermal elongation. By introducing load variables, a prediction model is constructed that includes terms of various orders of rotational speed and load, as well as their coupling terms with thermal state. This model comprehensively describes the dynamic balance process of heat generation (related to rotational speed and load) and heat dissipation (related to current thermal state, rotational speed, and load). The prediction model has high accuracy, good real-time performance, is easy to implement, and can effectively adapt to complex working conditions with varying rotational speed and load.

[0009] To achieve the above objectives, the present invention adopts the following technical solution.

[0010] A method for predicting spindle thermal expansion based on load current, rotational speed, and thermal expansion history includes the following steps: Step S1: Obtain the current spindle speed. and spindle load ; Step S2: Based on the preset prediction model, according to the spindle speed at the current moment... and spindle load And the predicted thermal elongation at the previous moment Calculate the predicted thermal elongation value at the current moment. ; The preset prediction model describes the dynamic balance process of heat generation and heat dissipation by introducing load variables and constructing expressions that include terms of rotational speed and load at various orders and their coupling with thermal state.

[0011] Specifically, the mathematical expression of the preset prediction model mentioned in step S2 is: ; In the above formula, This represents the axial thermal elongation of the spindle at the current sampling moment; This represents the axial thermal elongation of the spindle at the previous sampling time. L is the spindle speed at the current sampling moment; L is the spindle load at the current sampling moment; These are the model coefficients; Furthermore, the physical meaning of each expression in the prediction model is as follows: The inherent heat dissipation trend of the system is characterized based on the current accumulated thermal state; , , These terms represent the heat generation related to the spindle speed, and correspond to various mechanical and electromagnetic losses (such as friction loss, eddy current loss, and centrifugal preload additional loss) that are linear, quadratic, and cubic in relation to the spindle speed. It characterizes the coupling relationship between heat dissipation efficiency and rotational speed, reflecting the impact of rotational speed on the system's heat dissipation capacity; , To characterize the heat generation items directly related to the spindle load (current), the main components are the copper loss of the motor and other load-related losses; It characterizes the impact of load conditions on system heat dissipation conditions and reflects the change in heat dissipation rate when the load changes.

[0012] Specifically, the model coefficients were determined by conducting spindle thermal elongation experiments under different speed and load combinations, and then using multiple regression analysis to collect the data.

[0013] Specifically, the spindle load This refers to the real-time load current value of the spindle motor.

[0014] Based on the above technical solution, the present invention further provides a spindle thermal expansion compensation method based on spindle thermal expansion prediction results, including the following steps: Step 1: Signal sampling and preprocessing; With a fixed sampling period Real-time acquisition of spindle speed signal and spindle load signal ; Step 2: Calculation of thermal elongation prediction; Read the predicted thermal elongation from the previous cycle of storage. The processed current speed Current load Substituting the values ​​into the preset prediction model, the predicted thermal elongation for the current period is calculated. ; Step 3: Compensation generation and execution; The calculated predicted thermal elongation for the current period Invert the values ​​and perform smoothing filtering to obtain the axial thermal error compensation amount. ; Axial thermal error compensation amount The data is sent to the machine tool CNC system to provide real-time compensation for the axial positioning of the spindle and to offset the positioning error caused by the thermal expansion of the spindle. Step 4: Status Update; Current predicted thermal elongation Storage, as required for computation in the next sampling period .

[0015] Specifically, the axial thermal error compensation amount The commands are sent to the machine tool CNC system, including: dynamically offsetting the coordinate origin of the relevant axis of the machine tool or correcting the motion command of the axis in real time, in order to offset the positioning error caused by the thermal expansion of the spindle.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The method of this invention has high prediction accuracy and strong adaptability to operating conditions: The prediction model integrates information on rotational speed, load and thermal expansion history, with a clear physical mechanism, and can accurately describe the thermal dynamic process under complex operating conditions of variable speed and load, significantly improving prediction accuracy and model robustness.

[0017] 2. The method of the present invention has good real-time performance and is easy to integrate into engineering: it only needs to use the spindle motor current signal that can be obtained in real time by the CNC system, without the need to install an internal temperature sensor, the data source is reliable and has no lag; the prediction model is in polynomial form, the amount of calculation is small, and it can be easily embedded into the existing CNC system to achieve millisecond-level online prediction and compensation.

[0018] 3. The method of this invention is highly practical and easy to model: It overcomes the shortcomings of poor adaptability of traditional models and difficulty in deploying intelligent models. The predictive model coefficients can be determined by designing a limited number of typical working condition experiments and using conventional regression analysis, without the need for training with massive amounts of data, which greatly reduces the difficulty and cost of modeling.

[0019] 4. The method of this invention clarifies the load contribution: by introducing the load current and its coupling term with the thermal state, the influence of the processing load on the thermal error is clearly quantified, making the prediction model perform better in processing with frequent load changes. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this disclosure and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the spindle thermal elongation prediction method based on load current, rotational speed and thermal elongation history of the present invention.

[0022] Figure 2 This is a schematic diagram of a complex variable speed experimental condition used for model fitting in the embodiment.

[0023] Figure 3 This is a schematic diagram of the experimental measurement data of spindle thermal elongation under variable speed conditions.

[0024] Figure 4 This is a graph showing the prediction fitting effect of the prediction model on the design working condition data.

[0025] Figure 5 An experimental design was created to validate the prediction model.

[0026] Figure 6 To verify the spindle thermal elongation measurement data of the prediction model.

[0027] Figure 7 This represents the actual prediction performance of the prediction model.

[0028] Figure 8 A comparison chart showing the prediction results when the load model is incorporated versus when the load model is not considered.

[0029] Figure 9 To compare the residuals of predictions that incorporate the load model with those that do not. Detailed Implementation

[0030] To facilitate understanding and implementation of the present invention by those skilled in the art, the various steps of the method proposed in this invention are described in detail below. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various modifications or alterations to the invention, and these equivalent forms also fall within the scope defined by the claims of this application.

[0031] Example 1 like Figure 1 As shown, this invention discloses a method for predicting the thermal expansion of a spindle based on load current, rotational speed, and thermal expansion history, comprising the following steps: Step S1: Obtain the current spindle speed. and spindle load ; Step S2: Based on the preset prediction model, according to the spindle speed at the current moment... and spindle load And the predicted thermal elongation at the previous moment Calculate the predicted thermal elongation value at the current moment. ; The preset prediction model introduces load variables to construct an expression that includes terms of rotational speed and load at various orders and their coupling with thermal state, describing the dynamic balance process of heat generation and heat dissipation.

[0032] Specifically, the mathematical expression of the preset prediction model mentioned in step S2 is: ; In the above formula, This represents the axial thermal elongation of the spindle at the current sampling moment; This represents the axial thermal elongation of the spindle at the previous sampling time. L is the spindle speed at the current sampling moment; L is the spindle load at the current sampling moment; These are the model coefficients; Furthermore, the physical meaning of each expression in the prediction model is as follows: The inherent heat dissipation trend of the system is characterized based on the current accumulated thermal state; , , These terms represent the heat generation related to the spindle speed, and correspond to various mechanical and electromagnetic losses (such as friction loss, eddy current loss, and centrifugal preload additional loss) that are linear, quadratic, and cubic in relation to the spindle speed. It characterizes the coupling relationship between heat dissipation efficiency and rotational speed, reflecting the impact of rotational speed on the system's heat dissipation capacity; , To characterize the heat generation items directly related to the spindle load (current), the main components are the copper loss of the motor and other load-related losses; It characterizes the impact of load conditions on system heat dissipation conditions and reflects the change in heat dissipation rate when the load changes.

[0033] Specifically, the model coefficients were determined by conducting spindle thermal elongation experiments under different speed and load combinations, and then using multiple regression analysis to collect the data.

[0034] Specifically, the spindle load This refers to the real-time load current value of the spindle motor.

[0035] Example 2 Based on Example 1, this example further provides a spindle thermal expansion compensation method based on spindle thermal expansion prediction results, including the following steps: Step 1: Signal sampling and preprocessing; With a fixed sampling period Real-time acquisition of spindle speed signal and spindle load signal ; Step 2: Calculation of thermal elongation prediction; Read the predicted thermal elongation from the previous cycle of storage. The processed current speed Current load Substituting the values ​​into the preset prediction model, the predicted thermal elongation for the current period is calculated. ; Step 3: Compensation generation and execution; The calculated predicted thermal elongation for the current period Invert the values ​​and perform smoothing filtering to obtain the axial thermal error compensation amount. ; Axial thermal error compensation amount The data is sent to the machine tool CNC system to provide real-time compensation for the axial positioning of the spindle and to offset the positioning error caused by the thermal expansion of the spindle. Step 4: Status Update; Current predicted thermal elongation Storage, as required for computation in the next sampling period .

[0036] Specifically, the axial thermal error compensation amount The commands are sent to the machine tool CNC system, including: dynamically offsetting the coordinate origin of the relevant axis of the machine tool or correcting the motion command of the axis in real time, in order to offset the positioning error caused by the thermal expansion of the spindle.

[0037] The feasibility and effectiveness of the method of the present invention will be verified through an example below.

[0038] like Figure 2 As shown, a complex speed experiment was designed, including stepped speed increase, sudden speed drop, speed fluctuation, and shutdown. The spindle was in an unloaded state, and the axial thermal elongation of the spindle was measured using an eddy current displacement sensor to obtain a time-speed-thermal elongation dataset. Simultaneously, the host load L (spindle motor current value) was recorded. Figure 3 The figure shows the thermal elongation data measured in the experimental scheme of this example.

[0039] Using this dataset, perform multiple linear regression analysis on the prediction model to determine the coefficients. to The parameter values ​​obtained from the regression are shown in Table 1 below:

[0040] Table 1. Parameter values ​​of the spindle thermal elongation compensation model based on load current. ; like Figure 4 The image shown illustrates the fitting effect of the prediction model on the design working condition data in this example. To verify the model's prediction performance, the following steps were performed: Figure 5 The speed operating conditions shown are designed to verify the experimental scheme, such as... Figure 6 The image shows the thermal elongation data of the spindle measured by the eddy current displacement sensor in the verification scheme. The first elongation data measured by the sensor at the beginning of the experiment is taken as the first value of this iterative model. Perform iterative calculations to obtain, as follows Figure 7 The predicted results are shown.

[0041] like Figure 8 As shown, the model prediction results are compared with the prediction results of the model without load integration, as follows: Figure 9 The figure shows a comparison of the prediction residuals of the two models. The results show that the prediction model has a significantly better predictive ability than the model without load when considering the load.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for predicting spindle thermal expansion based on load current, rotational speed, and thermal expansion history, characterized in that, Includes the following steps: Step S1: Obtain the current spindle speed. and spindle load ; Step S2: Based on the preset prediction model, according to the spindle speed at the current moment... and spindle load And the predicted thermal elongation at the previous moment Calculate the predicted thermal elongation value at the current moment. ; The preset prediction model describes the dynamic balance process of heat generation and heat dissipation by introducing load variables and constructing expressions that include terms of rotational speed and load at various orders and their coupling with thermal state.

2. The method for predicting spindle thermal expansion based on load current, rotational speed, and thermal expansion history according to claim 1, characterized in that, The preset prediction model mentioned in step S2 has the following mathematical expression: ； In the above formula, This represents the axial thermal elongation of the spindle at the current sampling moment; This represents the axial thermal elongation of the spindle at the previous sampling time. L is the spindle speed at the current sampling moment; L is the spindle load at the current sampling moment; These are the model coefficients.

3. The method for predicting spindle thermal expansion based on load current, rotational speed, and thermal expansion history according to claim 2, characterized in that, The model coefficients were determined by conducting spindle thermal elongation experiments under different speed and load combinations, and the collected data were analyzed using multiple regression analysis.

4. The method for predicting spindle thermal expansion based on load current, rotational speed, and thermal expansion history according to claim 3, characterized in that, The spindle load This refers to the real-time load current value of the spindle motor.

5. A method for compensating for thermal expansion of a spindle, based on the spindle thermal expansion prediction results as described in any one of claims 1-4, characterized in that, Includes the following steps: Step 1: Signal sampling and preprocessing; With a fixed sampling period Real-time acquisition of spindle speed signal and spindle motor load current signal ; Step 2: Calculation of thermal elongation prediction; Read the predicted thermal elongation from the previous cycle of storage. The processed current speed Current load Substituting the values ​​into the preset prediction model, the predicted thermal elongation for the current period is calculated. ; Step 3: Compensation generation and execution; The calculated predicted thermal elongation for the current period Invert the values ​​and perform smoothing filtering to obtain the axial thermal error compensation amount. = ; Axial thermal error compensation amount The data is sent to the machine tool CNC system to provide real-time compensation for the axial positioning of the spindle and to offset the positioning error caused by the thermal expansion of the spindle. Step 4: Status Update; Current predicted thermal elongation Storage, as required for computation in the next sampling period .

6. The spindle thermal expansion compensation method according to claim 5, characterized in that, The axial thermal error compensation amount The commands are sent to the machine tool CNC system, including: dynamically offsetting the coordinate origin of the relevant axis of the machine tool or correcting the motion command of the axis in real time, in order to offset the positioning error caused by the thermal expansion of the spindle.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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