Thermal error compensation method and system for large bridge type gantry machining center

Abstract

The invention belongs to the technical field of numerical control machine tools, and relates to a thermal error compensation method and system for a large bridge type gantry machining center, and the system comprises a comprehensive data collection module, a key point determination module, a thermal error modeling module, a compensation execution module and a compensation optimization module. The comprehensive data acquisition module is used for synchronously acquiring three-way thermal displacement change data of a tool nose point and key point temperature data; the key point determination module is used for screening a representative key point temperature set required by modeling according to correlation and a classification principle; the thermal error modeling module is used for establishing a temperature difference-based multiple linear regression compensation model by taking a reference temperature as a reference; the compensation execution module is used for converting the compensation model into a real-time PLC program, and calculating and outputting a compensation amount to a servo system; and the compensation optimization module iteratively adjusts the compensation correction coefficient K through a dichotomy method, and verifies and optimizes the compensation effect. The problems that an existing thermal error compensation method is single in compensation object, difficult to debug and difficult to apply in an engineering mode can be effectively solved.

Description

Technical Field

[0001] This invention relates to the field of CNC machine tool technology, specifically to a thermal error compensation method and system for a large bridge-type gantry machining center. Background Technology

[0002] Large bridge-type gantry machining centers are widely used due to their high rigidity, high precision, and wide machining adaptability. Their machining accuracy directly affects the final quality of parts. Among various machine tool errors, thermal errors account for as much as 40% to 70%, becoming a key factor restricting the improvement of machining accuracy.

[0003] In the prior art, the invention patent with publication number CN102672527A discloses a method for thermal error compensation of the entire working stroke of a CNC machine tool feed system. Its features include: (1) Data acquisition: using a laser displacement measuring instrument, temperature sensors arranged at the main heat source positions of the feed system, and a temperature monitoring instrument, the displacement and temperature data of the feed system displacement measuring points are synchronously collected at set time intervals; (2) Thermal key point identification: the displacement and temperature data of the feed system displacement measuring points collected in step (1) are analyzed using a gray fuzzy clustering method, and the temperature measuring points are divided into several categories according to the gray correlation degree. By calculating the gray comprehensive correlation degree between the thermal error sequences of the temperature measuring points and the displacement measuring points in each category, the key points are selected from each category. Typical temperature measurement points are identified to realize the identification of the thermal key points of the feed system; (3) Determination of thermal error compensation value: Based on the thermal error value obtained by the laser displacement measuring instrument and the temperature rise value of the thermal key points, the thermal error compensation model of the feed system is constructed by multiple linear regression. Based on the temperature rise value of the thermal key points of the feed system and the displacement value of the feed system obtained by the grating ruler, the thermal error compensation value of the feed system is calculated and determined; (4) Dynamic compensation of thermal error of the feed system: Based on the thermal error compensation value of the feed system calculated and determined in step (3), the origin of the CNC machine tool feed system is translated by the central controller to implement dynamic compensation of thermal error of the CNC machine tool feed system. This can be used to solve the thermal error compensation problem of the CNC machine tool feed system and provide technical support for improving the machining accuracy and stability of the CNC machine tool.

[0004] The above technical solutions have made progress in modeling system thermal errors and selecting key points, but the following technical problems still exist: the existing thermal error compensation methods are complex to implement on large bridge gantry machining centers, have high costs, poor adaptability, and are difficult to debug the compensation effect.

[0005] In view of this, it is very necessary to provide a thermal error compensation method and system for a large bridge-type gantry machining center to solve the above-mentioned defects in the prior art. Summary of the Invention

[0006] The purpose of this invention is to address the technical problems of existing thermal error compensation methods in large bridge gantry machining centers, which are complex to implement, costly, have poor adaptability, and are difficult to debug. The invention provides a thermal error compensation method and system for large bridge gantry machining centers to solve the technical problems existing in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for thermal error compensation in a large bridge-type gantry machining center, comprising the following steps: Step S1: The comprehensive data acquisition steps include collecting the triaxial thermal displacement change data of the blade tip and the key point temperature data of key locations; Step S2: The step of determining representative key point temperature data. Based on the principles of correlation and classification, the key point temperature data is filtered to obtain representative key point temperature data. The filtered representative key point temperature data forms a representative key point temperature data set. Step S3: The thermal error modeling step, taking the temperature of the machine tool bed as the temperature reference point, and establishing a multiple linear regression compensation model based on temperature error and machine tool thermal error; Step S4: The compensation is executed in real time. The multiple linear regression compensation model is converted into a PLC program to calculate the compensation value in real time and drive the servo of each feed axis. Step S5: The step of verifying the compensation effect is to adjust the compensation correction coefficient K by the bisection method and iteratively optimize until the thermal error meets the requirements.

[0008] Secondly, the present invention also provides a thermal error compensation system for a large bridge-type gantry machining center, comprising: The integrated data acquisition module is used to simultaneously acquire triaxial thermal displacement data of the blade tip and temperature data of key points; The key point determination module is used to select a representative set of key point temperatures required for modeling based on correlation and classification principles. The thermal error modeling module is used to establish a multiple linear regression compensation model based on temperature difference, with a reference temperature as the standard temperature. The compensation execution module is used to convert the compensation model into a real-time PLC program, calculate and output the compensation amount to the servo system; The compensation optimization module is used to iteratively adjust the compensation correction coefficient K using the bisection method, and to verify and optimize the compensation effect.

[0009] The modules work together to achieve full-process thermal error compensation, from synchronous data acquisition, intelligent key point screening, robust model building, real-time compensation output to parameter closed-loop optimization.

[0010] The beneficial effects of this invention are as follows: This invention collects data on the three-dimensional thermal displacement change of the tool tip and the key temperature data of key locations, so that the thermal error characteristics are fully reflected under different working conditions and time scales. This provides a stable and complete data foundation for compensation modeling and solves the problem of complex schemes and difficult debugging caused by insufficient data when implementing thermal error compensation on large bridge gantry machining centers.

[0011] This invention selects representative temperature key points based on correlation analysis and classification principles, thereby reducing the number of temperature variables involved in modeling while ensuring the validity of temperature information. It simplifies the configuration of temperature measurement points and the data processing process, thus reducing implementation costs and improving the adaptability of the thermal error compensation method on different large bridge gantry machining centers.

[0012] This invention uses the machine bed as the temperature reference, introduces temperature difference variables, and combines the three-dimensional thermal displacement change data of the tool tip to establish a multiple linear regression compensation model. This allows thermal error compensation to simultaneously consider the comprehensive influence of multiple heat sources on thermal displacement in different directions, improving the stability of the thermal compensation effect under varying ambient temperatures around the machine tool and enhancing the applicability of thermal compensation under different machine tool ambient temperatures. It overcomes the shortcomings of existing technologies that only compensate for thermal errors in a single direction, thus improving the overall thermal error compensation effect. This invention converts the thermal error compensation model into a PLC executable program and calculates the compensation amount in real time during machining, outputting it to the CNC system to drive the servo. This achieves online execution of thermal error compensation, avoiding the inconvenience of manual intervention and offline correction, making the compensation process simpler and more reliable, and suitable for the long-term operation needs of large bridge-type gantry machining centers.

[0013] This invention uses a bisection method to iteratively adjust the compensation correction coefficient K, giving the tuning process of the compensation parameters a clear adjustment direction and convergence basis, significantly reducing the difficulty of debugging the compensation effect, ensuring that the thermal error can be stably controlled within the target range, thereby improving the consistency and repeatability of the thermal error compensation effect.

[0014] This invention forms a complete and continuous thermal error compensation process, enabling data acquisition, temperature screening, model building, real-time compensation, and effect optimization to work together. Without increasing the complexity of the hardware structure, it realizes a multi-directional, adjustable thermal error compensation method applicable to large bridge gantry machining centers. Overall, it reduces the implementation complexity and application cost, improves the adaptability and compensation stability of the method, and effectively solves the problems of existing thermal error compensation methods being complex to implement, costly, poorly adaptable, and difficult to debug the compensation effect.

[0015] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a flowchart of a thermal error compensation method for a large bridge-type gantry machining center; Figure 2 This is a schematic diagram of thermal error detection for a large bridge-type gantry machining center. Figure 3 This is a schematic diagram of the temperature sensor locations in a large bridge-type gantry machining center. Figure 4 This is a schematic diagram of the thermal error compensation system for a large bridge-type gantry machining center. Detailed Implementation

[0018] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following implementation methods.

[0019] Example 1: like Figure 1 As shown in the figure, this embodiment provides a thermal error compensation method for a large bridge-type gantry machining center, which includes the following steps: Step S1: The comprehensive data acquisition steps include collecting the triaxial thermal displacement change data of the blade tip and the key point temperature data of key locations; Step S2: The step of determining representative key point temperature data. Based on the principles of correlation and classification, the key point temperature data is filtered to obtain representative key point temperature data. The filtered representative key point temperature data forms a representative key point temperature data set. Step S3: The thermal error modeling step, taking the temperature of the machine tool bed as the temperature reference point, and establishing a multiple linear regression compensation model based on temperature error and machine tool thermal error; Step S4: The compensation is executed in real time. The multiple linear regression compensation model is converted into a PLC program to calculate the compensation value in real time and drive the servo of each feed axis. Step S5: The step of verifying the compensation effect is to adjust the compensation correction coefficient K by the bisection method and iteratively optimize until the thermal error meets the requirements.

[0020] In step S1: Using a large bridge-type gantry machining center as the compensation object, the triaxial thermal displacement change data of the tool tip and the key point temperature data of ten key locations are acquired under continuous operation and input into the machine tool control system. The machine tool control system performs time synchronization, data clearing and continuous recording processing on the acquired triaxial thermal displacement change data of the tool tip and the key point temperature data. The output is a composite data set containing time series, triaxial thermal displacement of the tool tip and key point temperature data.

[0021] like Figure 2 As shown, the three-dimensional thermal displacement change data of the tool tip is acquired through a displacement sensor fixture fixed on the worktable. The displacement sensor fixture simultaneously fixes three displacement sensors arranged perpendicularly to each other. Before detecting the thermal displacement change data in the Z direction, the machine tool is equipped with a standard ball joint and moved to the intersection of the axes of the three displacement sensors to complete the calibration. The position after calibration is the initial position, recorded as position A, and the displacement sensor data is cleared. Subsequently, the machine tool moves a certain distance along the Z direction, recorded as position B. The moving distance is 5-10 cm. The spindle rotates at a certain speed and performs multi-axis linkage motion. After running for 5 minutes, it returns to position B, stops rotating, and slowly returns to position A. The spindle speed is 1000-3000 r / min. The displacement sensors record the Z-direction displacement change as single Z-direction thermal displacement data. The spindle speed is adjusted every 2 hours. A spindle speed table is made according to the spindle speed, and the spindle speed is adjusted according to the requirements of the spindle speed table. The X and Y thermal displacement data are acquired using the same method to obtain the three-dimensional thermal displacement change data of the tool tip. The X and Y thermal displacement change data together reflect the spatial offset changes caused by the heat of the transverse feed axis and gantry structure. The Z thermal displacement change data reflects the vertical position changes of the column, crossbeam, slide, and spindle assembly caused by the heat. The acquisition time for the three-dimensional thermal displacement change data of the tool tip is no less than 72 hours.

[0022] The key temperature data of the ten key locations were collected by a stainless steel round bar PT100 temperature sensor, such as... Figure 3 As shown, 10 temperature sensors are respectively arranged on the right bed, the front middle side of the crossbeam, the rear middle side of the crossbeam, the front lower side of the slide, the front middle side of the slide, the front upper side of the slide, the rear lower side of the slide, the rear middle side of the slide, the rear upper side of the slide, and the spindle bearing position. The acquisition frequency of the PT100 temperature sensor is consistent with the acquisition frequency of the displacement sensor.

[0023] During machine tool operation, the collected data on the triaxial thermal displacement change of the tool tip and the temperature data of key points are synchronized in time, cleared, and continuously recorded. Specifically, before each round of testing begins, the moment when the machine tool returns to position A and completes the calibration of the displacement sensors is taken as the unified sampling start time. Zero-point calibration is performed on the current readings of the three displacement sensors to reset their initial output values ​​to zero. At the same time, the initial temperature values ​​of each key point temperature sensor at that moment are recorded as the temperature reference. During the rotation of the machine tool spindle and multi-axis linkage operation, the displacement sensors and key point temperature sensors are synchronously sampled according to a preset fixed sampling period of 1-10 seconds. The triaxial thermal displacement change data of the tool tip and the key point temperature data corresponding to each sampling moment are continuously stored in chronological order, thus forming a one-to-one correspondence between the triaxial thermal displacement change data of the tool tip and the key point temperature data on the same time axis. Finally, a data set containing the complete time series, the triaxial thermal displacement of the tool tip, and the corresponding key point temperature data is obtained.

[0024] Through the above steps, data on the three-dimensional thermal displacement change of the tool tip and the temperature data of key points caused by changes in ambient temperature, spindle heating and feed axis component heating can be obtained during a continuous test. This reduces the number of temperature sensors while ensuring the representativeness of temperature information, providing a complete, reliable and practically meaningful data foundation for subsequent determination of temperature key points and thermal error modeling.

[0025] In step S2: The dataset, which includes time series data, three-dimensional thermal displacement of the blade tip, and corresponding key point temperature data, is used as input. The key point temperature data is filtered based on the principles of correlation and classification. The output is a representative set of key point temperature data that is highly correlated with the change of thermal displacement of the blade tip and can represent the characteristics of different components and heat sources.

[0026] The correlation principle prioritizes temperature points closely related to the thermal error changes at the cutting edge when selecting key temperature points. Specifically, the temperature change at each selected point should reflect the thermal displacement trend of the cutting edge in the X, Y, and Z directions. This principle ensures a high correlation between the temperature data used in modeling and the thermal error, thereby improving the prediction accuracy and effectiveness of the thermal error compensation model. Correlation is typically assessed by calculating the Pearson correlation coefficient or linear correlation coefficient between the temperature change sequence at each temperature point and the corresponding time-varying thermal displacement sequence at the cutting edge. A higher correlation coefficient indicates a closer correlation between the temperature change at that temperature point and the thermal displacement of the cutting edge, making it a more suitable key temperature point.

[0027] The principle of categorized temperature point selection means that when choosing critical temperature points, not only should the correlation between a single point and thermal error be considered, but also the structure of the machine tool and the characteristics of heat sources. The machine tool should be divided according to its main components and heat source types, and at least one representative temperature point should be selected from each category. Specific classification methods include dividing the machine tool by major components (bed, beam, slide) and by heat source (spindle heat source, ambient heat source). This classification ensures that the critical temperature points reflect the temperature changes of the main heated parts of the machine tool while also covering the influence of different heat sources, avoiding the neglect of other important heat sources by selecting only a single highly correlated point.

[0028] In practice, the specific method for selecting key location points using the correlation principle and the classification principle is as follows: The correlation between the temperature data collected by each temperature sensor and the corresponding time-varying triaxial thermal displacement of the tool tip is calculated to obtain the correlation coefficient between the key point temperature data and the machine tool thermal error for each key location point; the machine tool temperature sensors are classified according to major components such as the bed, crossbeam, and slide, and labeled according to the heat source attributes; within each category, temperature points with higher correlation coefficients are preferentially selected as candidate key points; finally, while ensuring that each component and heat source category has at least one representative point, the representative key point temperature data set for modeling is determined by comprehensively considering the magnitude of the correlation coefficient.

[0029] The temperature key points selected by the above method not only ensure a high correlation with the thermal error of the tool tip, but also cover the main components of the machine tool and the types of heat sources, thus providing representative and robust input data for thermal error modeling.

[0030] In step S3: A representative set of key point temperature data and the three-dimensional thermal displacement of the tool tip are used as inputs, with the machine bed temperature as the temperature reference point. The temperature difference between other key points and the temperature reference point is calculated, and this temperature difference is used as the independent variable, with the machine tool thermal error as the dependent variable. The solution is obtained through a multiple linear regression method, outputting a multiple linear regression model for thermal error compensation. Here, the machine tool thermal error is the thermal displacement of the tool tip in the X, Y, and Z directions.

[0031] The specific operation is as follows: based on the machine tool bed temperature As a temperature reference point, calculate the temperature difference between each other temperature key point and the temperature reference point. ,in Let be the temperature of the i-th critical point. Then, place the blade tip at... As the dependent variable, construct a multiple linear regression model: ; ; ; in, The temperature difference between other critical temperature points and the temperature reference point. , , For regression coefficients, , , This is a constant term. The regression coefficients are solved using the least squares method, which completes the process of solving the thermal error compensation model using the differences between data from other key temperature points and data from the temperature reference point, along with the machine tool thermal error.

[0032] By constructing a multivariate linear model, the impact of overall environmental temperature fluctuations on the compensation results is effectively reduced, enabling the thermal error model to maintain good adaptability and robustness under different seasons and operating conditions.

[0033] In step S4: the multiple linear regression model and the representative key point temperature dataset are used as input. Taking the Siemens CNC system as an example, the thermal compensation function is debugged on the machine tool control terminal. The selected representative key point temperatures are marked, temperature sensors are placed at the corresponding positions, and the output of the temperature sensors is connected to the temperature acquisition card. Through the temperature digital signal processing method built into the Siemens CNC system, the acquired temperature sensor signals are converted into temperature variables that can be used by the Siemens CNC system. Subsequently, the multiple linear regression model is transcribed into a PLC program, the temperature variables are read in real time, and the corresponding compensation values ​​are calculated. The calculated compensation values ​​are used as NC variables to drive the servo of each feed axis, realizing real-time thermal error correction of the tool tip position.

[0034] During the debugging process, the PLC program judges the real-time calculated compensation value. If the calculated value exceeds 0.05mm, it will not be output to prevent excessive compensation value from causing tool overcutting or poor machining. The compensation output process is synchronized with the machine tool servo system to ensure that the compensation value can be applied to each axis in real time. The PLC program can convert the influence of temperature change on the tool tip position into compensation action in a timely manner to achieve real-time thermal error correction.

[0035] By combining a multiple linear regression model with the machine tool control terminal, real-time dynamic compensation is achieved. The mechanism for determining the compensation value ensures the safety and reliability of the compensation process, avoids processing abnormalities caused by excessive compensation, improves the machining accuracy of the machine tool and the stability of part dimensions, and reduces manual intervention and debugging costs.

[0036] In step S5: After the thermal compensation function is debugged, the compensation effect is verified. The data of the three-dimensional thermal displacement of the tool tip is used as the data input and compared with the data of the three-dimensional thermal displacement of the tool tip in the state without compensation. The compensation effect is evaluated. The thermal compensation correction coefficient K is adjusted according to the evaluation results, and the corrected compensation parameters are output and applied to the multiple linear regression model. After the compensation is enabled, the same thermal error test method as in step S1 is used.

[0037] The thermal compensation correction coefficient K, as a compensation multiplier factor, is multiplied by the compensation value calculated by the compensation model and then output. The initial value of K is set to 1. When the thermal error of the machine tool after compensation is significantly reduced compared to before compensation but still does not meet the accuracy requirements, the value of K is adjusted to 2; when the thermal error after compensation shows a reverse trend, the value of K is adjusted to 1.5; when the compensation effect exists but is not obvious, the value of K is further refined in adjacent value intervals according to the dichotomy principle, for example, adjusted to 1.75, 1.625, etc., until the thermal displacement of the tool tip stabilizes within the preset allowable range after compensation.

[0038] The thermal compensation correction coefficient K is a parameter used to adjust the output of thermal error compensation. Its function is to comprehensively correct the output amplitude of the compensation value without changing the multiple linear regression model and regression coefficients, thus adapting to the actual thermal characteristics differences of the machine tool under different operating conditions. The multiple linear regression model calculates the theoretical compensation value based on the temperature difference between other key temperature points and the temperature reference point, denoted as K. The thermal compensation correction coefficient K, as a compensation multiplier, is multiplied by the compensation value calculated by the compensation model and then output as the final compensation value. Expressed as: ; The initial value of K is set to 1, indicating that the compensation amount is directly output according to the model calculation results. The thermal compensation correction coefficient K is adjusted by observing the compensation effect. If the thermal error is continuously observed to be within a reasonable range, no adjustment is needed. When the machine tool thermal error after compensation is significantly reduced compared to before compensation but still does not meet the accuracy requirements, the value of K is adjusted to 2 to increase the compensation amplitude. When the thermal error after compensation shows a reverse change trend, indicating that the compensation direction or amplitude is too large, the value of K is adjusted to 1.5 to reduce the compensation ratio. When the compensation effect exists but is not obvious, the value of K is adjusted to 1.75, and so on, until the machine tool thermal error meets the requirements.

[0039] To improve the efficiency of K value adjustment, a bisection method is used to iteratively optimize the thermal compensation correction coefficient K. After determining the upper and lower limits of the effective compensation range, the K value is refined within adjacent value ranges according to the bisection principle, for example, adjusting the K value to 1.75, 1.625, etc., and the compensation effect is verified again after each adjustment. By continuously narrowing the value range of K, the thermal displacement of the blade tip after compensation gradually converges and stabilizes within the preset allowable range, thereby determining the final thermal compensation correction coefficient K.

[0040] Set the initial value of K to Enable thermal compensation and observe the thermal error at the tool tip after compensation. ;like If the value of K decreases compared to the uncompensated state but still does not reach the allowable range, then K is set to a larger value. If the thermal error changes in the opposite direction or overcompensates after compensation, then K should be set to a smaller value. In determining the valid interval [ Then, a new K value is selected using the bisection method: The compensation was then re-verified using this K value, and the range of K values ​​was continuously narrowed based on the changes in thermal error. Through multiple iterations, when the thermal error at the tool tip stabilized within the preset allowable range after compensation, the K value was considered to have converged to the optimal value.

[0041] By introducing a thermal compensation correction coefficient K and adjusting it using the bisection method, the thermal compensation effect can be rapidly optimized without re-collecting data or rebuilding the thermal error compensation model, thereby improving the success rate of thermal error compensation debugging and the flexibility of engineering applications.

[0042] Example 2: like Figure 4 As shown in the figure, this embodiment provides a thermal error compensation system for a large bridge-type gantry machining center, comprising: The integrated data acquisition module 1 synchronously acquires the triaxial thermal displacement change data of the tool tip and the key temperature data of the key positions by using displacement sensor fixtures arranged on the worktable and temperature sensors installed at various preset key points of the machine tool. Taking the machine tool's return to the initial position after calibration as the sampling starting point, the displacement signal is zero-point calibrated and the initial reference value of the temperature signal is recorded. During the rotation of the machine tool spindle and multi-axis linkage, the triaxial thermal displacement change data and the key temperature data of each key position are synchronized and stored according to a preset fixed sampling period. This ensures that the triaxial thermal displacement change data of the tool tip and the key temperature data of each key position correspond one-to-one on the same time axis, forming a composite data set containing a complete time series, the triaxial thermal displacement of the tool tip, and the corresponding key temperature data. This effectively reduces the number of temperature variables involved in modeling, reduces model complexity, improves modeling efficiency, and enhances the applicability and stability of the thermal error compensation model under different working conditions and different operating stages.

[0043] The key point determination module 2 takes a composite dataset as input and filters the key point temperature data by analyzing the relationship between the key point temperature data and the triaxial thermal displacement of the tool tip. Based on the correlation principle, it evaluates the correlation between the temperature change sequence of each temperature point in the key point temperature data and the triaxial thermal displacement of the tool tip, prioritizing temperature points with high consistency with the trend of machine tool thermal error changes. Based on the classification principle, the machine tool is divided into main components and heat source types, and representative temperature points are selected from each category. The module outputs a representative set of key temperature data that is highly correlated with the thermal displacement change of the tool tip and covers the characteristics of the main components and heat sources of the machine tool.

[0044] The thermal error modeling module 3 receives a set of representative key point temperature data and the three-dimensional thermal displacement of the tool tip. Using the machine tool bed temperature as the reference temperature, it calculates the temperature difference between other key temperature points and the reference temperature. Using the temperature difference as the independent variable and the machine tool thermal error as the dependent variable, it uses a multiple linear regression method to solve the mapping relationship between each error term and the machine tool thermal error, thus obtaining a multiple linear regression model to describe the relationship between temperature change and machine tool thermal error. This enables thermal error modeling based on temperature difference, improves the safety and reliability of system operation, and allows thermal error compensation to stably and continuously act on the actual machining process.

[0045] The compensation execution module 4 transcribes the multiple linear regression model into a PLC program that can run on the CNC system, establishes a data association with the temperature acquisition channel, reads the temperature data signals of representative key points, calculates the corresponding thermal error, substitutes it into the multiple linear regression model, and obtains the compensation value at the current moment. Subsequently, the compensation value is output in the form of a variable that the CNC system can recognize and applied to each feed axis servo to realize real-time thermal error correction of the tool tip position. The reasonableness of the calculation results is judged to avoid abnormal compensation values ​​from directly affecting the servo system, thus ensuring the stability and safety of the compensation process.

[0046] The compensation optimization module 5 evaluates and optimizes the compensation effect, using the three-dimensional thermal displacement of the tool tip after compensation is activated as the evaluation criterion. A compensation correction coefficient K is introduced to adjust the output of the compensation model. When the compensation effect is insufficient or an overcompensation trend occurs, the range of the compensation correction coefficient K is set, and a bisection method is used to iteratively adjust the value of K within the range, continuously refining the value of K. This allows the thermal displacement of the tool tip after compensation to gradually converge to the preset allowable range, thereby verifying and optimizing the compensation effect, improving the success rate of thermal error compensation debugging, reducing on-site debugging difficulty and time costs, and making the thermal error compensation method more practical for engineering and more worthy of promotion.

[0047] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0048] 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 herein 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 implementations should not be considered beyond the scope of this invention.

[0049] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0050] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0051] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0052] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0053] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0054] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0055] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. A method for thermal error compensation in a large bridge-type gantry machining center, characterized in that, Includes the following steps: Step S1: The comprehensive data acquisition steps include collecting the triaxial thermal displacement change data of the blade tip and the key point temperature data of key locations; Step S2: The step of determining representative key point temperature data. Based on the principles of correlation and classification, the key point temperature data is filtered to obtain representative key point temperature data. The filtered representative key point temperature data forms a representative key point temperature data set. Step S3: The thermal error modeling step, taking the temperature of the machine tool bed as the temperature reference point, and establishing a multiple linear regression compensation model based on temperature error and machine tool thermal error; Step S4: The compensation is executed in real time. The multiple linear regression compensation model is converted into a PLC program to calculate the compensation value in real time and drive the servo of each feed axis. Step S5: The step of verifying the compensation effect is to adjust the compensation correction coefficient K by the bisection method and iteratively optimize until the thermal error meets the requirements.

2. The thermal error compensation method for a large bridge-type gantry machining center according to claim 1, characterized in that, In step S1: using a large bridge-type gantry machining center as the compensation object, the triaxial thermal displacement change data of the tool tip and the key point temperature data of ten key locations are acquired under continuous operation and input into the machine tool control system; the machine tool control system performs time synchronization, data clearing and continuous recording processing on the acquired triaxial thermal displacement change data of the tool tip and the key point temperature data; and outputs a composite data set containing time series, triaxial thermal displacement of the tool tip and key point temperature data.

3. A thermal error compensation method for a large bridge-type gantry machining center according to claim 1 or 2, characterized in that, The triaxial thermal displacement data of the tool tip is acquired through a displacement sensor fixture fixed to the worktable. This fixture simultaneously fixes three displacement sensors arranged perpendicularly to each other. Before detecting the Z-axis thermal displacement data, the machine tool is fitted with a standard ball joint and moved to the intersection of the axes of the three displacement sensors to complete the calibration. The position after calibration is the initial position, denoted as position A. The displacement sensor data is then cleared. Subsequently, the machine tool moves a certain distance along the Z-direction, denoted as position B. The spindle rotates at a certain speed and performs multi-axis linkage motion. After running for 5 minutes, it returns to position B, stops rotating, and slowly returns to position A. The spindle speed is 1000–3000 RPM. r / min; the displacement sensor records the Z-axis displacement change as single Z-axis thermal displacement data; the spindle speed is adjusted every 2 hours, and a spindle speed table is made according to the spindle speed, and the spindle speed is adjusted according to the requirements of the spindle speed table; the X-axis and Y-axis thermal displacement change data are acquired in the same way to obtain the three-axis thermal displacement change data of the tool tip, where the X-axis thermal displacement change data and the Y-axis thermal displacement change data together reflect the spatial offset change caused by the heat of the transverse feed axis and the gantry structure, and the Z-axis thermal displacement change data reflects the vertical position change caused by the heat of the column, crossbeam, slide and spindle assembly. The acquisition time of the three-axis thermal displacement change data of the tool tip is not less than 72 hours; The key temperature data of the ten key locations are collected by stainless steel round bar PT100 temperature sensors. The ten temperature sensors are respectively arranged on the right bed, the front middle side of the crossbeam, the rear middle side of the crossbeam, the front lower side of the slide, the front middle side of the slide, the front upper side of the slide, the rear lower side of the slide, the rear middle side of the slide, the rear upper side of the slide, and the position of the spindle bearing. The acquisition frequency of the PT100 temperature sensors is consistent with the acquisition frequency of the displacement sensors.

4. The thermal error compensation method for a large bridge-type gantry machining center according to claim 2, characterized in that, The process of synchronizing the collected triaxial thermal displacement data of the tool tip and the temperature data of the key points with time, clearing the data and recording continuously is as follows: Before the start of each round of detection, the time when the machine tool returns to position A and completes the calibration of the displacement sensor is taken as the unified sampling start time. The current readings of the three displacement sensors are zeroed to make their initial output values ​​zero. The initial temperature values ​​of the temperature sensors at each key point are recorded as the temperature reference. During the rotation of the machine tool spindle and the multi-axis linkage operation, the displacement sensor and the key point temperature sensor are synchronously sampled according to a preset fixed sampling period of 1-10 seconds. The triaxial thermal displacement change data of the tool tip at each sampling moment and the key point temperature data are stored continuously in chronological order, thereby forming a one-to-one correspondence between the triaxial thermal displacement change data of the tool tip and the key point temperature data on the same time axis, resulting in a data set containing the complete time series, the triaxial thermal displacement of the tool tip, and the corresponding key point temperature data.

5. A thermal error compensation method for a large bridge-type gantry machining center according to claim 4, characterized in that, In step S2: the dataset containing time series, three-dimensional thermal displacement of the blade tip, and corresponding key point temperature data is used as input, and the key point temperature data is filtered through the correlation principle and the classification point selection principle. Output a set of representative key point temperature data that is highly correlated with the change in thermal displacement of the tool tip and can represent the characteristics of different components and heat sources; The key location points are selected by using the correlation principle and the classification point selection principle as follows: the correlation between the temperature data collected by each temperature sensor and the corresponding time of the three-dimensional thermal displacement of the tool tip is calculated to obtain the correlation coefficient between the key point temperature data and the machine tool thermal error of each key location point. The temperature sensors of the machine tools are classified and labeled according to their heat source properties; Within each category, temperature points with high correlation coefficients are selected as candidate key points; While ensuring that there is at least one representative point for each component and heat source category, the representative key point temperature data set is determined by comprehensively considering the correlation coefficient.

6. The thermal error compensation method for a large bridge-type gantry machining center according to claim 5, characterized in that, In step S3: the representative key point temperature data set and the three-dimensional thermal displacement of the tool tip are used as inputs, and the machine tool bed temperature is used as the temperature reference point. Calculate the temperature difference between other key locations and the temperature reference point, using the temperature difference as the independent variable and the machine tool thermal error as the dependent variable. Solve the model using a multiple linear regression method to obtain a multiple linear regression model for thermal error compensation. The machine tool thermal error is defined as the thermal displacement of the tool tip in the X, Y, and Z directions, specifically: Machine tool bed temperature As a temperature reference point, calculate the temperature difference between other key temperature points and the temperature reference point. ,in For the temperature of the i-th critical point, place the blade tip at... As the dependent variable, construct a multiple linear regression model: ； ； ； in, The temperature difference between other critical temperature points and the temperature reference point. , , For regression coefficients, , , The constant term is used to solve for the regression coefficients using the least squares method.

7. A thermal error compensation method for a large bridge-type gantry machining center according to claim 6, characterized in that, In step S4: the multiple linear regression model and the representative key point temperature dataset are used as input. Thermal compensation is debugged on the machine tool control terminal. The selected representative key point temperatures are marked, temperature sensors are placed at the corresponding locations, and the outputs of the temperature sensors are connected to the temperature acquisition card. Using the temperature digital signal processing method built into the Siemens CNC system, the acquired temperature sensor signals are converted into temperature variables usable by the Siemens CNC system. The multiple linear regression model is transcribed into a PLC program, which reads the temperature variables in real time and calculates the corresponding compensation values. The calculated compensation values ​​are used as NC variables to drive the servo of each feed axis, achieving real-time thermal error correction of the tool tip position. During the debugging process, the PLC program judges the compensation value calculated in real time. If the calculated value exceeds 0.05mm, it will not be output. The compensation output process is synchronized with the machine tool servo system.

8. A thermal error compensation method for a large bridge-type gantry machining center according to claim 7, characterized in that, In step S5: After the thermal compensation function is debugged, the compensation effect is verified. The data of the three-dimensional thermal displacement of the tool tip is used as the data input and compared with the data of the three-dimensional thermal displacement of the tool tip in the state without compensation. The compensation effect is evaluated. The thermal compensation correction coefficient K is adjusted according to the evaluation result, and the corrected compensation parameters are output and applied to the multiple linear regression model. After compensation is enabled, the same thermal error test method as in step S1 is used. The thermal compensation correction coefficient K is a parameter used to adjust the output of thermal error compensation. The theoretical compensation value is calculated by the multiple linear regression model based on the temperature difference between other key temperature points and the temperature reference point, denoted as K. The thermal compensation correction coefficient K, as a compensation multiplier, is multiplied by the compensation value calculated by the compensation model and then output as the final compensation value. Expressed as: ; Set the initial value of K to Enable thermal compensation and observe the thermal error at the tool tip after compensation. ;like If the value of K decreases compared to the uncompensated state but still does not reach the allowable range, then K is set to a larger value. ; If the thermal error changes in the opposite direction or overcompensates after compensation, then K should be set to a smaller value. ; in determining the effective interval [ Then, a new K value is selected using the bisection method: The compensation verification was performed again using this K value. The range of K values ​​was continuously narrowed based on the changes in thermal error. Through multiple iterations, when the thermal error at the blade tip stabilized within the preset allowable range after compensation, the K value was considered to have converged to the optimal value.

9. A thermal error compensation system for a large bridge-type gantry machining center, characterized in that, include: The system includes a comprehensive data acquisition module (1), a key point determination module (2), a thermal error modeling module (3), a compensation execution module (4), and a compensation optimization module (5). The integrated data acquisition module (1) is used to simultaneously acquire the triaxial thermal displacement change data of the blade tip and the temperature data of key points; The key point determination module (2) is used to screen the representative key point temperature set required for modeling based on correlation and classification principles. The thermal error modeling module (3) is used to establish a multiple linear regression compensation model based on temperature difference with reference to the reference temperature. The compensation execution module (4) is used to convert the compensation model into a real-time PLC program, calculate and output the compensation amount to the servo system; The compensation optimization module (5) is used to iteratively adjust the compensation correction coefficient K using the bisection method to verify and optimize the compensation effect.

10. A thermal error compensation system for a large bridge-type gantry machining center according to claim 9, characterized in that, The integrated data acquisition module (1) synchronously acquires the triaxial thermal displacement change data of the tool tip and the key temperature data of the key position points by means of the displacement sensor fixture arranged on the worktable and the temperature sensor installed at each preset key point of the machine tool. The sampling start point is taken as the machine tool completing the calibration and returning to the initial position. Zero-point calibration is performed on the displacement signal, and the initial reference value is recorded on the temperature signal. During the rotation of the machine tool spindle and multi-axis linkage, the triaxial thermal displacement change data and the key temperature data of each key position point are synchronized and stored according to the preset fixed sampling period, so that the triaxial thermal displacement change data of the tool tip and the key temperature data of each key position point correspond one-to-one on the same time axis, forming a composite data set containing a complete time series, the triaxial thermal displacement of the tool tip and the key temperature data of each key position point. The key point determination module (2) takes the composite dataset as input, analyzes the relationship between the key point temperature data and the three-dimensional thermal displacement of the tool tip, filters the key point temperature data, evaluates the correlation between the temperature change sequence of the key point temperature data and the three-dimensional thermal displacement of the tool tip based on the correlation principle, and prioritizes the selection of temperature points that are highly consistent with the trend of machine tool thermal error change. Based on the classification principle, the machine tools are divided into categories according to the main components and heat source types. Representative temperature points are selected in each category, and a set of representative key point temperature data that is highly correlated with the change of thermal displacement at the tool tip and covers the characteristics of the main components and heat sources of the machine tool is output. The thermal error modeling module (3) receives a set of representative key point temperature data and the three-dimensional thermal displacement of the tool tip. It uses the machine tool bed temperature as the reference temperature point and calculates the temperature difference between other key temperature points and the reference temperature. It uses the temperature difference as the independent variable and the machine tool thermal error as the dependent variable. It uses the multiple linear regression method to solve the mapping relationship between each error term and the machine tool thermal error, and obtains a multiple linear regression model to describe the relationship between temperature change and machine tool thermal error. The compensation execution module (4) transcribes the multiple linear regression model into a PLC program that can run in the CNC system, establishes a data association with the temperature acquisition channel, reads the temperature data signals of representative key points, calculates the corresponding thermal error, substitutes it into the multiple linear regression model, and obtains the compensation value at the current moment; then the compensation value is output in the form of a variable that the CNC system can recognize, and is applied to each feed axis servo to realize the real-time thermal error correction of the tool tip position, and makes a reasonable judgment on the calculation results; The compensation optimization module (5) evaluates and optimizes the compensation effect, using the three-dimensional thermal displacement of the blade tip after compensation is turned on as the evaluation basis, and introduces the compensation correction coefficient K to adjust the output result of the compensation model by a factor of 1. When the compensation effect is insufficient or an overcompensation trend occurs, the range of values ​​for the compensation correction coefficient K is set, and the bisection method is used to iteratively adjust within the range.

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