Blade five-axis machining center thermal stability active compensation method based on multi-source information fusion

Abstract

The invention discloses a blade five-axis machining center thermal stability active compensation method based on multi-source information fusion, and relates to the technical field of blade five-axis machining center precision control. Parameters are collected, a multi-source dynamic heat generation model is constructed to obtain heat generation characteristics under different heat working conditions and different power levels, then the heat generation characteristics and heat errors are correlated, a data set is integrated, then the fusion weight of multi-source heat information is calculated, the weight is adjusted according to the precision and stability of the machining process, and the multi-source dynamic heat generation characteristics are obtained. Testing parameters of the blade five-axis machining center are confirmed to achieve thermal error compensation, and finally the compensation effect is verified, adjusted and optimized. According to the scheme, by fusing the multi-source heat source information and the multi-dimensional monitoring data, active regulation and control of the heat generation characteristic and the thermal error are achieved, and the blank of multi-source thermal modeling and active compensation in the prior art is filled.

Description

Technical Field

[0001] This invention relates to the field of precision control technology for five-axis machining centers for blades, specifically to an active compensation method for thermal stability of five-axis machining centers for blades based on multi-source information fusion. Background Technology

[0002] As a core component in high-end equipment fields such as aerospace and energy power, blades have complex shapes and extremely high precision requirements, necessitating precision machining using a five-axis blade machining center. During prolonged high-speed, light-cutting processes, the machine tool contains internal heat sources such as spindle rotation friction and feed system transmission losses. Simultaneously, it is affected by environmental thermal loads such as ambient temperature fluctuations and changes in cutting fluid circulation temperature. Coupled with the dynamic heat transfer effect of the heat exchange medium, this can easily lead to thermal deformation of the machine tool structure, resulting in axial or radial thermal errors in the spindle and thermal errors in feed axis positioning, ultimately affecting the blade machining accuracy.

[0003] Existing technology, such as the invention patent application CN117260485A, discloses a belt sander for polishing blades. The central part is a six-axis robot system, including a six-axis robot, a six-dimensional force sensor, and a universal gripping mechanism, which grasps the blade to complete the sanding work. A blade surface processing system is arranged in front of the robot system, including belt sanders of various grit sizes. A motor drives a drive wheel, combined with an alignment wheel, tension wheel, and support wheel, to ensure high-speed and stable operation of the sanding belt. Next to the blade surface processing system is a blade edge processing system. On the other side is a detection system, including a contact measuring instrument, to complete blade positioning and blade profile dimension detection. The advantages of this invention are: real-time adjustment of the robot's posture based on a grinding force control algorithm to achieve the best sanding effect, and adaptive compensation and precise calibration of the robot's repeatability error. It determines the actual position of the blade, realizes blade positioning, and determines the machining allowance. However, the sanding method has low compliance, making it difficult to achieve high-precision sanding operations for blades with complex profiles.

[0004] As can be seen from the above solutions, the existing thermal error control technology for five-axis blade machining centers has the following shortcomings: First, the heat source analysis only focuses on a single type of heat source and does not construct a multi-source dynamic heat generation model, so it cannot accurately describe the effects of endogenous heat sources, environmental heat loads, and heat exchange media; Second, the thermal error compensation logic cannot accurately match the thermal error sensitivity characteristics of different regions, making it difficult to achieve high-precision machining of five-axis blades; Third, thermal error compensation is mostly passive, only correcting after the error occurs, without achieving active control based on real-time thermal state, making it difficult to cope with transient and time-varying thermal accuracy fluctuations. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the present invention aims to provide an active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion.

[0006] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides an active compensation method for the thermal stability of a five-axis machining center for blades based on multi-source information fusion, including the following steps: S1, identify the endogenous heat source, environmental heat load and heat transfer medium thermal effect of the five-axis machining center for blades, collect multi-source heat source parameters, construct a multi-source dynamic heat generation model, and obtain the heat generation characteristics under different thermal conditions and different power levels.

[0007] S2. Based on the heat generation characteristics under different thermal conditions and power levels, correlate the heat generation characteristics with thermal errors and integrate the dataset.

[0008] S3. Calculate the fusion weight of multi-source thermal information, adjust the weight according to the accuracy and stability of the processing process, and use the adjusted fusion weight of multi-source thermal information to confirm the test parameters of the five-axis machining center of the blade to achieve thermal error compensation.

[0009] S4. The thermal error compensation effect is verified by actual machining test of the prototype of the blade five-axis machining center. At the same time, the test results are used to make adjustments and optimizations to achieve active compensation for thermal stability.

[0010] The beneficial effects of this invention are as follows: 1. This invention provides an active compensation method for the thermal stability of a five-axis machining center for blades based on multi-source information fusion. First, it identifies the endogenous heat sources, environmental heat loads, and thermal effects of the heat exchange medium in the five-axis machining center. Parameters are collected, and a multi-source dynamic heat generation model is constructed to obtain the heat generation characteristics under different thermal conditions and power levels. Then, the heat generation characteristics are correlated with thermal errors, and the dataset is integrated. Next, the fusion weights of the multi-source thermal information are calculated, and the weights are adjusted according to the accuracy and stability of the machining process. The test parameters of the five-axis machining center for blades are confirmed to achieve thermal error compensation. Finally, the compensation effect is verified, and adjustments and optimizations are made. This solution achieves active control of heat generation characteristics and thermal errors by fusing multi-source heat source information and multi-dimensional monitoring data, filling the gap in existing technologies for multi-source thermal modeling and active compensation.

[0011] 2. More accurate multi-source modeling: By incorporating endogenous heat sources, environmental heat loads, and heat exchange media into a unified multi-source dynamic heat generation model, the problem of traditional single heat source modeling being unable to describe complex thermal coupling effects is solved, providing more comprehensive basic data for thermal characteristic analysis.

[0012] 3. More complete technical system: It forms a complete technical system from heat source modeling and active control to error compensation, which can be directly applied to mass production models of five-axis machining centers for blades, and has good prospects for engineering application. At the same time, it provides a reference for thermal precision control of other high-end machine tools. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] See Figure 1 As shown, the active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion includes the following steps: S1, identifying the endogenous heat source, environmental heat load and heat transfer medium thermal effect of the five-axis machining center for blades, collecting multi-source heat source parameters, constructing a multi-source dynamic heat generation model, and obtaining the heat generation characteristics under different thermal conditions and different power levels.

[0017] In a specific embodiment, the specific process of S1 is as follows: classify and identify the sources of heat in the five-axis machining center of the blade to obtain three types of thermal effects: endogenous heat source, environmental heat load, and heat transfer medium heat effect.

[0018] Multi-source heat source parameters are collected through corresponding sensing devices. These parameters include parameters related to endogenous heat sources, parameters related to environmental heat load, and parameters related to the thermal effect of the heat exchange medium.

[0019] Parameters related to the endogenous heat source are collected through temperature monitoring equipment, parameters related to the environmental heat load are collected through environmental monitoring equipment, and parameters related to the thermal effect of the heat exchange medium are collected through medium parameter monitoring equipment.

[0020] By constructing a multi-source dynamic heat generation model, the heat generation laws of the machine tool and key components under different thermal conditions and power levels are analyzed, and the corresponding heat generation characteristics are output.

[0021] Preferably, the specific process of constructing the multi-source dynamic heat generation model is as follows: based on the basic principles of thermodynamics and combined with the structural characteristics of the machine tool and its key components, a correlation mapping between the input multi-source thermal parameters and the heat generation characteristics of the machine tool is established. By associating different thermal condition parameters and different power level parameters, the heat generation characteristics of the machine tool and its key components are extracted and output in a standardized format, thus forming a multi-source dynamic heat generation model that can accurately describe the mapping relationship between thermal parameters and the heat generation characteristics of the machine tool.

[0022] Preferably, the specific process of analyzing the heat generation laws of the machine tool and key components under different thermal conditions and power levels, and outputting the corresponding heat generation characteristics, is as follows: inputting different thermal condition parameters and different power level parameters into the constructed multi-source dynamic heat generation model, and outputting the instantaneous thermal state data of the machine tool and key components under different thermal conditions and power level analysis scenarios.

[0023] Based on the instantaneous thermal state data output by the multi-source dynamic heat generation model, the heat generation law is analyzed, the extracted heat generation law is transformed into standardized heat generation characteristic parameters, and the heat generation characteristics under different thermal conditions and different power levels are compared to obtain the heat generation characteristic dataset of the multi-source dynamic heat generation model.

[0024] It should be noted that thermal conditions refer to the thermal-related operating states during machine tool processing, such as the parameters of rapid traverse and slow cutting of the feed axis.

[0025] Power level: The power output status of each energy-consuming component of the machine tool, such as the power output value of the spindle motor and the load power value of the feed motor, directly determines the heat generation intensity of the heat source inside the machine tool and is a key input for calculating the heat generation characteristics of the multi-source dynamic heat generation model.

[0026] Multi-source dynamic heat generation model: It consists of three types of thermal effects: endogenous heat source, environmental heat load and heat transfer medium thermal effect. By inputting different thermal condition parameters and power level parameters, it can dynamically describe the heat generation law of the whole machine tool and key components and output the corresponding heat generation characteristics model.

[0027] Instantaneous thermal state data: refers to the specific quantitative data output by the multi-source dynamic heat generation model at a specific moment, reflecting the thermal state of the machine tool and its key components, including the instantaneous temperature value and instantaneous heat generation power of key components.

[0028] S2. Based on the heat generation characteristics under different thermal conditions and power levels, correlate the heat generation characteristics with thermal errors and integrate the dataset.

[0029] In a specific embodiment, the specific process of S2 is as follows: extract the heat generation characteristics under different thermal conditions and different power levels output by the multi-source dynamic heat generation model, and at the same time, for the key components of the blade five-axis machining center that are sensitive to thermal errors, collect real-time temperature data of each key component through temperature sensing equipment, collect thermal deformation data of each key component through high-precision deformation detection equipment, and collect machine tool operation data through operating condition monitoring equipment to form a multi-dimensional raw dataset including heat generation, temperature, deformation and operation.

[0030] The multi-dimensional raw dataset is preprocessed, and then the mapping relationship between heat generation characteristics and thermal error under different thermal conditions and power levels is established. The heat generation characteristic parameters and thermal error parameters under different thermal conditions and power levels are associated to form corresponding parameter groups, resulting in a thermal scenario corresponding to each set of data, and finally forming a standardized integrated dataset.

[0031] Preferably, the specific process of establishing the mapping relationship between heat generation characteristics and thermal error under different thermal conditions and power levels is as follows: for datasets with different thermal conditions and power levels, based on the correspondence between heat generation characteristic parameters and thermal error parameters, a preliminary mapping relationship between heat generation characteristics and thermal error under different thermal conditions and power levels is established, and the influence weights of different thermal conditions and power levels on the mapping relationship are obtained.

[0032] The initially established mapping relationship is applied to different combinations of thermal conditions and power levels. The thermal error data output by the mapping relationship is compared with the actual thermal error data collected. If the thermal error data exceeds the preset thermal error data threshold, the weight of the mapping relationship needs to be adjusted to finally form an accurate thermal generation characteristic and thermal error mapping model.

[0033] It should be noted that the heat generation characteristic parameters refer to the standardized quantitative parameters output by the multi-source dynamic heat generation model that describe the heat generation law of the machine tool, including the spatial distribution of heat generation power and heat accumulation rate of the whole machine tool and key components.

[0034] Thermal error parameters: These are error data that reflect the thermal deformation caused by the blade in the five-axis machining center. They are the core indicators at the output end of the mapping relationship, including the thermal error of the feed axis positioning and the thermal deformation of key components.

[0035] The preset thermal error data threshold is a critical value used to determine whether the thermal error is acceptable. It is set by professionals according to adjustment needs, and no specific numerical limit is set here.

[0036] S3. Calculate the fusion weight of multi-source thermal information, adjust the weight according to the accuracy and stability of the processing process, and use the adjusted fusion weight of multi-source thermal information to confirm the test parameters of the five-axis machining center of the blade to achieve thermal error compensation.

[0037] In a specific embodiment, the specific process of S3 is as follows: extracting heat generation characteristic data, heat generation characteristic-thermal error correlation data, and multi-dimensional raw datasets collected in real time during the processing under different thermal conditions and power levels to form the basic dataset for weight calculation.

[0038] During the processing, workpiece processing accuracy data and thermal error fluctuation data are collected in real time, and the thermal error compensation of multi-source thermal information is analyzed, and weights are assigned to different types of thermal information.

[0039] If the processing accuracy corresponding to a certain type of thermal information is less than the preset processing accuracy threshold and the thermal error fluctuation is greater than the preset thermal error fluctuation threshold, then the weight of that type of thermal information is reduced; if the processing accuracy corresponding to a certain type of thermal information is greater than or equal to the preset processing accuracy threshold and the thermal error fluctuation is less than or equal to the preset thermal error fluctuation threshold, then the weight of that type of thermal information is increased, thereby realizing the dynamic adjustment of the weights of different types of thermal information.

[0040] Based on the dynamically adjusted weights of the multi-source thermal information fusion, thermal information with a correlation coefficient greater than the preset correlation threshold is selected first to determine the thermal characteristic test parameters of the five-axis machining center for the blade, thereby achieving thermal error compensation.

[0041] It should be noted that the preset machining accuracy threshold is a critical value used to determine whether the machining accuracy of the blade is qualified. It is set by professionals according to adjustment needs, and no specific numerical limit is set here.

[0042] The preset thermal error fluctuation threshold is a critical value used to determine whether the thermal error fluctuation is acceptable. It is set by professionals according to adjustment needs, and no specific numerical limit is set here.

[0043] The preset correlation threshold is a critical value used to determine whether the correlation is qualified. It is set by professionals according to adjustment needs, and no specific numerical limit is set here.

[0044] Preferably, the specific process of achieving thermal error compensation is as follows: based on the adjusted fusion weights and confirmed test parameters, a mapping relationship is established between thermal error and compensation instructions for the entire machine tool and key components, and compensation instructions are generated and issued in real time; at the same time, the processing accuracy and thermal error fluctuations after compensation are monitored in real time. If the processing accuracy or thermal error fluctuations corresponding to a certain type of thermal information deviate from the corresponding preset threshold, the compensation instructions are fine-tuned again based on the fusion results of multi-source thermal information, and finally thermal error compensation is achieved.

[0045] It should be noted that the corresponding preset thresholds refer to two types of critical values ​​that are set in advance based on the precision machining requirements of the blades and the thermal error control targets of the machine tool, namely the preset machining accuracy threshold and the preset thermal error fluctuation threshold.

[0046] S4. The thermal error compensation effect is verified by actual machining test of the prototype of the blade five-axis machining center. At the same time, the test results are used to make adjustments and optimizations to achieve active compensation for thermal stability.

[0047] In a specific embodiment, the specific process of S4 is as follows: based on the typical machining scenario of the blade five-axis machining center, obtain the core conditions of the long-term high-speed light cutting experiment, and set the duration, speed and cutting depth of the experiment, and obtain the temperature, deformation data of the key components of the machine tool and the machining accuracy data of the blade during the experiment.

[0048] Active control and compensation functions were activated on the five-axis blade machining center, and experiments were conducted according to the set long-term high-speed light cutting scheme. During the experiment, the temperature and deformation data of key machine tool components and the operating parameters during the machining process were collected in real time. After the experiment, the accuracy data of the machined blade specimens were collected.

[0049] Based on the collected experimental data, the machining accuracy of the blade specimens was tested, and the compensation effect was determined to be qualified.

[0050] If the thermal error fluctuation is less than or equal to the preset thermal error fluctuation threshold and the blade precision is greater than or equal to the preset blade machining precision threshold, the compensation effect is deemed qualified; if at least one of the following conditions is met: the thermal error fluctuation is greater than the preset thermal error fluctuation threshold and the blade precision is less than the preset blade machining precision threshold, the compensation effect is deemed unqualified and testing, adjustment and optimization are required.

[0051] Preferably, the specific process of testing, adjusting, and optimizing is as follows: based on the temperature and deformation data of key machine tool parts and the blade machining accuracy data collected experimentally, the causes of parameter deviations are located and targeted corrections are made: If the thermal error fluctuation exceeds the preset thermal error fluctuation threshold, the multi-source dynamic heat generation model is specifically modified based on the temperature and deformation data of the key machine tool components collected from the long-term high-speed light cutting experiment. The adjusted thermal error parameters are then applied to the key machine tool components and the experiment is repeated until the thermal error fluctuation drops to within the preset thermal error fluctuation threshold.

[0052] If the blade accuracy is less than the preset blade machining accuracy threshold, the correlation between thermal error and blade accuracy data is corrected by combining the blade accuracy data with the machine tool thermal error data of the corresponding machining period. The adjusted blade accuracy parameters are then applied to key machine tool components and the experiment is repeated until the blade accuracy reaches the preset blade machining accuracy threshold.

[0053] The adjusted thermal error parameters and blade accuracy parameters are applied to the blade five-axis machining center, and long-term high-speed light cutting experiments are repeated. Data is collected again and the compensation effect is verified. This process continues until the machine tool thermal error fluctuation is less than or equal to the preset thermal error fluctuation threshold and the blade accuracy is greater than or equal to the preset blade machining accuracy threshold, thus achieving active compensation for thermal stability.

[0054] The examples described in this invention are not limited to the specific embodiments listed above. The examples are merely illustrative to facilitate understanding of the invention and do not constitute a limitation on the scope of protection of this invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of this invention should be included within the scope of protection.

[0055] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for active compensation of thermal stability in a five-axis machining center for blades based on multi-source information fusion, characterized in that, Includes the following steps: S1. Identify the endogenous heat source, environmental heat load and heat transfer medium thermal effect of the five-axis machining center of the blade, collect multi-source heat source parameters, construct a multi-source dynamic heat generation model, and obtain the heat generation characteristics under different thermal conditions and different power levels. S2. Based on the heat generation characteristics under different thermal conditions and power levels, correlate the heat generation characteristics with thermal errors and integrate the dataset; S3. Calculate the fusion weight of multi-source thermal information, adjust the weight according to the accuracy and stability of the processing process, and use the adjusted fusion weight of multi-source thermal information to confirm the test parameters of the five-axis machining center of the blade to achieve thermal error compensation. S4. The thermal error compensation effect is verified by actual machining test of the prototype of the blade five-axis machining center. At the same time, the test results are used to make adjustments and optimizations to achieve active compensation for thermal stability.

2. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion as described in claim 1, characterized in that, The specific process of S1 is as follows: The sources of heat effects in the five-axis machining center of blades were classified and identified into three categories: endogenous heat sources, environmental heat loads, and heat effects of heat transfer media. Multi-source heat source parameters are collected through corresponding sensing devices. These parameters include parameters related to endogenous heat sources, parameters related to environmental heat load, and parameters related to the thermal effect of heat exchange medium. Parameters related to the endogenous heat source are collected through temperature monitoring equipment, parameters related to the environmental heat load are collected through environmental monitoring equipment, and parameters related to the thermal effect of the heat exchange medium are collected through medium parameter monitoring equipment. By constructing a multi-source dynamic heat generation model, the heat generation laws of the machine tool and key components under different thermal conditions and power levels are analyzed, and the corresponding heat generation characteristics are output.

3. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion as described in claim 2, characterized in that, The specific process for constructing the multi-source dynamic heat generation model is as follows: Based on the fundamental principles of thermodynamics and combined with the structural characteristics of the machine tool and its key components, a correlation mapping between the input multi-source thermal parameters and the heat generation characteristics of the machine tool is established. By associating different thermal operating parameters and different power level parameters, the heat generation characteristics of the machine tool and its key components are extracted and output in a standardized format, ultimately forming a multi-source dynamic heat generation model that can accurately describe the mapping relationship between thermal parameters and the heat generation characteristics of the machine tool.

4. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion as described in claim 2, characterized in that, The specific process of analyzing the heat generation patterns of the machine tool and its key components under different thermal conditions and power levels, and outputting the corresponding heat generation characteristics, is as follows: By inputting different thermal condition parameters and different power level parameters into the constructed multi-source dynamic heat generation model, the instantaneous thermal state data of the machine tool and key components under different thermal condition and power level analysis scenarios are output. Based on the instantaneous thermal state data output by the multi-source dynamic heat generation model, the heat generation law is analyzed, the extracted heat generation law is transformed into standardized heat generation characteristic parameters, and the heat generation characteristics under different thermal conditions and different power levels are compared to obtain the heat generation characteristic dataset of the multi-source dynamic heat generation model.

5. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion as described in claim 1, characterized in that, The specific process of S2 is as follows: Extract the heat generation characteristics under different thermal conditions and power levels from the output of the multi-source dynamic heat generation model. At the same time, for the key components of the blade five-axis machining center that are sensitive to thermal errors, collect real-time temperature data of each key component through temperature sensing equipment, collect thermal deformation data of each key component through high-precision deformation detection equipment, and collect machine tool operation data through operating condition monitoring equipment to form a multi-dimensional raw dataset including heat generation, temperature, deformation and operation. The multi-dimensional raw dataset is preprocessed, and then the mapping relationship between heat generation characteristics and thermal error under different thermal conditions and power levels is established. The heat generation characteristic parameters and thermal error parameters under different thermal conditions and power levels are associated to form corresponding parameter groups, resulting in a thermal scenario corresponding to each set of data, and finally forming a standardized integrated dataset.

6. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion according to claim 5, characterized in that, The specific process for establishing the mapping relationship between heat generation characteristics and thermal error under different thermal conditions and power levels is as follows: For datasets with different thermal conditions and power levels, based on the correspondence between heat generation characteristic parameters and thermal error parameters, a preliminary mapping relationship between heat generation characteristics and thermal error under different thermal conditions and power levels is established, and the influence weights of different thermal conditions and power levels on the mapping relationship are obtained. The initially established mapping relationship was applied to different thermal conditions and power level combinations, and the thermal error data output by the mapping relationship was compared with the actual collected thermal error data. If the thermal error data exceeds the preset thermal error data threshold, the weights of the mapping relationship need to be adjusted to ultimately form an accurate thermal generation characteristic and thermal error mapping model.

7. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion according to claim 1, characterized in that, The specific process of S3 is as follows: Extract heat generation characteristic data, heat generation characteristic-thermal error correlation data, and multi-dimensional raw datasets collected in real time during the processing under different thermal conditions and power levels to form the basic dataset for weight calculation. During the processing, workpiece processing accuracy data and thermal error fluctuation data are collected in real time, and the thermal error compensation of multi-source thermal information is analyzed to assign weights to different types of thermal information. If the processing accuracy corresponding to a certain type of thermal information is less than the preset processing accuracy threshold and the thermal error fluctuation is greater than the preset thermal error fluctuation threshold, then the weight of that type of thermal information will be reduced. If the processing accuracy corresponding to a certain type of thermal information is greater than or equal to the preset processing accuracy threshold and the thermal error fluctuation is less than or equal to the preset thermal error fluctuation threshold, then the weight of that type of thermal information is increased to achieve dynamic adjustment of the weights of different types of thermal information. Based on the dynamically adjusted weights of the multi-source thermal information fusion, thermal information with a correlation coefficient greater than the preset correlation threshold is selected first to determine the thermal characteristic test parameters of the five-axis machining center for the blade, thereby achieving thermal error compensation.

8. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion according to claim 7, characterized in that, The specific process for achieving thermal error compensation is as follows: Based on the adjusted fusion weights and confirmed test parameters, a mapping relationship is established between thermal error and compensation commands for the entire machine tool and key components. Compensation commands are generated and issued in real time. At the same time, the machining accuracy and thermal error fluctuations after compensation are monitored in real time. If the machining accuracy or thermal error fluctuations corresponding to a certain type of thermal information deviate from the corresponding preset threshold, the compensation commands are fine-tuned again based on the fusion results of multi-source thermal information, and finally thermal error compensation is achieved.

9. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion according to claim 1, characterized in that, The specific process of S4 is as follows: Based on the typical machining scenario of a five-axis machining center for blades, the core conditions for a long-term high-speed light cutting experiment were obtained, and the duration, speed and depth of cut of the experiment were set. During the experiment, the temperature and deformation data of key machine tool components and the machining accuracy data of the blades were obtained. Active control and compensation functions were activated on the five-axis blade machining center, and experiments were conducted according to the set long-term high-speed light cutting scheme. During the experiment, the temperature and deformation data of key machine tool components and the operating parameters during the machining process were collected in real time. After the experiment, the accuracy data of the machined blade specimens were collected. Based on the collected experimental data, the machining accuracy of the blade specimens was tested, and the compensation effect was determined to be qualified. If the thermal error fluctuation is less than or equal to the preset thermal error fluctuation threshold and the blade accuracy is greater than or equal to the preset blade machining accuracy threshold, then the compensation effect is deemed qualified. If at least one of the following conditions is met: thermal error fluctuation is greater than the preset thermal error fluctuation threshold or blade precision is less than the preset blade machining precision threshold, the compensation effect is deemed unqualified and testing, adjustment, and optimization are required.

10. The active compensation method for thermal stability of a five-axis machining center for blades based on multi-source information fusion according to claim 9, characterized in that, The specific process for testing, adjusting, and optimizing is as follows: Based on the temperature and deformation data of key machine tool components and the blade machining accuracy data collected in the experiment, the causes of parameter deviations were identified and corrected accordingly. If the thermal error fluctuation exceeds the preset thermal error fluctuation threshold, the multi-source dynamic heat generation model is specifically modified based on the temperature and deformation data of the key components of the machine tool collected from the long-term high-speed light cutting experiment. The adjusted thermal error parameters are then applied to the key components of the machine tool and the experiment is repeated until the thermal error fluctuation is reduced to within the preset thermal error fluctuation threshold. If the blade accuracy is less than the preset blade machining accuracy threshold, combine the blade accuracy data with the machine tool thermal error data of the corresponding machining period, correct the correlation between thermal error and blade accuracy data, apply the adjusted blade accuracy parameters to the key components of the machine tool and repeat the experiment until the blade accuracy reaches the preset blade machining accuracy threshold. The adjusted thermal error parameters and blade accuracy parameters are applied to the blade five-axis machining center, and long-term high-speed light cutting experiments are repeated. Data is collected again and the compensation effect is verified. This process continues until the machine tool thermal error fluctuation is less than or equal to the preset thermal error fluctuation threshold and the blade accuracy is greater than or equal to the preset blade machining accuracy threshold, thus achieving active compensation for thermal stability.

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