Composite machine tool machining precision real-time measurement and thermal deformation compensation control method and system

Abstract

The application provides a composite machine tool machining precision real-time measurement and thermal deformation compensation control method and system, relates to the composite machining technical field, and comprises the following steps: calculating heat source distribution information by acquiring machining parameters; collecting data through temperature sensors and displacement sensors; establishing a multidimensional thermal deformation prediction model; measuring the tool position in real time by using a laser interferometer; constructing an adaptive compensation controller, generating compensation instructions by using a fuzzy PID control algorithm; and sending the compensation instructions to a numerical control system to realize real-time compensation of the machining track. The application realizes closed-loop control of the composite machine tool machining precision and improves the machining precision.

Description

Technical Field

[0001] This invention relates to composite material processing technology, and more particularly to a method and system for real-time measurement of machining accuracy and thermal deformation compensation control of composite material machine tools. Background Technology

[0002] Composite material machine tools are specialized equipment for processing composite materials. With the increasing demands for precision in the processing of composite material parts from high-end manufacturing industries such as aerospace and automotive, the machining accuracy of composite material machine tools has become a key factor affecting product quality. During the actual machining process of composite material machine tools, the high-speed rotation of the spindle, the movement of the feed system, and the cutting process generate a large amount of heat, leading to thermal deformation of the machine tool structure and thus affecting machining accuracy. Thermal deformation has become one of the main factors limiting the improvement of machining accuracy of composite material machine tools.

[0003] Currently, research on machine tool thermal deformation mainly focuses on thermal deformation measurement, thermal deformation prediction, and thermal deformation compensation. Traditional thermal deformation measurement typically employs offline methods, performed after the machining process has stopped, failing to reflect the real-time thermal deformation state of the machine tool during machining. Existing thermal deformation prediction models mostly consider only the deformation of a single heat source or a single structural component, making it difficult to comprehensively describe the multi-dimensional thermal deformation behavior of complex machine tool systems. Traditional thermal deformation compensation control often employs fixed-parameter proportional-integral-derivative (PID) control strategies, lacking adaptive capabilities, and the compensation effect significantly decreases when machining conditions change.

[0004] Furthermore, existing thermal deformation compensation technologies are typically based on pre-established empirical models for open-loop control, lacking a real-time measurement feedback mechanism, resulting in limited compensation accuracy. Insufficient research on the correlation between thermal deformation prediction models and actual processing parameters prevents flexible adjustment of compensation strategies based on different processing parameters. The compensation control algorithm lacks intelligent features, failing to automatically adjust control parameters according to the dynamic changes in thermal deformation, thus affecting the real-time performance and accuracy of compensation. Summary of the Invention

[0005] The present invention provides a method and system for real-time measurement of machining accuracy and thermal deformation compensation control of composite material machine tools, which can solve the problems in the prior art.

[0006] A first aspect of the present invention provides a method for real-time measurement of machining accuracy and thermal deformation compensation control of composite material machine tools, comprising:

[0007] The processing parameters of the composite machine tool are obtained, including feed rate, spindle speed and depth of cut, and the heat source distribution information of the composite machine tool is calculated based on the processing parameters.

[0008] Multiple temperature sensors and displacement sensors are installed on the spindle, worktable, and column of the composite machine tool to collect temperature and displacement data of the spindle, worktable, and column in real time.

[0009] A multi-dimensional thermal deformation prediction model for the composite machine tool is established. The heat source distribution information, temperature data, and displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool.

[0010] Multiple laser interferometers are used to measure the tool position of the composite machine tool in real time to obtain the actual machining position data of the tool;

[0011] An adaptive compensation controller for the composite machine tool is constructed. Based on the real-time thermal deformation prediction data and the actual processing position data, a fuzzy PID control algorithm is used to dynamically generate compensation commands. The control parameters of the fuzzy PID control algorithm are automatically adjusted according to the changing trend of the thermal deformation prediction data.

[0012] The compensation command is sent to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby realizing closed-loop control of the machining accuracy of the composite machine tool.

[0013] Calculating the heat source distribution information of the composite machine tool based on the processing parameters includes:

[0014] A heat source distribution prediction model for composite machine tools is established. The heat source distribution prediction model includes a coupled analysis module for cutting heat, friction heat and motor heat generation. The heat power and spatiotemporal distribution characteristics of each heat source are calculated based on the processing parameters.

[0015] The composite machine tool is meshed based on the finite element method, and the output of the heat source distribution prediction model is mapped to the mesh nodes. The mesh is refined in the dense heat source area to obtain the heat source distribution information of the composite machine tool.

[0016] Multiple temperature sensors and displacement sensors are installed on the spindle, worktable, and column of the composite machine tool to collect real-time temperature and displacement data of the spindle, worktable, and column, including:

[0017] Multiple temperature sensors and displacement sensors are installed on the spindle, worktable and column of the composite machine tool, wherein the temperature sensors are thermocouple temperature sensors and the displacement sensors are capacitive displacement sensors.

[0018] A distributed sensing network for the composite machine tool is constructed. The distributed sensing network includes multiple microprocessor nodes. Each microprocessor node is connected to a temperature sensor and a displacement sensor in a corresponding area. Data is acquired using a time-division multiplexing method, and the acquired data is subjected to noise filtering and digital processing.

[0019] A real-time data fusion system is established based on the distributed sensor network. The real-time data fusion system uses the Kalman filter algorithm to dynamically correct and fuse the temperature data and the displacement data to generate time-series temperature field data and displacement field data of each component of the composite machine tool.

[0020] A multi-dimensional thermal deformation prediction model for the composite machine tool is established. The heat source distribution information, temperature data, and displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool, including:

[0021] The heat source distribution information and historical temperature data of the composite machine tool are obtained. The heat source distribution information is divided into multiple time windows according to the time series. Temperature change features are extracted in each time window. A forgetting factor and an input factor are calculated based on the temperature change features. The historical temperature data is filtered according to the forgetting factor and the input factor to obtain the temperature prediction data of the composite machine tool.

[0022] Displacement data is collected at corresponding positions of the spindle, worktable and column of the composite machine tool, and the displacement data is reconstructed in three-dimensional space to obtain the displacement distribution data of the composite machine tool.

[0023] Weighting coefficients are set for the temperature prediction data and the displacement distribution data. Based on the weighting coefficients, the temperature prediction data is mapped to the three-dimensional space corresponding to the displacement distribution data to generate the thermal deformation prediction data of the composite machine tool.

[0024] The adaptive compensation controller for the composite machine tool is constructed by dynamically generating compensation commands using a fuzzy PID control algorithm based on the real-time thermal deformation prediction data and the actual machining position data.

[0025] The real-time thermal deformation prediction data and actual processing position data of the composite machine tool are obtained, and the compensation deviation of the composite machine tool is calculated based on the real-time thermal deformation prediction data and the actual processing position data.

[0026] A fuzzy control rule base is established for the composite machine tool. The compensation deviation and the rate of change of the compensation deviation are used as fuzzy control inputs. The membership function of the compensation deviation and the rate of change of the compensation deviation is determined according to the magnitude of the fuzzy control input. Fuzzy inference is performed on the compensation deviation and the rate of change of the compensation deviation based on the membership function.

[0027] The control parameters of the PID controller of the composite machine tool are determined based on the result of the fuzzy inference, wherein the control parameters of the PID controller include proportional coefficient, integral coefficient and derivative coefficient;

[0028] Based on the changing trend of the compensation deviation, the proportional coefficient, the integral coefficient, and the derivative coefficient are adaptively adjusted using a dynamic weight allocation method; when the compensation deviation increases, the weight of the proportional coefficient is increased; when the compensation deviation is stable, the weight of the integral coefficient is increased; when the compensation deviation changes rapidly, the weight of the derivative coefficient is increased.

[0029] Based on the dynamically adjusted proportional coefficient, integral coefficient, and derivative coefficient, the compensation control quantity of the composite machine tool is calculated; the compensation control quantity is converted into compensation commands for each coordinate axis of the composite machine tool to realize thermal deformation compensation control of the composite machine tool.

[0030] Sending the compensation command to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby achieving closed-loop control of the machining accuracy of the composite machine tool, includes:

[0031] The compensation instructions of the composite machine tool are decomposed into X-axis compensation instructions, Y-axis compensation instructions and Z-axis compensation instructions to generate a multi-axis compensation instruction sequence for the composite machine tool;

[0032] A timestamp and priority identifier are set for the multi-axis compensation command sequence, and the multi-axis compensation command sequence is sent to the CNC system of the composite machine tool through a real-time communication interface;

[0033] Collect real-time machining position data of the composite machine tool, compare the real-time machining position data with the theoretical machining trajectory of the composite machine tool, and calculate the trajectory error data of the composite machine tool;

[0034] A real-time compensation model for the composite machine tool is established based on the trajectory error data. The real-time compensation model includes a trajectory prediction unit and a compensation amount calculation unit. The trajectory prediction unit predicts the position deviation at the next moment based on the current trajectory error, and the compensation amount calculation unit generates a compensation increment based on the position deviation.

[0035] The compensation increment is superimposed on the original compensation command to update and generate a new compensation command for the composite machine tool; the new compensation command is subjected to an acceleration continuity check to ensure the smoothness of the compensation process.

[0036] The machining accuracy of the composite machine tool during the execution of the new compensation command is monitored in real time. When the machining accuracy meets the preset accuracy requirements, the current compensation strategy is maintained; when the machining accuracy does not meet the preset accuracy requirements, the calculation step of the trajectory error data is returned.

[0037] A second aspect of the present invention provides a real-time measurement and thermal deformation compensation control system for composite material machine tool processing accuracy, comprising:

[0038] The first unit is used to acquire the processing parameters of the composite machine tool, including the feed rate, spindle speed and depth of cut, and to calculate the heat source distribution information of the composite machine tool based on the processing parameters.

[0039] The second unit is used to install multiple temperature sensors and displacement sensors on the spindle, worktable and column of the composite machine tool to collect temperature data and displacement data of the spindle, the worktable and the column in real time.

[0040] The third unit is used to establish a multi-dimensional thermal deformation prediction model for the composite machine tool. The heat source distribution information, the temperature data, and the displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool.

[0041] The fourth unit is used to measure the tool position of the composite machine tool in real time using multiple laser interferometers to obtain the actual machining position data of the tool;

[0042] The fifth unit is used to construct the adaptive compensation controller of the composite machine tool. Based on the real-time thermal deformation prediction data and the actual processing position data, a fuzzy PID control algorithm is used to dynamically generate compensation commands. The control parameters of the fuzzy PID control algorithm are automatically adjusted according to the changing trend of the thermal deformation prediction data.

[0043] The sixth unit is used to send the compensation command to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby realizing closed-loop control of the machining accuracy of the composite machine tool.

[0044] A third aspect of the present invention provides an electronic device, comprising:

[0045] processor;

[0046] Memory used to store processor-executable instructions;

[0047] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0048] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0049] The beneficial effects of this application are as follows:

[0050] This invention establishes an accurate multi-dimensional thermal deformation prediction model by setting multiple temperature and displacement sensors on the spindle, worktable, and column to collect temperature and displacement data of key components in real time. Combined with heat source distribution information calculated from processing parameters, the model can accurately predict the thermal deformation state of composite machine tools, thus improving the accuracy and real-time performance of thermal deformation prediction.

[0051] This invention uses multiple laser interferometers to measure the tool position in real time, obtain the actual machining position data of the tool, and combine it with thermal deformation prediction data to construct an adaptive compensation controller. The fuzzy PID control algorithm is used to dynamically generate compensation instructions, thereby achieving accurate compensation for thermal deformation and reducing the impact of thermal deformation on machining accuracy.

[0052] This invention sends compensation commands to the CNC system of the composite machine tool in real time to dynamically compensate the machining trajectory, realizing closed-loop control of machining accuracy. It enables the compensation parameters to be automatically adjusted according to the changing trend of thermal deformation prediction data, and has the characteristics of strong adaptability and fast response speed. It effectively improves the machining accuracy and stability of the composite machine tool and extends the service life of the equipment. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the method for real-time measurement of machining accuracy and thermal deformation compensation control of composite materials in an embodiment of the present invention.

[0054] Figure 2 This is a flowchart illustrating the multi-level data acquisition and fusion process for monitoring thermal deformation of composite machine tools according to an embodiment of the present invention.

[0055] Figure 3 This is a flowchart of the data acquisition and prediction modeling process for thermal deformation of composite machine tools according to an embodiment of the present invention;

[0056] Figure 4 This is a flowchart of the real-time compensation control process for multi-axis thermal deformation of composite machine tools according to an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0059] Figure 1 This is a flowchart illustrating the real-time measurement of machining accuracy and thermal deformation compensation control method for composite material machine tools according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0060] The processing parameters of the composite machine tool are obtained, including feed rate, spindle speed and depth of cut, and the heat source distribution information of the composite machine tool is calculated based on the processing parameters.

[0061] Multiple temperature sensors and displacement sensors are installed on the spindle, worktable, and column of the composite machine tool to collect temperature and displacement data of the spindle, worktable, and column in real time.

[0062] A multi-dimensional thermal deformation prediction model for the composite machine tool is established. The heat source distribution information, temperature data, and displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool.

[0063] Multiple laser interferometers are used to measure the tool position of the composite machine tool in real time to obtain the actual machining position data of the tool;

[0064] An adaptive compensation controller for the composite machine tool is constructed. Based on the real-time thermal deformation prediction data and the actual processing position data, a fuzzy PID control algorithm is used to dynamically generate compensation commands. The control parameters of the fuzzy PID control algorithm are automatically adjusted according to the changing trend of the thermal deformation prediction data.

[0065] The compensation command is sent to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby realizing closed-loop control of the machining accuracy of the composite machine tool.

[0066] In one optional implementation, calculating the heat source distribution information of the composite machine tool based on the processing parameters includes:

[0067] A heat source distribution prediction model for composite machine tools is established. The heat source distribution prediction model includes a coupled analysis module for cutting heat, friction heat and motor heat generation. The heat power and spatiotemporal distribution characteristics of each heat source are calculated based on the processing parameters.

[0068] The composite machine tool is meshed based on the finite element method, and the output of the heat source distribution prediction model is mapped to the mesh nodes. The mesh is refined in the dense heat source area to obtain the heat source distribution information of the composite machine tool.

[0069] A heat source distribution prediction model for composite machine tools needs to be established. This model includes a coupled analysis module for cutting heat, frictional heat, and motor heating. The heat power and spatiotemporal distribution characteristics of each heat source are calculated by analyzing the processing parameters. Subsequently, the composite machine tool is meshed using the finite element method. The output results of the heat source distribution prediction model are mapped to the mesh nodes, and the mesh is refined in areas with dense heat sources to obtain the heat source distribution information of the composite machine tool.

[0070] The establishment of a heat source distribution prediction model for composite machine tools is the core of the entire method. This model comprehensively considers three main heat sources: cutting heat, frictional heat, and motor heat generation. Cutting heat mainly originates from the interaction between the tool and the workpiece and is affected by machining parameters such as cutting speed, feed rate, and depth of cut. In practical applications, the relationship between machining parameters and cutting heat can be established by measuring the temperature rise under different cutting conditions. For example, when a composite machine tool processes carbon fiber reinforced composite materials at a spindle speed of 1500 rpm and a feed rate of 0.2 mm / r, the heat power generated in the cutting zone is approximately 250 W, of which about 60% of the heat is absorbed by the workpiece and 40% by the tool.

[0071] Frictional heat mainly originates from friction between moving parts of a machine tool, such as guideways, bearings, and transmission devices. The generation of frictional heat is closely related to the relative speed, contact pressure, and coefficient of friction of the moving parts. For a linear guideway, when the speed is 30 m / min and the load is 500 kg, the coefficient of friction is approximately 0.05, and the generated heat power is approximately 120 W. This heat is mainly concentrated on the contact surface between the guideway and the slider, forming a localized heat source.

[0072] Motor heat generation primarily includes heat from the spindle motor and feed motor. The heat generated by a motor is related to its power, efficiency, and load condition. In practical applications, the motor's thermal power can be calculated using its rated power, efficiency curve, and actual load rate. For example, a spindle motor with a rated power of 5.5kW operating at 80% load with an efficiency of 0.88 will generate approximately 960W of thermal power. This heat is concentrated around the motor housing and transferred to the surrounding structure through thermal conduction.

[0073] The coupling analysis module is designed to account for the interactions between various heat sources. For example, motor heating can raise the temperature of the surrounding structure, thus affecting the generation of frictional heat; cutting heat can alter the material properties of the workpiece, influencing its mechanical behavior and heat generation during machining. By establishing the coupling relationships between these heat sources, the temperature field distribution of composite machine tools can be predicted more accurately.

[0074] Determining the spatiotemporal distribution characteristics of the heat source is a crucial component of the heat source model. Temporal distribution characteristics describe the variation of heat source intensity over time, such as continuous constancy, periodic variation, or transient variation. Spatial distribution characteristics describe the location of the heat source in space and the distribution of heat flux density. For cutting heat, it can be considered a heat source moving along the tool path, and the heat flux density distribution can be described using a Gaussian distribution model; for frictional heat, it can be considered a line heat source along the motion path; for motor heating, it can be considered a surface heat source distributed on the surface of the motor housing.

[0075] After completing the heat source distribution prediction model, the composite machine tool needs to be meshed using the finite element method. The quality of the mesh directly affects the accuracy of the calculation results. For complex geometries, tetrahedral or hexahedral elements can be used for meshing. In the initial stage, a relatively uniform mesh can be used, with an element size of approximately 10-20 mm. After the initial meshing is completed, the mesh needs to be optimized according to the heat source distribution.

[0076] The output of the heat source distribution prediction model needs to be mapped onto the grid nodes. The mapping process must ensure heat conservation, that is, the total heat before and after mapping should remain unchanged. For point heat sources, heat can be directly applied to the nearest node; for line heat sources, heat can be distributed to nearby nodes according to the distance ratio; for area heat sources, shape functions can be used for interpolation to distribute heat to each node of the area element.

[0077] Mesh refinement in areas with dense heat sources is a crucial step in ensuring computational accuracy. For areas with dense heat sources, such as cutting regions, friction contact surfaces, and motor surfaces, finer meshes should be used. For example, in cutting regions, the element size can be reduced to 1-2 mm; on guide rail contact surfaces, the element size along the contact surface direction can be reduced to 5 mm; and on motor surfaces, the element size can be reduced to 3-5 mm. During mesh refinement, attention should be paid to the transition between adjacent element sizes to avoid numerical instability caused by excessive size changes.

[0078] Through the above steps, the heat source distribution information of the composite machine tool can be obtained, including the location, intensity, and spatiotemporal distribution characteristics of each heat source. This information can be used for subsequent temperature field calculations and thermal deformation analysis, providing a theoretical basis for thermal error compensation and precision control of the composite machine tool.

[0079] In one optional embodiment, multiple temperature sensors and displacement sensors are installed on the spindle, worktable, and column of the composite machine tool to collect real-time temperature and displacement data of the spindle, worktable, and column, including:

[0080] Multiple temperature sensors and displacement sensors are installed on the spindle, worktable and column of the composite machine tool, wherein the temperature sensors are thermocouple temperature sensors and the displacement sensors are capacitive displacement sensors.

[0081] A distributed sensing network for the composite machine tool is constructed. The distributed sensing network includes multiple microprocessor nodes. Each microprocessor node is connected to a temperature sensor and a displacement sensor in a corresponding area. Data is acquired using a time-division multiplexing method, and the acquired data is subjected to noise filtering and digital processing.

[0082] A real-time data fusion system is established based on the distributed sensor network. The real-time data fusion system uses the Kalman filter algorithm to dynamically correct and fuse the temperature data and the displacement data to generate time-series temperature field data and displacement field data of each component of the composite machine tool.

[0083] The specific implementation method for installing multiple temperature and displacement sensors on the spindle, worktable, and column of the composite material machine tool to collect real-time temperature and displacement data of the spindle, worktable, and column is as follows:

[0084] When setting up a sensor network on a composite material machine tool, K-type thermocouple temperature sensors are selected, with a measurement range of -50℃ to 1300℃, an accuracy of ±0.5℃, and a response time of less than 0.5 seconds. In the spindle section, one temperature sensor is arranged every 50mm along the axial direction, for a total of 8 sensors; 25 temperature sensors are evenly distributed on the worktable surface in a 5×5 grid; and 6 temperature sensors are evenly spaced on each of the four sides of the column, for a total of 24 sensors. Capacitive displacement sensors are used, with a measurement range of 0-2mm, a resolution of 0.1μm, and a response frequency of 10kHz. Four displacement sensors are installed around the spindle bearing housing, one at each of the four corners of the worktable bottom, and three at the top and bottom of the column. Dedicated mounting brackets are used for sensor installation to ensure measurement stability and reduce external interference.

[0085] When constructing the distributed sensor network, a hierarchical architecture is adopted, dividing the composite machine tool into five areas: the spindle area, the front area of ​​the worktable, the rear area of ​​the worktable, the upper column area, and the lower column area. Each area is equipped with a microprocessor node, using a 32-bit ARM Cortex-M4 processor with a main frequency of 120MHz, a built-in 12-bit ADC converter, and 256KB of RAM. Each microprocessor node is connected to the central data processing unit via an RS-485 bus, forming a star network topology.

[0086] Data acquisition employs time-division multiplexing, sampling each sensor sequentially according to a preset scan cycle (20ms). To ensure data quality, a three-stage noise filtering system is implemented: hardware filtering uses an RC low-pass filter circuit with a cutoff frequency of 200Hz; software filtering employs digital filtering algorithms, including a combination of moving average filtering and median filtering; the filtering window size is 9 sampling points, smoothing the data. In the signal processing circuit design, the analog front-end uses an AD620 instrumentation amplifier with a gain set to 100 to amplify weak signals to a suitable range. The ADC sampling resolution is 12 bits, and the sampling frequency is set to 1kHz. After local processing, the sampled data is transmitted to the central data processing unit at a frequency of 100Hz.

[0087] For the temperature sensor, the signal conditioning circuit includes a cold junction compensation module to ensure thermocouple measurement accuracy. The gain of each temperature channel is adjusted to 41mV / ℃, resulting in an output voltage of 1.025V at 25℃. After conversion to a digital signal, a piecewise linear approximation algorithm is used to calculate the temperature value, with the conversion error controlled within ±0.2℃. For the displacement sensor, the conditioning circuit employs lock-in amplification technology with a driving frequency of 50kHz. The sensitivity of the detection circuit is set to 4V / mm, achieving sub-micron displacement measurement accuracy. During data acquisition, the microprocessor node collects 50 data points per second from each sensor. After local filtering, 10 valid data points are retained and transmitted to the central processing unit.

[0088] The real-time data fusion system is implemented on an industrial-grade PC platform, equipped with an eight-core processor with a main frequency of 3.0GHz, 16GB of memory, and running a real-time operating system. Data fusion employs an improved Kalman filter algorithm to dynamically correct and fuse temperature and displacement data. In the processing flow, a state-space model is first established, including temperature and displacement state variables. The state transition matrix is ​​pre-calibrated based on the machine tool's thermal characteristics. The system dynamically adjusts the measurement noise covariance matrix based on historical sensor performance data to improve filtering accuracy. For temperature data, the process noise variance is set to 0.01℃², and the measurement noise variance to 0.25℃²; for displacement data, the process noise variance is set to 0.0001mm², and the measurement noise variance to 0.0004mm².

[0089] In the Kalman filtering process, the prediction step calculates the prior estimate and covariance of the current state; the update step combines new observations to calculate the Kalman gain and update the state estimate and covariance. The filtering algorithm outputs noise-suppressed temperature and displacement estimates, improving temperature estimation accuracy by 50% and displacement estimation accuracy by 65%. The data fusion system stores the processed data in time series, forming temperature field data and displacement field data. The temperature field data records the temperature change over time at each measuring point with a resolution of 0.1℃; the displacement field data records displacement changes with a resolution of 0.5μm. The system-generated time series data is updated at a frequency of 10Hz, supporting real-time status monitoring and offline analysis. Data storage uses a time series database, supporting high-speed writing and efficient querying. The data volume of a single measuring point is approximately 3MB per day, and the total daily data volume of the entire system is approximately 200MB.

[0090] Practice has shown that this system can accurately capture thermal deformation caused by temperature changes during composite material machining. The temperature range in the spindle area is 22℃ to 45℃, corresponding to a thermal expansion displacement of 5μm to 28μm; the temperature range in the worktable area is 20℃ to 38℃, with a thermal deformation range of 3μm to 22μm; and the temperature range in the column area is 19℃ to 35℃, with a thermal deformation range of 6μm to 35μm. By monitoring these changes in real time, the system provides accurate data support for subsequent thermal error compensation.

[0091] Figure 2 The flowchart below illustrates the multi-level data acquisition and fusion process for monitoring thermal deformation of composite machine tools according to an embodiment of the present invention:

[0092] This diagram details the system architecture for thermal deformation data acquisition and processing of a composite machine tool. The entire system is implemented in three layers. The first layer is the sensor deployment layer, where multiple sensors are strategically deployed in key structural parts of the composite machine tool (including the spindle system, worktable platform, and column support structure). Temperature monitoring utilizes high-precision and stable thermocouple temperature sensors to capture real-time temperature changes in various parts of the machine tool; displacement monitoring employs precision capacitive displacement sensors to accurately detect minute displacement changes in various structural components. The second layer is the distributed sensor network construction layer. The system is designed with a network architecture consisting of multiple microprocessor nodes. Each microprocessor node is responsible for connecting and managing the temperature and displacement sensors within its corresponding area. Time-division multiplexing technology is used for data acquisition to improve acquisition efficiency. Simultaneously, the acquired raw data undergoes noise reduction filtering and digitization conversion to ensure data quality and reliability. The third layer is the data fusion and processing layer, which establishes a real-time data fusion system. This system innovatively employs the Kalman filter algorithm to dynamically correct and intelligently fuse temperature and displacement data from different sensors. Ultimately, it generates temperature and displacement field data that accurately reflect the time-varying changes of various components of the composite machine tool, providing reliable data support for subsequent thermal deformation compensation and precision control. These three layers work closely together to form a complete closed-loop monitoring system for data acquisition, processing, and fusion, enabling high-precision real-time monitoring of thermal deformation in composite machine tools.

[0093] In one optional implementation, a multi-dimensional thermal deformation prediction model for the composite machine tool is established. The heat source distribution information, the temperature data, and the displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool, including:

[0094] The heat source distribution information and historical temperature data of the composite machine tool are obtained. The heat source distribution information is divided into multiple time windows according to the time series. Temperature change features are extracted in each time window. A forgetting factor and an input factor are calculated based on the temperature change features. The historical temperature data is filtered according to the forgetting factor and the input factor to obtain the temperature prediction data of the composite machine tool.

[0095] Displacement data is collected at corresponding positions of the spindle, worktable and column of the composite machine tool, and the displacement data is reconstructed in three-dimensional space to obtain the displacement distribution data of the composite machine tool.

[0096] Weighting coefficients are set for the temperature prediction data and the displacement distribution data. Based on the weighting coefficients, the temperature prediction data is mapped to the three-dimensional space corresponding to the displacement distribution data to generate the thermal deformation prediction data of the composite machine tool.

[0097] Acquire heat source distribution information and historical temperature data for the composite machine tool. Heat source distribution information is obtained by installing temperature sensors at key locations on the machine tool, such as multiple sensors arranged around major heat sources like the spindle motor, bearing housing, and drive motor, with a sampling frequency set to 10Hz. Historical temperature data includes records of temperature changes under different operating conditions, typically collecting at least 100 hours of operational data to ensure comprehensiveness and representativeness.

[0098] The acquired heat source distribution information is divided into multiple time windows according to the time series. The size of the time window is determined based on the thermal response characteristics of the machine tool. In this embodiment, the time window is set to 5 minutes, meaning that the temperature data for each 5-minute period is processed as a separate data unit. Temperature change characteristics are extracted within each time window, including maximum, minimum, average, rate of change, and standard deviation. For example, for the spindle bearing housing, within a certain 5-minute window, the temperature rises from 22.5°C to 25.8°C, and the calculated average temperature is 24.2°C, the rate of change is 0.66°C / minute, and the standard deviation is 1.22°C.

[0099] The forgetting factor and input factor are calculated based on the extracted temperature change characteristics. The forgetting factor characterizes the influence of historical data on the current prediction, with a value ranging from 0.8 to 0.95. When the temperature change rate is large, it indicates a significant change in machine tool operating conditions, and the forgetting factor takes a smaller value, such as 0.82; when the temperature change is gradual, the forgetting factor takes a larger value, such as 0.93. The input factor measures the importance of the current input data, with a value ranging from 0.05 to 0.2. When the temperature fluctuation is large (high standard deviation), the input factor takes a smaller value, such as 0.08; when the temperature is stable, the input factor takes a larger value, such as 0.17. By setting these two factors, dynamic weighting of historical data is achieved.

[0100] Historical temperature data is filtered based on the calculated forgetting factor and input factor. For the spindle area, if the forgetting factor is 0.88 and the input factor is 0.12, the temperature data from the past 24 hours is weighted. Data from further back in time has a lower weight, ultimately selecting a representative subset of temperature data. This method yields predicted temperature data for the composite machine tool, which is then used for subsequent thermal deformation calculations.

[0101] Displacement data was collected at corresponding positions on the spindle, worktable, and column of the composite material machine tool. A high-precision displacement sensor with an accuracy of 0.001 mm was installed at the end of the spindle; displacement sensors were installed at the four corners of the worktable; and multiple displacement measurement points were arranged on the front and rear surfaces of the column. The acquisition frequency was set to 5 Hz, synchronized with temperature data acquisition. For example, after the machine tool operated continuously for 2 hours, the spindle end displacement in the X direction was 0.015 mm, in the Y direction was 0.008 mm, and in the Z direction was 0.023 mm; the average upward warping deformation at the four corners of the worktable was 0.018 mm.

[0102] The collected displacement data is reconstructed in three-dimensional space. First, a three-dimensional coordinate system for the machine tool is established, using the machine tool origin as a reference, to determine the relative positions of each measurement point. Then, using a spatial interpolation algorithm, the discrete displacement measurement points are extended to the entire machine tool structure, forming a continuous displacement field. For example, using displacement data from the spindle end and bearing housing, the spatial displacement state of various parts of the spindle can be calculated; using displacement data from the four corners of the worktable, the planar deformation of the worktable can be calculated. Finally, the displacement distribution data of the composite machine tool is obtained and represented in the form of a three-dimensional mesh.

[0103] Weighting coefficients were set for the temperature prediction data and displacement distribution data. These coefficients were determined based on the thermal sensitivity of different parts of the machine tool: 0.45 for the spindle, 0.35 for the table, and 0.2 for the column. Simultaneously, considering the nonlinear relationship between temperature and displacement, a temperature-displacement mapping function was introduced to convert temperature changes into corresponding displacement changes. For example, when the spindle bearing housing temperature increases by 1°C, the spindle end experiences approximately 0.005 mm of thermal deformation in the Z-direction.

[0104] Based on set weighting coefficients, the predicted temperature data is mapped to the corresponding three-dimensional space of the displacement distribution data. In actual operation, the predicted temperature data is converted into the expected displacement change through a mapping function, and then superimposed on the displacement distribution data to generate the predicted thermal deformation data of the composite machine tool. This predicted data is presented in the form of a three-dimensional thermal deformation field, which can intuitively display the thermal deformation state of various parts of the machine tool. For example, the prediction results show that after 4 hours of continuous machining, the maximum thermal deformation at the spindle end will reach 0.032 mm, and the maximum warpage deformation of the worktable will reach 0.025 mm. These data can be used for subsequent thermal error compensation.

[0105] The multi-dimensional thermal deformation prediction model established using the above method can predict the thermal deformation state of composite machine tools in real time, providing an effective basis for machine tool precision control. This model fully considers temperature change characteristics and displacement distribution features, and improves the accuracy and adaptability of predictions through a dynamic weight adjustment mechanism.

[0106] Figure 3The flowchart below shows the data acquisition and prediction modeling process for thermal deformation of composite machine tools according to an embodiment of the present invention:

[0107] This diagram details the complete data processing flow for predicting the thermal deformation of a composite machine tool. The first stage is data acquisition and preprocessing. The system acquires heat source distribution information and historical temperature data of the composite machine tool. Using time series analysis, the heat source distribution information is divided into multiple consecutive time windows, and the temperature change characteristics are analyzed in depth within each window. Based on these temperature change characteristics, the system calculates corresponding forgetting factors and input factors. These two factors serve as key parameters for filtering and preprocessing historical temperature data, ultimately generating temperature prediction data. The second stage focuses on displacement data acquisition and spatial reconstruction. The system collects displacement data from key structural parts of the composite machine tool (spindle, worktable, and column), transforming this two-dimensional displacement data into three-dimensional space using a spatial reconstruction algorithm to form complete displacement distribution data. Finally, in the data fusion and predictive modeling stage, the system innovatively introduces a weighting coefficient mechanism. By setting reasonable weighting coefficients, the temperature prediction data is accurately mapped to the three-dimensional space where the displacement distribution data resides, achieving effective fusion of the temperature field and displacement field, ultimately generating the thermal deformation prediction data for the composite machine tool. This progressive data processing method ensures the accuracy and reliability of thermal deformation prediction, providing crucial data support for the precision machining of composite machine tools.

[0108] In one optional implementation, an adaptive compensation controller for the composite machine tool is constructed, which dynamically generates compensation commands using a fuzzy PID control algorithm based on the real-time thermal deformation prediction data and the actual processing position data, including:

[0109] The real-time thermal deformation prediction data and actual processing position data of the composite machine tool are obtained, and the compensation deviation of the composite machine tool is calculated based on the real-time thermal deformation prediction data and the actual processing position data.

[0110] A fuzzy control rule base is established for the composite machine tool. The compensation deviation and the rate of change of the compensation deviation are used as fuzzy control inputs. The membership function of the compensation deviation and the rate of change of the compensation deviation is determined according to the magnitude of the fuzzy control input. Fuzzy inference is performed on the compensation deviation and the rate of change of the compensation deviation based on the membership function.

[0111] The control parameters of the PID controller of the composite machine tool are determined based on the result of the fuzzy inference, wherein the control parameters of the PID controller include proportional coefficient, integral coefficient and derivative coefficient;

[0112] Based on the changing trend of the compensation deviation, the proportional coefficient, the integral coefficient, and the derivative coefficient are adaptively adjusted using a dynamic weight allocation method; when the compensation deviation increases, the weight of the proportional coefficient is increased; when the compensation deviation is stable, the weight of the integral coefficient is increased; when the compensation deviation changes rapidly, the weight of the derivative coefficient is increased.

[0113] Based on the dynamically adjusted proportional coefficient, integral coefficient, and derivative coefficient, the compensation control quantity of the composite machine tool is calculated; the compensation control quantity is converted into compensation commands for each coordinate axis of the composite machine tool to realize thermal deformation compensation control of the composite machine tool.

[0114] When constructing an adaptive compensation controller for a composite machine tool, the first step is to acquire real-time thermal deformation prediction data and actual machining position data. Real-time thermal deformation prediction data is obtained by collecting temperature data from multiple temperature sensors positioned at key locations on the composite machine tool and combining this data with a thermal deformation prediction model. Actual machining position data is acquired in real-time using linear encoders or servo encoders on each axis of the machine tool. In a specific example, the predicted thermal deformation value for the X-axis is 0.035 mm, the actual machining position is 100.042 mm, while the theoretical position should be 100.000 mm. The calculated compensation deviation is 0.042 mm.

[0115] Establishing a fuzzy control rule base is a crucial step in achieving adaptive control. In this method, the compensation deviation *e* and the rate of change of the compensation deviation *ec* are used as inputs to the fuzzy control. For the compensation deviation *e*, the linguistic variable set is set as {NB, NM, NS, ZO, PS, PM, PB}, representing negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively. The rate of change *ec* also uses the same linguistic variable set. Based on practical engineering experience, the domain of each linguistic variable is as follows:

[0116] NB[-0.1,-0.06), NM[-0.06,-0.03), NS[-0.03,-0.01), ZO[-0.01,0.01], PS(0.01,0.03], PM(0.03,0.06], PB(0.06,0.1], in millimeters.

[0117] The membership function was determined using a combination of triangle and trapezoidal methods, which effectively reflects the fuzzy characteristics of the compensation deviation. When the measured compensation deviation is 0.042 mm, the corresponding linguistic variable is PS, with a membership degree of 0.7; it also partially belongs to PM, with a membership degree of 0.3. When the deviation change rate is 0.005 mm / s, the membership degrees for the two linguistic variables, ZO and PS, are 0.5 and 0.5, respectively.

[0118] The fuzzy inference process is based on the Mamdani model and performs inference according to the fuzzy control rule base. There are a total of 49 fuzzy control rules, covering various combinations of compensation deviation and rate of change. A typical rule example is: if e is PS and ec is ZO, then Kp is PM, Ki is PS, and Kd is NS. For the above input example, the fuzzy output results of the PID parameters obtained through fuzzy inference are: Kp corresponds to the linguistic variable PM, with a membership degree of 0.6; Ki corresponds to PS, with a membership degree of 0.7; and Kd corresponds to NS, with a membership degree of 0.5.

[0119] The center-of-gravity method is used for fuzzy model demodeling to convert the fuzzy output into precise PID control parameter values. Under standard operating conditions, the basic values ​​of the PID parameters are set as Kp0=1.5, Ki0=0.8, and Kd0=0.2. After model demodeling calculation, the PID parameters for practical application are obtained as Kp=2.1, Ki=1.05, and Kd=0.15.

[0120] Based on the changing trend of the compensation deviation, this method employs a dynamic weight allocation strategy to adaptively adjust the PID control parameters. Weight coefficients wp, wi, and wd are introduced to represent the relative importance of the three control parameters. When the compensation deviation increases, the weight wp of the proportional coefficient Kp increases; when the compensation deviation remains stable, the weight wi of the integral coefficient Ki increases; and when the compensation deviation changes rapidly, the weight wd of the derivative coefficient Kd increases. The weight coefficients satisfy the constraint wp + wi + wd = 1.

[0121] When the deviation increases from 0.042 mm to 0.048 mm, the system detects the increasing trend and adjusts wp from 0.4 to 0.6, wi from 0.4 to 0.2, and keeps wd unchanged at 0.2. Correspondingly, the PID parameters are adjusted to Kp=2.5, Ki=0.85, and Kd=0.15. When the deviation remains relatively stable around 0.048 mm, the system adjusts wi to 0.5, decreases wp to 0.3, and sets wd to 0.2, resulting in new PID parameters Kp=1.9, Ki=1.25, and Kd=0.15. When the deviation rapidly changes from 0.048 mm to 0.032 mm within a short period, the system increases wd to 0.4, and adjusts wp and wi to 0.3 and 0.3 respectively, resulting in new PID parameters Kp=1.9, Ki=0.95, and Kd=0.25.

[0122] The compensation control quantity is calculated using a typical PID control algorithm, combined with dynamically adjusted control parameters, to generate the corresponding compensation quantity. For a compensation deviation of 0.042mm on the X-axis, using the calculated Kp=2.1, Ki=1.05, and Kd=0.15, the compensation control quantity for the X-axis is calculated to be 0.038mm.

[0123] The compensation control quantity is converted into actual compensation commands for each axis of the machine tool. The format of the compensation command may differ depending on the machine tool control system. Taking a common CNC system as an example, the X-axis compensation command can be expressed as G54.2 X-0.038, indicating a compensation of -0.038mm offset in the X-axis direction in the workpiece coordinate system. After the machine tool executes this compensation command, the actual machining position will be adjusted from 100.042mm to 100.004mm, significantly improving machining accuracy. Through this adaptive compensation method, the thermal deformation error of the composite machine tool can be controlled within ±0.005mm, meeting the requirements of high-precision machining.

[0124] In one optional implementation, the compensation command is sent to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby achieving closed-loop control of the machining accuracy of the composite machine tool, including:

[0125] The compensation instructions of the composite machine tool are decomposed into X-axis compensation instructions, Y-axis compensation instructions and Z-axis compensation instructions to generate a multi-axis compensation instruction sequence for the composite machine tool;

[0126] A timestamp and priority identifier are set for the multi-axis compensation command sequence, and the multi-axis compensation command sequence is sent to the CNC system of the composite machine tool through a real-time communication interface;

[0127] Collect real-time machining position data of the composite machine tool, compare the real-time machining position data with the theoretical machining trajectory of the composite machine tool, and calculate the trajectory error data of the composite machine tool;

[0128] A real-time compensation model for the composite machine tool is established based on the trajectory error data. The real-time compensation model includes a trajectory prediction unit and a compensation amount calculation unit. The trajectory prediction unit predicts the position deviation at the next moment based on the current trajectory error, and the compensation amount calculation unit generates a compensation increment based on the position deviation.

[0129] The compensation increment is superimposed on the original compensation command to update and generate a new compensation command for the composite machine tool; the new compensation command is subjected to an acceleration continuity check to ensure the smoothness of the compensation process.

[0130] The machining accuracy of the composite machine tool during the execution of the new compensation command is monitored in real time. When the machining accuracy meets the preset accuracy requirements, the current compensation strategy is maintained; when the machining accuracy does not meet the preset accuracy requirements, the calculation step of the trajectory error data is returned.

[0131] In the composite material machining process, to achieve closed-loop control of machining accuracy, this invention sends compensation commands to the CNC system of the composite material machine tool to perform real-time compensation on the machining trajectory. Specifically, the compensation commands of the composite material machine tool are decomposed into X-axis compensation commands, Y-axis compensation commands, and Z-axis compensation commands to generate a multi-axis compensation command sequence for the composite material machine tool.

[0132] For contour machining of composite material parts, the original compensation command may include the displacement of spatial coordinates (x1, y1, z1) to (x2, y2, z2). Decomposition yields the X-axis compensation command as Δx = x2 - x1, the Y-axis compensation command as Δy = y2 - y1, and the Z-axis compensation command as Δz = z2 - z1. If the original command is from (10.25, 15.36, 5.42) to (12.76, 18.29, 4.58), the decomposition yields an X-axis compensation command of 2.51 mm, a Y-axis compensation command of 2.93 mm, and a Z-axis compensation command of -0.84 mm.

[0133] The generated multi-axis compensation command sequence is timestamped and assigned a priority identifier. The timestamp marks the command generation time, accurate to the millisecond, such as "2023-05-15 09:23:45.632"; the priority identifier is set to an integer value from 0 to 9, with smaller values ​​indicating higher priority. For example, a priority of 2 is set for compensation commands for critical contour machining points, and a priority of 5 is set for compensation commands for non-critical areas. The multi-axis compensation command sequence with timestamps and priority identifiers is sent to the CNC system of the composite machine tool via a real-time communication interface. The real-time communication interface uses the industrial Ethernet protocol, with a data transmission rate of 100Mbps and a communication latency controlled within 2 milliseconds, ensuring that the compensation commands are delivered and executed in a timely manner.

[0134] During the machining process, real-time machining position data of the composite machining tool is collected. The acquisition frequency is set to 500Hz, meaning the actual position of each axis of the machine tool is collected every 2 milliseconds. The collected real-time machining position data is compared with the theoretical machining trajectory of the composite machining tool to calculate the trajectory error data. The trajectory error data includes the position deviation values ​​in the X, Y, and Z directions.

[0135] In the machining process of a certain composite material part, the theoretical machining point coordinates are (156.325, 89.764, 25.431) mm, while the actual machining position is (156.342, 89.781, 25.415) mm. The calculated trajectory errors are 0.017 mm for the X-axis, 0.017 mm for the Y-axis, and -0.016 mm for the Z-axis.

[0136] A real-time compensation model for the composite machine tool is established based on the calculated trajectory error data. This model includes a trajectory prediction unit and a compensation calculation unit. The trajectory prediction unit predicts the position deviation at the next moment based on the current trajectory error. It employs time-series data analysis to predict the position deviation based on the trajectory error trends of the most recent 10 sampling points. For example, if the trajectory errors of the most recent 10 sampling points on the X-axis are 0.012, 0.013, 0.015, 0.016, 0.017, 0.017, 0.018, 0.019, 0.020, and 0.021 mm respectively, analysis can predict that the X-axis position deviation will reach 0.022 mm at the next moment.

[0137] The compensation calculation unit generates a compensation increment based on the predicted position deviation. The calculation of the compensation increment takes into account the dynamic characteristics of the machine tool and appropriately amplifies the predicted deviation. The coefficient is usually set between 1.05 and 1.2. If the predicted X-axis position deviation at the next moment is 0.022 mm, and a compensation coefficient of 1.1 is used, the generated X-axis compensation increment will be -0.0242 mm.

[0138] The calculated compensation increment is overlaid with the original compensation command to generate a new compensation command for the composite material machine tool. If the original compensation command is 5.000 mm movement on the X-axis, 3.000 mm movement on the Y-axis, and 1.000 mm movement on the Z-axis, and the calculated compensation increment is -0.0242 mm on the X-axis, -0.0187 mm on the Y-axis, and 0.0175 mm on the Z-axis, then the new compensation command after overlay and update is 4.9758 mm movement on the X-axis, 2.9813 mm movement on the Y-axis, and 1.0175 mm movement on the Z-axis.

[0139] The newly generated compensation commands undergo an acceleration continuity check to ensure the smoothness of the compensation process. This check involves calculating the acceleration change between adjacent compensation commands and ensuring it does not exceed the maximum permissible rate of acceleration change for each axis of the machine tool. For example, if the maximum permissible rate of acceleration change for the X-axis is 0.5 m / s³, the acceleration change generated by two adjacent compensation commands should not exceed this value. If a discontinuity in acceleration is detected, the system smooths the compensation commands by inserting transition points to ensure the continuity of acceleration changes.

[0140] The machining accuracy of the composite material machine tool is monitored in real time during the execution of new compensation commands. Machining accuracy monitoring employs an online measurement system, including a laser tracker or optical sensor, with a measurement accuracy of ±0.005 mm. When the monitored machining accuracy meets the preset accuracy requirements, the system maintains the current compensation strategy and continues execution.

[0141] If the preset machining accuracy requirement is a contour deviation of no more than 0.02 mm, and the currently measured contour deviation is 0.015 mm, then the existing compensation strategy remains unchanged. When the monitored machining accuracy does not meet the preset accuracy requirement, the system returns to the calculation step of the trajectory error data, recalculates the trajectory error, and updates the compensation strategy. Experimental data shows that using this method can improve the machining accuracy of composite material parts from ±0.05 mm to ±0.015 mm, an improvement of approximately 70%.

[0142] Figure 4 The flowchart below shows the real-time compensation control process for multi-axis thermal deformation of a composite machine tool according to an embodiment of the present invention:

[0143] This diagram details the complete closed-loop control process for thermal deformation compensation in a composite machine tool. First, at the command generation level, the system decomposes compensation commands into X-axis, Y-axis, and Z-axis commands according to the machine tool's motion characteristics, forming an ordered multi-axis compensation command sequence. To ensure effective execution of the compensation commands, the system assigns a timestamp and execution priority flag to each command, accurately transmitting these flagged commands to the CNC system of the composite machine tool via a real-time communication interface. In the execution monitoring phase, the system collects real-time data on the machine tool's actual machining position, precisely compares this data with the theoretical machining trajectory, and calculates the real-time trajectory error data. Based on the obtained error data, the system establishes a real-time compensation model including a trajectory prediction unit and a compensation calculation unit. The trajectory prediction unit predicts the possible position deviation at the next moment based on the current trajectory error, while the compensation calculation unit generates the corresponding compensation increment based on the predicted position deviation. The system intelligently overlays and updates the calculated compensation increment with the original compensation command, generating a new compensation command. Simultaneously, it checks the acceleration continuity of the newly generated compensation command to ensure the smoothness of the compensation process. Finally, the system continuously monitors the machining accuracy of the machine tool during the execution of compensation commands. When the accuracy meets the preset requirements, the current compensation strategy is maintained; when the accuracy does not meet the requirements, the system returns to recalculate the trajectory error data, forming a complete accuracy guarantee closed loop. This multi-level compensation control system design achieves high-precision real-time compensation for thermal deformation of composite machine tools.

[0144] A second aspect of the present invention provides a real-time measurement and thermal deformation compensation control system for composite material machine tool processing accuracy, comprising:

[0145] The first unit is used to acquire the processing parameters of the composite machine tool, including the feed rate, spindle speed and depth of cut, and to calculate the heat source distribution information of the composite machine tool based on the processing parameters.

[0146] The second unit is used to install multiple temperature sensors and displacement sensors on the spindle, worktable and column of the composite machine tool to collect temperature data and displacement data of the spindle, the worktable and the column in real time.

[0147] The third unit is used to establish a multi-dimensional thermal deformation prediction model for the composite machine tool. The heat source distribution information, the temperature data, and the displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool.

[0148] The fourth unit is used to measure the tool position of the composite machine tool in real time using multiple laser interferometers to obtain the actual machining position data of the tool;

[0149] The fifth unit is used to construct the adaptive compensation controller of the composite machine tool. Based on the real-time thermal deformation prediction data and the actual processing position data, a fuzzy PID control algorithm is used to dynamically generate compensation commands. The control parameters of the fuzzy PID control algorithm are automatically adjusted according to the changing trend of the thermal deformation prediction data.

[0150] The sixth unit is used to send the compensation command to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby realizing closed-loop control of the machining accuracy of the composite machine tool.

[0151] A third aspect of the present invention provides an electronic device, comprising:

[0152] processor;

[0153] Memory used to store processor-executable instructions;

[0154] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0155] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0156] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time measurement of machining accuracy and thermal deformation compensation control of composite materials on machine tools, characterized in that, include: The processing parameters of the composite machine tool are obtained, including feed rate, spindle speed and depth of cut, and the heat source distribution information of the composite machine tool is calculated based on the processing parameters. Multiple temperature sensors and displacement sensors are installed on the spindle, worktable, and column of the composite machine tool to collect temperature and displacement data of the spindle, worktable, and column in real time. A multi-dimensional thermal deformation prediction model for the composite machine tool is established. The heat source distribution information, temperature data, and displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool. Multiple laser interferometers are used to measure the tool position of the composite machine tool in real time to obtain the actual machining position data of the tool; An adaptive compensation controller for the composite machine tool is constructed. Based on the real-time thermal deformation prediction data and the actual processing position data, a fuzzy PID control algorithm is used to dynamically generate compensation commands. The control parameters of the fuzzy PID control algorithm are automatically adjusted according to the changing trend of the thermal deformation prediction data. The compensation command is sent to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby realizing closed-loop control of the machining accuracy of the composite machine tool.

2. The method according to claim 1, characterized in that, Calculating the heat source distribution information of the composite machine tool based on the processing parameters includes: A heat source distribution prediction model for composite machine tools is established. The heat source distribution prediction model includes a coupled analysis module for cutting heat, friction heat and motor heat generation. The heat power and spatiotemporal distribution characteristics of each heat source are calculated based on the processing parameters. The composite machine tool is meshed based on the finite element method, and the output of the heat source distribution prediction model is mapped to the mesh nodes. The mesh is refined in the dense heat source area to obtain the heat source distribution information of the composite machine tool.

3. The method according to claim 1, characterized in that, Multiple temperature sensors and displacement sensors are installed on the spindle, worktable, and column of the composite machine tool to collect real-time temperature and displacement data of the spindle, worktable, and column, including: Multiple temperature sensors and displacement sensors are installed on the spindle, worktable and column of the composite machine tool, wherein the temperature sensors are thermocouple temperature sensors and the displacement sensors are capacitive displacement sensors. A distributed sensing network for the composite machine tool is constructed. The distributed sensing network includes multiple microprocessor nodes. Each microprocessor node is connected to a temperature sensor and a displacement sensor in a corresponding area. Data is acquired using a time-division multiplexing method, and the acquired data is subjected to noise filtering and digital processing. A real-time data fusion system is established based on the distributed sensor network. The real-time data fusion system uses the Kalman filter algorithm to dynamically correct and fuse the temperature data and the displacement data to generate time-series temperature field data and displacement field data of each component of the composite machine tool.

4. The method according to claim 1, characterized in that, A multi-dimensional thermal deformation prediction model for the composite machine tool is established. The heat source distribution information, temperature data, and displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool, including: The heat source distribution information and historical temperature data of the composite machine tool are obtained. The heat source distribution information is divided into multiple time windows according to the time series. Temperature change features are extracted in each time window. A forgetting factor and an input factor are calculated based on the temperature change features. The historical temperature data is filtered according to the forgetting factor and the input factor to obtain the temperature prediction data of the composite machine tool. Displacement data is collected at corresponding positions of the spindle, worktable and column of the composite machine tool, and the displacement data is reconstructed in three-dimensional space to obtain the displacement distribution data of the composite machine tool. Weighting coefficients are set for the temperature prediction data and the displacement distribution data. Based on the weighting coefficients, the temperature prediction data is mapped to the three-dimensional space corresponding to the displacement distribution data to generate the thermal deformation prediction data of the composite machine tool.

5. The method according to claim 1, characterized in that, The adaptive compensation controller for the composite machine tool is constructed by dynamically generating compensation commands using a fuzzy PID control algorithm based on the real-time thermal deformation prediction data and the actual machining position data. The real-time thermal deformation prediction data and actual processing position data of the composite machine tool are obtained, and the compensation deviation of the composite machine tool is calculated based on the real-time thermal deformation prediction data and the actual processing position data. A fuzzy control rule base is established for the composite machine tool. The compensation deviation and the rate of change of the compensation deviation are used as fuzzy control inputs. The membership function of the compensation deviation and the rate of change of the compensation deviation is determined according to the magnitude of the fuzzy control input. Fuzzy inference is performed on the compensation deviation and the rate of change of the compensation deviation based on the membership function. The control parameters of the PID controller of the composite machine tool are determined based on the result of the fuzzy inference, wherein the control parameters of the PID controller include proportional coefficient, integral coefficient and derivative coefficient; Based on the changing trend of the compensation deviation, the proportional coefficient, the integral coefficient, and the derivative coefficient are adaptively adjusted using a dynamic weight allocation method; when the compensation deviation increases, the weight of the proportional coefficient is increased; when the compensation deviation is stable, the weight of the integral coefficient is increased; when the compensation deviation changes rapidly, the weight of the derivative coefficient is increased. The compensation control quantity of the composite machine tool is calculated based on the dynamically adjusted proportional coefficient, integral coefficient, and differential coefficient; the compensation control quantity is converted into compensation commands for each coordinate axis of the composite machine tool to realize thermal deformation compensation control of the composite machine tool.

6. The method according to claim 1, characterized in that, Sending the compensation command to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby achieving closed-loop control of the machining accuracy of the composite machine tool, includes: The compensation instructions of the composite machine tool are decomposed into X-axis compensation instructions, Y-axis compensation instructions and Z-axis compensation instructions to generate a multi-axis compensation instruction sequence for the composite machine tool; A timestamp and priority identifier are set for the multi-axis compensation command sequence, and the multi-axis compensation command sequence is sent to the CNC system of the composite machine tool through a real-time communication interface; Collect real-time machining position data of the composite machine tool, compare the real-time machining position data with the theoretical machining trajectory of the composite machine tool, and calculate the trajectory error data of the composite machine tool; A real-time compensation model for the composite machine tool is established based on the trajectory error data. The real-time compensation model includes a trajectory prediction unit and a compensation amount calculation unit. The trajectory prediction unit predicts the position deviation at the next moment based on the current trajectory error, and the compensation amount calculation unit generates a compensation increment based on the position deviation. The compensation increment is superimposed on the original compensation command to update and generate a new compensation command for the composite machine tool; the new compensation command is subjected to an acceleration continuity check to ensure the smoothness of the compensation process. The machining accuracy of the composite machine tool during the execution of the new compensation command is monitored in real time. When the machining accuracy meets the preset accuracy requirements, the current compensation strategy is maintained; when the machining accuracy does not meet the preset accuracy requirements, the calculation step of the trajectory error data is returned.

7. A real-time measurement and thermal deformation compensation control system for composite material machine tool processing accuracy, used to implement the method described in any one of claims 1-6, characterized in that, include: The first unit is used to acquire the processing parameters of the composite machine tool, including the feed rate, spindle speed and depth of cut, and to calculate the heat source distribution information of the composite machine tool based on the processing parameters. The second unit is used to install multiple temperature sensors and displacement sensors on the spindle, worktable and column of the composite machine tool to collect temperature data and displacement data of the spindle, the worktable and the column in real time. The third unit is used to establish a multi-dimensional thermal deformation prediction model for the composite machine tool. The heat source distribution information, the temperature data, and the displacement data are input into the multi-dimensional thermal deformation prediction model to obtain real-time thermal deformation prediction data for the composite machine tool. The fourth unit is used to measure the tool position of the composite machine tool in real time using multiple laser interferometers to obtain the actual machining position data of the tool; The fifth unit is used to construct the adaptive compensation controller of the composite machine tool. Based on the real-time thermal deformation prediction data and the actual processing position data, a fuzzy PID control algorithm is used to dynamically generate compensation commands. The control parameters of the fuzzy PID control algorithm are automatically adjusted according to the changing trend of the thermal deformation prediction data. The sixth unit is used to send the compensation command to the CNC system of the composite machine tool to perform real-time compensation on the machining trajectory of the composite machine tool, thereby realizing closed-loop control of the machining accuracy of the composite machine tool.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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