Machine tool thermal error compensation and control method, circuit, part and storage medium

Abstract

The present disclosure provides a machine tool thermal error compensation and control method, circuit, part and storage medium, the method comprising: acquiring an ambient temperature of an environment in which a machine tool is located, the ambient temperature being the air temperature of a space in which the machine tool is located; determining, based on the ambient temperature, a thermal error of a motion axis of the machine tool in a target axial direction through a preset temperature-thermal error relationship, the temperature-thermal error relationship representing a corresponding relationship between the ambient temperature and the thermal error in the target axial direction; compensating and correcting coordinates of the motion axis in the target axial direction using the determined thermal error to obtain corrected control parameters; and controlling the motion axis to machine a part according to the corrected control parameters. Thus, the temperature quantity on which the compensation is based can be free from the influence of thermal conduction lag between the temperature of a structure measurement point and macroscopic thermal deformation thereof, the thermal error of the motion axis in the target axial direction can be reduced, and a part with smaller dimensional deviation can be obtained.

Description

Technical Field

[0001] This invention relates to the field of machine tool control technology, and in particular to machine tool thermal error compensation and control methods, circuits, components and storage media. Background Technology

[0002] During parts machining, components such as the machine tool spindle and leadscrew undergo thermal deformation, causing a shift in the relative position between the tool and the workpiece. In related technologies, to reduce machining errors caused by thermal deformation, temperature sensors are typically placed on the heat-generating components and structural parts of the machine tool to collect their temperatures. A model is then established based on the relationship between temperature and thermal error, and the compensation amount obtained from the model is applied to the coordinates of the motion axes. This approach relies on the component's temperature affecting its macroscopic thermal deformation. However, the temperature sensors collect the surface or near-surface temperature of the structural parts. This temperature conducts to the interior of the structural parts, causing macroscopic thermal deformation throughout the component, requiring a relatively long heat conduction process. Therefore, the collected temperature and the thermal deformation are not synchronized in time. The machine tool's controller has limited computing power, making it difficult to perform real-time calculations of complex models. During machining processes with fluctuating ambient temperatures, the coordinates of the motion axes cannot be corrected in real time to match the actual thermal deformation, causing the workpiece's machining dimensions to deviate from the target value during periods of temperature change.

[0003] It is evident that reducing dimensional deviations in machined parts is a technical issue worthy of attention. Summary of the Invention

[0004] According to a first aspect of the present disclosure, a method for compensating and controlling thermal errors in a machine tool is provided, the method comprising: The ambient temperature of the environment in which the machine tool is located is obtained, wherein the ambient temperature is the air temperature of the space where the machine tool is located; Based on the ambient temperature, the thermal error of the machine tool's motion axis in the target axis is determined by a preset temperature-thermal error relationship. The temperature-thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis. Using the determined thermal error, the coordinates of the motion axis along the target axis are compensated and corrected to obtain the corrected control parameters; The motion axis is controlled to process parts according to the modified control parameters.

[0005] In some optional implementations, the temperature-thermal error relationship is represented by a univariate linear model, where the independent variable of the univariate linear model represents the ambient temperature or the temperature difference between the ambient temperature and the reference temperature.

[0006] In some optional implementations, the temperature thermal error relationship is obtained through pre-calibration, and the step of calibrating the temperature thermal error relationship includes: According to a first preset frequency, the ambient temperature and the thermal error of the motion axis along the target axis are collected; Determine the first temperature difference between the collected ambient temperature and the first reference temperature; A linear regression analysis was performed on the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship.

[0007] In some optional implementations, the step of calibrating the temperature thermal error relationship further includes: According to the second preset frequency, the component temperature of the heating component of the machine tool and the thermal error of the motion axis in the target axis are collected; Determine the second temperature difference between the collected component temperature and the second reference temperature; Determine whether the first temperature difference and the second temperature difference meet preset conditions, wherein the preset conditions include a linear relationship between ambient temperature and thermal error. If the first temperature difference meets the preset condition and the second temperature difference does not meet the preset condition, then the first temperature difference is used as the independent variable of the univariate linear model, and the second temperature difference is excluded from being used as the independent variable of the univariate linear model.

[0008] In some optional implementations, the step of performing linear regression analysis on the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship includes: Linear regression analysis was performed on the determined first temperature difference and the collected thermal error to determine the residuals of each data point; Data points whose residuals fall outside the preset confidence interval are identified as outliers, and outliers are removed from the determined first temperature difference and the collected thermal error. A linear regression analysis was performed again on the first temperature difference after removing outliers and the collected thermal error to obtain the temperature-thermal error relationship.

[0009] In some alternative implementations, the reference temperature represents the temperature of the machine tool in a cold state.

[0010] According to a second aspect of the present disclosure, a part is provided, which is processed by any embodiment of the machine tool thermal error compensation and control method of the first aspect of the present disclosure.

[0011] According to a third aspect of the present disclosure, a machine tool thermal error compensation and control circuit is provided, the circuit comprising: The input module is configured to acquire the ambient temperature of the environment in which the machine tool is located, wherein the ambient temperature is the air temperature of the space where the machine tool is located; The processing module is configured to determine the target temperature difference between the ambient temperature and the reference temperature, and based on the target temperature difference, determine the thermal error of the machine tool's motion axis in the target axis through a preset temperature thermal error relationship. The temperature thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis, and use the determined thermal error to compensate and correct the coordinates of the motion axis in the target axis to obtain the corrected control parameters. The control module is configured to control the motion axis to process parts according to the modified control parameters.

[0012] According to a fourth aspect of the present disclosure, a machine tool is provided, comprising: Memory, used to store computer programs; A processor is configured to execute a computer program stored in the aforementioned memory, and when the aforementioned computer program is executed, to implement the method of any embodiment of the machine tool thermal error compensation and control method of the first aspect of this disclosure.

[0013] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method of any embodiment of the machine tool thermal error compensation and control method of the first aspect described above.

[0014] According to a sixth aspect of the present disclosure, a computer program product is provided, including computer-readable code, which, when executed by a processor, implements the method of any embodiment of the machine tool thermal error compensation and control method of the first aspect described above.

[0015] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: In this embodiment, by acquiring the ambient temperature of the machine tool's environment (the air temperature of the space where the machine tool is located) and determining the target temperature difference between the ambient temperature and the reference temperature, and then by characterizing the temperature-thermal error relationship between the ambient temperature and the thermal error along the target axis, the thermal error of the motion axis along the target axis is determined. This ensures that the temperature value used to determine the thermal error is not affected by the thermal conduction hysteresis between the temperature of the structural component's measuring point and its internal macroscopic thermal deformation. By using the determined thermal error to compensate and correct the coordinates of the motion axis along the target axis to obtain corrected control parameters, and controlling the motion axis to process parts according to the corrected control parameters, the coordinates of the motion axis along the target axis can be corrected to match the actual thermal deformation, reducing the thermal error along the target axis and obtaining parts with smaller dimensional deviations. Furthermore, the timeliness of machine tool thermal error compensation can be improved.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this disclosure, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0018] Figure 1 This disclosure is a flowchart illustrating a machine tool thermal error compensation and control method according to an exemplary embodiment.

[0019] Figure 2 This is a flowchart illustrating the calibration of temperature and thermal error relationship in a machine tool thermal error compensation and control method according to an exemplary embodiment of the present disclosure.

[0020] Figure 3 This is a schematic diagram of a one-dimensional thermoelastic analysis model according to an exemplary embodiment of the present disclosure.

[0021] Figure 4 This is a schematic diagram illustrating the mounting position of a thermal sensor on a machine tool according to an exemplary embodiment of this disclosure.

[0022] Figure 5 This is a schematic diagram illustrating the arrangement of a thermal error measuring instrument according to an exemplary embodiment of the present disclosure.

[0023] Figure 6 This is a schematic diagram illustrating temperature test results according to an exemplary embodiment of the present disclosure.

[0024] Figure 7 This is a schematic diagram illustrating temperature test results under a temperature-controlled environment according to an exemplary embodiment of this disclosure.

[0025] Figure 8 This disclosure presents a scatter plot illustrating the relationship between target axial thermal error and ambient temperature according to an exemplary embodiment.

[0026] Figure 9 This is a schematic diagram illustrating the hysteresis relationship between the temperature of a first measuring point and the target axial thermal error according to an exemplary embodiment of this disclosure.

[0027] Figure 10 This is a schematic diagram illustrating the hysteresis relationship between the temperature of the second measuring point and the target axial thermal error according to an exemplary embodiment of this disclosure.

[0028] Figure 11 This disclosure illustrates an error linearity graph of ambient temperature along a target axis according to an exemplary embodiment.

[0029] Figure 12 This is a schematic diagram illustrating the regression residuals and prediction intervals according to an exemplary embodiment of this disclosure.

[0030] Figure 13 This is a schematic diagram of a univariate linear model illustrated in an exemplary embodiment of the present disclosure.

[0031] Figure 14 This disclosure illustrates a hardware architecture diagram of a PLC-based thermal error compensation system according to an exemplary embodiment.

[0032] Figures 15-17 This is a program logic block diagram illustrated in this disclosure according to an exemplary embodiment.

[0033] Figure 18 This is a schematic diagram illustrating a cutting verification specimen and its dimensions according to an exemplary embodiment of the present disclosure.

[0034] Figure 19 This disclosure illustrates a trend diagram of the error variation of the target axis before compensation, according to an exemplary embodiment.

[0035] Figure 20 This disclosure illustrates a trend diagram of the error variation in the target axis after compensation, according to an exemplary embodiment.

[0036] Figure 21 This is a comparison diagram of the precision of field-machined parts illustrated in this disclosure according to an exemplary embodiment.

[0037] Figure 22 This disclosure is a block diagram illustrating a machine tool thermal error compensation and control circuit according to an exemplary embodiment.

[0038] Figure 23 This disclosure is a block diagram illustrating a machine tool according to an exemplary embodiment. Detailed Implementation

[0039] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0040] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0041] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0042] The acquisition and processing of data in this disclosure comply with relevant laws, regulations, social ethics, and provisions concerning public interests. For example, the acquisition, storage, and processing of data in this disclosure were all carried out with the knowledge and consent of the users, and have obtained the full authorization of the relevant users.

[0043] The machine tool thermal error compensation and control method provided in this disclosure can be executed by a controller configured on the machine tool or by an electronic device communicatively connected to the machine tool. In some embodiments, the machine tool includes a bed, a spindle, a feed axis, and a leadscrew that drives the feed axis to move. The feed axis drives the tool or workpiece to move along the target axis. A temperature sensor is arranged in the space where the machine tool is located to collect the ambient temperature of the environment in which the machine tool is located. The controller includes an analog input module and a calculation module. The analog input module is connected to the temperature sensor and is used to receive the temperature signal collected by the temperature sensor. The calculation module is used to determine the thermal error of the motion axis along the target axis based on the temperature signal. The controller is communicatively connected to the CNC system of the machine tool, and the CNC system is used to control the motion axis according to the corrected control parameters. It is understood that the names of the above components are for illustrative purposes only, and this disclosure does not limit the specific implementation of the controller. For example, the controller can be a programmable logic controller or other control device with computing capabilities.

[0044] The embodiments of this disclosure will now be described in detail.

[0045] Figure 1 This is a flowchart illustrating a machine tool thermal error compensation and control method according to an exemplary embodiment. This method can be applied to one or more electronic devices, such as machine tools. Furthermore, the execution entity of this method can be hardware or software. When the execution entity is hardware, it can be one or more of the aforementioned electronic devices. For example, a single electronic device can execute this method, or multiple electronic devices can cooperate with each other to execute this method. When the execution entity is software, this method can be implemented as multiple software programs or software modules, or as a single software program or software module. No specific limitations are made herein.

[0046] Step 101: Obtain the ambient temperature of the environment where the machine tool is located. The ambient temperature is the air temperature of the space where the machine tool is located.

[0047] In this embodiment, the machine tool can be a CNC device used for cutting parts, such as a precision horizontal machining center.

[0048] The environment in which a machine tool is located can be the workshop space where the machine tool is located.

[0049] Ambient temperature refers to the air temperature in the space where the machine tool is located, that is, the temperature of the air in the environment, which is different from the temperature of the machine tool's heat-generating components and structural parts themselves. Air temperature refers to the temperature of the air.

[0050] In some optional implementations, the ambient temperature of the machine tool's environment can be collected or read in a preset manner, for example, by collecting it through a temperature sensor placed in the space where the machine tool is located, or by reading it from a module that collects the temperature.

[0051] In some alternative implementations, the ambient temperature can be acquired by periodically collecting data at a preset frequency or by collecting data when a collection command is received; this disclosure does not limit the method.

[0052] Step 102: Based on the ambient temperature, determine the thermal error of the machine tool's motion axis in the target axis by using a preset temperature-thermal error relationship. The temperature-thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis.

[0053] In this embodiment, the motion axis can be a feed component in a machine tool that can move in a certain direction to drive the tool or workpiece to move, such as a feed axis driven by a lead screw.

[0054] The target axis can be the direction in which the motion axis generates thermal error. In some embodiments, the target axis can be the feed direction of the motion axis, such as the X, Y, or Z direction of a horizontal machining center.

[0055] Thermal error can be caused by thermal deformation, or by the deviation between the actual size and the standard size of a workpiece.

[0056] The temperature-thermal error relationship can characterize the correspondence between ambient temperature and thermal error along the target axis.

[0057] In some alternative implementations, the temperature-thermal error relationship can be represented by a univariate linear model, where the independent variable represents the ambient temperature or the temperature difference between the ambient temperature and the reference temperature.

[0058] In some alternative implementations, the target temperature difference can be used as input, substituted into the temperature-thermal error relationship, and the thermal error of the motion axis along the target axis can be calculated.

[0059] In some optional implementations, the temperature-thermal error relationship can directly represent the correspondence between the ambient temperature and the thermal error along the target axis. For example, the temperature-thermal error relationship can be a functional relationship with the ambient temperature as the independent variable and the thermal error along the target axis as the dependent variable. Based on this, the ambient temperature obtained in step 101 can be input into this functional relationship to calculate the thermal error of the machine tool's motion axis along the target axis.

[0060] In some optional implementations, the temperature-thermal error relationship can also indirectly represent the correspondence between the ambient temperature and the thermal error along the target axis. For example, the temperature-thermal error relationship can be a functional relationship with the temperature difference between the ambient temperature and the reference temperature as the independent variable and the thermal error along the target axis as the dependent variable. Based on this, the temperature difference between the ambient temperature and the reference temperature obtained in step 101 can be calculated first to obtain the target temperature difference. Then, the target temperature difference is input into the functional relationship to calculate the thermal error of the machine tool's motion axis along the target axis.

[0061] The reference temperature can be a temperature used as a comparison benchmark. In some embodiments, the reference temperature can be the initial ambient temperature collected before compensation begins, a preset benchmark temperature, or the temperature of the machine tool in a cold state. This disclosure does not limit the method of determining the reference temperature.

[0062] The target temperature difference can be obtained by subtracting the ambient temperature from the reference temperature, and it can characterize the amount of change in the ambient temperature relative to the reference temperature.

[0063] In some alternative implementations, the target temperature difference can be calculated based on the ambient temperature and the reference temperature. For example, the target temperature difference can be obtained by subtracting the reference temperature from the ambient temperature collected this time.

[0064] Step 103: Using the determined thermal error, the coordinates of the motion axis in the target axis are compensated and corrected to obtain the corrected control parameters.

[0065] In this embodiment, coordinates can represent the position of the motion axis along the target axis. Control parameters can be parameters used to control the movement of the motion axis, such as the coordinates or coordinate offsets of the motion axis along the target axis.

[0066] Compensation correction can be achieved by correcting the coordinates using the determined thermal error. For example, the thermal error can be converted into a coordinate offset, and the coordinates of the motion axis on the target axis can be corrected through the origin offset function of the CNC system.

[0067] The corrected control parameters can be control parameters obtained after compensation correction.

[0068] Step 104: Control the motion axis to process the part according to the corrected control parameters.

[0069] In this embodiment, the part can be a workpiece processed by a machine tool.

[0070] In some alternative implementations, the machine tool can drive the motion axis according to control parameters, and drive the motion axis with corrected control parameters so that the motion axis drives the tool or workpiece to process the part at a position after taking thermal error into account.

[0071] Based on the embodiments of this disclosure, by acquiring the ambient temperature of the space where the machine tool is located and determining the target temperature difference between the ambient temperature and the reference temperature, and then determining the thermal error of the motion axis in the target axis based on the target temperature difference using the temperature-thermal error relationship, the temperature quantity on which the thermal error is determined can be the ambient temperature of the space where the machine tool is located, rather than relying on the temperature of the structural component measuring points. This avoids the influence of the thermal conduction lag between the temperature of the structural component measuring points and its internal macroscopic thermal deformation on the determination of the thermal error. By using the determined thermal error to compensate and correct the coordinates of the motion axis in the target axis to obtain corrected control parameters, and controlling the motion axis to process parts according to the corrected control parameters, the coordinates of the motion axis in the target axis can be corrected to match the actual thermal deformation, reducing the thermal error of the motion axis in the target axis. This results in parts with smaller dimensional deviations during periods of ambient temperature fluctuation. Furthermore, the timeliness of machine tool thermal error compensation can be improved.

[0072] In some optional implementations of this embodiment, the temperature-thermal error relationship is represented by a univariate linear model, where the independent variable of the univariate linear model represents the ambient temperature or the temperature difference between the ambient temperature and the reference temperature.

[0073] Among them, a univariate linear model can be a linear model containing only one independent variable, and its output has a linear relationship with the independent variable.

[0074] The independent variable can be the input of a univariate linear model, where the independent variable represents the ambient temperature or the temperature difference between the ambient temperature and the reference temperature.

[0075] Here, the ambient temperature or the temperature difference between the ambient temperature and the reference temperature can be used as the independent variable, and the thermal error along the target axis can be used as the dependent variable. The two are related by a linear relationship.

[0076] It is understandable that by using a univariate linear model with ambient temperature or the temperature difference between ambient temperature and reference temperature as the independent variable to represent the temperature thermal error relationship, the determination of the thermal error of the motion axis in the target axis can be made to involve only one linear operation, thereby reducing the amount of computation when determining the thermal error and enabling the determination process to be executed on a controller with limited computing power.

[0077] In some optional implementations of this embodiment, the ambient temperature of the machine tool's environment can be obtained in the following way: the ambient temperature of the machine tool's environment is collected according to a third preset frequency.

[0078] The third preset frequency can be a pre-set frequency used to collect ambient temperature. The third preset frequency can be the same as or different from at least one of the first preset frequency and the second preset frequency.

[0079] Based on this, steps 102 to 104 above can be executed at the third preset frequency to compensate for the thermal error of the machine tool in real time.

[0080] It is understandable that by collecting ambient temperature at the third preset frequency and compensating for machine tool thermal errors in real time, the dynamic trend of ambient temperature changes can be effectively tracked, avoiding compensation lag or advance caused by temperature changes, thereby improving the machining accuracy and stability of the machine tool under complex working conditions.

[0081] In some optional implementations of this embodiment, the reference temperature (including at least one of the reference temperature involved in the independent variable of the univariate linear model, the first reference temperature, and the second reference temperature) represents the temperature of the machine tool in a cold state.

[0082] The "cold machine state" refers to the state of the machine tool after a long period of shutdown, when the temperature of each component reaches thermal equilibrium with the ambient temperature. In this state, the thermal deformation inside the machine tool is basically eliminated, and the motion axes are in their initial geometric positions. Therefore, the machine tool temperature at this time can be set as the reference temperature for subsequent calculations of temperature differences and thermal error compensation.

[0083] It is understood that, among the above optional implementation methods, by setting the temperature under cold conditions as the reference temperature, the influence of the initial thermal deformation of the machine tool on the compensation model can be eliminated, ensuring the consistency of the benchmark for temperature difference calculation, thereby improving the accuracy and reliability of thermal error prediction.

[0084] It should be noted that, where there is no conflict, the technical features described in different alternative implementations can be included in the same embodiment. For the sake of brevity, they will not be elaborated here.

[0085] Figure 2 This is a flowchart illustrating the calibration of the temperature-thermal error relationship in a machine tool thermal error compensation and control method according to an exemplary embodiment of this disclosure. Figure 2 As shown, the steps for calibrating the temperature thermal error relationship include: Step 201: Collect the ambient temperature and the thermal error of the motion axis along the target axis according to the first preset frequency.

[0086] In this embodiment, the first preset frequency can be a pre-set frequency used in the calibration phase, such as collecting data according to a fixed time period.

[0087] In some optional implementations, the ambient temperature and the thermal error of the motion axis along the target axis can be collected simultaneously at the same acquisition time, making the two correspond to each other in time. Here, the thermal error is the deviation of the motion axis along the target axis caused by thermal deformation, which is obtained through measurement during the index determination stage.

[0088] In some alternative implementations, the ambient temperature and thermal error collected in step 201 can be matched one-to-one.

[0089] Step 202: Determine the first temperature difference between the collected ambient temperature and the first reference temperature.

[0090] In this embodiment, the first reference temperature can be the ambient temperature used as a comparison benchmark during the calibration phase, such as the ambient temperature collected at the beginning of calibration.

[0091] The first temperature difference can be the difference between the ambient temperature collected during the calibration phase and the first reference temperature. Here, the first temperature difference can be obtained by subtracting the corresponding first reference temperature from the collected ambient temperature.

[0092] Step 203: Perform linear regression analysis on the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship.

[0093] In this embodiment, linear regression can be a process that uses the first temperature difference as the independent variable and the thermal error as the dependent variable to fit the linear relationship between the two.

[0094] In some alternative implementations, the coefficients in the linear relationship can be determined through linear regression, thereby obtaining the temperature-thermal error relationship.

[0095] In some optional implementations, the coefficients obtained from linear regression can be amplified to integers by a preset factor, and integer operations can be performed between the amplified integer coefficients and the ambient temperature. Then, the coefficients can be restored to thermal error by a preset factor to accommodate controllers that do not support decimal operations.

[0096] It is understandable that by collecting the ambient temperature and the thermal error of the motion axis along the target axis at the first preset frequency, and performing linear regression analysis on the first temperature difference between the ambient temperature and the first reference temperature and the collected thermal error to obtain the temperature-thermal error relationship, the temperature-thermal error relationship can be made to match the correspondence between the ambient temperature and thermal error in the actual operation of the machine tool, so that the thermal error of the motion axis along the target axis determined accordingly is closer to the actual thermal error.

[0097] In some optional implementations of this embodiment, the step of calibrating the temperature thermal error relationship further includes: The first step is to collect the component temperature of the machine tool's heating parts and the thermal error of the motion axis along the target axis at the second preset frequency.

[0098] The second step is to determine the second temperature difference between the collected component temperature and the second reference temperature.

[0099] The third step is to determine whether the first temperature difference and the second temperature difference meet the preset conditions, which include that the ambient temperature and the thermal error have a linear relationship.

[0100] Fourth step: If the first temperature difference meets the preset conditions and the second temperature difference does not meet the preset conditions, then the first temperature difference is used as the independent variable of the univariate linear model, and the second temperature difference is excluded from being used as the independent variable of the univariate linear model.

[0101] The second preset frequency can be a pre-set frequency used to collect the temperature of the sensor. The second preset frequency can be the same as or different from the first preset frequency.

[0102] Heat-generating components can be parts of the machine tool that generate heat during operation, such as the spindle or the lead screw of the feed axis.

[0103] The component temperature can be the temperature of the heat-generating component itself, which is different from the ambient temperature of the space where the machine tool is located.

[0104] The second reference temperature can be the temperature used as a benchmark when comparing component temperatures.

[0105] The second temperature difference can be the difference between the collected component temperature and the second reference temperature.

[0106] The preset conditions can be used to determine whether the ambient temperature (e.g., the first temperature difference, the second temperature difference) can be used as the independent variable of a univariate linear model. The preset conditions include that the ambient temperature and the thermal error have a linear relationship of the first degree. Meeting the preset conditions means that the ambient temperature and the thermal error have a linear relationship of the first degree.

[0107] In some alternative implementations, when the first temperature difference has a linear relationship with the thermal error but the second temperature difference does not, the first temperature difference can be selected as the independent variable of the univariate linear model, while the second temperature difference is not selected as the independent variable.

[0108] It is understandable that by judging whether the first temperature difference corresponding to the ambient temperature and the second temperature difference corresponding to the component temperature of the heating component are linearly related to the thermal error, and selecting the first temperature difference as the independent variable of the univariate linear model when the first temperature difference meets the condition but the second temperature difference does not, the univariate linear model can use the ambient temperature, which is linearly related to the thermal error, as the input, and avoid using the component temperature, which is not linearly related to the thermal error. This reduces the deviation between the thermal error of the motion axis determined in this way on the target axis and the actual thermal error.

[0109] In some optional implementations of this embodiment, the temperature-thermal error relationship can be obtained by performing linear regression analysis on the determined first temperature difference and the collected thermal error in the following manner: The first step is to perform linear regression analysis on the determined first temperature difference and the collected thermal error to determine the residuals of each data point; The second step is to identify outliers by defining data points whose residuals fall outside the preset confidence interval, and to remove outliers from the determined first temperature difference and the collected thermal error. The third step is to perform a linear regression analysis on the first temperature difference after removing outliers and the collected thermal error to obtain the temperature-thermal error relationship.

[0110] The data points are determined by the first temperature difference and the thermal error. For example, each data point can be represented as (first temperature difference, thermal error).

[0111] The residual can be the difference between the actual thermal error of a data point and the thermal error predicted by the regression relationship.

[0112] Preset confidence intervals can be pre-defined intervals used to determine whether a data point is an outlier, such as prediction intervals.

[0113] Outliers can be data points whose residuals fall outside a predefined confidence interval.

[0114] Outlier removal can be achieved by removing the data points corresponding to outliers from the collected ambient temperature and thermal error data.

[0115] In some alternative implementations, the linear relationship between ambient temperature and thermal error can be refitted using data points after removing outliers, thereby obtaining the temperature-thermal error relationship.

[0116] It is understandable that by determining the residuals of each data point, identifying data points whose residuals fall outside the preset confidence interval as outliers and removing them, and then performing linear regression analysis on the data after removing outliers, the obtained temperature thermal error relationship can be made unaffected by data points that deviate from the overall pattern. This makes the temperature thermal error relationship more consistent with the overall correspondence between ambient temperature and thermal error, further reducing the thermal error of the processed parts.

[0117] It should be noted that, in addition to the contents described above, this embodiment may also include... Figure 1 The corresponding technical features described in the corresponding embodiments, thereby achieving Figure 1 For details on the technical effects of the machine tool thermal error compensation and control method shown, please refer to [link / reference needed]. Figure 1 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0118] The following describes the embodiments of this disclosure by way of example. However, it should be noted that the following content is only used to understand the technical solutions of the embodiments of this disclosure and does not constitute a limitation on the protection scope of the embodiments of this disclosure.

[0119] With the increasing demands for precision in machining complex components for humanoid robots and new energy vehicles in high-end manufacturing, the thermal stability of high-precision CNC machine tools has become a key factor restricting their performance. During operation, critical components such as the spindle and lead screw undergo complex thermal deformation due to internal friction, motor heating, and ambient temperature fluctuations, leading to a shift in the relative position between the tool and the workpiece. This shift in position causes machining errors, preventing the machine tool from meeting the ever-increasing precision requirements of high-end manufacturing. Furthermore, research indicates that thermal errors account for 40% to 70% of the total machine tool error, and the higher the machine tool's precision, the more significant the impact of thermal errors.

[0120] To address this challenge, reducing machining errors caused by thermal deformation has become an effective way to improve machine tool accuracy. Related methods are mainly divided into compensation based on mechanistic models and compensation based on data. While methods based on theoretical models such as the finite element method can reveal the laws of thermal deformation, they are usually complex to model and have limited adaptability. Furthermore, due to high computational costs and diverse error sources, these methods are difficult to use for real-time optimization and determining the reliability of results. In contrast, modeling methods based on experimental data, such as multiple regression and neural networks, establish a direct mapping between temperature and error, making them more practical for engineering applications. In recent years, embedded real-time compensation systems built around programmable logic controllers (PLCs) have gained widespread attention and application due to their high reliability, strong anti-interference capabilities, and ease of integration with CNC systems.

[0121] However, accurately establishing a thermal error model under the coupling effect of multiple heat sources for a specific machine tool structure and achieving efficient and stable integration with a PLC control system remains an urgent engineering problem to be solved. This solution takes a high-precision horizontal machining center as the research object. Addressing the issues of strong multi-source thermal error coupling interference and insufficient real-time compensation, the system achieves accurate detection and optimized modeling of multi-dimensional thermal errors in the environment, spindle, and feed axes. It innovatively develops a low-latency online control strategy based on the machine tool's native PLC architecture, ultimately realizing the design and engineering application of an integrated real-time thermal error compensation system. This provides a technical solution for improving the accuracy and stability of domestically produced high-end machine tools and thus meeting the machining accuracy requirements of high-end components.

[0122] To effectively suppress machining errors caused by thermal deformation in machine tools, this solution proposes a PLC-based thermal error modeling and compensation technique. First, a multi-channel temperature measurement system is constructed to monitor the temperature field of key structural points in the machining center in real time. Then, using a spindle rotation error analyzer and a multi-point measurement method, the coupled effects of ambient temperature fluctuations and internal heat sources such as the spindle and leadscrew on the machine tool's thermal errors are quantified. Next, based on measured data, a high-precision thermal error prediction model is established through multivariate regression analysis. Finally, using a PLC as the core controller, combined with a thermal sensor and an analog signal acquisition module, an online real-time thermal error compensation system is designed and implemented. This system can drive model calculations based on temperature sensor data and dynamically correct the machine tool's motion axis coordinates. Experimental results show that after applying this compensation system, the thermal error between the machine tool spindle and the Z-axis leadscrew is effectively controlled within 8 μm, significantly improving machining accuracy and verifying the effectiveness and engineering applicability of the proposed method.

[0123] Specifically, the proposed solution is as follows: 1. Theoretical analysis of the thermoelastic process.

[0124] To theoretically explain the common "hysteresis" phenomenon between thermal error and temperature signal, the local structure of the machine tool can be simplified as follows: Figure 3 The analysis is performed using a one-dimensional thermoelastic model. The model is a homogeneous cylinder with one end fixed and the other end free. The fixed end is excited by a periodic heat source, and the surface of the cylinder dissipates heat through convection and radiation. Temperature as a function of axial position With time A changing function, for example, Temperature as a function of axial position ,time A changing function, Temperature as a function of axial position ,time A changing function. This indicates the amount of thermal deformation displacement at the free end as a function of time. A changing function.

[0125] The heat source of a machine tool changes dynamically during operation, therefore it is necessary to study the dynamic characteristics of the temperature field and structural thermal deformation caused by the changing heat source. If the heat exchange between the cylinder and the surrounding air is not considered, the one-dimensional heat transfer equation of the cylinder can be expressed by formula (1):

[0126] Formula (1) In formula (1), Indicates temperature (e.g., machine tool temperature, ambient temperature); The axial position (the length of the free end of the machine tool relative to a reference position (e.g., the shortest position); For time; ,in For the thermal conductivity of the machine tool, For the material density of the machine tool, This refers to the specific heat capacity of the machine tool.

[0127] The heat source is a periodically changing heat flow, which is equivalent to the temperature boundary condition at the fixed end on the left. The temperature can be expressed as a harmonic function, which can be represented by formula (2):

[0128] Formula (2) In formula (2), Temperature of the fixed end on the left side over time A changing function; The amplitude; It is frequency; For phase.

[0129] The heat source is continuous in time, satisfying the Dirichlet continuity condition. Equation (2) can be expanded into a Fourier series in the time domain, i.e., equation (3):

[0130] Formula (3) In formula (3), The zeroth harmonic coefficient; For harmonic order; For the first Second cosine harmonic coefficient; For the first Subsine harmonic coefficient; For time; ; For the first Second harmonic amplitude; It is frequency; For the first The initial phase of the second harmonic, by and synthesis.

[0131] Solving the one-dimensional heat transfer differential equation, i.e., equation (1), and considering the boundary conditions of equation (3), we obtain the solution as equation (4):

[0132] Formula (4) In formula (4), is the temperature distribution function; the meanings of other symbols are consistent with those in formulas (1) and (3).

[0133] After obtaining the temperature distribution, the expression for thermal deformation displacement can be obtained by integrating the thermal expansion characteristics of the material. Here, the cylinder is discretized as... A length of The displacement of the right free end in the discrete element can be expressed as formula (5):

[0134] Formula (5) In formula (5), The coefficient of thermal expansion; Let be the length of the cylinder. From formula (4), it can be seen that the temperature response at a certain measuring point on the cylinder is a harmonic response. It can be seen from the item that the amplitude of temperature fluctuation varies with As it increases, it decreases. The term indicates that the lag in temperature fluctuations at the measuring point is location-dependent. The larger the value, the greater the lag. Combining this with formula (5), we can see that thermal deformation... Temperature at different measuring points The two phenomena exhibit varying degrees of "leading" or "lagging," known as hysteresis. This effect provides a theoretical basis for the nonlinear and hysteretic relationship between the temperature at the structural measuring points and the thermal error in subsequent experimental observations.

[0135] 2. The thermal error measurement experimental system of this scheme.

[0136] 2.1 Research object and temperature measurement point layout.

[0137] This study focuses on a high-precision horizontal machining center. Analysis of the machine tool's heat sources reveals three main sources: ambient temperature fluctuations (external heat source), spindle system heat generation (internal heat source 1), and feed system heat generation (especially the Z-axis) (internal heat source 2). To comprehensively monitor the temperature field distribution, temperature sensors were installed at key heat-sensitive locations on the machine tool, as shown in the diagram. Figure 4 As shown in the diagram, all sensor signals are connected to the data acquisition system via analog input modules. Temperature sensors are located at points T1-T9.

[0138] 2.2 Thermal error measurement method.

[0139] Thermal error measurement was conducted according to relevant standards. Spindle thermal drift under the influence of ambient temperature was measured using a standard test bar and a dial indicator, arranged as follows: Figure 5 As shown. The thermal error caused by the spindle's own heating was measured using a spindle rotation error analyzer. The feed axis thermal error was measured with high precision using a laser interferometer. During the experiment, the machine tool was fully preheated, and the system automatically recorded the temperature and error data of all measuring points at a cycle of 5 minutes, continuously monitoring for more than 24 hours to obtain complete temperature-deformation time-series data.

[0140] 3. Experimental results analysis and thermal error modeling.

[0141] 3.1 Basic characteristics of temperature field and thermal error.

[0142] Temperature field test results under different working conditions are as follows Figure 6 and Figure 7 As shown. The analysis revealed that: (1) the temperature change of the structural measuring point lags behind the change of the ambient temperature, and the lag time is related to the position of the measuring point and the heat capacity; (2) affected by the rising of hot air, the temperature of the measuring point at the higher position of the machine tool is generally higher than that of the measuring point at the lower position; (3) when there is a horizontal temperature difference in the workshop, the machine tool structure will also generate a horizontal temperature gradient.

[0143] The non-uniform and time-varying characteristics of the temperature field are the fundamental cause of complex thermal deformation in machine tools. The heat capacity and heat conduction paths of different components within a machine tool vary. When the ambient temperature fluctuates or internal heat sources (such as the spindle or motor) are activated, heat is not instantly and uniformly distributed but rather transferred along specific paths. This transfer process takes time, resulting in a significant time lag in the temperature response at different structural locations. Furthermore, the rising hot air creates a significant vertical temperature difference between the upper structure and the lower bed of the machine tool, directly causing pitching or tilting deformation in critical components such as the column.

[0144] Analysis of the thermal error data shows that, on this horizontal machining center, the thermal error in the Z-axis direction exhibits a significant linear correlation with the ambient temperature. Figure 8 The thermal errors in the X and Y directions exhibit complex nonlinear and hysteresis characteristics. A clear hysteresis loop exists between the temperature at the structural measurement points (e.g., T2, T4) and the Z-axis error. Figure 9 and Figure 10 This is consistent with the theoretical analysis above.

[0145] The hysteresis loop phenomenon is a key characteristic in the formation mechanism of thermal errors, intuitively revealing the asynchronous nature between thermal deformation and temperature changes. Its physical mechanism lies in the fact that temperature sensors, typically located on or near the surface of machine tool structural components, can respond rapidly to temperature changes. However, for the temperature measured by the sensors to be transmitted to the interior of the structural component and cause macroscopic thermal errors throughout the entire part, a relatively long heat conduction process is required. Therefore, when the temperature begins to decrease, the error may continue to increase, thus forming a closed "hysteresis loop" in the temperature-error coordinate system. This fully demonstrates that thermal errors are not only related to the current temperature value but also closely related to the thermal history and dynamic heat transfer process of the entire system.

[0146] 3.2 Z-axis thermal error modeling under the influence of ambient temperature.

[0147] Using the ambient temperature from a certain day's test as input, the analysis results show a good linear relationship between the ambient temperature and the Z-axis thermal error. Figure 11 As shown.

[0148] Correlation analysis and scatter plots were performed on the collected temperature and error data. The analysis results show that under the typical working conditions studied in this scheme, there is a strong linear correlation between the temperature changes of several key temperature measurement points (especially those near the heat source) and the thermal error in the Z direction (i.e., the target axis mentioned above). In addition, the core objective of this scheme is to develop a compensation system that can operate stably and efficiently in the PLC controller. The simple structure, small computational load, and strong robustness of the univariate linear regression model can ensure engineering practicality and computational efficiency. Therefore, in order to obtain the linear relationship between ambient temperature and Z-direction thermal error, a univariate linear regression analysis was performed on the data, which can be expressed as formula (6):

[0149] Formula (6) In formula (6), x represents the ambient temperature, or the temperature difference between the ambient temperature and the reference temperature; y represents the thermal error; β0 and β1 represent the regression coefficients; and ε represents the error.

[0150] The regression results are shown in Table 1: ; Figure 12 The table shows the residuals and prediction intervals of the regression. It can be observed that the residual intervals for data points 12, 13, 25, and 26 do not contain zero, indicating that the residuals are greater than expected in 95% of the new observations. These four data points are outliers. Figure 12 As shown.

[0151] The model was improved by re-performing linear regression analysis on the temperature difference after removing outliers and the collected thermal error, and the relationship between the Z-axis thermal error and the ambient temperature was obtained as formula (7):

[0152] Formula (7) The results of the linear equation obtained by fitting are as follows Figure 13 As shown.

[0153] 4. Implementation of a PLC-based real-time thermal error compensation system.

[0154] 4.1 Hardware architecture and software process of the compensation system.

[0155] The core hardware of the compensation system uses a high-reliability industrial PLC, and its architecture is as follows: Figure 14 As shown. Figure 14 As shown, the electrical cabinet 30 includes a PLC 301 and an input module 302. The PLC 301 is connected to the control panel 303. Temperature sensor signals are input to the PLC 301 via the input module 302. The PLC 301 interacts with the control panel 303 of the machine tool's FANUC CNC system in real time via a high-speed fieldbus (MODBUS / TCP). The compensation value is ultimately used to dynamically correct the coordinate system of each axis by calling the FANUC system's "Extended External Mechanical Origin Offset" function.

[0156] 4.2 Compensation value calculation and integration.

[0157] The compensation value is calculated in real time in the PLC using a ladder diagram program. First, the ambient temperature is read and the difference (ΔT) is calculated with the initial value (i.e., the reference temperature). Then, each component error is substituted into the model (formula (7)) to calculate the error of each component. Finally, the values ​​are summed and converted into coordinate offsets, which are then compensated using the external origin offset function of the CNC system. The program logic diagram of the key calculation steps (such as ambient temperature calculation, model coefficient multiplication, and coordinate related compensation calculation) is shown below. Figures 15 to 17 As shown.

[0158] Using the multiplication instruction MULB, the ambient temperature difference E360 (2 bytes) is multiplied by the coefficient 5.3369 in the ambient temperature compensation model. Since the PLC does not support decimal calculations, the coefficient is multiplied by 10,000 to calculate the thermal error compensation value E364 (2 bytes) under the influence of ambient temperature, in units of 0.00001 μm. Figure 16 As shown.

[0159] 4.3 Verification of compensation effect.

[0160] To verify the effectiveness of the system, specific tests and production verification were conducted. Figure 18 Test cut pieces and dimensions for cutting verification. Figures 19-20To compare the Z-axis error changes of test pieces processed over a long period of time before and after compensation, the error after compensation was suppressed to within 8μm. Figure 21 The image shows a comparison of the precision of parts processed on-site. After compensation is enabled, the accuracy error of critical positions is reduced to within 8μm.

[0161] The above description uses the Z-axis direction as an example to illustrate the thermal error compensation and control method. The thermal error compensation and control of the X and Y axes can be implemented in a similar way, and will not be elaborated here.

[0162] This solution targets a high-precision horizontal machining center, constructing a multi-channel temperature field monitoring system, quantitatively analyzing the characteristics of multi-source thermal errors, establishing a high-precision model based on multivariate regression, and successfully developing a PLC-based embedded real-time thermal error compensation system. Without compensation, the maximum thermal error of the machine tool under the same operating conditions reached 40μm. PLC-based thermal error modeling and compensation for the precision machining center can control the machine tool's thermal error within 8μm, achieving a thermal error suppression rate of 80%, and meeting engineering compensation requirements, effectively improving the machining accuracy and stability of the machine tool. This solution clarifies the characteristics of the machine tool's thermal error: the Z-axis error is significantly linearly correlated with ambient temperature; there is a hysteresis effect between the temperature and error at structural measuring points; and the thermal error of the feed axis leadscrew is linearly correlated with the coordinate system.

[0163] A precision horizontal machining center is used as the machine tool. The machining center's feed axes include a Z-axis, which is driven by a leadscrew. The coordinate of the motion axis in the target axis is the Z-coordinate. Temperature sensors are installed in the workshop space where the machining center is located to collect the ambient temperature. A programmable logic controller (PLC) is used as the controller, and its analog input module is connected to the temperature sensors. The PLC communicates with the CNC system of the machining center via a fieldbus.

[0164] During the calibration phase, the machining center is fully preheated, and then the ambient temperature and thermal error in the Z-axis are collected at a first preset frequency. The ambient temperature collected at the start of calibration is used as the first reference temperature, and a first temperature difference is determined between the collected ambient temperature and the first reference temperature. Linear regression analysis is performed on the determined first temperature difference and the collected thermal error to determine the residuals for each data point. Data points whose residuals fall outside the prediction interval are identified as outliers and removed. Linear regression analysis is then performed again on the first temperature difference and thermal error after outlier removal to obtain a univariate linear model with ambient temperature as the independent variable, representing the temperature-thermal error relationship. During the calibration phase, the component temperatures of heat-generating parts such as the spindle and leadscrew can also be collected at a second preset frequency to determine a second temperature difference between the collected component temperatures and the second reference temperature. When the first temperature difference corresponding to the ambient temperature shows a linear relationship with the thermal error, but the second temperature difference corresponding to the component temperature does not show a linear relationship, the first temperature difference is selected as the independent variable of the univariate linear model, and the second temperature difference is not included as the independent variable.

[0165] In the compensation and control phase, the programmable logic controller (PLC) collects ambient temperature at a third preset frequency, using the previously collected ambient temperature as a reference temperature to determine the target temperature difference between the currently collected ambient temperature and the reference temperature. The target temperature difference is then substituted into a univariate linear model to determine the thermal error in the Z-axis. In some embodiments, considering that some controllers do not support decimal arithmetic, the coefficients of the univariate linear model can be amplified to integers by a preset factor. Integer operations are then performed between the amplified integer coefficients and the target temperature difference to obtain the amplified value, which is then restored to the thermal error by the preset factor. The determined thermal error is converted into a Z-axis coordinate offset, and the Z-axis coordinate is compensated and corrected using the origin offset function of the CNC system to obtain the corrected control parameters. The CNC system controls the Z-axis feed axis movement according to the corrected control parameters to machine the part.

[0166] In the above application scenario, the technical problem to be solved is to correct the coordinates of the motion axis along the target axis to match the actual thermal deformation during machining processes with fluctuating ambient temperatures. The adopted technical solution is to use the ambient temperature of the machine tool's space as input, determine the thermal error of the motion axis along the target axis using a univariate linear model with ambient temperature as the independent variable, and correct the coordinates and control the machining accordingly. The achieved technical effect is that the thermal error of the motion axis along the target axis is reduced, and the machining dimensions of the workpiece no longer deviate from the target value during temperature changes. It should be noted that the above content is only used to understand the technical solution of the embodiments of this disclosure and does not constitute a limitation on the scope of protection of the embodiments of this disclosure. The features of amplifying the coefficients of the above univariate linear model to integers, using the previously collected ambient temperature as a reference temperature, removing outliers, and selecting independent variables based on linear relationships are only one combination.

[0167] It should be noted that, in addition to the contents described above, this embodiment may also include the technical features described in the above embodiments, thereby achieving the technical effects of the machine tool thermal error compensation and control method shown above. For details, please refer to the above description. For the sake of brevity, it will not be elaborated here.

[0168] This disclosure also provides a part that is machined using the machine tool thermal error compensation and control method described in any of the preceding claims. As an example, the machine tool thermal error compensation and control method may include: Obtain the ambient temperature of the environment where the machine tool is located; the ambient temperature is the air temperature of the space where the machine tool is located. Based on the ambient temperature, the thermal error of the machine tool's motion axis in the target axis is determined by the preset temperature thermal error relationship. The temperature thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis. Using the determined thermal error, the coordinates of the motion axis in the target axis are compensated and corrected to obtain the corrected control parameters; The motion axis is controlled to process the parts according to the revised control parameters.

[0169] In some alternative implementations, the temperature-thermal error relationship is represented by a univariate linear model, where the independent variable of the univariate linear model represents the ambient temperature or the temperature difference between the ambient temperature and the reference temperature.

[0170] In some optional implementations, the temperature thermal error relationship is obtained through pre-calibration, and the step of calibrating the temperature thermal error relationship includes: According to the first preset frequency, the ambient temperature and the thermal error of the motion axis along the target axis are collected; Determine the first temperature difference between the collected ambient temperature and the first reference temperature; A linear regression analysis was performed on the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship.

[0171] In some optional implementations, the step of calibrating the temperature thermal error relationship further includes: According to the second preset frequency, the component temperature of the heat-generating parts of the machine tool and the thermal error of the motion axis in the target axis are collected; Determine the second temperature difference between the collected component temperature and the second reference temperature; Determine whether the first temperature difference and the second temperature difference meet preset conditions respectively. The preset conditions include that the temperature difference (e.g., the first temperature difference and the second temperature difference) and the thermal error have a linear relationship. If the first temperature difference meets the preset conditions and the second temperature difference does not meet the preset conditions, then the first temperature difference is used as the independent variable of the univariate linear model, and the second temperature difference is excluded from being used as the independent variable of the univariate linear model.

[0172] In some optional implementations, a linear regression analysis is performed on the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship, including: Linear regression analysis was performed on the determined first temperature difference and the collected thermal error to determine the residuals of each data point; Data points whose residuals fall outside the preset confidence interval are identified as outliers, and outliers are removed from the determined first temperature difference and the collected thermal error. A linear regression analysis was performed again on the first temperature difference after removing outliers and the collected thermal error to obtain the temperature-thermal error relationship.

[0173] In some alternative implementations, the reference temperature represents the temperature of the machine tool in a cold state.

[0174] It should be noted that, in addition to the contents described above, this embodiment may also include the technical features described in the above embodiments, thereby achieving the technical effects of the machine tool thermal error compensation and control method shown above. For details, please refer to the above description. For the sake of brevity, it will not be elaborated here.

[0175] Figure 22 This is a structural diagram of a machine tool thermal error compensation and control circuit according to an exemplary embodiment of the present disclosure. The circuit specifically includes: Input module 401 is configured to: obtain the ambient temperature of the environment in which the machine tool is located, where the ambient temperature is the air temperature of the space where the machine tool is located.

[0176] In some embodiments, the input module 401 may be an analog input module connected to a temperature sensor, used to receive temperature signals collected by the temperature sensor.

[0177] The processing module 402 is configured to: determine the target temperature difference between the ambient temperature and the reference temperature; determine the thermal error of the machine tool's motion axis in the target axis based on the target temperature difference and a preset temperature-thermal error relationship; the temperature-thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis; and use the determined thermal error to compensate and correct the coordinates of the motion axis in the target axis to obtain the corrected control parameters.

[0178] In some embodiments, the processing module 402 may be implemented by an arithmetic unit in a programmable logic controller, or by a digital signal processor, an application-specific integrated circuit, or a field-programmable gate array.

[0179] The control module 403 is configured to control the motion axis to process parts according to the modified control parameters.

[0180] In some embodiments, the control module 403 can communicate with the CNC system of the machine tool and correct the coordinates of the motion axis on the target axis through the origin offset function of the CNC system, thereby controlling the motion axis.

[0181] The input module 401, processing module 402 and control module 403 can be integrated into the same control circuit or distributed in multiple control circuits that are interconnected. This disclosure does not limit this.

[0182] In some optional implementations, the temperature-thermal error relationship is represented by a univariate linear model, where the independent variable of the univariate linear model represents the ambient temperature or the temperature difference between the ambient temperature and the reference temperature.

[0183] In some optional implementations, the temperature thermal error relationship is obtained through pre-calibration, and the step of calibrating the temperature thermal error relationship includes: According to a first preset frequency, the ambient temperature and the thermal error of the motion axis along the target axis are collected; Determine the first temperature difference between the collected ambient temperature and the first reference temperature; A linear regression analysis was performed on the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship.

[0184] In some optional implementations, the step of calibrating the temperature thermal error relationship further includes: According to the second preset frequency, the component temperature of the heating component of the machine tool and the thermal error of the motion axis in the target axis are collected; Determine the second temperature difference between the collected component temperature and the second reference temperature; Determine whether the first temperature difference and the second temperature difference meet preset conditions, wherein the preset conditions include a linear relationship between ambient temperature and thermal error. If the first temperature difference meets the preset condition and the second temperature difference does not meet the preset condition, then the first temperature difference is used as the independent variable of the univariate linear model, and the second temperature difference is excluded from being used as the independent variable of the univariate linear model.

[0185] In some optional implementations, the step of performing linear regression analysis on the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship includes: Linear regression analysis was performed on the determined first temperature difference and the collected thermal error to determine the residuals of each data point; Data points whose residuals fall outside the preset confidence interval are identified as outliers, and outliers are removed from the determined first temperature difference and the collected thermal error. A linear regression analysis was performed again on the first temperature difference after removing outliers and the collected thermal error to obtain the temperature-thermal error relationship.

[0186] In some alternative implementations, the reference temperature represents the temperature of the machine tool in a cold state.

[0187] The machine tool thermal error compensation and control circuit provided in this embodiment can execute the corresponding steps of the machine tool thermal error compensation and control methods described above, thereby achieving the technical effects of the machine tool thermal error compensation and control methods described above. The machine tool thermal error compensation and control circuit and the machine tool thermal error compensation and control methods can refer to and cite each other in terms of specific implementation and technical effects. For the sake of brevity, they will not be elaborated here.

[0188] Figure 23 This disclosure is a block diagram illustrating a machine tool according to an exemplary embodiment. Figure 23 The machine tool 500 shown includes at least one processor 501, a memory 502, at least one network interface 504, and a user interface 503. The various components in the machine tool 500 are coupled together via a bus system 505. It is understood that the bus system 505 is used to enable communication between these components. In addition to a data bus, the bus system 505 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 23 The general designated all buses as Bus System 505.

[0189] The user interface 503 may include a display, keyboard, or clicking device, such as a mouse, trackball, touchpad, or touchscreen.

[0190] It is understood that the memory 502 in this embodiment can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 502 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0191] In some implementations, memory 502 stores elements, executable units or data structures, or subsets thereof, or extended sets thereof: operating system 5021 and application program 5022.

[0192] The operating system 5021 includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 5022 includes various applications, such as a media player and a browser, used to implement various application functions. The program implementing the method of this disclosure embodiment can be included in the application program 5022.

[0193] In this embodiment, by calling the program or instructions stored in memory 502, specifically the program or instructions stored in application program 5022, processor 501 executes the method steps provided in each method embodiment, including, for example: The ambient temperature of the machine tool's environment is obtained; based on the ambient temperature, the thermal error of the machine tool's motion axis in the target axis is determined using a preset temperature-thermal error relationship. The temperature-thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis. The determined thermal error is used to compensate and correct the coordinates of the motion axis in the target axis to obtain the corrected control parameters. The motion axis is controlled to process parts according to the corrected control parameters.

[0194] The methods disclosed in the embodiments of this disclosure can be applied to or implemented by processor 501. Processor 501 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuit of the hardware or by instructions in the form of software in processor 501. The processor 501 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor. The software units may be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 502. Processor 501 reads the information in memory 502 and, in conjunction with its hardware, completes the steps of the above method.

[0195] It is understood that the embodiments described herein can be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described above, or combinations thereof.

[0196] For software implementation, the techniques described above can be implemented by units that perform the functions described above. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processor or externally.

[0197] The machine tool provided in this embodiment can be as follows: Figure 23 The machine tool shown can execute all the steps of the above-described machine tool thermal error compensation and control methods, thereby achieving the technical effects of the above-described machine tool thermal error compensation and control methods. For details, please refer to the above descriptions. For the sake of brevity, further details are omitted here.

[0198] This disclosure also provides a storage medium (computer-readable storage medium). This storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; the memory may also include combinations of the above types of memory.

[0199] One or more programs in the storage medium can be executed by one or more processors to implement the above-mentioned machine tool thermal error compensation and control method.

[0200] The processor described above is used to execute the machine tool thermal error compensation and control program stored in the memory to implement the following steps of the machine tool thermal error compensation and control method: The ambient temperature of the machine tool's environment is obtained; based on the ambient temperature, the thermal error of the machine tool's motion axis in the target axis is determined using a preset temperature-thermal error relationship. The temperature-thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis. The determined thermal error is used to compensate and correct the coordinates of the motion axis in the target axis to obtain the corrected control parameters. The motion axis is controlled to process parts according to the corrected control parameters.

[0201] Furthermore, the computer program product provided in this disclosure embodiment may include computer-readable code, which, when executed on a device, causes a processor in the device to implement the steps of the machine tool thermal error compensation and control method: The ambient temperature of the machine tool's environment is obtained; based on the ambient temperature, the thermal error of the machine tool's motion axis in the target axis is determined using a preset temperature-thermal error relationship. The temperature-thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis. The determined thermal error is used to compensate and correct the coordinates of the motion axis in the target axis to obtain the corrected control parameters. The motion axis is controlled to process parts according to the corrected control parameters.

[0202] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this solution can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0203] The steps of the methods or algorithms described in conjunction with the embodiments disclosed in this solution can be implemented in hardware, software modules executed by a processor, or a combination of both. The software modules can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art.

[0204] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0205] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for compensating and controlling thermal errors in machine tools, comprising: The ambient temperature of the environment in which the machine tool is located is obtained, wherein the ambient temperature is the air temperature of the space where the machine tool is located; Based on the ambient temperature, the thermal error of the machine tool's motion axis in the target axis is determined by a preset temperature-thermal error relationship. The temperature-thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis. Using the determined thermal error, the coordinates of the motion axis along the target axis are compensated and corrected to obtain the corrected control parameters; The motion axis is controlled to process parts according to the modified control parameters.

2. The method according to claim 1, characterized in that, The temperature-thermal error relationship is represented by a univariate linear model, where the independent variable of the univariate linear model represents the ambient temperature or the temperature difference between the ambient temperature and the reference temperature [Agent 1].

3. The method according to claim 2, characterized in that, The temperature thermal error relationship is obtained through pre-calibration, and the steps for calibrating the temperature thermal error relationship include: According to a first preset frequency, the ambient temperature and the thermal error of the motion axis along the target axis are collected; Determine the first temperature difference between the collected ambient temperature and the first reference temperature; A linear regression analysis was performed on the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship.

4. The method according to claim 3, characterized in that, The step of calibrating the temperature thermal error relationship further includes: According to the second preset frequency, the component temperature of the heating component of the machine tool and the thermal error of the motion axis in the target axis are collected; Determine the second temperature difference between the collected component temperature and the second reference temperature; Determine whether the first temperature difference and the second temperature difference meet preset conditions, wherein the preset conditions include a linear relationship between the temperature difference and the thermal error. If the first temperature difference meets the preset condition and the second temperature difference does not meet the preset condition, then the first temperature difference is used as the independent variable of the univariate linear model, and the second temperature difference is excluded from being used as the independent variable of the univariate linear model.

5. The method according to claim 3, characterized in that, The linear regression analysis of the determined first temperature difference and the collected thermal error to obtain the temperature-thermal error relationship includes: Linear regression analysis was performed on the determined first temperature difference and the collected thermal error to determine the residuals of each data point; Data points whose residuals fall outside the preset confidence interval are identified as outliers, and outliers are removed from the determined first temperature difference and the collected thermal error. A linear regression analysis was performed again on the first temperature difference after removing outliers and the collected thermal error to obtain the temperature-thermal error relationship.

6. The method according to claim 2, characterized in that, The reference temperature indicates the temperature of the machine tool in a cold state.

7. A component, characterized in that, The part is processed using the machine tool thermal error compensation and control method as described in any one of claims 1 to 6.

8. A machine tool thermal error compensation and control circuit, characterized in that, The circuit includes: The input module is configured to: acquire the ambient temperature of the environment in which the machine tool is located, wherein the ambient temperature is the air temperature of the space where the machine tool is located; The processing module is configured to: determine the thermal error of the machine tool's motion axis in the target axis based on the ambient temperature using a preset temperature thermal error relationship, wherein the temperature thermal error relationship characterizes the correspondence between the ambient temperature and the thermal error in the target axis; and use the determined thermal error to compensate and correct the coordinates of the motion axis in the target axis to obtain the corrected control parameters. The control module is configured to control the motion axis to process parts according to the modified control parameters.

9. A machine tool, comprising: Memory, used to store computer programs; A processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, it implements the machine tool thermal error compensation and control method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the machine tool thermal error compensation and control method as described in any one of claims 1 to 6.

Images