A turning process ambient temperature compensation method, system and electronic device

Abstract

The application provides a turning process environment temperature compensation method, system and electronic equipment, and relates to the technical field of turning processing. The method comprises the following steps: acquiring temperature data of a machine tool at a preset position as environment temperature data; obtaining current thermal deformation amounts of machine tool structural parts corresponding to a workpiece and a current thermal deformation amount of the workpiece according to the environment temperature data and geometric structure parameters of a processing device of the machine tool; predicting the future thermal deformation amounts of the machine tool structural parts and the workpiece in a future preset time period through a long short-term memory fusion prediction model in combination with a historical thermal deformation amount of the workpiece; then generating a tool servo shaft compensation amount, a main shaft hydraulic pre-tightening force compensation amount and a cutting fluid temperature control compensation amount, generating a compensation program according to the compensation amounts, and inserting the compensation program into a processing program of the workpiece to complete real-time compensation. The application significantly improves the precision and effect of environment temperature compensation through real-time monitoring, dynamic prediction and multi-dimensional compensation.

Description

Technical Field

[0001] This invention relates to the field of turning technology, and more specifically, to a method, system, and electronic device for compensating for ambient temperature during the turning process. Background Technology

[0002] In high-precision turning, the final dimensions of the workpiece are affected by the combined errors of three factors: the relative displacement between the tool and the workpiece, tool wear, and transient drift of ambient temperature. Currently, ambient temperature is typically compensated for offline using a temperature-controlled workshop and empirical allowances.

[0003] In related technologies, the average ambient temperature and the fixed coefficient of thermal expansion of the workpiece result in compensation accuracy that cannot meet the requirements of high-precision turning. Specifically, while a constant-temperature workshop can control the ambient temperature to some extent, it cannot eliminate the impact of local temperature gradients and transient temperature changes on machining accuracy. Furthermore, the method of compensation using a fixed coefficient of thermal expansion cannot adapt to the differences in thermal expansion of different materials and under different processing conditions, further reducing the accuracy of compensation. Consequently, it cannot respond to transient temperature changes in real time and cannot adapt to complex processing conditions. Summary of the Invention

[0004] The problem addressed by this invention is how to improve the compensation effect of ambient temperature during turning.

[0005] To address the above problems, this invention provides a method, system, and electronic device for compensating for ambient temperature during the turning process.

[0006] In a first aspect, the present invention provides a method for compensating for ambient temperature during a turning process, comprising:

[0007] Acquire temperature data of the machine tool at a preset position, and use the temperature data as the ambient temperature data for processing the workpiece;

[0008] Based on the ambient temperature data and the geometric parameters of the machine tool's processing equipment, the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece are obtained.

[0009] By using a long short-term memory fusion prediction model, the current thermal deformation of the workpiece and the machine tool structural component are combined with the historical thermal deformation of the workpiece to predict the future thermal deformation of the machine tool structural component and the workpiece within a preset time period.

[0010] Based on the future thermal deformation of the machine tool structural components and the future thermal deformation of the workpiece being machined, the tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation are generated.

[0011] A compensation program is generated based on the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount. The compensation program is then inserted into the machining program of the workpiece to complete real-time compensation.

[0012] Optionally, acquiring temperature data of the machine tool at a preset position and using the temperature data as the ambient temperature data for processing the workpiece includes:

[0013] Temperature data for each of the preset locations is acquired by temperature sensors installed at multiple preset locations according to a preset sampling period.

[0014] The ambient temperature data of the workpiece being processed is obtained based on the temperature data of all the preset locations.

[0015] Optionally, obtaining the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece based on the ambient temperature data and the geometric parameters of the machine tool's processing equipment includes:

[0016] The ambient temperature data is input into the machine tool-workpiece three-dimensional transient temperature field model. The geometric parameters are solved using the machine tool-workpiece three-dimensional transient temperature field model to obtain the current temperature of the machine tool structure at each node and the current temperature of the machined workpiece at each node.

[0017] The current thermal deformation of the machine tool structure is determined based on the current temperature of each node of the machine tool structure and the thermal expansion coefficient of the material of the machine tool structure.

[0018] The current thermal deformation of the workpiece is determined based on the current temperature of each node of the workpiece and the coefficient of thermal expansion of the workpiece material.

[0019] Optionally, the prediction using a long short-term memory fusion model, based on the current thermal deformation of the workpiece and the machine tool structural component, combined with the historical thermal deformation of the workpiece, to predict the future thermal deformation of the machine tool structural component and the workpiece within a preset time period, includes:

[0020] Based on the current thermal deformation of the machine tool structural component and the workpiece, and combined with the historical thermal deformation of the machine tool structural component and the workpiece in the previous sampling period, the thermal deformation variation curves of the machine tool structural component and the workpiece are obtained.

[0021] The time-series features of the thermal deformation variation curve are extracted to obtain the time-series feature vector of the thermal deformation variation curve;

[0022] The processing parameters of the workpiece within the future preset time period are extracted to obtain the processing feature vector of the processing parameters;

[0023] The temporal feature vector and the machining feature vector are input into the long short-term memory fusion prediction model for prediction, so as to obtain the future thermal deformation of the machine tool structural component and the machined workpiece within the future preset time period.

[0024] Optionally, generating tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation based on the future thermal deformation of the machine tool structural component and the future thermal deformation of the workpiece includes:

[0025] The component of the future thermal deformation that corresponds to the relative displacement of the tool is mapped to the tool servo axis compensation amount.

[0026] The component of the future thermal deformation corresponding to the axial elongation of the spindle is converted into the hydraulic preload compensation of the spindle.

[0027] The component of the future thermal deformation that corresponds to the temperature rise on the workpiece surface is converted into the cutting fluid temperature control compensation amount.

[0028] Optionally, generating a compensation program based on the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount includes:

[0029] The tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount are prioritized and synchronized in time to obtain a synchronous compensation command sequence.

[0030] According to the synchronous compensation instruction sequence, the compensation amount of the tool servo axis, the compensation amount of the spindle hydraulic preload, and the compensation amount of the cutting fluid temperature control are discretized at the interpolation cycle level to form a discrete compensation value sequence corresponding to the interpolation cycle of the CNC system.

[0031] The discrete compensation value sequence is encapsulated into a macro variable to generate the compensation program.

[0032] Optionally, inserting the compensation program into the machining program of the workpiece to complete real-time compensation includes:

[0033] Write the compensation procedure into the compensation call entry point of the machining program before the current tool position;

[0034] The CNC system calls the compensation program in real time during the next interpolation cycle, and simultaneously executes the tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation.

[0035] Optionally, it also includes:

[0036] Once the real-time compensation is completed, the actual dimensions of the machined surface of the workpiece are obtained.

[0037] Based on the actual dimensions, the residual error between the actual dimensions and the theoretical dimensions of the machined workpiece is obtained;

[0038] The residual error is converted into a thermal deformation correction value, and the tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation of the workpiece in the next sampling cycle are updated according to the thermal deformation correction value until the workpiece is finished.

[0039] Secondly, the present invention provides a turning process ambient temperature compensation system, comprising:

[0040] The data acquisition unit is used to acquire temperature data of the machine tool at a preset position and use the temperature data as the ambient temperature data of the workpiece being processed.

[0041] The thermal deformation calculation unit is used to obtain the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece based on the ambient temperature data and the geometric structural parameters of the machine tool's processing equipment.

[0042] The prediction unit is used to predict the future thermal deformation of the machine tool structure and the machine tool structural component within a preset time period by using a long short-term memory fusion prediction model, based on the current thermal deformation of the workpiece and the machine tool structural component, combined with the historical thermal deformation of the workpiece.

[0043] The compensation calculation unit is used to generate the tool servo axis compensation amount, spindle hydraulic preload compensation amount, and cutting fluid temperature control compensation amount based on the future thermal deformation amount of the machine tool structural component and the future thermal deformation amount of the workpiece.

[0044] The compensation unit generates a compensation program based on the compensation amount of the tool servo axis, the compensation amount of the spindle hydraulic preload, and the compensation amount of the cutting fluid temperature control, and inserts the compensation program into the machining program of the workpiece to complete real-time compensation.

[0045] Thirdly, an electronic device according to the present invention includes a memory and a processor;

[0046] The memory is used to store computer programs;

[0047] The processor is configured to implement the above-described method for compensating for ambient temperature during the turning process when executing the computer program.

[0048] The present invention relates to a turning process environmental temperature compensation method, system, and electronic equipment. By acquiring temperature data at a preset location on the machine tool, it can reflect real-time changes in ambient temperature during machining. Compared with traditional constant-temperature workshops, this method can more accurately capture local temperature gradients and transient temperature changes, thus providing a more accurate basis for compensation. Furthermore, by combining the geometric parameters of the machining equipment, it can accurately calculate the current thermal deformation of the machine tool structural components and the machined workpiece. The present invention considers the actual structure of the machine tool and the workpiece, enabling a more accurate reflection of thermal deformation rather than simply using a fixed coefficient of thermal expansion.

[0049] Simultaneously, by utilizing a Long Short-Term Memory (LSTM) fusion prediction model, combining current and historical thermal deformation amounts, the thermal deformation amount within a preset future time period is predicted. This model effectively processes time-series data, captures the dynamic trend of thermal deformation, and thus provides advance compensation, avoiding machining errors caused by transient temperature changes. Based on the predicted future thermal deformation amount, compensation amounts for the tool servo axis, spindle hydraulic preload, and cutting fluid temperature control are generated. This multi-dimensional compensation strategy adjusts machining parameters from multiple aspects, comprehensively reducing the impact of ambient temperature changes on machining accuracy. Furthermore, the generated compensation program can be inserted into the machining program in real time, achieving dynamic compensation. Compared to traditional offline compensation methods, this invention can respond to changes in ambient temperature in real time, ensuring the continuity and accuracy of the machining process.

[0050] The technical solution of this invention does not rely on a fixed coefficient of thermal expansion, and can adapt to the differences in thermal expansion of different materials and under different processing conditions. This makes the compensation method more flexible and can be widely applied to various high-precision turning machining scenarios. Through real-time compensation, machining errors caused by changes in ambient temperature can be significantly reduced, improving machining efficiency and quality; this is especially important for high-precision turning, effectively reducing scrap rates and lowering production costs. Furthermore, in high-precision turning, the final dimensions of the workpiece are affected by the superposition of multiple errors. This invention, through real-time monitoring and dynamic compensation, can effectively reduce errors caused by changes in ambient temperature, thereby reducing the superposition of errors and improving machining accuracy. Through precise temperature compensation, the stability and reliability of the machining process can be ensured, reducing machining anomalies caused by temperature changes and improving the controllability and repeatability of the machining process.

[0051] In summary, the ambient temperature compensation method for the turning process of the present invention significantly improves the accuracy and effect of ambient temperature compensation through real-time monitoring, dynamic prediction and multi-dimensional compensation, which can effectively solve the problem of insufficient compensation accuracy in the prior art and meet the strict requirements of high-precision turning. Attached Figure Description

[0052] Figure 1This is a flowchart of the environmental temperature compensation method for the turning process according to an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the structure of the ambient temperature compensation system for the turning process according to an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0055] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0056] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0057] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0058] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0059] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0060] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties. The collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0061] Combination Figure 1 As shown in the figure, an embodiment of the present invention provides a method for compensating for ambient temperature during a turning process, comprising:

[0062] The temperature data of the machine tool at a preset position is acquired, and the temperature data is used as the ambient temperature data for processing the workpiece.

[0063] Specifically, to acquire temperature data of the machine tool at preset locations, multiple high-precision temperature sensors are installed in key areas of the machine tool, such as near the spindle, the tool holder area, and the workpiece clamping area. These sensors can monitor and record temperature changes at these locations in real time. The temperature data collected by the temperature sensors is collected by a data acquisition system and transmitted to the machine tool's control unit, i.e., the numerical control system (CNC) or a dedicated control computer. The control unit performs preliminary processing on this temperature data, such as filtering and data calibration, to ensure the accuracy and reliability of the data. The processed temperature data is then stored for subsequent analysis and calculation.

[0064] Based on the ambient temperature data and the geometric parameters of the machine tool's processing equipment, the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece are obtained.

[0065] Specifically, based on the acquired ambient temperature data and the geometric parameters of the machining equipment, the current thermal deformation of the machine tool components and the machined workpiece is calculated. The geometric parameters of the machining equipment include the dimensions, shape, and material properties of each component, which are crucial for accurately calculating thermal deformation. First, a three-dimensional model of the machine tool components and the machined workpiece is established using finite element analysis software, and the ambient temperature data and the geometric parameters of the machining equipment are input into the model. Through finite element analysis, the thermal deformation of the machine tool components and the machined workpiece under the current ambient temperature is simulated, obtaining their thermal deformation distribution. The thermal deformation of key areas is extracted from the thermal deformation distribution. These key areas are typically locations closely related to machining accuracy, such as the tool-workpiece contact area and the spindle support area. The extracted thermal deformation is the current thermal deformation of the machine tool components and the machined workpiece, and this data will serve as the basis for subsequent predictions of future thermal deformation.

[0066] In a preferred embodiment of the present invention, the process of establishing a three-dimensional model of the machine tool structure and the workpiece using finite element analysis software includes:

[0067] First, detailed geometric dimensions and material properties of the machine tool structural components and machined workpieces are collected. Geometric dimensions include parameters such as the length, width, height, diameter, and thickness of each component, which are usually obtained from the machine tool's design drawings or technical documents. Material properties include the material's coefficient of thermal expansion, thermal conductivity, and modulus of elasticity; these parameters are crucial for accurately simulating thermal deformation. These material properties are typically obtained by consulting material handbooks or through experimental measurements.

[0068] Next, in finite element analysis software (such as ANSYS, ABAQUS, etc.), modeling tools are used to create 3D models of the machine tool structure and the workpiece based on the collected geometric dimensions. During the modeling process, the shape and dimensions of each component are first precisely defined to ensure that the model is as consistent as possible with the actual machine tool structure and the workpiece. For example, for the machine tool spindle, its cylindrical main body and the geometry of its connecting parts need to be accurately modeled; for the workpiece, if it is a shaft-type part, its outer cylindrical surface, end faces, and any possible steps need to be modeled.

[0069] After the 3D model is built, it is meshed. Meshing discretizes the continuous geometric model into a finite set of small elements, which can be tetrahedral, hexahedral, quadrilateral, triangular, or other shapes. The density and quality of the mesh directly affect the accuracy and computational efficiency of the finite element analysis. In models of machine tool structures and machined workpieces, denser meshes are typically used in critical areas (such as regions subjected to high stress or sensitive to thermal deformation) to improve analysis accuracy. For example, finer meshes are needed in the tool-workpiece contact area and the spindle support area because thermal deformation in these areas significantly affects machining accuracy.

[0070] After mesh generation, the boundary conditions of the model are set. Boundary conditions include temperature boundary conditions, constraint conditions, and load conditions. Temperature boundary conditions are set based on the temperature distribution in the actual machining environment. For example, some surfaces of the model can be set to be in contact with the ambient temperature, or certain areas can be designated as having heat sources. Constraint conditions are used to simulate the fixing or restriction of machine tool components and workpieces during actual machining. For example, the spindle support points can be set as fixed constraints, and the clamping locations of the workpiece can also be configured with appropriate constraints based on actual conditions. Load conditions include various forces that may be generated during machining, such as cutting forces. These load conditions can be set according to the actual machining parameters.

[0071] Finally, material properties are assigned to the individual elements in the model. Based on the previously collected material property data, parameters such as the coefficient of thermal expansion, thermal conductivity, and modulus of elasticity are assigned to the corresponding elements, resulting in a complete three-dimensional finite element model of the machine tool structure and the machined workpiece. This three-dimensional model contains information such as the geometry, mesh generation, boundary conditions, and material properties of the machine tool structure and the machined workpiece, which can be used for subsequent thermal deformation analysis and calculation. Using the solver of the finite element analysis software, thermal analysis can be performed on this model to calculate the thermal deformation distribution of the machine tool structure and the machined workpiece under given temperature and boundary conditions, thereby obtaining their thermal deformation amounts.

[0072] By using a long short-term memory fusion prediction model, the future thermal deformation of the machine tool structure and the machine tool structural component is predicted based on the current thermal deformation of the workpiece and the machine tool structural component, combined with the historical thermal deformation of the workpiece. This prediction yields the future thermal deformation of the machine tool structural component and the workpiece within a preset time period.

[0073] Specifically, firstly, historical thermal deformation data of the machined workpiece is collected. This data includes records of thermal deformation under different ambient temperatures, processing times, and processing conditions. These historical data, along with current thermal deformation data, are used as input to construct a long short-term memory (LSTM) fusion prediction model. This model learns the time-series features from the historical data to capture the changing patterns of thermal deformation over time. During model training, parameters such as the learning rate and the number of hidden layer nodes are continuously adjusted to improve the model's prediction accuracy. After training, the model, combined with current and historical thermal deformation data, is used to predict the future thermal deformation of the machine tool structure and the machined workpiece within a preset time period. The prediction results provide a basis for subsequent compensation calculations, enabling compensation to adapt to potential future thermal deformation conditions in advance, thereby improving processing accuracy.

[0074] Based on the future thermal deformation of the machine tool structural components and the future thermal deformation of the workpiece being machined, the tool servo axis compensation amount, spindle hydraulic preload compensation amount, and cutting fluid temperature control compensation amount are generated.

[0075] Specifically, based on the predicted future thermal deformation of the machine tool structural components and the workpiece, the specific impact of this thermal deformation on the machining process is analyzed. For the tool servo axis compensation, the change in the relative position between the tool and the workpiece is calculated based on the thermal deformation, determining the tool position compensation amount that needs adjustment to ensure the tool can accurately machine along the predetermined trajectory. For the spindle hydraulic preload compensation, the hydraulic preload that needs adjustment is calculated based on the spindle's future thermal deformation to compensate for the stiffness change caused by thermal deformation, ensuring the spindle's machining accuracy and stability. For the cutting fluid temperature control compensation, the cutting fluid temperature that needs adjustment is determined based on the future thermal deformation of the workpiece. By adjusting the cutting fluid temperature, the temperature change in the machining area is controlled, thereby reducing the impact of thermal deformation on machining accuracy.

[0076] A compensation program is generated based on the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount. The compensation program is then inserted into the machining program of the workpiece to complete real-time compensation.

[0077] Specifically, the generated compensation program is inserted into the machining program of the workpiece to achieve real-time compensation during the machining process. The compensation program contains specific instructions for tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation. The compensation program is embedded into the machining program of the workpiece through the programming interface of the control computer. During machining, the control computer adjusts the position of the tool servo axis, the spindle hydraulic preload, and the temperature of the cutting fluid in real time according to the compensation program. When executing the machining program, the control computer dynamically adjusts relevant parameters during the machining process according to the preset compensation instructions, thereby achieving real-time compensation for changes in ambient temperature. This real-time compensation method can effectively reduce the impact of ambient temperature changes on machining accuracy, improve the stability of the machining process, and enhance the reliability of machining quality.

[0078] The ambient temperature compensation method for turning processes in this embodiment acquires temperature data at preset positions on the machine tool, enabling real-time reflection of ambient temperature changes during machining. Compared to traditional constant-temperature workshops, this method more accurately captures local temperature gradients and transient temperature changes, providing a more accurate basis for compensation. Furthermore, by combining the geometric parameters of the machining equipment, the current thermal deformation of the machine tool components and the workpiece is precisely calculated. This embodiment considers the actual structure of the machine tool and the workpiece, enabling a more accurate reflection of thermal deformation rather than simply using a fixed coefficient of thermal expansion.

[0079] Simultaneously, by utilizing a Long Short-Term Memory (LSTM) fusion prediction model, combining current and historical thermal deformation amounts, the model predicts the thermal deformation amount within a preset time period. This model effectively processes time-series data, captures the dynamic trend of thermal deformation, and thus performs compensation in advance, avoiding machining errors caused by transient temperature changes. Based on the predicted future thermal deformation amount, compensation amounts for the tool servo axis, spindle hydraulic preload, and cutting fluid temperature control are generated. This multi-dimensional compensation strategy adjusts machining parameters from multiple aspects, comprehensively reducing the impact of ambient temperature changes on machining accuracy. Furthermore, the generated compensation program can be inserted into the machining program in real time to achieve dynamic compensation. Compared to traditional offline compensation methods, this embodiment can respond to changes in ambient temperature in real time, ensuring the continuity and accuracy of the machining process.

[0080] The technical solution of this embodiment does not rely on a fixed coefficient of thermal expansion, and can adapt to the differences in thermal expansion of different materials and under different processing conditions. This makes the compensation method more flexible and can be widely applied to various high-precision turning machining scenarios. Through real-time compensation, machining errors caused by changes in ambient temperature can be significantly reduced, improving machining efficiency and quality; this is especially important for high-precision turning, as it can effectively reduce scrap rates and lower production costs. Furthermore, in high-precision turning, the final dimensions of the workpiece are affected by the superposition of multiple errors. This embodiment, through real-time monitoring and dynamic compensation, can effectively reduce errors caused by changes in ambient temperature, thereby reducing the superposition of errors and improving machining accuracy. Through precise temperature compensation, the stability and reliability of the machining process can be ensured, reducing machining anomalies caused by temperature changes and improving the controllability and repeatability of the machining process.

[0081] In summary, the ambient temperature compensation method for the turning process in this embodiment significantly improves the accuracy and effectiveness of ambient temperature compensation through real-time monitoring, dynamic prediction, and multi-dimensional compensation. It can effectively solve the problem of insufficient compensation accuracy in the prior art and meet the stringent requirements of high-precision turning.

[0082] Optionally, acquiring temperature data of the machine tool at a preset position and using the temperature data as the ambient temperature data for processing the workpiece includes:

[0083] Temperature data for each of the preset locations is acquired by temperature sensors installed at multiple preset locations according to a preset sampling period.

[0084] The ambient temperature data of the workpiece being processed is obtained based on the temperature data of all the preset locations.

[0085] Specifically, multiple high-precision temperature sensors are installed in key areas of the machine tool, such as near the spindle, the tool holder area, and the workpiece clamping area. These sensors can monitor and record temperature changes at these locations in real time. The selection and installation location of the temperature sensors are determined based on the machine tool's structure and the heat distribution characteristics during the machining process to ensure a comprehensive reflection of temperature changes throughout the machining process.

[0086] In this embodiment, the temperature sensor collects data according to a preset sampling period. The selection of the sampling period depends on the dynamic characteristics of temperature changes during processing. For example, if the temperature changes rapidly during processing, the sampling period may be set to a shorter period, such as once per second; if the temperature changes slowly, the sampling period can be appropriately extended, such as once per minute. The setting of the sampling period needs to be determined through experiments and experience to ensure that key information on temperature changes can be captured in a timely manner, while avoiding excessive data processing burden caused by overly frequent data collection.

[0087] The collected temperature data from each preset location is then transmitted to the machine tool's control unit or a dedicated data processing system. In the data processing system, this temperature data from different locations is comprehensively analyzed. Since there may be temperature gradients within the machine tool, the temperature at different locations may vary. Therefore, this embodiment does not directly use the temperature data from a single location as the ambient temperature data for the workpiece. Instead, it calculates the temperature data from all preset locations, for example, by calculating the average of these temperature data or using a weighted average method, taking into account the different weights of each location's influence on the workpiece temperature. In this way, a comprehensive data set representing the actual ambient temperature of the workpiece is obtained. This data will be used as the ambient temperature data for subsequent thermal deformation calculations and compensation strategy formulation.

[0088] In this optional embodiment, by installing temperature sensors at multiple key locations on the machine tool, comprehensive monitoring of temperature changes in different areas of the machine tool can be achieved. These locations include the area near the spindle, the tool holder area, and the workpiece clamping area, where temperature changes significantly impact machining accuracy. Multi-point monitoring captures potential temperature gradients and localized temperature changes within the machine tool, providing more comprehensive temperature information. Compared to traditional single-temperature sensors or constant-temperature workshops, this embodiment more accurately reflects the actual temperature environment during machining, reducing machining errors caused by temperature variations.

[0089] By comprehensively calculating temperature data from multiple preset locations, the ambient temperature data of the workpiece can be obtained, effectively enhancing the representativeness of the temperature data. Data collected by temperature sensors at different locations may vary; by calculating the average or weighted average of these data, a more accurate and representative ambient temperature of the workpiece can be obtained. This avoids misjudgments caused by localized temperature anomalies and improves the reliability and stability of the temperature data.

[0090] By acquiring temperature data through a preset sampling period, temperature changes can be monitored in real time. In this embodiment, the sampling period can be adjusted according to the dynamic characteristics of temperature changes during processing, ensuring that key information about temperature changes can be captured in a timely manner. This real-time monitoring capability enables the compensation system to respond quickly to temperature changes and adjust the compensation strategy promptly, thereby improving the stability and accuracy of the processing.

[0091] Ambient temperature data obtained through multi-point monitoring and comprehensive calculation provides a more accurate basis for subsequent thermal deformation calculations and compensation strategies. This precise temperature data helps to more accurately calculate the thermal deformation of machine tool structural components and machined workpieces, thereby improving compensation accuracy. Through real-time monitoring and dynamic compensation, machining errors caused by changes in ambient temperature can be effectively reduced, improving the reliability and quality of the machining process.

[0092] This embodiment does not rely on a single temperature sensor or a fixed temperature environment, enabling it to adapt to different processing conditions and machine tool configurations. Through multi-point monitoring and comprehensive calculation, it can flexibly address the complex temperature distribution within the machine tool, ensuring accurate temperature data under various processing conditions, thereby improving the adaptability and flexibility of the compensation strategy. Furthermore, in high-precision turning, the final dimensions of the workpiece are affected by the superposition of multiple errors. By accurately monitoring and comprehensively calculating ambient temperature data, errors caused by temperature changes can be effectively reduced, thereby minimizing the superposition of errors and improving machining accuracy.

[0093] Optionally, obtaining the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece based on the ambient temperature data and the geometric parameters of the machine tool's processing equipment includes:

[0094] The ambient temperature data is input into the machine tool-workpiece three-dimensional transient temperature field model. The geometric parameters are solved using the machine tool-workpiece three-dimensional transient temperature field model to obtain the current temperature of the machine tool structure at each node and the current temperature of the machined workpiece at each node.

[0095] The current thermal deformation of the machine tool structure is determined based on the current temperature of each node of the machine tool structure and the thermal expansion coefficient of the material of the machine tool structure.

[0096] The current thermal deformation of the workpiece is determined based on the current temperature of each node of the workpiece and the coefficient of thermal expansion of the workpiece material.

[0097] Specifically, after inputting ambient temperature data into the machine tool-workpiece three-dimensional transient temperature field model, the model initiates the solution process based on the geometric parameters of the machine tool and workpiece. These geometric parameters meticulously characterize the shape, size, and material properties of the machine tool and workpiece; they are precisely set during model construction to ensure the model accurately reflects the physical entities. In this embodiment, the solution process employs numerical methods such as finite element analysis, discretizing the continuous temperature field into a finite number of nodes and elements. By solving the heat conduction equations, the model calculates the temperature value of each node, thereby obtaining the temperature distribution of the machine tool structure and the machined workpiece under the current ambient temperature.

[0098] In a preferred embodiment of the present invention, a three-dimensional transient temperature field model of the machine tool and workpiece is used to simulate the temperature distribution of the machine tool and workpiece during machining. This model, through finite element analysis (FEA) combined with the heat conduction equation, can dynamically calculate the temperature changes of the machine tool structure and the machined workpiece at different times and spatial locations. This model considers not only the geometry of the machine tool and workpiece, but also the thermophysical properties of the materials (such as the coefficient of thermal expansion and thermal conductivity) and the distribution of heat sources during machining.

[0099] In this optional embodiment, the machine tool-workpiece three-dimensional transient temperature field model includes: a geometric model, comprising the three-dimensional geometry of the machine tool structural components and the workpiece. These geometries are precisely modeled using CAD software to ensure consistency with the dimensions of the actual machining equipment and workpiece. Examples include components such as the machine tool spindle, tool post, and bed, as well as the specific shape and dimensions of the workpiece. Material properties, including the thermophysical properties of the materials used in the machine tool structural components and the workpiece, such as the coefficient of thermal expansion, thermal conductivity, and specific heat capacity. These parameters are typically obtained from material handbooks or determined experimentally. Boundary conditions, including temperature boundary conditions and heat flux boundary conditions. Temperature boundary conditions define the temperature interaction between the model and the environment; for example, some surfaces may be in contact with a constant ambient temperature, while some areas may have heat sources. Heat flux boundary conditions define the input and output of heat flux. Initial conditions define the temperature distribution of the model at the initial moment; generally, it is assumed that the machine tool and workpiece are at the ambient temperature at the initial moment. Heat source distribution: During the machining process, the cutting process generates heat, which affects the temperature distribution of the machine tool and the workpiece. The heat source distribution is obtained through experimental measurement or theoretical calculation and is defined in the model.

[0100] In this embodiment, the specific application steps of the machine tool-workpiece three-dimensional transient temperature field model are as follows:

[0101] Create 3D geometric models of the machine tool structure and workpiece using CAD software, ensuring the model's dimensions match the actual equipment. Import the geometric model into finite element analysis software (such as ANSYS or ABAQUS) and define material properties, including coefficient of thermal expansion, thermal conductivity, and specific heat capacity. Set boundary and initial conditions; for example, set certain machine tool surfaces to be in contact with ambient temperature and set the initial temperature to ambient temperature. Define the heat source distribution, setting the location and intensity of heat sources based on the heat generation during the cutting process.

[0102] Meshing the 3D geometric model discretizes the continuous geometry into a finite number of nodes and elements. The density and quality of the mesh directly affect the accuracy and computational efficiency of the finite element analysis. Denser meshes are applied to critical areas (such as the tool-workpiece contact area and the spindle support area) to improve analysis accuracy.

[0103] Using ambient temperature data as input, the finite element analysis software solves the heat conduction equation to calculate the current temperature distribution at each node of the machine tool structure and the workpiece. During the solution process, the software dynamically calculates the temperature change at each node based on the defined material properties, boundary conditions, and initial conditions, generating a temperature field distribution map.

[0104] Based on the current temperature and the coefficient of thermal expansion of the material at each node, the thermal deformation of each node is calculated. The specific calculation formula is: Δx = α × ΔT × L, where Δx is the thermal deformation, α is the coefficient of thermal expansion, ΔT is the temperature change, and L is the initial length of the node. By calculating the thermal deformation of each node, the overall thermal deformation distribution of the machine tool structural components and the machined workpiece is obtained.

[0105] Based on the calculated thermal deformation, corresponding compensation strategies are formulated, such as tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation. A compensation program is generated and inserted into the machining program of the workpiece to achieve real-time compensation during the machining process.

[0106] Through the above steps, the three-dimensional transient temperature field model of the machine tool and workpiece is used in this embodiment to accurately calculate the thermal deformation of the machine tool structure and the workpiece, thereby providing a real-time temperature compensation strategy for high-precision turning.

[0107] When determining the current thermal deformation of a machine tool structural component, the current temperature of each node is used as a basis, combined with the coefficient of thermal expansion of the component's material. The coefficient of thermal expansion reflects the dimensional change characteristics of a material when temperature changes. For each node of the machine tool structural component, its thermal deformation is calculated using the thermal deformation formula based on its current temperature and coefficient of thermal expansion. The calculation process must consider the geometry and boundary conditions of the machine tool structural component, as these factors affect the distribution of thermal deformation. For example, a node at a fixed end may not experience thermal deformation due to constraints, while a node at a free end may experience significant thermal deformation due to temperature changes. By calculating the thermal deformation of each node, the current thermal deformation of the entire machine tool structural component is ultimately determined.

[0108] Determining the current thermal deformation of the workpiece is based on the current temperature of each node and the coefficient of thermal expansion of the workpiece material. The calculation of the workpiece's thermal deformation also needs to consider its constraints during processing, such as clamping methods, as these constraints affect the workpiece's thermal deformation. By calculating the thermal deformation at each node, the overall thermal deformation of the workpiece is obtained. This process is crucial for subsequent processing compensation because it accurately reflects the actual deformation state of the workpiece under the current ambient temperature.

[0109] This optional embodiment inputs ambient temperature data into the three-dimensional transient temperature field model of the machine tool and workpiece, and solves it according to the geometric parameters to obtain the current temperature of the machine tool structural components and the machined workpiece at each node, thereby determining their current thermal deformation.

[0110] By inputting ambient temperature data into the machine tool-workpiece three-dimensional transient temperature field model, the temperature distribution of the machine tool structure and the machined workpiece during the machining process can be accurately simulated. This model considers the geometric parameters of the machine tool and workpiece, and can dynamically reflect temperature changes over time and space. Compared with traditional static temperature models, the three-dimensional transient temperature field model can more accurately capture transient temperature changes, providing more precise temperature data for thermal deformation calculations.

[0111] After obtaining the current temperature of the machine tool structure and the workpiece at each node by solving the model, the thermal deformation of each node can be accurately calculated by combining the thermal expansion coefficient of the material. This embodiment can provide detailed thermal deformation distribution, not just the overall thermal deformation; the node-level thermal deformation calculation makes the compensation strategy more refined, enabling precise compensation for thermal deformation in different parts, thereby improving machining accuracy.

[0112] By precisely calculating the thermal deformation of the machine tool components and the workpiece, more accurate compensation strategies can be developed. For example, tool servo axis compensation can be adjusted based on the thermal deformation of the machine tool components, spindle hydraulic preload compensation can be adjusted based on the thermal deformation of the spindle, and cutting fluid temperature control compensation can be adjusted based on the thermal deformation of the workpiece. This multi-dimensional compensation strategy can comprehensively reduce the impact of ambient temperature changes on machining accuracy and significantly improve compensation precision.

[0113] This embodiment can adapt to different processing conditions and machine tool configurations. Through a three-dimensional transient temperature field model, it can flexibly handle the complex temperature distribution within the machine tool, ensuring accurate temperature data and thermal deformation under various processing conditions. This embodiment does not rely on a fixed coefficient of thermal expansion or a single temperature sensor, and can adapt to the differences in thermal expansion of different materials and processing conditions, improving the adaptability and flexibility of the compensation strategy. The three-dimensional transient temperature field model can calculate the thermal deformation of machine tool structural components and processed workpieces in real time, thereby achieving real-time compensation for the processing process. This real-time compensation capability allows the processing process to dynamically respond to temperature changes, ensuring the continuity and stability of the processing, and improving processing efficiency and quality.

[0114] In summary, this embodiment inputs ambient temperature data into the three-dimensional transient temperature field model of the machine tool and workpiece, and solves the problem based on geometric parameters to obtain the current temperature of the machine tool structural components and the machined workpiece at each node, thereby determining their current thermal deformation. This technical solution significantly improves the accuracy of temperature field simulation, the refinement of thermal deformation calculation, compensation accuracy, adaptability, ability to reduce error superposition, and real-time compensation capability, thereby significantly improving the accuracy and reliability of turning.

[0115] Optionally, the prediction using a long short-term memory fusion model, based on the current thermal deformation of the workpiece and the machine tool structural component, combined with the historical thermal deformation of the workpiece, to predict the future thermal deformation of the machine tool structural component and the workpiece within a preset time period, includes:

[0116] Based on the current thermal deformation of the machine tool structural component and the workpiece, and combined with the historical thermal deformation of the machine tool structural component and the workpiece in the previous sampling period, the thermal deformation variation curves of the machine tool structural component and the workpiece are obtained.

[0117] The time-series features of the thermal deformation variation curve are extracted to obtain the time-series feature vector of the thermal deformation variation curve;

[0118] The processing parameters of the workpiece within the future preset time period are extracted to obtain the processing feature vector of the processing parameters;

[0119] The temporal feature vector and the machining feature vector are input into the long short-term memory fusion prediction model for prediction, so as to obtain the future thermal deformation of the machine tool structural component and the machined workpiece within the future preset time period.

[0120] Specifically, the thermal deformation data of the machine tool structural components and the machined workpiece at the current moment are collected, along with their historical thermal deformation data from the previous sampling period. These data are arranged chronologically to form a data sequence of thermal deformation changing over time. Through data fitting or interpolation methods, these discrete data points are connected to generate a smooth curve, namely the thermal deformation change curve. This curve can intuitively reflect the dynamic trend of thermal deformation of the machine tool structural components and the machined workpiece over time.

[0121] The generated thermal deformation variation curve undergoes time-series feature extraction. The purpose of time-series feature extraction is to extract key information from the curve that characterizes its variation pattern. These features include the curve's periodicity, trend, and fluctuation amplitude. In this embodiment of the invention, mathematical tools such as wavelet transform and Fourier transform can be used to analyze the curve and extract features of different frequency components. In addition, statistical features of the curve, such as mean, variance, and slope, can be calculated. These extracted features are combined to form a time-series feature vector, which is used as input for subsequent prediction models.

[0122] The machining parameters of the workpiece are feature extracted over a preset time period. These parameters include cutting speed, feed rate, and depth of cut, which directly affect heat generation and transfer during machining. Each machining parameter is analyzed to extract its key features. For example, for cutting speed, features such as average, maximum, minimum, and range of variation can be extracted; similarly, statistical features are extracted for feed rate and depth of cut. These features are then combined to form a machining feature vector.

[0123] The extracted temporal and machining feature vectors are input into a long short-term memory (LSTM) fusion prediction model. This model combines temporal and machining features, comprehensively considering the historical trends of thermal deformation and the influence of future machining conditions, to predict the amount of thermal deformation. During the training phase, the model learns from a large amount of historical data to understand the patterns of thermal deformation variation. In the prediction phase, based on the input temporal and machining feature vectors, the model outputs the future thermal deformation of the machine tool structure and the machined workpiece within a preset time period.

[0124] In another preferred embodiment of the present invention, the Long Short-Term Memory Fusion Prediction Model (LSTM FusionPrediction Model) is a deep learning-based prediction model that combines a Long Short-Term Memory network (LSTM) and feature fusion technology. It can effectively capture long-term dependencies in time series data and comprehensively consider the impact of multiple features on the prediction results.

[0125] The long short-term memory fusion prediction model in this embodiment includes: a long short-term memory network (LSTM), which specifically includes an input gate to control the degree to which new information enters the cell state; a forget gate to determine which information in the cell state needs to be forgotten; an output gate to determine which information in the cell state needs to be output; a cell state that stores long-term memory information and is updated through the forget gate and the input gate; and a hidden state that stores short-term memory information and is used to pass it to the next time step.

[0126] The feature extraction layer specifically includes: temporal feature extraction, which extracts temporal features from the thermal deformation variation curve, such as periodicity, trend, and fluctuation amplitude; and machining parameter feature extraction, which extracts key features from machining parameters, such as statistical features of cutting speed, feed rate, and depth of cut.

[0127] The feature fusion layer specifically includes: feature concatenation, which concatenates the temporal feature vector and the processed feature vector into a comprehensive feature vector; and feature transformation, which transforms the comprehensive feature vector through fully connected layers or convolutional layers to extract higher-level feature representations.

[0128] The prediction layer specifically includes: an output layer, which uses a fully connected layer to map the output of the feature fusion layer to the prediction target, i.e., the future thermal deformation. The activation function is typically a linear activation function, as the thermal deformation in this embodiment is a continuous value.

[0129] In this embodiment, the specific application steps of the long short-term memory fusion prediction model are as follows:

[0130] First, collect the current thermal deformation data of the machine tool structural components and the workpiece, as well as their historical thermal deformation data in the previous sampling period; collect the machining parameter data of the workpiece in the future preset time period, such as cutting speed, feed rate, and depth of cut.

[0131] Then, time-series feature extraction is performed on the thermal deformation change curve to extract features such as periodicity, trend, and fluctuation amplitude, forming a time-series feature vector; feature extraction is performed on the machining parameters to extract statistical features of cutting speed, feed rate, and depth of cut, forming a machining feature vector.

[0132] The temporal feature vector and the processing feature vector are then concatenated into a comprehensive feature vector; the comprehensive feature vector is then transformed through a fully connected layer or a convolutional layer to extract a higher-level feature representation.

[0133] Furthermore, a Long Short-Term Memory (LSTM) network model is constructed, including an input gate, a forget gate, an output gate, cell states, and hidden states. The model is trained using historical data, and its parameters are adjusted through backpropagation, enabling the model to learn the patterns of thermal deformation changes. During training, the model learns how to combine temporal and processing features to accurately predict future thermal deformation.

[0134] The extracted temporal feature vector and the processed feature vector are then input into the trained long short-term memory fusion prediction model. The model processes the temporal features through the LSTM layer, combines the processed features through the feature fusion layer, and finally outputs the predicted value of thermal deformation within a preset time period in the prediction layer.

[0135] Finally, based on the predicted future thermal deformation, corresponding compensation strategies are formulated, such as tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation. A compensation program is generated and inserted into the machining program of the workpiece to achieve real-time compensation of the machining process.

[0136] Through the above steps, the long short-term memory fusion prediction model in this embodiment is used to accurately predict the future thermal deformation of machine tool structural components and machined workpieces, thereby providing a real-time temperature compensation strategy for high-precision turning, effectively reducing machining errors caused by changes in ambient temperature, and improving machining accuracy and quality.

[0137] In this embodiment of the invention, by combining current and historical thermal deformation data to generate a thermal deformation variation curve, a more comprehensive reflection of the dynamic trend of thermal deformation can be achieved. The time-series-based method of this embodiment can capture both short-term fluctuations and long-term trends in thermal deformation, providing a foundation for accurate prediction. Compared to using only current data or a fixed model, this embodiment can more accurately predict future thermal deformation, thereby improving the accuracy of compensation.

[0138] By extracting time-series features from the thermal deformation variation curve, a time-series feature vector is obtained. Simultaneously, features are extracted from the processing parameters to obtain a processing feature vector. This multi-dimensional feature fusion method comprehensively considers the influence of historical thermal deformation data and future processing conditions, enabling the prediction model to more fully understand the complexity of thermal deformation. Compared to single-dimensional feature extraction, multi-dimensional feature fusion significantly improves the accuracy and reliability of predictions.

[0139] By extracting feature vectors from machining parameters, the model can consider specific conditions during future machining processes, such as cutting speed, feed rate, and depth of cut. These parameters directly affect heat generation and heat transfer during machining; therefore, incorporating them into the prediction model enables it to dynamically adapt to changes in machining conditions. Compared to static prediction models, this embodiment can better handle dynamic changes during machining, improving the adaptability and flexibility of the compensation strategy. By inputting temporal feature vectors and machining feature vectors into the long short-term memory fusion prediction model, the amount of thermal deformation within a preset time period can be predicted in real time. This real-time prediction capability allows the compensation strategy to be adjusted in advance, ensuring the continuity and stability of the machining process. Compared to traditional offline compensation methods, real-time prediction and compensation can significantly reduce machining errors caused by changes in ambient temperature, improving machining accuracy and quality. Furthermore, by learning from a large amount of historical data, the long short-term memory fusion prediction model can grasp the patterns of thermal deformation changes, thereby improving the model's generalization ability. This model is not only applicable to current machining conditions but can also adapt to different machining scenarios and machine tool configurations, exhibiting strong versatility and scalability. Compared with the traditional fixed model, this embodiment can better cope with complex and ever-changing processing environments and improve the robustness of the compensation strategy.

[0140] In summary, by using a long short-term memory fusion prediction model, which combines current and historical thermal deformation amounts with machining parameters, the future thermal deformation amounts of machine tool structural components and machined workpieces can be predicted. This significantly improves the accuracy of thermal deformation prediction, the ability to fuse multi-dimensional features, the ability to dynamically adapt to changes in machining conditions, the real-time performance and effectiveness of compensation strategies, the ability to reduce error superposition, and the model's generalization ability, thereby significantly improving the accuracy and reliability of turning machining.

[0141] Optionally, generating tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation based on the future thermal deformation of the machine tool structural component and the future thermal deformation of the workpiece includes:

[0142] The component of the future thermal deformation that corresponds to the relative displacement of the tool is mapped to the tool servo axis compensation amount.

[0143] The component of the future thermal deformation corresponding to the axial elongation of the spindle is converted into the hydraulic preload compensation of the spindle.

[0144] The component of the future thermal deformation that corresponds to the temperature rise on the workpiece surface is converted into the cutting fluid temperature control compensation amount.

[0145] Specifically, in turning, the relative displacement between the tool and the workpiece is mainly reflected in the tool's feed direction. Therefore, the component of future thermal deformation related to the relative displacement of the tool is mainly the thermal deformation component along the tool feed direction. By performing vector decomposition on the thermal deformation, the component along the tool feed direction is extracted. Next, based on the machine tool's geometry and kinematic model, a mapping relationship between the thermal deformation and the tool servo axis movement is established. Specific steps include: determining the direction and magnitude of the future thermal deformation, where the future thermal deformation is a vector containing deformation information of the machine tool structure and the workpiece in different directions; and decomposing the future thermal deformation into a component along the tool feed direction. In this embodiment, this is achieved through vector projection, i.e., calculating the projection value of the thermal deformation in the tool feed direction.

[0146] Assuming the future thermal deformation is Δd, and the unit vector in the tool feed direction is u, then the thermal deformation component Δd in the tool feed direction... 进给 It can be calculated using the following formula:

[0147] Δd 进给 =Δd•u, where • represents the dot product operation of vectors.

[0148] Based on the machine tool's geometry and kinematic model, and combined with the machine tool's structural parameters, such as the initial distance between the tool and the workpiece and the tool's installation position, the relationship between thermal deformation and tool servo axis movement is determined.

[0149] A mapping function f is established to map the thermal deformation component Δd feed to the tool servo axis compensation amount Δx tool. The mapping function f can be a linear or nonlinear function, depending on the machine tool's structure and kinematic characteristics. For example, if the machine tool's kinematic model is linear, the mapping function can be expressed as:

[0150] Δx 刀具 =f(Δd 进给 )=k•Δd 进给 Where k is a proportionality coefficient, representing the amount of tool servo axis movement corresponding to a unit amount of thermal deformation. The proportionality coefficient k can be obtained through experimental calibration or theoretical calculation.

[0151] Calculate the compensation amount Δx of the tool servo axis based on the mapping function f. 刀具 The thermal deformation component Δd 进给 Substituting into the mapping function, we obtain the compensation amount for the tool servo axis:

[0152] Δx 刀具 =f(Δd 进给 The calculated compensation amount Δx 刀具 This indicates the distance the tool servo axis needs to move to compensate for the effect of thermal deformation on the relative displacement between the tool and the workpiece.

[0153] The calculated tool servo axis compensation amount Δx 刀具 This technology is applied to tool servo control systems. The tool servo control system adjusts the tool position based on compensation amounts to ensure that the relative displacement between the tool and the workpiece remains within a predetermined machining accuracy range. The compensation amount can be achieved through compensation commands from the CNC system, either by directly inputting the compensation amount into the CNC system's compensation program or by adjusting it through a dedicated compensation module.

[0154] Through the above steps, the component of future thermal deformation corresponding to the relative displacement of the tool can be accurately mapped to the tool servo axis compensation amount, thereby achieving effective compensation for thermal deformation and improving the accuracy and reliability of turning. This embodiment, by incorporating the machine tool's geometry and kinematic characteristics, can accurately calculate the compensation amount and implement the compensation through the CNC system, ensuring the continuity and stability of the machining process.

[0155] In this embodiment of the invention, by mapping, converting and converting different components of future thermal deformation into tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation, accurate compensation for thermal deformation during turning is achieved, significantly improving machining accuracy and reliability.

[0156] By mapping the component of future thermal deformation corresponding to the relative displacement of the tool to a tool servo axis compensation amount, the tool position can be precisely adjusted. This embodiment considers the influence of thermal deformation on the relative position between the tool and the workpiece. By calculating the component of thermal deformation in the tool feed direction and converting it into the movement of the tool servo axis, it ensures that the tool can accurately perform machining along a predetermined trajectory. Compared with traditional fixed compensation methods, this embodiment can dynamically adjust the tool position to adapt to changes in thermal deformation during machining, thereby significantly improving machining accuracy.

[0157] By converting the component of future thermal deformation corresponding to the axial elongation of the spindle into a hydraulic preload compensation amount, the rigidity change of the spindle caused by thermal expansion can be effectively compensated. The axial elongation of the spindle affects its machining accuracy and stability; by adjusting the hydraulic preload, this elongation can be counteracted, maintaining the spindle's rigidity and machining accuracy. This embodiment considers the spindle's structural and material properties, converting thermal deformation into a hydraulic preload adjustment amount through a mechanical model, ensuring stable spindle performance during machining. Compared to traditional static compensation methods, this embodiment can respond to changes in thermal deformation in real time, improving the spindle's machining accuracy and reliability.

[0158] By converting the component of future thermal deformation corresponding to the workpiece surface temperature rise into a cutting fluid temperature control compensation, the temperature change of the workpiece surface can be effectively controlled. A rise in workpiece surface temperature causes thermal deformation, affecting machining accuracy. By adjusting the cutting fluid temperature, the temperature rise of the workpiece surface can be reduced, thereby minimizing the impact of thermal deformation on machining accuracy. This embodiment considers the thermophysical properties of the workpiece material and the cooling effect of the cutting fluid. A heat conduction model is used to convert the workpiece surface temperature rise component into cutting fluid temperature control parameters, ensuring that the workpiece surface temperature remains within a reasonable range. Compared with traditional cooling methods, this embodiment can dynamically adjust the cutting fluid temperature to adapt to thermal changes during machining, improving machining quality.

[0159] By comprehensively considering the compensation amounts of the tool servo axis, the spindle hydraulic preload, and the cutting fluid temperature control, a multi-dimensional compensation strategy is achieved. This embodiment not only considers the impact of thermal deformation on tool position and spindle rigidity but also the changes in workpiece surface temperature, thus comprehensively reducing the impact of thermal deformation on machining accuracy. Compared with single-dimensional compensation methods, the multi-dimensional compensation strategy can more comprehensively address the problem of thermal deformation, improving the stability and accuracy of the machining process.

[0160] By mapping, transforming, and converting different components of future thermal deformation into corresponding compensation amounts, real-time compensation for the machining process can be achieved. This embodiment can dynamically respond to changes in thermal deformation, adjusting tool position, spindle hydraulic preload, and cutting fluid temperature in a timely manner to ensure the continuity and stability of the machining process. Compared with traditional offline compensation methods, real-time compensation can significantly reduce machining errors caused by thermal deformation, improving machining efficiency and quality.

[0161] This embodiment can adapt to different machining conditions and machine tool configurations. By dynamically adjusting the compensation amount, it can flexibly respond to various changes during the machining process, such as different cutting parameters, workpiece materials, and machining environments. Compared with traditional fixed compensation methods, this embodiment has stronger adaptability and flexibility, and can be widely applied to various high-precision turning machining scenarios.

[0162] In high-precision turning, the final dimensions of the workpiece are affected by the superposition of multiple errors. By accurately compensating for thermal deformation, errors caused by temperature changes can be effectively reduced, thereby reducing the superposition of errors and improving machining accuracy. This embodiment can significantly improve machining accuracy, meet the stringent requirements of high-precision turning, and ensure the dimensional accuracy and surface quality of the machined workpiece.

[0163] In summary, by mapping, transforming, and converting different components of future thermal deformation into tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation, the embodiments of the present invention significantly improve the accuracy and reliability of turning, achieve precise compensation for thermal deformation, adapt to complex and variable machining conditions, reduce error superposition, and improve machining efficiency and quality.

[0164] Optionally, generating a compensation program based on the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount includes:

[0165] The tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount are prioritized and synchronized in time to obtain a synchronous compensation command sequence.

[0166] According to the synchronous compensation instruction sequence, the compensation amount of the tool servo axis, the compensation amount of the spindle hydraulic preload, and the compensation amount of the cutting fluid temperature control are discretized at the interpolation cycle level to form a discrete compensation value sequence corresponding to the interpolation cycle of the CNC system.

[0167] The discrete compensation value sequence is encapsulated into a macro variable to generate the compensation program.

[0168] Specifically, when prioritizing the tool servo axis compensation, spindle hydraulic preload compensation, and coolant temperature control compensation, the priority is first determined based on the degree of influence of each compensation on machining accuracy. For example, the tool servo axis compensation directly affects the relative position of the tool and the workpiece, having the greatest impact on machining dimensional accuracy, and is therefore given the highest priority; the spindle hydraulic preload compensation affects the rigidity and stability of the spindle, indirectly affecting machining accuracy, and is given a medium priority; the coolant temperature control compensation reduces thermal deformation by controlling the workpiece surface temperature, and has a relatively small impact on machining accuracy, thus being given a lower priority.

[0169] During synchronization timing alignment, the timestamps of compensation quantities are analyzed and adjusted to align their time sequences to the same time base, ensuring consistent execution times during machining. For example, suppose the time sequence of the tool servo axis compensation quantity is updated every 10 milliseconds, the spindle hydraulic preload compensation quantity every 20 milliseconds, and the coolant temperature control compensation quantity every 30 milliseconds. To achieve synchronization timing alignment, the time sequences of all compensation quantities can be unified to a least common multiple time interval, i.e., updated every 60 milliseconds. Thus, each compensation quantity has a corresponding compensation value at each 60-millisecond time point, forming a synchronized compensation instruction sequence. After obtaining the synchronized compensation instruction sequence, compensation quantities of different priorities are arranged in chronological order, and the values ​​of each compensation quantity at each time point are integrated to form an ordered instruction sequence. For example, at each 60-millisecond time point, the highest priority tool servo axis compensation quantity is executed first, followed by the spindle hydraulic preload compensation quantity, and finally the coolant temperature control compensation quantity. In this way, it is ensured that the compensation operation is executed accurately in the predetermined sequence and at the predetermined time during the processing, thereby achieving effective compensation for thermal deformation and improving processing accuracy and reliability.

[0170] For example, suppose there is the following compensation data in a high-precision turning process:

[0171] Tool servo axis compensation: updated every 10 milliseconds, data is [0.01, 0.02, 0.03, ...] (unit: mm); Spindle hydraulic preload compensation: updated every 20 milliseconds, data is [100, 105, 110, ...] (unit: Newton); Cutting fluid temperature control compensation: updated every 30 milliseconds, data is [25, 26, 27, ...] (unit: degree Celsius).

[0172] The next step is to prioritize the following: Tool servo axis compensation directly affects the relative position of the tool and workpiece, having the greatest impact on machining dimensional accuracy, and therefore has the highest priority. Spindle hydraulic preload compensation affects the spindle's rigidity and stability, indirectly impacting machining accuracy, and has a medium priority. Cutting fluid temperature control compensation reduces thermal deformation by controlling the workpiece surface temperature, and has a relatively small impact on machining accuracy, thus having the lowest priority.

[0173] When performing synchronization timing alignment, the time series of all compensation quantities are unified to a common time interval, such as choosing 60 milliseconds as the unified time interval (60 is the least common multiple of 10, 20 and 30).

[0174] The tool servo axis compensation amount is updated every 10 milliseconds. Within 60 milliseconds, there are 6 data points: [0.01, 0.02, 0.03, 0.04, 0.05, 0.06]. The spindle hydraulic preload compensation amount is updated every 20 milliseconds. Within 60 milliseconds, there are 3 data points: [100, 105, 110]. The cutting fluid temperature control compensation amount is updated every 30 milliseconds. Within 60 milliseconds, there are 2 data points: [25, 26]. These compensation amounts are integrated at each 60-millisecond time point to form a synchronous compensation instruction sequence. The compensation instructions at each time point are arranged in priority order.

[0175] Table 1. Time series table of dynamic compensation during turning process

[0176]

[0177] At each 60-millisecond time point, the tool servo axis compensation is executed first, followed by the spindle hydraulic preload compensation, and finally the coolant temperature control compensation. For time points where no data is updated, the compensation amount remains unchanged (indicated by "-"). Referring to Table 1, the spindle hydraulic preload compensation is updated at time points 20, 40, and 60 milliseconds, but not at 40 and 50 milliseconds, and therefore remains unchanged. Similarly, the coolant temperature control compensation is updated at time points 30 and 60 milliseconds, but not at other time points, and therefore remains unchanged. This results in a synchronized compensation instruction sequence, ensuring that all compensation operations are executed accurately according to the predetermined order and time points during machining. Through the above steps, the tool servo axis compensation, spindle hydraulic preload compensation, and coolant temperature control compensation are prioritized and synchronized, forming a synchronized compensation instruction sequence. This method effectively coordinates the execution of different compensation amounts, ensuring the stability and accuracy of the machining process.

[0178] Optionally, inserting the compensation program into the machining program of the workpiece to complete real-time compensation includes:

[0179] Write the compensation procedure into the compensation call entry point of the machining program before the current tool position;

[0180] The CNC system calls the compensation program in real time during the next interpolation cycle, and simultaneously executes the tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation.

[0181] Optionally, it also includes:

[0182] Once the real-time compensation is completed, the actual dimensions of the machined surface of the workpiece are obtained.

[0183] Based on the actual dimensions, the residual error between the actual dimensions and the theoretical dimensions of the machined workpiece is obtained;

[0184] The residual error is converted into a thermal deformation correction value, and the tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation of the workpiece in the next sampling cycle are updated according to the thermal deformation correction value until the workpiece is finished.

[0185] Specifically, after real-time compensation is completed, precision measuring tools, such as coordinate measuring machines or laser scanners, are used to measure the dimensions of the machined surface of the workpiece, thereby ensuring the accuracy of the measurement results. During the measurement process, the tool moves along the workpiece surface, recording the dimensional information of each key point. This data is then transmitted to a computer system for subsequent error analysis.

[0186] After obtaining the actual dimensional data, it is compared with the theoretical dimensions to calculate the residual error. The theoretical dimensions are pre-defined dimensions based on the workpiece design requirements and can be obtained through CAD models or process documents. During the comparison process, the computer system automatically calculates the difference between the actual and theoretical dimensions, obtaining the residual error at each measurement point; these error data reflect the dimensional deviations present in the current machining process.

[0187] Based on the material's coefficient of thermal expansion and the machine tool's thermal characteristics, dimensional errors are converted into corresponding thermal deformation. During the conversion process, the computer system uses the material's coefficient of thermal expansion to convert dimensional errors into thermal deformation caused by temperature changes.

[0188] In an optional embodiment of the present invention, this conversion process can be implemented using mathematical formulas, for example:

[0189] ΔT = ΔL / α•L0, where ΔT is the temperature change, ΔL is the dimensional error, α is the coefficient of thermal expansion of the material, and L0 is the original length.

[0190] Based on the calculated thermal deformation correction values, the tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation for the machined workpiece are updated for the next sampling cycle. During the update process, the computer system adjusts the calculation formulas or parameters of the compensation amounts according to the thermal deformation correction values.

[0191] For example, if the thermal deformation correction value indicates that the workpiece size is too large, the system may increase the compensation amount of the tool servo axis, or adjust the spindle hydraulic preload and cutting fluid temperature to counteract excessive thermal deformation. These adjustments can be made by modifying the compensation parameters in the CNC system to ensure that the compensation strategy can more accurately adapt to changes in thermal deformation during machining in the next sampling cycle.

[0192] The above process is repeated until the workpiece is finished. In this embodiment, after each sampling cycle, the residual error needs to be remeasured, recalculated, converted into a thermal deformation correction value, and the compensation amount updated. Through this closed-loop control method, the compensation strategy can be continuously optimized until the workpiece is finished, ensuring that the machining accuracy meets the design requirements.

[0193] In this optional embodiment, by obtaining the actual dimensions of the machined surface of the workpiece after real-time compensation is completed and comparing them with the theoretical dimensions, the residual error can be accurately calculated. This step allows the error during the machining process to be quantified, thus providing a basis for further error correction. Converting the residual error into a thermal deformation correction value and updating the compensation amount accordingly ensures that the compensation strategy better matches the actual machining situation, effectively improving machining accuracy.

[0194] Converting residual errors into thermal deformation correction values ​​and updating the compensation accordingly allows the compensation strategy to adapt to dynamic changes during processing. This adaptability is crucial for handling unpredictable factors such as temperature fluctuations and changes in material properties, helping to maintain the stability and consistency of the processing.

[0195] A closed-loop control system was implemented by repeatedly measuring, calculating residual errors, converting them into thermal deformation correction values, and updating the compensation amounts. In this system, feedback from actual machining results guides and adjusts subsequent machining processes. This closed-loop control helps continuously optimize the machining process until the workpiece is finished, thereby improving overall machining quality and efficiency.

[0196] Precise error measurement and compensation updates can significantly reduce scrap caused by machining errors. By adjusting compensation strategies in real time to adapt to actual changes during the machining process, it is possible to ensure that workpiece dimensions are closer to theoretical dimensions, thereby reducing scrap rates and improving material utilization and production efficiency.

[0197] Combination Figure 2 As shown, the present invention provides a turning process ambient temperature compensation system, comprising:

[0198] The data acquisition unit is used to acquire temperature data of the machine tool at a preset position and use the temperature data as the ambient temperature data of the workpiece being processed.

[0199] The thermal deformation calculation unit is used to obtain the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece based on the ambient temperature data and the geometric structural parameters of the machine tool's processing equipment.

[0200] The prediction unit is used to predict the future thermal deformation of the machine tool structure and the machine tool structural component within a preset time period by using a long short-term memory fusion prediction model, based on the current thermal deformation of the workpiece and the machine tool structural component, combined with the historical thermal deformation of the workpiece.

[0201] The compensation calculation unit is used to generate the tool servo axis compensation amount, spindle hydraulic preload compensation amount, and cutting fluid temperature control compensation amount based on the future thermal deformation amount of the machine tool structural component and the future thermal deformation amount of the workpiece.

[0202] The compensation unit generates a compensation program based on the compensation amount of the tool servo axis, the compensation amount of the spindle hydraulic preload, and the compensation amount of the cutting fluid temperature control, and inserts the compensation program into the machining program of the workpiece to complete real-time compensation.

[0203] The turning process ambient temperature compensation system of the present invention has the same advantages over the prior art as the above-mentioned turning process ambient temperature compensation method, and will not be repeated here.

[0204] Combination Figure 3 As shown, an electronic device according to the present invention includes a memory and a processor;

[0205] The memory is used to store computer programs;

[0206] The processor is configured to implement the above-described method for compensating for ambient temperature during the turning process when executing the computer program.

[0207] The electronic device of the present invention has the same advantages over the prior art as the above-mentioned method for compensating for ambient temperature during the turning process, and will not be repeated here.

[0208] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for compensating for ambient temperature during turning, characterized in that, include: Acquire temperature data of the machine tool at a preset position, and use the temperature data as the ambient temperature data for processing the workpiece; Based on the ambient temperature data and the geometric parameters of the machine tool's processing equipment, the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece are obtained. The long short-term memory fusion prediction model predicts the future thermal deformation of the machine tool structure and the workpiece within a preset time period by combining the current thermal deformation of the workpiece and the machine tool structure with the historical thermal deformation of the workpiece. Specifically, it includes obtaining the thermal deformation change curves of the machine tool structure and the workpiece by combining the current thermal deformation of the machine tool structure and the workpiece with the historical thermal deformation of the machine tool structure and the workpiece in the previous sampling period. The time-series features of the thermal deformation variation curve are extracted to obtain the time-series feature vector of the thermal deformation variation curve; The processing parameters of the workpiece within the future preset time period are extracted to obtain the processing feature vector of the processing parameters; The time-series feature vector and the processing feature vector are input into the long short-term memory fusion prediction model for prediction, so as to obtain the future thermal deformation of the machine tool structural component and the processed workpiece in the future preset time period; Based on the future thermal deformation of the machine tool structural components and the future thermal deformation of the workpiece being machined, the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount are generated; specifically, this includes mapping the component of the future thermal deformation amount corresponding to the relative displacement of the tool to the tool servo axis compensation amount. The component of the future thermal deformation corresponding to the axial elongation of the spindle is converted into the hydraulic preload compensation of the spindle. The component of the future thermal deformation that corresponds to the temperature rise of the workpiece surface is converted into the cutting fluid temperature control compensation amount. Based on the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount, a compensation program is generated, specifically including: prioritizing and synchronizing the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount to obtain a synchronous compensation instruction sequence; According to the synchronous compensation instruction sequence, the compensation amount of the tool servo axis, the compensation amount of the spindle hydraulic preload, and the compensation amount of the cutting fluid temperature control are discretized at the interpolation cycle level to form a discrete compensation value sequence corresponding to the interpolation cycle of the CNC system. The discrete compensation value sequence is encapsulated into a macro variable to generate the compensation program; and the compensation program is inserted into the machining program of the workpiece to complete real-time compensation.

2. The method for compensating for ambient temperature during the turning process according to claim 1, characterized in that, The step of acquiring temperature data of the machine tool at a preset position and using the temperature data as the ambient temperature data for processing the workpiece includes: Temperature data for each of the preset locations is acquired by temperature sensors installed at multiple preset locations according to a preset sampling period. The ambient temperature data of the workpiece being processed is obtained based on the temperature data of all the preset locations.

3. The method for compensating for ambient temperature during the turning process according to claim 1, characterized in that, The step of obtaining the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece based on the ambient temperature data and the geometric parameters of the machine tool's processing equipment includes: The ambient temperature data is input into the machine tool-workpiece three-dimensional transient temperature field model. The geometric parameters are solved using the machine tool-workpiece three-dimensional transient temperature field model to obtain the current temperature of the machine tool structure at each node and the current temperature of the machined workpiece at each node. The current thermal deformation of the machine tool structure is determined based on the current temperature of each node of the machine tool structure and the thermal expansion coefficient of the material of the machine tool structure. The current thermal deformation of the workpiece is determined based on the current temperature of each node of the workpiece and the coefficient of thermal expansion of the workpiece material.

4. The method for compensating for ambient temperature during the turning process according to claim 1, characterized in that, The step of inserting the compensation program into the machining program of the workpiece to complete real-time compensation includes: Write the compensation procedure into the compensation call entry point of the machining program before the current tool position; The CNC system calls the compensation program in real time during the next interpolation cycle, and simultaneously executes the tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation.

5. The method for compensating for ambient temperature during the turning process according to claim 1, characterized in that, Also includes: Once the real-time compensation is completed, the actual dimensions of the machined surface of the workpiece are obtained. Based on the actual dimensions, the residual error between the actual dimensions and the theoretical dimensions of the machined workpiece is obtained; The residual error is converted into a thermal deformation correction value, and the tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation of the workpiece in the next sampling cycle are updated according to the thermal deformation correction value until the workpiece is finished.

6. A temperature compensation system for the turning process, characterized in that, include: The data acquisition unit is used to acquire temperature data of the machine tool at a preset position and use the temperature data as the ambient temperature data of the workpiece being processed. The thermal deformation calculation unit is used to obtain the current thermal deformation of the machine tool structural component corresponding to the workpiece and the current thermal deformation of the workpiece based on the ambient temperature data and the geometric structural parameters of the machine tool's processing equipment. The prediction unit is used to predict the future thermal deformation of the machine tool structure and the workpiece within a preset time period by using a long short-term memory fusion prediction model, based on the current thermal deformation of the workpiece and the machine tool structural component, combined with the historical thermal deformation of the workpiece. Specifically, it includes: obtaining the thermal deformation change curves of the machine tool structure and the workpiece based on the current thermal deformation of the machine tool structure and the workpiece, combined with the historical thermal deformation of the machine tool structure and the workpiece in the previous sampling period. The time-series features of the thermal deformation variation curve are extracted to obtain the time-series feature vector of the thermal deformation variation curve; The processing parameters of the workpiece within the future preset time period are extracted to obtain the processing feature vector of the processing parameters; The time-series feature vector and the processing feature vector are input into the long short-term memory fusion prediction model for prediction, so as to obtain the future thermal deformation of the machine tool structural component and the processed workpiece in the future preset time period; The compensation calculation unit is used to generate tool servo axis compensation, spindle hydraulic preload compensation, and cutting fluid temperature control compensation based on the future thermal deformation of the machine tool structural component and the future thermal deformation of the workpiece. Specifically, it includes mapping the component of the future thermal deformation that corresponds to the relative displacement of the tool to the tool servo axis compensation. The component of the future thermal deformation corresponding to the axial elongation of the spindle is converted into the hydraulic preload compensation of the spindle. The component of the future thermal deformation that corresponds to the temperature rise of the workpiece surface is converted into the cutting fluid temperature control compensation amount. The compensation unit generates a compensation program based on the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount. Specifically, it includes prioritizing and synchronizing the tool servo axis compensation amount, the spindle hydraulic preload compensation amount, and the cutting fluid temperature control compensation amount to obtain a synchronous compensation instruction sequence. According to the synchronous compensation instruction sequence, the compensation amount of the tool servo axis, the compensation amount of the spindle hydraulic preload, and the compensation amount of the cutting fluid temperature control are discretized at the interpolation cycle level to form a discrete compensation value sequence corresponding to the interpolation cycle of the CNC system. The discrete compensation value sequence is encapsulated into a macro variable to generate the compensation program; and the compensation program is inserted into the machining program of the workpiece to complete real-time compensation.

7. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the turning process ambient temperature compensation method as described in any one of claims 1-5 when executing the computer program.

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