Hybrid drive numerical control machine tool feeding system thermal error modeling method and system

Abstract

The invention discloses a thermal error modeling method and system for a feeding system of a hybrid-driven numerical control machine tool, and the method comprises the steps: obtaining the temperature data of a lead screw feeding system under a simple working condition, identifying the heat source intensity and heat exchange coefficient of the lead screw feeding system under different working conditions, and achieving the parameter identification under the simple working condition; temperature and thermal error data of the lead screw feeding system under the complex working condition are collected, and temperature data of unmeasurable points of the lead screw under the corresponding complex working condition are calculated according to the parameter identification result under the simple working condition; establishing a mechanism and data hybrid driven numerical control machine tool feeding system thermal error model; and inputting the theoretical temperature data and the actually acquired temperature data of the unmeasurable points of the lead screw feeding system under the complex working condition into the constructed numerical control machine tool feeding system thermal error model, and outputting an error value. According to the method, the thermal error of the lead screw feeding system under the complex working condition is effectively predicted, the number of sensors is greatly reduced, the cost and wiring complexity are reduced, and the robustness and economical efficiency of the thermal error system of the lead screw feeding system are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of thermal error compensation of CNC machine tool feed systems, and in particular relates to a hybrid-driven CNC machine tool feed system thermal error modeling method and system. Background Art

[0002] Precision CNC machine tools play a vital, fundamental role in driving the transformation and upgrading of my country's manufacturing industry toward precision, modernization, and intelligent manufacturing, as well as in promoting high-quality development of the real economy. Precision is a core technical indicator of CNC machine tools, directly determining the quality and market acceptance of machine tool products and forming the core of a company's image and competitiveness. Currently, with the continuous advancement of the design, manufacturing, and assembly of complete machine tools and their components, the proportion of geometric errors, servo errors, and other errors in machining errors has gradually decreased. In contrast, thermal errors caused by thermal deformation of various components are becoming increasingly significant and have become the primary factor affecting machining accuracy. Numerous studies and machining results have shown that thermal errors can account for as much as 40% to 70% of the total machining errors of CNC machine tools, and this phenomenon is particularly pronounced in precision machine tools. Therefore, reducing thermal errors is crucial for improving the machining accuracy and stability of high-end precision machine tools.

[0003] Based on current domestic and international trends, methods for suppressing thermal errors in machine tools are primarily categorized into two types: error prevention and error compensation. Error prevention primarily relies on structural design and auxiliary devices, which become costly as precision requirements increase. Furthermore, their applicability to existing machine tools is limited. Error compensation, on the other hand, compensates for existing errors by artificially setting "reverse errors." Compared to error prevention, compensation methods are more convenient, flexible, and easy to implement in existing machine tools, leading to their widespread application in engineering practice. Effective error compensation requires the establishment of an accurate thermal error model. Common models are often data-driven. Accurately identifying thermally sensitive points requires the deployment of a large number of sensors and optimization of measurement points. Thermal error prediction is then achieved using various regression algorithms.

[0004] However, when applied in industrial fields, a large number of sensors will increase costs, and the wiring and installation of sensors is also very inconvenient, and may even affect the normal processing process. In addition, data-driven models are essentially statistical models and are highly dependent on experimental data. However, the experimental conditions in industrial fields are often less than ideal, which leads to the robustness of the established thermal error model not being guaranteed. At the same time, the temperature measurement points of data-driven models are mostly selected by statistical methods, and are limited by the installation of sensors. The collected temperature is mostly the temperature of the outermost side of the component. For screw feed systems, sensors cannot be arranged on the surface of the screw, and the collected temperature cannot accurately reflect the thermal characteristics of the screw. Therefore, it is necessary to propose a feed system thermal error modeling method that is driven by a hybrid mechanism and data to improve the robustness and accuracy of the feed system thermal error prediction model. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned prior art and provide a hybrid-driven CNC machine tool feed system thermal error modeling method and system to solve the technical problems in the prior art such as poor model robustness, high cost and complex wiring caused by an excessive number of sensors.

[0006] The present invention adopts the following technical solutions: A thermal error modeling method for a hybrid-driven CNC machine tool feed system includes the following steps: Obtain temperature data of the screw feed system under simple working conditions and identify the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient , to achieve parameter identification under simple working conditions; Collect the temperature and thermal error data of the screw feed system under complex working conditions, and calculate the temperature data of the unmeasurable points of the screw under complex working conditions based on the parameter identification results under simple working conditions; Establish a thermal error model of CNC machine tool feed system driven by hybrid mechanism and data; The theoretical temperature and actual temperature data of the unmeasurable points of the screw feed system under complex working conditions are input into the constructed thermal error model of the CNC machine tool feed system, and the error value is output.

[0007] Preferably, parameter identification under simple working conditions is achieved as follows: Under a single feed speed, reciprocate and collect the temperature data of the four-bar feed system during the whole process. Change the feed speed and sample again until all the temperature data under the set simple working condition are collected. Then, obtain the temperature data of the screw feed system under the simple working condition. The root mean square error between the experimental value and the theoretical value of the three measuring points is used as the optimization target, the heat source intensity and heat transfer coefficient at the three measuring points are used as variables, and the optimization is performed with the help of the fmincon function in MATLAB. The point corresponding to the minimum root mean square error is selected. and heat transfer coefficient To identify the results, parameter identification corresponding to simple working conditions is achieved.

[0008] Preferably, temperature sensors are arranged on the outside of the nut and the bearing seats at both ends of the screw feed system. At the same time, in order to monitor the influence of the ambient temperature, sensors are arranged to monitor the ambient temperature around the feed system.

[0009] Preferably, the temperature of the i-th measuring point at time t is for:

[0010] in, represents the ambient temperature, represents the heat source intensity at the i-th measuring point, represents heat capacity, represents the heat transfer coefficient, represents the heat exchange area, is an exponential constant.

[0011] Preferably, the temperature and thermal error data of the screw feed system under complex working conditions are collected, and the temperature data of the unmeasurable points of the screw under complex working conditions are calculated based on the parameter identification results under simple working conditions, specifically: Before the feed system starts to move, thermal error data is collected as a benchmark. After the start of movement, thermal error data is collected every t time interval to collect the temperature and thermal error data of the screw feed system under complex working conditions. After setting the screw feed system parameters, the screw is divided into M units, and the total simulation time is divided into N time steps. The position of the nut heat source is determined at each time step, and the calculation is performed unit by unit from the first unit to the last unit. After recording the temperature of each unmeasurable point in this process, the temperature distribution at the next time step is calculated until the temperature distribution at the last time step is recorded. Based on the identification results, the temperature data of the unmeasurable points of the screw under the corresponding complex working conditions are calculated.

[0012] Preferably, the process of collecting primary thermal error data is as follows: Move the workbench to the specified 0 position, and measure the positioning error every x millimeters until the workbench reaches the limit. Subtract the positioning error data measured each time from the thermal error data measured before the movement to obtain the thermal error data for the different positions. Temperature sensors are arranged on the nut of the screw feed system and the outside of the bearing seats at both ends, and sensors are arranged to monitor the ambient temperature around the feed system.

[0013] Preferably, the formula for calculating the temperature step by step according to the difference method is as follows:

[0014] in, is the cross-sectional area of ​​the structural member, is the heat dissipation area of ​​the microelement; is the convective heat exchange coefficient between the structural component and the surrounding air, is the thermal diffusivity of the system, is the thermal conductivity of the structural member, is the material density of the structural component; is the specific heat capacity of the structural material, is the body heat source W / m 3 Indicates the heat source generation intensity of each unit.

[0015] Preferably, the thermal error model of the CNC machine tool feed system driven by a hybrid mechanism and data is established as follows: The theoretically calculated temperature data of the unmeasurable point of the screw is mixed with the corresponding actual temperature data and used as the input features of the thermal error model. The slope of the thermal error of the screw feed system is calculated with the screw position as the horizontal coordinate at different times. and intercept As output, a multiple linear regression model is constructed with the help of fitlm function.

[0016] Preferably, the slope at different times calculated according to the model and intercept , the time-varying thermal error of the screw feed system at different positions can be calculated, and the thermal error value of the x position at the i-th moment is for:

[0017] in, represents the slope value at time i, Indicates the position of the specified 0 bit, Represents the intercept value at time i.

[0018] In a second aspect, an embodiment of the present invention provides a hybrid-driven CNC machine tool feed system thermal error modeling system, comprising: Identification module, which obtains the temperature data of the screw feed system under simple working conditions and identifies the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient , to achieve parameter identification under simple working conditions; The calculation module collects the temperature and thermal error data of the screw feed system under complex working conditions, and calculates the temperature data of the unmeasurable points of the screw under complex working conditions based on the parameter identification results under simple working conditions; Construct a module to establish a thermal error model of CNC machine tool feed system driven by mechanism and data hybrid; The output module inputs the theoretical temperature and actual temperature data of the unmeasurable points of the screw feed system under complex working conditions into the constructed thermal error model of the CNC machine tool feed system and outputs the error value.

[0019] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned hybrid-driven CNC machine tool feed system thermal error modeling method when executing the computer program.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned hybrid-driven CNC machine tool feed system thermal error modeling method.

[0021] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the thermal error modeling method of the hybrid-driven CNC machine tool feed system are implemented.

[0022] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned hybrid-driven CNC machine tool feed system thermal error modeling method.

[0023] Compared with the prior art, the present invention has at least the following beneficial effects: A hybrid-driven thermal error modeling method for the feed system of a CNC machine tool accurately calibrates the heat source intensity (Q) and heat transfer coefficient (h) under controllable conditions, providing a physical basis for complex working conditions and avoiding the high cost and uncertainty of direct complex experiments; it uses calibrated parameters to calculate the temperature of unmeasurable points, breaking through the limitations of sensor layout and solving the problem of temperature monitoring at key positions in actual processing; it combines mechanism models with data-driven methods to retain the interpretability of heat conduction theory while compensating for unmodeled factors through data; it feeds back model errors through the difference between theoretical and measured temperatures to achieve closed-loop calibration and improve the adaptability of the model under variable working conditions; it significantly reduces the cost of full-working-condition experiments, takes into account both the physical rationality of the model and its actual generalization ability, and is suitable for dynamic thermal compensation control of CNC machine tools.

[0024] Furthermore, the variables are separated by reciprocating motion of a single feed speed to avoid interference from multiple heat source coupling and ensure the accuracy of parameter identification; the root mean square error optimization of the three measuring points: with the goal of minimizing the temperature error at key positions, the fmincon function is used to efficiently find the optimal solution, improve the calibration accuracy of Q and h, and provide reliable input for subsequent calculations.

[0025] Furthermore, the nut and bearing seat are the main heat sources, and ambient temperature monitoring can eliminate external interference, ensure data representativeness, and provide effective boundary conditions for the mechanism model.

[0026] Furthermore, the heat transfer process is simplified into a lumped parameter model, which explicitly expresses the temperature variation over time. It has high computational efficiency and clear physical meaning, and is suitable for real-time prediction.

[0027] Furthermore, the lead screw is discretized into units to dynamically track the position changes of the heat source of the moving nut, accurately simulate the transient temperature field under complex motion, and make up for the lack of measured data; through pre-motion benchmark and periodic sampling, the thermal deformation error and mechanical error are separated to improve data quality.

[0028] Furthermore, the influence of initial error is eliminated and the pure thermally induced deformation is directly extracted; the data acquisition process is standardized to ensure the spatial continuity of thermal error data.

[0029] Furthermore, the heat conduction partial differential equation is discretized into iterative calculations to explicitly express the temperature update process, taking into account heat conduction, convection heat dissipation and heat source terms, to achieve efficient numerical solution.

[0030] Furthermore, the theoretical temperature and the actual temperature are integrated to enhance the completeness of the model input information; the slope k and intercept b are used to characterize the spatial distribution of thermal errors at different times, which simplifies the model structure and conforms to the linear characteristics of the screw thermal elongation.

[0031] Furthermore, through the dynamic k and b parameters, the thermal error is expressed as a linear function of the position x, which has low computational complexity and is easy to integrate into the real-time compensation module of the CNC system.

[0032] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0033] In summary, the present invention achieves a step-by-step fusion of mechanism and data, which not only ensures the physical interpretability of the model but also significantly improves its adaptability to dynamic machining environments, thus providing a cost-effective solution for thermal error compensation of CNC machine tools.

[0034] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the process of the present invention; Figure 2 This is a flow chart of calculating the temperature change of unmeasurable points of the screw using the differential method; Figure 3 This is the actual temperature data diagram for a simple working condition in Example 1; Figure 4 This is the temperature identification result diagram of a simple working condition in Example 1; Figure 5 This is the error data graph collected from a complex working condition in Example 1; Figure 6 This is the temperature calculation result diagram of the unmeasurable point of the screw in a complex working condition in Example 1; Figure 7This is a comparison chart of the error prediction value and the actual value of a complex working condition in Example 1; Figure 8 A schematic diagram of a computer device provided in accordance with an embodiment of the present invention; Figure 9 The block diagram of a chip provided according to one embodiment of the present invention is shown.

[0036] Among them, 60. Computer device; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / Utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0038] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0039] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0040] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0041] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0042] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0043] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0044] This paper provides a method for modeling the thermal errors of a hybrid-driven CNC machine tool feed system. Based on boundary conditions such as heat source intensity identified through temperature experiments under simple operating conditions, the theoretical temperature of unmeasurable points in the screw feed system under complex operating conditions is calculated. This theoretical temperature data, along with actual temperature data, is used as input features to construct a hybrid-driven CNC machine tool feed system thermal error model. This model effectively predicts the thermal errors of the screw feed system under complex operating conditions, significantly reducing the number of sensors required, lowering costs and wiring complexity, and significantly improving the robustness and cost-effectiveness of the screw feed system's thermal error system.

[0045] See also Figure 1 The present invention provides a hybrid drive CNC machine tool feed system thermal error modeling method, comprising the following steps: S1. Obtain the temperature data of the screw feed system under simple working conditions and identify the heat source intensity of the screw feed system under different working conditions. and heat transfer coefficient ; S101, obtaining temperature data of the screw feed system under simple working conditions; Temperature sensors are arranged on the outside of the nut and bearing seats at both ends of the screw feed system. At the same time, in order to monitor the influence of ambient temperature, sensors are arranged to monitor the ambient temperature around the feed system. Perform reciprocating motion at a single feed speed, collect temperature data of the screw feed system throughout the entire process, change the feed speed, and perform sampling again; using the feed speed of the feed system during the actual machining process of the machine tool as a reference, take multiple constant feed speed and no-load working conditions as simple working conditions, and repeat the temperature collection steps under a single constant feed speed until all temperature data under the set simple working conditions are collected.

[0046] S102. Identify the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient ; The temperature calculation formula of the nut and the bearings on both sides in the present invention is as follows:

[0047] in, represents the temperature of the i-th measuring point at time t, represents the ambient temperature, represents the heat source intensity at the i-th measuring point, Represents heat capacity (mass Specific heat capacity ), represents the heat transfer coefficient, represents the heat exchange area, is an exponential constant.

[0048] The root mean square error between the experimental and theoretical values ​​at the three measuring points is used as the optimization target. The heat source intensity and heat transfer coefficient at the three measuring points are used as variables. The fmincon function in MATLAB is used to search for the optimal solution. The heat source intensity and heat transfer coefficient corresponding to the minimum root mean square error are used as the identification results to achieve parameter identification under the corresponding simple working conditions. S2. Collect the temperature and thermal error data of the screw feed system under complex working conditions, and calculate the temperature data of the unmeasurable points of the screw under complex working conditions based on the identification results; S201, collecting temperature and thermal error data of the screw feed system under complex working conditions; The temperature data collection process is the same as that under simple working conditions. Sensors are arranged on the outside of the nut and the bearings on both sides to collect temperature data changes under the set complex working conditions.

[0049] Before the feed system starts to move, thermal error data is collected as a benchmark. After the start of movement, thermal error data is collected every t time. The collection process of thermal error data is as follows: Move the workbench to the specified "0 position" and measure the positioning error every x millimeters until the workbench reaches the limit. Subtract the positioning error data of each measurement from the thermal error data measured before the movement to obtain the thermal error data for the different positions.

[0050] S202, calculating the temperature data of the unmeasurable points of the screw under the corresponding complex working conditions according to the identification results; The heat source intensity under the simple working condition is converted into the heat source intensity at the corresponding position of the screw. The temperature data of the unmeasurable point of the screw under the complex working condition is calculated by finite difference method. For the specific process, please refer to Figure 2 , as follows: After setting the screw feed system parameters, the screw is divided into M units, and the total simulation time is divided into N time steps. The location of the nut heat source is determined at each time step, and the calculation is performed unit by unit from the first unit to the last unit. After recording the temperature of each unmeasurable point in this process, the temperature distribution at the next time step is calculated until the temperature distribution at the last time step is recorded. The formula for calculating the temperature step by step according to the difference method is as follows:

[0051] in, is the cross-sectional area of ​​the structural member, is the heat dissipation area of ​​the microelement; is the convective heat exchange coefficient between the structural component and the surrounding air, which changes with time and position. is the thermal diffusivity of the system, is the thermal conductivity of the structural member, is the material density of the structural component; is the specific heat capacity of the structural material, is the body heat source W / m 3 Indicates the heat source generation intensity of each unit.

[0052] S3. Establish a thermal error model of CNC machine tool feed system driven by mechanism and data hybrid; The theoretically calculated temperature data of the unmeasurable point of the screw is mixed with the corresponding actual temperature data and used as the input features of the thermal error model. The slope of the thermal error of the screw feed system is calculated with the screw position as the horizontal coordinate at different times. and intercept The multiple linear regression model is constructed with the help of fitlm function in MATLAB.

[0053] S4. Integrate the output of the thermal error model into an error value.

[0054] The slope at different times calculated according to the model and intercept , the time-varying thermal error of the screw feed system at different positions can be calculated. The specific calculation formula is as follows;

[0055] in, Represents the thermal error value of the x position at time i, represents the slope value at time i, Indicates the position of the specified "0 bit", Represents the intercept value at time i.

[0056] In another embodiment of the present invention, a hybrid-driven CNC machine tool feed system thermal error modeling system is provided, which can be used to implement the above-mentioned hybrid-driven CNC machine tool feed system thermal error modeling method. Specifically, the hybrid-driven CNC machine tool feed system thermal error modeling system includes an identification module, a calculation module, a construction module and an output module.

[0057] Among them, the identification module obtains the temperature data of the screw feed system under simple working conditions and identifies the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient , to achieve parameter identification under simple working conditions; The calculation module collects the temperature and thermal error data of the screw feed system under complex working conditions, and calculates the temperature data of the unmeasurable points of the screw under complex working conditions based on the parameter identification results under simple working conditions; Construct a module to establish a thermal error model of CNC machine tool feed system driven by mechanism and data hybrid; The output module inputs the theoretical temperature and actual temperature data of the unmeasurable points of the screw feed system under complex working conditions into the constructed thermal error model of the CNC machine tool feed system and outputs the error value.

[0058] The present invention provides a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the thermal error modeling method of the hybrid-driven CNC machine tool feed system, including: Obtain temperature data of the screw feed system under simple working conditions and identify the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient , realize parameter identification under simple working conditions; collect temperature and thermal error data of the screw feed system under complex working conditions, and calculate the temperature data of the unmeasurable points of the screw under complex working conditions based on the parameter identification results under simple working conditions; establish a thermal error model of the CNC machine tool feed system driven by a hybrid of mechanism and data; input the theoretical temperature and actual temperature data of the unmeasurable points of the screw feed system under complex working conditions into the constructed thermal error model of the CNC machine tool feed system, and output the error value.

[0059] See also Figure 8 The terminal device is a computer device. Computer device 60 in this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in memory 62 and executable by processor 61. When executed by processor 61, computer program 63 implements the method for estimating the concentration of radioactive iodine species in a post-accident containment vessel described in this embodiment. To avoid repetition, this description is omitted here. Alternatively, when executed by processor 61, computer program 63 implements the functions of each model / unit in the thermal error modeling system for a hybrid-driven CNC machine tool feed system described in this embodiment. To avoid repetition, this description is omitted here.

[0060] The computer device 60 may be a desktop computer, a notebook computer, a PDA, a cloud server, or other computing devices. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. It will be understood by those skilled in the art that Figure 8 This is merely an example of the computer device 60 and does not constitute a limitation of the computer device 60 . The computer device 60 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0061] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), 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, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0062] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 60.

[0063] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is about to be output.

[0064] See also Figure 9 The terminal device is an electronic device 600, which is implemented as a general-purpose computing device. The components of the electronic device may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), and a display unit 640.

[0065] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0066] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0067] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0068] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0069] The electronic device 600 may also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem). Such communication may occur via an input / output interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network, a wide area network, and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0070] Example 4 The present invention also provides a storage medium, specifically a computer-readable storage medium. The computer-readable storage medium is a memory device in a terminal device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that more specific examples of the computer-readable storage medium herein include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0071] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program code. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0072] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network or a wide area network, or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0073] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for modeling thermal errors of a hybrid-driven CNC machine tool feed system in the above embodiment. The processor may load and execute the following steps: Obtain temperature data of the screw feed system under simple working conditions and identify the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient , realize parameter identification under simple working conditions; collect temperature and thermal error data of the screw feed system under complex working conditions, and calculate the temperature data of the unmeasurable points of the screw under complex working conditions based on the parameter identification results under simple working conditions; establish a thermal error model of the CNC machine tool feed system driven by a hybrid of mechanism and data; input the theoretical temperature and actual temperature data of the unmeasurable points of the screw feed system under complex working conditions into the constructed thermal error model of the CNC machine tool feed system, and output the error value.

[0074] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0075] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0076] Thermal error modeling for the Z-axis screw feed system of a certain type of inclined lathe includes the following steps: S1. Obtain the temperature data of the screw feed system under simple working conditions and identify the heat source intensity of the screw feed system under different working conditions. and heat transfer coefficient ; S101, obtaining temperature data of the screw feed system under simple working conditions; Temperature sensors are arranged on the outside of the nut and the bearing seats at both ends of the screw feed system. At the same time, in order to monitor the influence of the ambient temperature, sensors are arranged to monitor the ambient temperature around the feed system.

[0077] Four simple working conditions were set: 2400 mm / min, 3600 mm / min, 4800 mm / min, and 6000 mm / min, and the data was collected for 2 hours for each working condition.

[0078] Under a single feed speed, reciprocate and collect the temperature data of the screw feed system during the whole process. Change the feed speed and sample again until all the temperature data under the set simple working conditions are collected. For the collected temperature data results, please refer to Figure 3 .

[0079] S102. Identify the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient ; The root mean square error between the experimental and theoretical values ​​at the three measuring points is used as the optimization target. The heat source intensity and heat transfer coefficient at the three measuring points are used as variables. The fmincon function in MATLAB is used to find the optimal solution. The heat source intensity and heat transfer coefficient corresponding to the minimum root mean square error are used as the identification results to achieve parameter identification under the corresponding simple working conditions. Please refer to the identification results for details. Figure 4 .

[0080] S2. Collect the temperature and thermal error data of the screw feed system under complex working conditions, and calculate the temperature data of the unmeasurable points of the screw under complex working conditions based on the identification results; S201, collecting temperature and thermal error data of the screw feed system under complex working conditions; The temperature data collection process is the same as that under simple working conditions. Sensors are arranged on the outside of the nut and the bearings on both sides to collect temperature data changes under the set complex working conditions.

[0081] The complex working conditions are set as 6000-4800-3600-2400, 2400-3600-4800-6000, 6000-6000-3600-3600, and 6000-2400-6000-2400. Each set of working conditions contains four feed speeds, with one speed every 30 minutes, and a total running time of 2 hours.

[0082] Before the feed system starts to move, thermal error data is collected as a benchmark. After the start of movement, thermal error data is collected every 10 minutes. The total time is 2 hours, that is, a total of 13 data collections. The collection process of thermal error data is as follows: Move the workbench to the specified "0 position" and measure the positioning error every 100 mm until the workbench reaches the limit. Subtract the positioning error data measured each time from the thermal error data measured before the movement to obtain the thermal error data for the different positions. Figure 5 ; S202, calculating the temperature data of the unmeasurable points of the screw under the corresponding complex working conditions according to the identification results; The heat source intensity under the simple working condition is converted into the heat source intensity at the corresponding position of the screw. The temperature data of the unmeasurable point of the screw under the complex working condition is calculated by finite difference method. For the specific process, please refer to Figure 2 , as follows: The screw is divided into 500 units, and the total simulation time is divided into 0.1s per time step. The position of the nut heat source is determined at each time step, and the calculation is performed unit by unit from the first unit to the last unit. After recording the temperature of each unmeasurable point in this process, the temperature distribution at the next time step is calculated until the temperature distribution at the last time step is recorded. The temperature calculation results of the 10 unmeasurable points can be found in Figure 6 .

[0083] S3. Establish a thermal error model of CNC machine tool feed system driven by mechanism and data hybrid; The theoretically calculated temperature data of the unmeasurable point of the screw is mixed with the corresponding actual temperature data and used as the input features of the thermal error model. The slope of the thermal error of the screw feed system is calculated with the screw position as the horizontal coordinate at different times. and intercept The output is . A multiple linear regression model is constructed using the fitlm function in MATLAB. The 6000-4800-3600-2400 and 2400-3600-4800-6000 groups are used as training data, and the 6000-6000-3600-3600 and 6000-2400-6000-2400 groups are used as test data.

[0084] S4. Integrate the output of the thermal error model into an error value.

[0085] The slope at different times calculated according to the model and intercept , the time-varying thermal error of the screw feed system at different positions can be calculated. Figure 7 .

[0086] The core effects are reflected in the following aspects: Significantly reduce sensor dependency and application costs: By calculating the temperature at unmeasurable points within the screw using a mechanistic model, the problem of installing sensors on the screw surface is overcome. This significantly reduces the number of sensors required, placing them only at key locations such as the nut and bearing seat, as well as at environmental monitoring points. This significantly reduces hardware costs, wiring complexity, and disruption to normal processing, improving the feasibility of engineering applications.

[0087] Improve model prediction accuracy and robustness: Physical mechanisms ensure basic accuracy: Accurately identifying core physical parameters such as heat source intensity (Q) and heat transfer coefficient (h) under simple working conditions lays a solid physical foundation for temperature field deduction under complex working conditions, avoiding the poor extrapolation ability of pure data models due to the lack of physical laws.

[0088] Data-driven uncertainty compensation: When building a hybrid model, both the theoretical temperature calculated by the mechanism model and the actual temperature collected are input as features. The actual temperature incorporates field information such as actual environmental disturbances and unmodeled factors, effectively compensating for the simplified assumptions and parameter uncertainties of the mechanism model, significantly improving the model's prediction accuracy under complex and variable operating conditions.

[0089] Closed-loop feedback dynamic calibration: The model outputs the error value between the theoretical temperature and the actual temperature. This error value can be used as a feedback signal to calibrate model parameters online or evaluate model status, further enhancing the dynamic adaptability and long-term stability of the model.

[0090] Enhance the interpretability and generalization ability of the model: The core of the model is based on the physical laws of heat conduction. The model structure has clear physical meaning (reflecting the linear characteristics of the screw's thermal expansion), making it easy to understand and analyze. This physical embeddedness allows the model to generalize to different machine tool models or even minor structural modifications, making it easier to transfer and apply compared to purely "black box" data models.

[0091] Improve modeling efficiency and engineering practicality: A phased modeling strategy is used: first, efficient parameter identification is performed under simple working conditions, and then the results are used to infer the unmeasurable temperature under complex working conditions, thus avoiding the need for extensive, time-consuming, and costly experimental calibration under complex working conditions.

[0092] Efficient calculation model: The final thermal error output model has a simple structure and small calculation amount, which is convenient for real-time integration into the thermal error compensation module of the CNC system to achieve dynamic compensation.

[0093] In summary, the hybrid-drive thermal error modeling method and system for CNC machine tool feed systems, through the ingenious integration of physical mechanism models and field-measured data, successfully addresses key challenges in traditional thermal error modeling, including limited sensor placement (especially within the lead screw), the accuracy of pure mechanism models affected by parameters and simplification, and the poor robustness of pure data models, which rely on extensive experiments. With a significantly reduced number of sensors (reducing cost and interference), the method achieves accurate estimation of the internal temperature field of the lead screw under complex operating conditions, ultimately constructing a thermal error prediction model that combines high accuracy, strong robustness, good interpretability, and engineering practicality. This method provides efficient and reliable technical support for real-time dynamic compensation of thermal errors in CNC machine tools, especially high-end precision machine tools, and is of great significance for improving the machining accuracy and market competitiveness of domestically produced precision machine tools.

[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0095] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0096] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0097] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0098] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0099] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0100] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0101] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0104] The above content is only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A thermal error modeling method for a hybrid-driven CNC machine tool feed system, characterized in that: The following steps are involved: Obtain temperature data of the screw feed system under simple working conditions and identify the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient , to achieve parameter identification under simple working conditions; Collect the temperature and thermal error data of the screw feed system under complex working conditions, and calculate the temperature data of the unmeasurable points of the screw under complex working conditions based on the parameter identification results under simple working conditions; Establish a thermal error model of CNC machine tool feed system driven by hybrid mechanism and data; The theoretical temperature and actual temperature data of the unmeasurable points of the screw feed system under complex working conditions are input into the constructed thermal error model of the CNC machine tool feed system, and the error value is output.

2. The thermal error modeling method of the hybrid drive CNC machine tool feed system according to claim 1 is characterized in that: The parameter identification under simple working conditions is as follows: Under a single feed speed, reciprocate and collect the temperature data of the four-bar feed system during the whole process. Change the feed speed and sample again until all the temperature data under the set simple working condition are collected. Then, obtain the temperature data of the screw feed system under the simple working condition. The root mean square error between the experimental value and the theoretical value of the three measuring points is used as the optimization target, the heat source intensity and heat transfer coefficient at the three measuring points are used as variables, and the optimization is performed with the help of the fmincon function in MATLAB. The point corresponding to the minimum root mean square error is selected. and heat transfer coefficient To identify the results, parameter identification corresponding to simple working conditions is achieved.

3. The thermal error modeling method of the hybrid drive CNC machine tool feed system according to claim 2, characterized in that: Temperature sensors are arranged on the outside of the nut and the bearing seats at both ends of the screw feed system. At the same time, in order to monitor the influence of the ambient temperature, sensors are arranged to monitor the ambient temperature around the feed system.

4. The thermal error modeling method of the hybrid drive CNC machine tool feed system according to claim 2, characterized in that: The temperature of the i-th measuring point at time t for: in, represents the ambient temperature, represents the heat source intensity at the i-th measuring point, represents heat capacity, represents the heat transfer coefficient, represents the heat exchange area, is an exponential constant.

5. The thermal error modeling method of the hybrid drive CNC machine tool feed system according to claim 1, characterized in that: The temperature and thermal error data of the screw feed system under complex working conditions are collected, and the temperature data of the unmeasurable points of the screw under complex working conditions are calculated based on the parameter identification results under simple working conditions. Specifically: Before the feed system starts to move, thermal error data is collected as a benchmark. After the start of movement, thermal error data is collected every t time interval to collect the temperature and thermal error data of the screw feed system under complex working conditions. After setting the screw feed system parameters, the screw is divided into M units, and the total simulation time is divided into N time steps. The position of the nut heat source is determined at each time step, and the calculation is performed unit by unit from the first unit to the last unit. After recording the temperature of each unmeasurable point in this process, the temperature distribution at the next time step is calculated until the temperature distribution at the last time step is recorded. Based on the identification results, the temperature data of the unmeasurable points of the screw under the corresponding complex working conditions are calculated.

6. The thermal error modeling method for a hybrid-driven CNC machine tool feed system according to claim 5, characterized in that: The collection process of primary thermal error data is as follows: Move the workbench to the specified 0 position, and measure the positioning error every x millimeters until the workbench reaches the limit. Subtract the positioning error data measured each time from the thermal error data measured before the movement to obtain the thermal error data for the different positions. Temperature sensors are arranged on the nut of the screw feed system and the outside of the bearing seats at both ends, and sensors are arranged to monitor the ambient temperature around the feed system.

7. The thermal error modeling method for a hybrid-driven CNC machine tool feed system according to claim 5, characterized in that: The formula for calculating temperature step by step according to the difference method is as follows: in, is the cross-sectional area of ​​the structural member, is the heat dissipation area of ​​the microelement; is the convective heat exchange coefficient between the structural component and the surrounding air, is the thermal diffusivity of the system, is the thermal conductivity of the structural member, is the material density of the structural component; is the specific heat capacity of the structural material, is the body heat source W / m 3 Indicates the heat source generation intensity of each unit.

8. The thermal error modeling method for a hybrid-driven CNC machine tool feed system according to claim 1, characterized in that: The thermal error model of the CNC machine tool feed system driven by a hybrid mechanism and data is established as follows: The theoretically calculated temperature data of the unmeasurable point of the screw is mixed with the corresponding actual temperature data and used as the input features of the thermal error model. The slope of the thermal error of the screw feed system is calculated with the screw position as the horizontal coordinate at different times. and intercept As output, a multiple linear regression model is constructed with the help of fitlm function.

9. The thermal error modeling method for a hybrid-driven CNC machine tool feed system according to claim 1, characterized in that: The slope at different times calculated according to the model and intercept , the time-varying thermal error of the screw feed system at different positions can be calculated, and the thermal error value of the x position at the i-th moment is for: in, represents the slope value at time i, Indicates the position of the specified 0 bit, Represents the intercept value at time i.

10. A hybrid drive CNC machine tool feed system thermal error modeling system, characterized in that: include: Identification module, which obtains the temperature data of the screw feed system under simple working conditions and identifies the heat source intensity of the screw feed system under different working conditions and heat transfer coefficient , to achieve parameter identification under simple working conditions; The calculation module collects the temperature and thermal error data of the screw feed system under complex working conditions, and calculates the temperature data of the unmeasurable points of the screw under complex working conditions based on the parameter identification results under simple working conditions; Construct a module to establish a thermal error model of CNC machine tool feed system driven by mechanism and data hybrid; The output module inputs the theoretical temperature and actual temperature data of the unmeasurable points of the screw feed system under complex working conditions into the constructed thermal error model of the CNC machine tool feed system and outputs the error value.

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