Galvanometer mirror reflective sheet temperature measurement system and method with environmental temperature control and compensation

Abstract

The application provides a galvanometer mirror reflecting sheet temperature measurement system and method with environmental temperature control and compensation, and relates to the technical field of laser processing. The method comprises collecting historical processing data, performing clustering feature analysis on the processing object, and forming object processing temperature control data; obtaining to-be-processed data of the target processing object, combining the object processing temperature control data to perform temperature control compensation analysis, and forming target processing prediction compensation temperature control data; obtaining real-time processing data of the target processing object, combining the target processing prediction compensation temperature control data to perform deviation monitoring analysis, and forming deviation adjustment compensation temperature control data; and performing temperature control compensation processing according to the deviation adjustment compensation temperature control data. The method efficiently and accurately controls the situation that the processing precision is reduced due to the thermal deformation of the galvanometer mirror in the laser processing process through efficient temperature control prediction compensation analysis, and effectively guarantees and improves the processing precision.

Description

Technical Field

[0001] This invention relates to the field of laser processing technology, and more specifically, to a galvanometer reflector temperature measurement system and method with environmental temperature control and compensation. Background Technology

[0002] In the field of high-end manufacturing, laser processing equipment is an important processing technology, characterized by high efficiency and high precision. Laser processing utilizes a galvanometer to focus reflected light for machining. Due to the high and concentrated energy of the laser beam, thermal deformation of the galvanometer is often caused. Therefore, proper temperature monitoring and compensation are crucial compensation methods in the machining process. This method eliminates or compensates for the thermal deformation of the galvanometer reflector caused by its own heating and changes in ambient temperature, thereby stabilizing the laser path and ensuring the long-term accuracy and repeatability of the machining focal point.

[0003] However, the current temperature compensation for galvanometers is mainly based on simulation fitting of the working conditions to establish corresponding predictive temperature control compensation data. This method is time-consuming and resource-intensive, and it has become an important process that restricts the efficiency of high-efficiency laser processing technology. It can be said that the accuracy of simulation prediction analysis determines the rationality and accuracy of temperature control compensation.

[0004] Therefore, designing a temperature measurement system and method for galvanometer reflectors with environmental temperature control and compensation, and using efficient temperature control prediction and compensation analysis to effectively control the reduction in processing accuracy caused by thermal deformation of the galvanometer during laser processing, thereby effectively ensuring and improving processing accuracy, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method for measuring the temperature of a galvanometer reflector with environmental temperature control and compensation. This method extracts feature reference data that can be used for temperature control compensation prediction analysis by clustering historical processing data. It also fully integrates the processing information of the target processing object to form reasonable and effective temperature control compensation prediction data for the processing process. During processing, it combines real-time processing data, especially temperature measurement data, to monitor the initial temperature control compensation prediction data for reasonable temperature control compensation deviations and achieve effective and real-time temperature control compensation adjustment and correction. This effectively ensures the timeliness and accuracy of temperature control compensation during real-time processing, greatly reduces processing deviations caused by galvanometer deformation due to temperature, and effectively improves processing accuracy.

[0006] The present invention also aims to provide a galvanometer reflector temperature measurement system with environmental temperature control and compensation. This system completes all the basic data required for analysis by a data acquisition unit, and then uses a feature extraction unit to obtain the correlation between processing-related parameters and temperature measurement parameters. Based on this correlation, and combined with the data to be processed, a compensation analysis unit generates accurate and effective predictive compensation temperature control data. Real-time monitoring and analysis by a real-time monitoring unit adjusts the predictive compensation temperature data in real time to fully ensure control over galvanometer thermal deformation during processing. Different functional units work together to form a complete temperature measurement system with environmental temperature control and compensation, effectively controlling the reduction in processing accuracy caused by galvanometer thermal deformation during laser processing, thus effectively guaranteeing and improving processing precision.

[0007] In a first aspect, the present invention provides a method for measuring the temperature of a galvanometer reflector with environmental temperature control and compensation, comprising: collecting historical processing data, performing cluster feature analysis on the processing object to form object processing temperature control data; acquiring the processing data of the target processing object, performing temperature control compensation analysis in combination with the object processing temperature control data to form target processing prediction compensation temperature control data; acquiring real-time processing data of the target processing object, performing deviation monitoring analysis in combination with the target processing prediction compensation temperature control data to form deviation adjustment compensation temperature control data; and performing temperature control compensation processing based on the deviation adjustment compensation temperature control data.

[0008] In this invention, the method extracts feature reference data that can be used for temperature control compensation prediction analysis by clustering historical processing data, and fully combines the processing information of the target processing object to form reasonable and effective temperature control compensation prediction data for the processing process. During the processing, the method combines real-time processing data, especially temperature measurement data, to reasonably monitor the temperature control compensation deviation of the initially formed temperature control compensation prediction data, and realizes effective and real-time temperature control compensation adjustment and correction. This effectively ensures the timeliness and accuracy of temperature control compensation during real-time processing, greatly reduces the processing deviation caused by galvanometer deformation due to temperature, and effectively improves the accuracy of processing.

[0009] One possible approach is to collect historical processing data, perform cluster feature analysis on the processed objects, and form object processing temperature control data. This includes: clustering historical data corresponding to processed objects of different materials based on historical processing data to form object processing historical datasets of different material types; extracting features of processing parameters from different object processing historical datasets to form object type processing feature data; and combining object type processing feature data corresponding to different material types to form object processing temperature control data.

[0010] In this invention, the parameters causing thermal deformation of the galvanometer vary significantly depending on the type of material processed by laser processing. Therefore, when extracting features based on historical data, it is necessary to consider clustering according to different material types to ensure the relevance of the clustered feature extraction results and the accuracy of the feature data. Of course, even with the same type of material, different processed objects will have varying effects on the thermal deformation of the galvanometer due to processing conditions such as structural features and size. Therefore, it is necessary to perform correlation feature analysis on the impact of specific processing conditions on the thermal deformation of the galvanometer, thereby obtaining the correlation and synergistic relationship affecting the thermal deformation of the galvanometer under the same material. This provides a more efficient and accurate predictive analysis method for directly utilizing these correlation features to make reasonable temperature control compensation predictions.

[0011] One possible implementation involves extracting features from different object processing history datasets based on processing parameters to form object type processing feature data. This includes: extracting historical processing data from different object processing history datasets according to the processed object to form individual object processing history data corresponding to different processed objects; splitting the individual object processing history data based on processing feature conditions to form object processing feature historical split data corresponding to different processing features; clustering the different object processing feature historical split data corresponding to the same type of processing feature conditions for different processed objects to form object processing feature condition historical clustering data corresponding to different processing feature conditions under the object processing history dataset; performing correlation analysis on the different object processing feature condition historical clustering data based on processing parameters to form corresponding object processing feature condition correlation data; and combining the different object processing feature condition correlation data under the object processing history dataset to form corresponding object type processing feature data.

[0012] In this invention, historical processing data is clustered according to the material type of the processed object, thus resolving the significant impact of material differences on the accuracy of subsequent feature extraction. It is also necessary to consider that even for the same type of processed object made of the same material, differences in structure and size can affect the temperature control of the galvanometer. After all, different processing feature sizes and structures provide significantly different processing conditions, including power, absorption rate, and scanning mode. Therefore, this application considers splitting processed objects of the same material type based on processing feature conditions, and performing reasonable clustering according to the processed feature objects. For example, processing data with all blind holes are clustered together, processing data with all through holes are clustered together, and processing data with all horizontal straight lines are clustered together, etc. The determination of processing feature conditions can be based on the feature identification by the processing center, or it can be further refined based on actual conditions, such as whether the relative coordinate system positions are in the same direction (e.g., distinguishing between horizontal and vertical straight lines), or whether the hole types are the same (e.g., distinguishing between blind holes, through holes, and non-circular holes). Extracting data according to processing feature conditions can form more processing-specific clustered data. It should be noted that even when clustering is performed based on processing feature conditions, there may be differences in the size of the data clustered under the same processing feature conditions. For example, the length of straight lines and the depth and size of holes may vary. However, considering that these size differences are all for the same type of feature, they will not fundamentally cause significant deviations in subsequent correlation analysis. Therefore, it is reasonable and effective to use correlation analysis to cover such variations in clustering analysis.

[0013] As one possible implementation, correlation analysis is performed on historical clustering data of different object processing characteristic conditions for processing parameters to form corresponding object processing characteristic condition correlation data. This includes: extracting size condition parameters, initial processing parameters, process processing parameters, process environment parameters, and process temperature measurement parameters from the historical split data of different object processing characteristic conditions in the cluster set to form corresponding object splitting historical extracted parameter data; and establishing the object processing characteristic condition relationship formula.

[0014] Where m represents the number of different dimensional condition parameters, n represents the number of different initial processing parameters, k represents the number of different process processing parameters, and u represents the number of different process environment parameters. This represents the dimension-dependent factor for the dimension condition parameter numbered m. This represents the initial processing related factor for the initial processing parameter numbered n. This represents the process-related factor for process parameter k. This represents the process environment-related factor for the process environment parameter numbered u. This represents the parameter value for the dimension condition parameter numbered m. This represents the parameter value for the initial processing parameter numbered n. This represents the function that describes the variation of the process parameter for process number k over the entire processing time. This represents the function representing the change of process environment parameter number u over the entire processing time. The function represents the change of temperature measurement parameters during the process; the parameter data of different object splitting history obtained from the historical clustering data of object processing feature conditions are combined with the object processing feature condition relationship to perform analysis based on correlation accuracy, and form the corresponding object processing feature condition correlation formula.

[0015] In this invention, the purpose of correlation analysis is to establish accurate correlation formulas. For processing parameters, the interaction between different parameters leads to the final effect of galvanometer deformation. The interaction between parameters is not a simple independent superposition, but a combined effect. This application extracts the processing parameters that affect the thermal deformation of the galvanometer to reverse the analysis of the correlation formulas and thus characterizes the corresponding correlation under the processing feature conditions. Understandably, since the data is extracted based on processing feature conditions, and the extracted data has a correlation with the processing information of the previous processing feature conditions, obtaining the initial processing parameters is necessary. These initial processing parameters include power, ambient temperature, ambient humidity, air velocity, and measured galvanometer temperature values ​​obtained at adjacent time points before the start. This can be correlated with the types of parameters that need to be collected during the processing to ensure the sufficiency of the types of initial parameters obtained. Furthermore, since processing feature conditions are important factors affecting the thermal deformation of the galvanometer, it is necessary to use them as important parameters for correlation analysis. For example, the size or percentage of the processing feature. For linear processing, it is necessary to pay attention to the total processing length, the cutting thickness, and the positional relationship of the line in the absolute coordinate system. After all, the total processing length and the cutting thickness will affect parameters such as the initial power, and their relationship in the absolute coordinate system will also affect the initial... Other parameters for processing or continuous processing need to be considered, thus the influence of gravity on energy needs to be taken into account. For example, in horizontal and vertical processing, the effect of gravity may be considered in the final part of vertical processing, where the processing power can be appropriately reduced due to the gravity of the material being cut. As processing continues, the processing parameters and environmental parameters are important parameters affecting the thermal deformation of the galvanometer. Unlike dimensional and initial processing parameters, these parameters vary over time, so the role of time parameters needs to be considered. These parameters include, but are not limited to, power, ambient temperature, humidity, and absorptivity. Expressing these parameters as functions of time allows these processing parameters to reflect the galvanometer temperature at each time point throughout the processing, thus providing a reference for the underlying information on the thermal deformation of the galvanometer. Of course, for process temperature measurement parameters, the synergy with the process processing parameters in the time dimension needs to be considered. Since temperature measurement cannot directly obtain the temperature at the point of action but is determined through other locations, there will be a time delay due to the thermal conductivity of the material. Therefore, time-delay data processing is required to obtain matching data in the time dimension to ensure the accuracy of correlation analysis. In addition, the temperature measurement data should be processed data that is not affected by the temperature control unit. The data can be processed by the temperature control data of the temperature control unit and the thermal conductivity of the material.It is important to emphasize that this application provides a correlation formula based on relevant factors for the established correlation relationship. The specific expression of the relationship can also be set according to the actual situation. In particular, for process parameters, it may be a multinomial term about time parameters. In this case, it is necessary to provide the correlation factor corresponding to the degree of the term. During the analysis, the number of terms also needs to be considered to obtain a sufficient number of extraction parameters to ensure complete correlation analysis. Of course, the more terms there are, the higher the accuracy of the correlation relationship, but the greater the difficulty and complexity of the analysis. Therefore, it is necessary to take comprehensive consideration to establish a reasonable correlation relationship that meets the requirements.

[0016] As one possible implementation, the historical extraction parameter data of different object splitting obtained from the historical clustering data of object processing feature conditions is analyzed based on correlation accuracy in combination with the object processing feature condition relationship to form the corresponding object processing feature condition relationship. This includes: determining the total number of unknown constants to be analyzed based on the object processing feature condition relationship; grouping different object splitting historical extraction parameter data into different object splitting historical extraction parameter data groups based on the total number of unknown constants to be analyzed, the total number of size condition parameters, and the total number of initial processing parameters in the different object splitting historical extraction parameter data groups; randomly determining the corresponding number of discrete time points over the entire processing time based on the total number of unknown constants to be analyzed, the total number of size condition parameters, and the total number of initial processing parameters, and extracting discrete parameter values ​​at different discrete time points on different process processing parameter change functions and different process environment parameter change functions; analyzing the object processing feature condition relationship in combination with different size condition parameters and different initial processing parameters for the discrete parameter values ​​extracted at different discrete time points from the different object splitting historical extraction parameter data in the object splitting historical extraction parameter data groups; and if the relationship formed after analysis is... If the difference between the process temperature parameter value determined by the relational formula and the actual process temperature parameter value obtained at any time point does not exceed the analytical deviation threshold, then the analytical relational formula is determined as the single-item processing feature conditional relational formula corresponding to the historical parameter data extracted from object splitting. If the relational formula formed after analysis guarantees that the difference between the process temperature parameter value determined by the relational formula and the actual process temperature parameter value obtained at any time point exceeds the analytical deviation threshold, then the corresponding number of discrete time points are selected non-repeatingly for parameter value extraction and analysis until the relational formula formed after analysis guarantees that the difference between the process temperature parameter value determined by the relational formula and the actual process temperature parameter value obtained at any time point exceeds the analytical deviation threshold, then the analytical relational formula is determined as the single-item processing feature conditional relational formula corresponding to the historical parameter data extracted from object splitting. The different single-item processing feature conditional relational formulas in the historical parameter data group of object splitting are homogenized to form a single-group processing feature conditional relational formula corresponding to the historical parameter data group of object splitting. The single-group processing feature conditional relational formulas corresponding to different historical parameter data groups of object splitting are homogenized to form the object processing feature conditional correlation formula corresponding to the historical clustering data of object processing features.

[0017] In this invention, parsing the relational expression requires consideration of two aspects. Firstly, the historical data breakdown of each object's processing features must ensure consistency between the predicted temperature parameters obtained through the relational expression and the actual measured parameters. This consistency is not absolute; it is defined by a reasonable tolerance range, i.e., a deviation threshold, considering the impact of actual conditions. The magnitude of this deviation threshold is set based on the accuracy of the relational expression. Secondly, it ensures that the relational expression can accurately predict temperature parameters under similar processing feature conditions, satisfying historical data breakdown of different objects under the same processing feature. While there are some differences under similar feature conditions, these differences are small, and the relational expression is averaged to ensure relative stability in predicting data. The averaging process mainly involves obtaining the average value at each time point from the time-dimensional functional relational expression established for different object processing feature historical data breakdown, and then fitting this average value to form a new functional relational expression. Of course, due to the large volume of historical data on object processing features, this application first considers grouping the data according to the amount of data that can just be used to parse the relational formula. After parsing each group and obtaining the corresponding averaged relational formula, the averaged relational formulas of each group are then averaged to form the final correlation formula. The averaging process avoids excessive deviation in temperature prediction for processing conditions with certain characteristics, ensuring the stability and rationality of the prediction data. For the grouping of historical data on object processing features, there may be situations where the remaining data after grouping by quantity is insufficient to form a group. Considering the large volume of data, the remaining data that cannot form a group can be directly filtered out. Alternatively, grouping can be done according to certain patterns, such as grouping based on size condition parameters or grouping based on process temperature measurement data, to more reasonably control the synergistic correlation of each parameter on the temperature measurement results.

[0018] One possible implementation involves acquiring the processing data of the target object, combining it with the object's processing temperature control data for temperature compensation analysis, and forming target processing predictive compensation temperature control data. This includes: splitting the processing data of the target object based on processing characteristic conditions and sorting it according to the processing order to form different ordered processing steps; for different ordered processing steps, determining the corresponding process environment parameter prediction functions based on current environmental parameters and combined with historical clustering data of the corresponding object's processing characteristic conditions; for different ordered processing steps, obtaining the corresponding object processing characteristic condition correlation formulas and the process environment parameter prediction functions corresponding to different process environment parameters for temperature measurement analysis to determine the corresponding process temperature measurement parameter prediction functions; performing compensation analysis based on the process temperature measurement parameter prediction functions corresponding to different ordered processing steps to form corresponding process predictive compensation temperature control functions; and combining the process predictive compensation temperature control functions corresponding to different ordered processing steps to form target processing predictive compensation temperature control data.

[0019] In this invention, the purpose of temperature control compensation analysis is to determine how the temperature control compensation unit of the galvanometer needs to control the cooling to ensure that the cooling effect transmitted to the target area can just offset the temperature rise causing thermal deformation or keep the temperature rise causing thermal deformation within a reasonable range. This application divides the pre-set processing data by splitting the processing data of the target object according to processing characteristic conditions. It should be noted that the processing data is mainly based on CAE processing simulation, including process processing parameters, initial processing parameters, and dimensional condition parameters. Since the processing parameters required to process the target object, especially the process processing parameters, are active parameters, such as the power used for cutting, they are mainly related to the material itself and the equipment itself. Therefore, they can be obtained through simulation analysis before processing. Furthermore, the processing time is also actively determined, so the total processing time for each step can be directly determined. As for the process environment parameters, since only current environmental parameter information can be obtained, reasonable prediction is needed to ensure accuracy. Therefore, the focus is on determining the process environment parameters. This application considers providing reasonable prediction data by analyzing historical data with the same processing characteristic conditions, thereby ensuring the accuracy and rationality of the compensation analysis. After obtaining temperature measurement data based on the correlation formula, since the temperature measurement data is the temperature change data of the target deformation area, and the compensation unit works at the fixed position of the galvanometer and reaches the target area by means of heat transfer of the material, it is necessary to use the temperature measurement data to transform from the target area to the compensation position. This transformation can be achieved in the simplest way through simulation analysis such as flow field analysis. It can be understood that by how much the temperature measurement data increases, the temperature compensation unit should decrease by how much to ensure a reasonable thermal balance to eliminate thermal deformation.

[0020] As one possible implementation, for different ordered processing steps, based on the current environmental parameters and combined with the corresponding historical clustering data of object processing characteristics, a process environment parameter prediction function is determined for different process environment parameters. This includes: extracting process environment parameter features from the historical clustering data of object processing characteristics corresponding to different ordered processing steps in the following way: for different process environment parameters, obtaining the process environment parameter change function of the corresponding split item in the historical splitting data of different object processing characteristics under the historical clustering data of object processing characteristics. 'v' represents the number of the historical split data of different object processing features in the historical clustering data of object processing feature conditions; the standard time consumption of processing feature conditions is set, and the process environment parameter change function of different split items is used. The process is scaled according to the ratio of the time consumed to the standard time consumed under the processing characteristics, resulting in a corresponding decomposed process environment standard function. And according to the standard function of the process environment for splitting items. Determine the corresponding process environmental standard change rate function for the split item. For different process environment parameters, the corresponding process environment standard variation function for all decomposed items is used. The process environment parameters are homogenized to form a prediction function for the rate of change of process environment parameters. Based on the order of data for different ordered processing steps, a prediction function for the rate of change of process environmental parameters is established, with the current environmental parameter as the first priority. The process environmental parameter prediction function is determined by inputting the processing parameter values ​​and sequentially determining the process environmental parameters under different ordered processing steps.

[0021] In this invention, since the prediction of temperature measurement data of the target processing object needs to take into account the influence of environmental parameters, but the environmental parameter value is a changing value over time, it can only be predicted by using historical data to perform reasonable feature extraction, thereby ensuring the accuracy and rationality of the temperature measurement prediction data. Environmental parameters are data that change over time, and for the same processing characteristic conditions, previous processes can influence them, making it impossible to quantify distance parameter values ​​using historical data. However, historical data can be used to extract features of the changing trends under corresponding processing characteristic conditions. Therefore, the change rate feature is extracted using the change function of environmental parameters corresponding to the same processing characteristic conditions in historical data. During extraction, it is considered that even under the same processing characteristic conditions, the processing time may differ due to size differences. Therefore, the time span needs to be standardized to provide a unified basis for analysis. This unified basis for analysis can be achieved by scaling the historical data by setting a standard time consumption. The average value is used as the target for feature extraction to obtain representative feature data of the change rate. Then, using the feature data and the current environmental parameter value as the starting point for quantification, the change function of environmental parameters corresponding to different ordered processing steps is determined according to the processing sequence. It can be understood that the starting value of the environmental parameter value of the subsequent step is the environmental parameter value at the end of the previous step. In this way, the change function of different environmental parameters corresponding to all steps can be obtained. Then, the temperature measurement function can be predicted by using the correlation formula to provide data reference for subsequent temperature compensation.

[0022] One possible approach is to acquire real-time processing data of the target object and combine it with target processing prediction compensation temperature control data for deviation monitoring and analysis to form deviation adjustment compensation temperature control data. This includes: determining all completed process parameters and environmental parameters corresponding to different ordered processing steps based on real-time processing data; determining the actual calculated temperature change information within the processing time based on all completed process parameters and environmental parameters corresponding to different ordered processing steps, combined with all dimensional condition parameters, all initial processing parameters, and the corresponding object processing characteristic condition correlation formulas; and performing real-time deviation analysis based on the actual calculated temperature change information, the corresponding process prediction compensation temperature control function, and the actual temperature measurement information for different ordered processing steps to form deviation adjustment compensation temperature control data.

[0023] In this invention, the main purpose of real-time temperature monitoring during the processing is to determine whether insufficient thermal deformation has occurred in the target area. The analysis and judgment are based on whether the theoretical temperature compensation determined from real-time acquired environmental and processing parameter data differs from the predicted compensation data. It is important to note that while the temperature measurement data obtained using real-time data combined with correlation equations utilizes actual data, the theoretical nature of the correlation equations does not guarantee the actual measured temperature values. However, since the predicted temperature compensation data is related to the correlation equations, it can be used to assess the deviation of the predicted temperature compensation data under the same correlation relationship, providing a reference for temperature compensation control. Of course, the essence of the monitoring and analysis still needs to be guided by the actual temperature measurement results. Since the direct temperature measurement data is comprehensive data after temperature compensation, it needs to be compared with the deviation between the temperature measurement information obtained using actual data combined with correlation equations and the predicted temperature measurement information to accurately determine the effect of the current temperature compensation.

[0024] As one possible implementation, for different ordered processing steps, real-time deviation analysis is performed based on actual calculated temperature change information, the corresponding process prediction compensation temperature control function, and actual temperature measurement information to form deviation adjustment compensation temperature control data. This includes: determining the total cumulative temperature value within the processing time that has occurred for different ordered processing steps based on actual calculated temperature change information. Based on the process prediction compensation temperature control function, the total cumulative target temperature control value within the already occurred processing time is determined. The total actual temperature value during the processing time is determined based on the actual temperature measurement information. Based on the total cumulative temperature value already issued Total cumulative temperature control value of the target already issued and the total actual temperature measurement value Perform the following real-time deviation analysis: If This generates information about controllable deviations, where... Indicates the allowable tolerance threshold; if Then the cumulative deviation is determined. The cumulative deviation is averaged over the remaining processing time to form a deviation constant. This data is then merged into the corresponding process temperature measurement parameter prediction function for further compensation analysis, forming the process deviation adjustment compensation temperature control function. .

[0025] In this invention, the purpose of real-time deviation analysis is to ensure that the temperature compensation of the target area by the temperature control unit keeps the direct temperature measurement value of the target area within a controllable range to avoid unsatisfactory thermal deformation. By comparing the difference between the cumulative total temperature value and the cumulative total temperature control value with the actual total temperature measurement value, it can be determined whether the temperature compensation meets the requirements. The out-of-tolerance threshold is the limiting condition for judgment, and the out-of-tolerance threshold can be set according to the actual situation. If the out-of-tolerance threshold is not exceeded, it is considered that the thermal deformation caused by the galvanometer is still within a controllable deviation range. If the out-of-tolerance threshold is exceeded, adjustment is required. The adjustment method is to average the part exceeding the threshold over the remaining processing time span, that is, to determine the unit average temperature compensation amount of the excess part over the remaining time. The unit average compensation amount is the quotient of the total value of the excess threshold and the remaining processing time. The unit average temperature compensation amount is then superimposed on the originally determined process temperature measurement parameter prediction function, thereby determining the compensation temperature value of the compensation unit in the subsequent remaining processing time through compensation analysis. It is important to note here that the total cumulative temperature control value is not the process prediction compensation temperature control function, but rather the cumulative temperature value converted by the compensation unit to the target area. All other cumulative total values ​​are also for the target area.

[0026] Secondly, the present invention provides a galvanometer reflector temperature measurement system with environmental temperature control and compensation, comprising: a data acquisition unit for acquiring historical processing data, data to be processed, and real-time machining data; a feature extraction unit for performing cluster feature analysis on the historical processing data acquired by the data acquisition unit for the processing object, forming object processing temperature control data; a compensation analysis unit for performing temperature control compensation analysis based on the data to be processed acquired by the data acquisition unit and the object processing temperature control data formed by the feature extraction unit, forming target processing prediction compensation temperature control data; a real-time monitoring unit for performing deviation monitoring analysis based on the real-time processing data acquired by the data acquisition unit and the target processing prediction compensation temperature control data formed by the compensation analysis unit, forming deviation adjustment compensation temperature control data; and an output execution unit for outputting the deviation adjustment compensation temperature control data formed by the real-time monitoring unit.

[0027] In this invention, the system completes all the basic data required for analysis by the direct temperature measurement system through the data acquisition unit. Based on this, the feature extraction unit obtains the correlation between processing-related parameters and temperature measurement parameters. Then, using this correlation, and combining it with the data to be processed, the compensation analysis unit generates accurate and effective predictive compensation temperature control data. The real-time monitoring unit then performs real-time adjustments to the predictive compensation temperature data to fully ensure control over the thermal deformation of the galvanometer during processing. Different functional units work together to form a complete temperature measurement system with environmental temperature control and compensation, effectively controlling the reduction in processing accuracy caused by thermal deformation of the galvanometer during laser processing, thus effectively guaranteeing and improving processing precision.

[0028] The beneficial effects of the galvanometer reflector temperature measurement system and method with environmental temperature control and compensation provided by this invention are as follows:

[0029] This method extracts feature reference data that can be used for temperature control compensation prediction analysis by clustering historical processing data. It also fully combines the processing information of the target processing object to form reasonable and effective temperature control compensation prediction data for the processing process. During the processing, it combines real-time processing data, especially temperature measurement data, to monitor the temperature control compensation deviation of the initially formed temperature control compensation prediction data and realize effective and real-time temperature control compensation adjustment and correction. This effectively ensures the timeliness and accuracy of temperature control compensation during real-time processing, greatly reduces the processing deviation caused by galvanometer deformation due to temperature, and effectively improves the accuracy of processing.

[0030] This system uses a data acquisition unit to collect all the basic data required for analysis by the direct temperature measurement system. Based on this, a feature extraction unit obtains the correlation between processing-related parameters and temperature measurement parameters. Then, using this correlation, combined with the data to be processed, a compensation analysis unit generates accurate and effective predictive compensation temperature control data. Real-time monitoring by the real-time monitoring unit allows for real-time adjustments to the predicted compensation temperature data to fully ensure control over galvanometer thermal deformation during processing. These different functional units work together to form a complete temperature measurement system with environmental temperature control and compensation, effectively controlling the reduction in processing accuracy caused by galvanometer thermal deformation during laser processing, thus effectively guaranteeing and improving processing precision. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating the steps of a galvanometer reflector temperature measurement method with environmental temperature control and compensation provided in an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of a galvanometer reflector temperature measurement system with environmental temperature control and compensation, provided in an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0035] In the field of high-end manufacturing, laser processing equipment is an important processing technology, characterized by high efficiency and high precision. Laser processing utilizes a galvanometer to focus reflected light for machining. Due to the high and concentrated energy of the laser beam, thermal deformation of the galvanometer is often caused. Therefore, proper temperature monitoring and compensation are crucial compensation methods in the machining process. This method eliminates or compensates for the thermal deformation of the galvanometer reflector caused by its own heating and changes in ambient temperature, thereby stabilizing the laser path and ensuring the long-term accuracy and repeatability of the machining focal point.

[0036] However, the current temperature compensation for galvanometers is mainly based on simulation fitting of the working conditions to establish corresponding predictive temperature control compensation data. This method is time-consuming and resource-intensive, and it has become an important process that restricts the efficiency of high-efficiency laser processing technology. It can be said that the accuracy of simulation prediction analysis determines the rationality and accuracy of temperature control compensation.

[0037] refer to Figures 1-2 This invention provides a method for measuring the temperature of a galvanometer reflector with environmental temperature control and compensation. This method extracts feature reference data that can be used for temperature control compensation prediction analysis by clustering historical processing data. It also fully integrates the processing information of the target processing object to form reasonable and effective temperature control compensation prediction data for the processing process. During processing, it combines real-time processing data, especially temperature measurement data, to monitor the initial temperature control compensation prediction data for reasonable temperature control compensation deviations and achieve effective and real-time temperature control compensation adjustment and correction. This effectively ensures the timeliness and accuracy of temperature control compensation during real-time processing, greatly reducing processing deviations caused by galvanometer deformation due to temperature, and effectively improving processing accuracy.

[0038] A method for measuring temperature using a galvanometer reflector with environmental temperature control and compensation specifically includes the following steps:

[0039] S1: Collect historical processing data, perform cluster feature analysis on the processing objects, and form object processing temperature control data.

[0040] Collect historical processing data and perform cluster feature analysis on the processed objects to form object processing temperature control data. This includes: clustering historical data corresponding to processed objects of different materials based on historical processing data to form object processing historical datasets of different material types; extracting features of processing parameters from different object processing historical datasets to form object type processing feature data; and combining object type processing feature data corresponding to different material types to form object processing temperature control data.

[0041] The parameters causing thermal deformation of the galvanometer vary significantly depending on the type of material processed by laser processing. Therefore, when extracting features based on historical data, it is necessary to consider clustering according to different material types to ensure the relevance of the clustered feature extraction results and the accuracy of the feature data. Of course, even with the same type of material, different processed objects will have varying effects on the thermal deformation of the galvanometer due to processing conditions such as structural features and size. Therefore, it is necessary to conduct correlation feature analysis on the impact of specific processing conditions on galvanometer thermal deformation, thereby obtaining the correlation and synergistic relationship affecting galvanometer thermal deformation under the same material. This provides a more efficient and accurate predictive analysis method for subsequently using these correlation features to make reasonable temperature control compensation predictions.

[0042] For different object processing history datasets, feature extraction based on processing parameters is performed to form object type processing feature data. This includes: extracting historical processing data according to the processed object from different object processing history datasets to form individual object processing history data corresponding to different processed objects; splitting the individual object processing history data based on processing feature conditions to form object processing feature historical split data corresponding to different processing features; clustering the different object processing feature historical split data corresponding to the same type of processing feature conditions for different processed objects to form object processing feature condition historical clustering data corresponding to different processing feature conditions under the object processing history dataset; performing correlation analysis based on processing parameters on the different object processing feature condition historical clustering data to form corresponding object processing feature condition correlation data; and combining the different object processing feature condition correlation data under the object processing history dataset to form corresponding object type processing feature data.

[0043] Historical processing data was clustered according to the material type of the processed object, resolving the significant impact of material differences on the accuracy of subsequent feature extraction. It is also necessary to consider that even for the same type of processed object made of the same material, differences in structure and size can affect the temperature control of the galvanometer. After all, different processing feature sizes and structures provide significantly different processing conditions, including power, absorption rate, and scanning mode. Therefore, this application considers splitting processed objects of the same material type based on processing feature conditions, and performing reasonable clustering according to the processed feature objects. For example, processing data with all blind holes are clustered together, processing data with all through holes are clustered together, and processing data with all horizontal straight lines are clustered together. The determination of processing feature conditions can be based on the feature identification of the processing center, or it can be further refined based on actual conditions, such as whether the relative coordinate system positions are in the same direction (e.g., distinguishing between horizontal and vertical straight lines), or whether the hole types are the same (e.g., distinguishing between blind holes, through holes, and non-circular holes). Extracting data based on processing feature conditions can form more processing-specific clustered data. It should be noted that even when clustering is performed based on processing feature conditions, there may be differences in the size of the data clustered under the same processing feature conditions. For example, the length of straight lines and the depth and size of holes may vary. However, considering that these size differences are all for the same type of feature, they will not fundamentally cause significant deviations in subsequent correlation analysis. Therefore, it is reasonable and effective to use correlation analysis to cover such variations in clustering analysis.

[0044] For historical clustering data of different object processing characteristics, correlation analysis is performed on processing parameters to form corresponding object processing characteristic condition correlation data. This includes: extracting size condition parameters, initial processing parameters, process processing parameters, process environment parameters, and process temperature measurement parameters from the historical split data of different object processing characteristics in the clusters, forming corresponding object splitting historical extracted parameter data; and establishing the relationship between object processing characteristic conditions.

[0045] Where m represents the number of different dimensional condition parameters, n represents the number of different initial processing parameters, k represents the number of different process processing parameters, and u represents the number of different process environment parameters. This represents the dimension-dependent factor for the dimension condition parameter numbered m. This represents the initial processing related factor for the initial processing parameter numbered n. This represents the process-related factor for process parameter k. This represents the process environment-related factor for the process environment parameter numbered u. This represents the parameter value for the dimension condition parameter numbered m. This represents the parameter value for the initial processing parameter numbered n. This represents the function that describes the variation of the process parameter for process number k over the entire processing time. This represents the function representing the change of process environment parameter number u over the entire processing time. The function represents the change of temperature measurement parameters during the process; the parameter data of different object splitting history obtained from the historical clustering data of object processing feature conditions are combined with the object processing feature condition relationship to perform analysis based on correlation accuracy, and form the corresponding object processing feature condition correlation formula.

[0046] The purpose of correlation analysis is to establish accurate correlation formulas. For processing parameters, the interaction between different parameters leads to the final effect of galvanometer deformation. The interaction between parameters is not a simple independent superposition, but a combined effect. This application extracts the processing parameters that affect the thermal deformation of the galvanometer to reverse the analysis of the correlation formulas and thus characterizes the corresponding correlation under the processing characteristic conditions. Understandably, since the data is extracted based on processing feature conditions, and the extracted data has a correlation with the processing information of the previous processing feature conditions, obtaining the initial processing parameters is necessary. These initial processing parameters include power, ambient temperature, ambient humidity, air velocity, and measured galvanometer temperature values ​​obtained at adjacent time points before the start. This can be correlated with the types of parameters that need to be collected during the processing to ensure the sufficiency of the types of initial parameters obtained. Furthermore, since processing feature conditions are important factors affecting the thermal deformation of the galvanometer, it is necessary to use them as important parameters for correlation analysis. For example, the size or percentage of the processing feature. For linear processing, it is necessary to pay attention to the total processing length, the cutting thickness, and the positional relationship of the line in the absolute coordinate system. After all, the total processing length and the cutting thickness will affect parameters such as the initial power, and their relationship in the absolute coordinate system will also affect the initial... Other parameters for processing or continuous processing need to be considered, thus the influence of gravity on energy needs to be taken into account. For example, in horizontal and vertical processing, the effect of gravity may be considered in the final part of vertical processing, where the processing power can be appropriately reduced due to the gravity of the material being cut. As processing continues, the processing parameters and environmental parameters are important parameters affecting the thermal deformation of the galvanometer. Unlike dimensional and initial processing parameters, these parameters vary over time, so the role of time parameters needs to be considered. These parameters include, but are not limited to, power, ambient temperature, humidity, and absorptivity. Expressing these parameters as functions of time allows these processing parameters to reflect the galvanometer temperature at each time point throughout the processing, thus providing a reference for the underlying information on the thermal deformation of the galvanometer. Of course, for process temperature measurement parameters, the synergy with the process processing parameters in the time dimension needs to be considered. Since temperature measurement cannot directly obtain the temperature at the point of action but is determined through other locations, there will be a time delay due to the thermal conductivity of the material. Therefore, time-delay data processing is required to obtain matching data in the time dimension to ensure the accuracy of correlation analysis. In addition, the temperature measurement data should be processed data that is not affected by the temperature control unit. The data can be processed by the temperature control data of the temperature control unit and the thermal conductivity of the material.It is important to emphasize that this application provides a correlation formula based on relevant factors for the established correlation relationship. The specific expression of the relationship can also be set according to the actual situation. In particular, for process parameters, it may be a multinomial term about time parameters. In this case, it is necessary to provide the correlation factor corresponding to the degree of the term. During the analysis, the number of terms also needs to be considered to obtain a sufficient number of extraction parameters to ensure complete correlation analysis. Of course, the more terms there are, the higher the accuracy of the correlation relationship, but the greater the difficulty and complexity of the analysis. Therefore, it is necessary to take comprehensive consideration to establish a reasonable correlation relationship that meets the requirements.

[0047] For historical object splitting extraction parameter data obtained from historical clustering data of object processing feature conditions, and combined with the object processing feature condition relationship, a correlation-based analysis is performed to form the corresponding object processing feature condition relationship, including: determining the total number of unknown constants to be analyzed based on the object processing feature condition relationship; grouping different object splitting historical extraction parameter data into different object splitting historical extraction parameter data groups based on the total number of unknown constants to be analyzed, the total number of size condition parameters, and the total number of initial processing parameters; randomly determining a corresponding number of discrete time points over the entire processing time based on the total number of unknown constants to be analyzed, the total number of size condition parameters, and the total number of initial processing parameters, and extracting discrete parameter values ​​at different discrete time points on different process processing parameter change functions and different process environment parameter change functions; and analyzing the object processing feature condition relationship based on different size condition parameters and different initial processing parameters for the discrete parameter values ​​extracted at different discrete time points from different object splitting historical extraction parameter data groups: if the relationship formed after analysis guarantees that at any time... If the difference between the process temperature parameter value determined by the relational formula at any given time point and the actual process temperature parameter value obtained does not exceed the analytical deviation threshold, then the analytical relational formula is determined as the single-item processing feature conditional formula corresponding to the historical parameter data extracted from object splitting. If the relational formula formed after analysis guarantees that the difference between the process temperature parameter value determined by the relational formula and the actual process temperature parameter value obtained at any given time point exceeds the analytical deviation threshold, then the corresponding number of discrete time points are selected non-repeatingly for parameter value extraction and analysis until the relational formula formed after analysis guarantees that the difference between the process temperature parameter value determined by the relational formula and the actual process temperature parameter value obtained at any given time point exceeds the analytical deviation threshold, then the analytical relational formula is determined as the single-item processing feature conditional formula corresponding to the historical parameter data extracted from object splitting. The different single-item processing feature conditional formulas in the historical parameter data group of object splitting are homogenized to form a single-group processing feature conditional formula corresponding to the historical parameter data group of object splitting. The single-group processing feature conditional formulas corresponding to different historical parameter data groups of object splitting are homogenized to form the object processing feature conditional correlation formula corresponding to the historical clustering data of object processing features.

[0048] Analyzing the relational expression requires considering two aspects. Firstly, the historical data breakdown of each object's processing features must ensure consistency between the predicted temperature parameters obtained through the relational expression and the actual measured parameters. This consistency does not mean complete identical values; a reasonable tolerance range, known as the analytical deviation threshold, is used to account for the impact of actual conditions. The magnitude of this threshold is set based on the accuracy of the relational expression. Secondly, the relational expression must be able to accurately predict temperature parameters under similar processing feature conditions, satisfying historical data breakdown of different objects with the same processing feature. While there may be some differences under similar feature conditions, these differences are relatively small. Furthermore, the relational expression is averaged to ensure relative stability in predicting data. The averaging process primarily involves obtaining the average value at each time point from the time-dimensional functional relational expression established for different object processing feature historical data breakdowns, and then fitting this average value to form a new functional relational expression. Of course, due to the large volume of historical data on object processing features, this application first considers grouping the data according to the amount of data that can just be used to parse the relational formula. After parsing each group and obtaining the corresponding averaged relational formula, the averaged relational formulas of each group are then averaged to form the final correlation formula. The averaging process avoids excessive deviation in temperature prediction for processing conditions with certain characteristics, ensuring the stability and rationality of the prediction data. For the grouping of historical data on object processing features, there may be situations where the remaining data after grouping by quantity is insufficient to form a group. Considering the large volume of data, the remaining data that cannot form a group can be directly filtered out. Alternatively, grouping can be done according to certain patterns, such as grouping based on size condition parameters or grouping based on process temperature measurement data, to more reasonably control the synergistic correlation of each parameter on the temperature measurement results.

[0049] S2: Obtain the processing data of the target object, combine it with the processing temperature control data of the object to perform temperature control compensation analysis, and form the target processing predictive compensation temperature control data.

[0050] The process involves acquiring the processing data of the target object, combining it with the object's processing temperature control data for temperature compensation analysis, and forming target processing predictive compensation temperature control data. This includes: splitting the processing data of the target object based on processing characteristic conditions and sorting it according to the processing order to form different ordered processing steps; for different ordered processing steps, determining the corresponding process environment parameter prediction functions based on current environmental parameters and historical clustering data of the corresponding object processing characteristic conditions; for different ordered processing steps, obtaining the corresponding object processing characteristic condition correlation formulas and the process environment parameter prediction functions corresponding to different process environment parameters for temperature measurement analysis to determine the corresponding process temperature measurement parameter prediction functions; performing compensation analysis based on the process temperature measurement parameter prediction functions corresponding to different ordered processing steps to form corresponding process predictive compensation temperature control functions; and combining the process predictive compensation temperature control functions corresponding to different ordered processing steps to form target processing predictive compensation temperature control data.

[0051] The purpose of temperature control compensation analysis is to determine how the temperature control compensation unit of the galvanometer needs to control the cooling to ensure that the cooling effect transmitted to the target area can just offset the temperature rise causing thermal deformation or keep the temperature rise causing thermal deformation within a reasonable range. This application divides the pre-set processing data by splitting the processing data of the target object according to processing characteristic conditions. It should be noted that the processing data is mainly based on CAE processing simulation, including process processing parameters, initial processing parameters, and dimensional condition parameters. Since the processing parameters required to process the target object, especially the process processing parameters, are active parameters, such as the power used for cutting, they are mainly related to the material itself and the equipment itself. Therefore, they can be obtained through simulation analysis before processing. In addition, the processing time is also actively determined, so the total processing time for each process can be directly determined. As for the process environment parameters, since only current environmental parameter information can be obtained, reasonable prediction is needed to ensure accuracy. Therefore, the focus is on determining the process environment parameters. This application considers providing reasonable prediction data by analyzing historical data with the same processing characteristic conditions, thereby ensuring the accuracy and rationality of the compensation analysis. After obtaining temperature measurement data based on the correlation formula, since the temperature measurement data is the temperature change data of the target deformation area, and the compensation unit works at the fixed position of the galvanometer and reaches the target area by means of heat transfer of the material, it is necessary to use the temperature measurement data to transform from the target area to the compensation position. This transformation can be achieved in the simplest way through simulation analysis such as flow field analysis. It can be understood that by how much the temperature measurement data increases, the temperature compensation unit should decrease by how much to ensure a reasonable thermal balance to eliminate thermal deformation.

[0052] For different ordered processing steps, based on the current environmental parameters and combined with the historical clustering data of the corresponding object processing characteristics, process environment parameter prediction functions are determined for different process environment parameters. This includes: extracting process environment parameter features from the historical clustering data of the object processing characteristics corresponding to different ordered processing steps in the following way: for different process environment parameters, obtaining the process environment parameter change functions of the corresponding split items in the historical splitting data of different object processing characteristics under the historical clustering data of the object processing characteristics. 'v' represents the number of the historical split data of different object processing features in the historical clustering data of object processing feature conditions; the standard time consumption of processing feature conditions is set, and the process environment parameter change function of different split items is used. The process is scaled according to the ratio of the time consumed to the standard time consumed under the processing characteristics, resulting in a corresponding decomposed process environment standard function. And according to the standard function of the process environment for splitting items. Determine the corresponding process environmental standard change rate function for the split item. For different process environment parameters, the corresponding process environment standard variation function for all decomposed items is used. The process environment parameters are homogenized to form a prediction function for the rate of change of process environment parameters. Based on the order of data for different ordered processing steps, a prediction function for the rate of change of process environmental parameters is established, with the current environmental parameter as the first priority. The process environmental parameter prediction function is determined by inputting the processing parameter values ​​and sequentially determining the process environmental parameters under different ordered processing steps.

[0053] Predicting temperature data for the target processing object requires considering the influence of environmental parameters. However, environmental parameter values ​​are constantly changing, so prediction can only be achieved by using historical data for reasonable feature extraction, thereby ensuring the accuracy and rationality of the temperature prediction data. Environmental parameters are data that change over time, and for the same processing characteristic conditions, previous processes can influence them, making it impossible to quantify distance parameter values ​​using historical data. However, historical data can be used to extract features of the changing trends under corresponding processing characteristic conditions. Therefore, the change rate feature is extracted using the change function of environmental parameters corresponding to the same processing characteristic conditions in historical data. During extraction, it is considered that even under the same processing characteristic conditions, the processing time may differ due to size differences. Therefore, the time span needs to be standardized to provide a unified basis for analysis. This unified basis for analysis can be achieved by scaling the historical data by setting a standard time consumption. The average value is used as the target for feature extraction to obtain representative feature data of the change rate. Then, using the feature data and the current environmental parameter value as the starting point for quantification, the change function of environmental parameters corresponding to different ordered processing steps is determined according to the processing sequence. It can be understood that the starting value of the environmental parameter value of the subsequent step is the environmental parameter value at the end of the previous step. In this way, the change function of different environmental parameters corresponding to all steps can be obtained. Then, the temperature measurement function can be predicted by using the correlation formula to provide data reference for subsequent temperature compensation.

[0054] S3: Obtain real-time processing data of the target processing object, combine it with the target processing prediction compensation temperature control data to perform deviation monitoring and analysis, and form deviation adjustment compensation temperature control data.

[0055] Real-time processing data of the target object is acquired, and deviation monitoring and analysis are performed in conjunction with the target processing prediction and compensation temperature control data to form deviation adjustment and compensation temperature control data. This includes: determining all completed process parameters and environmental parameters corresponding to different ordered processing steps based on the real-time processing data; determining the actual calculated temperature change information within the processing time based on all completed process parameters and environmental parameters corresponding to different ordered processing steps, combined with all dimensional condition parameters, all initial processing parameters, and the corresponding object processing characteristic condition correlation formulas; and performing real-time deviation analysis based on the actual calculated temperature change information, the corresponding process prediction and compensation temperature control function, and the actual temperature measurement information for different ordered processing steps to form deviation adjustment and compensation temperature control data.

[0056] The main purpose of real-time temperature monitoring during the processing is to determine whether insufficient thermal deformation has occurred in the target area. The analysis and judgment are based on whether the theoretical temperature compensation determined from real-time acquired environmental and processing parameter data differs from the predicted compensation data. It's important to note that while temperature measurement data obtained using real-time data combined with correlation equations utilizes actual data, the theoretical nature of the correlation equations does not guarantee the actual measured temperature values. However, since the predicted temperature compensation data is related to the correlation equations, it can be used to assess the deviation of the predicted temperature compensation data under the same correlation, providing a reference for temperature compensation control. Of course, the essence of monitoring and analysis still needs to be guided by actual temperature measurement results. Since direct temperature measurement data is comprehensive data after temperature compensation, it needs to be compared with the deviation between the temperature measurement information obtained using actual data combined with correlation equations and the predicted temperature measurement information to accurately determine the effectiveness of the current temperature compensation.

[0057] For different ordered processing steps, real-time deviation analysis is performed based on actual calculated temperature change information, the corresponding process prediction compensation temperature control function, and actual temperature measurement information to generate deviation adjustment compensation temperature control data. This includes determining the total cumulative temperature value within the processing time that has occurred, based on actual calculated temperature change information, for different ordered processing steps. Based on the process prediction compensation temperature control function, the total cumulative target temperature control value within the already occurred processing time is determined. The total actual temperature value during the processing time is determined based on the actual temperature measurement information. Based on the total cumulative temperature value already issued Total cumulative temperature control value of the target already issued and the total actual temperature measurement value Perform the following real-time deviation analysis: If This generates information about controllable deviations, where... Indicates the allowable tolerance threshold; if Then the cumulative deviation is determined. The cumulative deviation is averaged over the remaining processing time to form a deviation constant. This data is then merged into the corresponding process temperature measurement parameter prediction function for further compensation analysis, forming the process deviation adjustment compensation temperature control function. .

[0058] The purpose of real-time deviation analysis is to ensure that the temperature compensation of the temperature control unit for the target area keeps the direct temperature measurement value of the target area within a controllable range to avoid unsatisfactory thermal deformation. By comparing the difference between the cumulative total temperature value and the cumulative total temperature control value with the actual total temperature measurement value, it can be determined whether the temperature compensation meets the requirements. The out-of-tolerance threshold is the limiting condition for judgment, and this threshold can be set according to actual conditions. If the out-of-tolerance threshold is not exceeded, it is considered that the deviation is still within the controllable deviation range for thermal deformation caused by the galvanometer. If the out-of-tolerance threshold is exceeded, adjustment is required. The adjustment method is to average the portion exceeding the threshold over the remaining processing time, i.e., determine the unit average temperature compensation amount over the remaining time. This unit average compensation amount is the quotient of the total value exceeding the threshold and the remaining processing time. This unit average temperature compensation amount is then superimposed onto the originally determined process temperature measurement parameter prediction function. Through compensation analysis, the compensation temperature value of the compensation unit in the subsequent remaining processing time is determined. It is important to note here that the total cumulative temperature control value is not the process prediction compensation temperature control function, but rather the cumulative temperature value converted by the compensation unit to the target area. All other cumulative total values ​​are also for the target area.

[0059] S4: Perform temperature control compensation processing based on the deviation adjustment and compensation temperature control data.

[0060] Once the adjusted temperature control compensation function is determined, its output can be used as the basis data for temperature control compensation to achieve reasonable and accurate real-time temperature control compensation adjustment.

[0061] This invention also provides a galvanometer reflector temperature measurement system with environmental temperature control and compensation. The system includes: a data acquisition unit for acquiring historical processing data, data to be processed, and real-time machining data; a feature extraction unit for performing cluster feature analysis on the historical processing data acquired by the data acquisition unit to form object processing temperature control data; a compensation analysis unit for performing temperature control compensation analysis based on the data to be processed acquired by the data acquisition unit and the object processing temperature control data formed by the feature extraction unit, forming target processing prediction compensation temperature control data; a real-time monitoring unit for performing deviation monitoring analysis based on the real-time processing data acquired by the data acquisition unit and the target processing prediction compensation temperature control data formed by the compensation analysis unit, forming deviation adjustment compensation temperature control data; and an output execution unit for outputting the deviation adjustment compensation temperature control data formed by the real-time monitoring unit.

[0062] This system uses a data acquisition unit to collect all the basic data required for analysis by the direct temperature measurement system. Based on this, a feature extraction unit obtains the correlation between processing-related parameters and temperature measurement parameters. Then, using this correlation, combined with the data to be processed, a compensation analysis unit generates accurate and effective predictive compensation temperature control data. Real-time monitoring by the real-time monitoring unit allows for real-time adjustments to the predicted compensation temperature data to fully ensure control over galvanometer thermal deformation during processing. These different functional units work together to form a complete temperature measurement system with environmental temperature control and compensation, effectively controlling the reduction in processing accuracy caused by galvanometer thermal deformation during laser processing, thus effectively guaranteeing and improving processing precision.

[0063] In summary, the beneficial effects of the galvanometer reflector temperature measurement system and method with environmental temperature control and compensation provided by the embodiments of the present invention are as follows:

[0064] This method extracts feature reference data that can be used for temperature control compensation prediction analysis by clustering historical processing data. It also fully combines the processing information of the target processing object to form reasonable and effective temperature control compensation prediction data for the processing process. During the processing, it combines real-time processing data, especially temperature measurement data, to monitor the temperature control compensation deviation of the initially formed temperature control compensation prediction data and realize effective and real-time temperature control compensation adjustment and correction. This effectively ensures the timeliness and accuracy of temperature control compensation during real-time processing, greatly reduces the processing deviation caused by galvanometer deformation due to temperature, and effectively improves the accuracy of processing.

[0065] This system uses a data acquisition unit to collect all the basic data required for analysis by the direct temperature measurement system. Based on this, a feature extraction unit obtains the correlation between processing-related parameters and temperature measurement parameters. Then, using this correlation, combined with the data to be processed, a compensation analysis unit generates accurate and effective predictive compensation temperature control data. Real-time monitoring by the real-time monitoring unit allows for real-time adjustments to the predicted compensation temperature data to fully ensure control over galvanometer thermal deformation during processing. These different functional units work together to form a complete temperature measurement system with environmental temperature control and compensation, effectively controlling the reduction in processing accuracy caused by galvanometer thermal deformation during laser processing, thus effectively guaranteeing and improving processing precision.

[0066] In the embodiments of this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In the specific implementation process, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. At the same time, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0067] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be repeated here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In the specific implementation process, the required indication method can be selected according to specific needs. This application embodiment does not limit the selected indication method; therefore, the indication methods involved in this application embodiment should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0068] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this application embodiment. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0069] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or communication device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0070] The “protocol” mentioned in the embodiments of this application may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future communication systems. The embodiments of this application do not specifically limit this.

[0071] In the embodiments of this application, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0072] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0073] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0074] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0075] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0076] It should be understood that, in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0077] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0078] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0082] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for measuring temperature using a galvanometer reflector with environmental temperature control and compensation, characterized in that, include: Collect historical processing data, perform cluster feature analysis on the processed objects, and generate object processing temperature control data; Obtain the processing data of the target object, and perform temperature control compensation analysis in combination with the processing temperature control data of the object to form target processing prediction compensation temperature control data; Real-time processing data of the target processing object is obtained, and deviation monitoring and analysis are performed in combination with the target processing prediction and compensation temperature control data to form deviation adjustment and compensation temperature control data. Temperature control compensation processing is performed based on the temperature control data adjusted according to the aforementioned deviation. The step of acquiring the processing data of the target object, and combining it with the processing temperature control data of the object to perform temperature control compensation analysis to form target processing predictive compensation temperature control data includes: The data to be processed of the target processing object is split according to the processing feature conditions and sorted according to the processing order to form different ordered processing steps data; For different ordered processing steps, based on the current environmental parameters and combined with the historical clustering data of the corresponding object processing characteristics, a process environment parameter prediction function is determined for different process environment parameters. For different ordered processing steps, obtain the corresponding object processing feature condition correlation formula and the process environment parameter prediction function corresponding to different process environment parameters, perform temperature measurement analysis, and determine the corresponding process temperature measurement parameter prediction function; Compensation analysis is performed based on the process temperature measurement parameter prediction function corresponding to different ordered processing steps to form a corresponding process prediction compensation temperature control function; The process prediction compensation temperature control function corresponding to different ordered processing process data is collected to form the target processing prediction compensation temperature control data; The step of acquiring real-time processing data of the target processing object, and combining it with the target processing prediction compensation temperature control data for deviation monitoring and analysis to form deviation adjustment compensation temperature control data includes: Based on the real-time processing data, determine all completed process processing parameter information and completed process environmental parameter information corresponding to different ordered processing steps; For all the completed process processing parameter information and the completed process environmental parameter information corresponding to different ordered processing steps, combined with all dimensional condition parameters, all initial processing parameters and the corresponding object processing characteristic condition correlation formulas, the actual calculated temperature measurement change information within the processing time that has occurred is determined; For different ordered processing steps, real-time deviation analysis is performed based on the actual calculated temperature change information, the corresponding process prediction compensation temperature control function, and the actual temperature measurement information to form deviation adjustment compensation temperature control data.

2. The method for measuring temperature using a galvanometer reflector with environmental temperature control and compensation according to claim 1, characterized in that, The collected historical processing data is used to perform cluster feature analysis on the processed objects to form object processing temperature control data, including: Based on the historical processing data, the historical data corresponding to the processing objects of different materials are clustered to form historical processing datasets of objects of different material types; For different historical datasets of object processing, feature extraction is performed based on processing parameters to form object type processing feature data; The processing characteristic data of the object type corresponding to different material types are collected to form the processing temperature control data of the object.

3. The method for measuring temperature using a galvanometer reflector with environmental temperature control and compensation according to claim 2, characterized in that, The step of extracting features based on processing parameters from different historical datasets of object processing to form object type processing feature data includes: For different object processing history datasets, historical processing data is extracted according to the processing object to form object-specific historical processing data for different processing objects; The historical processing data of different individual objects are split based on processing feature conditions to form historical processing feature split data of objects corresponding to different processing features; For different processing objects, the historical data of different processing features corresponding to the same type of processing feature conditions are clustered to form historical cluster data of object processing feature conditions corresponding to different processing feature conditions under the object processing historical dataset; For historical clustering data of different object processing feature conditions, a correlation analysis is performed on the processing parameters to form corresponding object processing feature condition correlation data; The correlation data of different object processing feature conditions under the object processing historical dataset are collected to form the corresponding object type processing feature data.

4. The method for measuring temperature using a galvanometer reflector with environmental temperature control and compensation according to claim 3, characterized in that, The process of performing correlation analysis on historical clustering data for different object processing feature conditions to form corresponding object processing feature condition correlation data includes: For historical clustering data with different processing characteristics of the objects, extract the size condition parameters, initial processing parameters, process processing parameters, process environment parameters, and process temperature measurement parameters of the historical split data of different processing characteristics of the objects in the cluster set to form the corresponding object splitting historical extraction parameter data; Establish the conditional relationship of object processing characteristics: Where m represents the number of different dimensional condition parameters, n represents the number of different initial processing parameters, k represents the number of different process processing parameters, and u represents the number of different process environment parameters. This represents the dimension-dependent factor for the dimension condition parameter numbered m. This represents the initial processing related factor for the initial processing parameter numbered n. This represents the process-related factor for process parameter k. This represents the process environment-related factor for the process environment parameter numbered u. This represents the parameter value for the dimension condition parameter numbered m. This represents the parameter value for the initial processing parameter numbered n. This represents the function that describes the variation of the process parameter for process number k over the entire processing time. This represents the function representing the change of process environment parameter number u over the entire processing time. This represents a function representing the change in process temperature parameters; Different object splitting historical extraction parameter data obtained from the historical clustering data of the object processing feature conditions are analyzed based on correlation accuracy in combination with the object processing feature condition relationship to form the corresponding object processing feature condition correlation.

5. The method for measuring temperature using a galvanometer reflector with environmental temperature control and compensation according to claim 4, characterized in that, The different object splitting historical extraction parameter data obtained from the historical clustering data of the object processing feature conditions are combined with the object processing feature condition relationship to perform correlation-based analysis, forming the corresponding object processing feature condition relationship, including: Based on the object processing characteristic condition relationship, the total number of unknown constants to be analyzed is determined; Based on the total number of unknown constants to be parsed, the historical extracted parameter data of different objects are divided into groups to form different object splitting historical extracted parameter data groups; For different object splitting history extraction parameter data in different object splitting history extraction parameter data groups, according to the total number of unknown constants to be parsed, the total number of size condition parameters, and the total number of initial processing parameters, a corresponding number of discrete time points are randomly determined over the entire processing time, and discrete parameter values ​​at different discrete time points are extracted on different process processing parameter change functions and different process environment parameter change functions respectively. The discrete parameter values ​​extracted from different historical extraction parameter data of the object splitting at different discrete time points in the object splitting historical extraction parameter data group are analyzed in combination with different size condition parameters and different initial processing parameters to parse the object processing feature condition relationship: If the relational expression formed after parsing ensures that the difference between the process temperature measurement parameter value determined by the relational expression and the actual obtained process temperature measurement parameter value at any point in time does not exceed the parsing deviation threshold, then the parsed relational expression is determined as the single processing feature conditional relational expression corresponding to the historical extracted parameter data of the object splitting. If the relational expression formed after parsing guarantees that the difference between the process temperature measurement parameter value determined by the relational expression and the actual process temperature measurement parameter value obtained at any time point exceeds the parsing deviation threshold, then the corresponding number of discrete time points will continue to be selected non-repeatingly for parameter value extraction and parsing until the relational expression formed after parsing guarantees that the difference between the process temperature measurement parameter value determined by the relational expression and the actual process temperature measurement parameter value obtained at any time point exceeds the parsing deviation threshold. Then the parsed relational expression will be determined as the single processing feature condition relational expression corresponding to the historical extracted parameter data of the object. The different single processing feature conditional relationships in the object splitting historical extraction parameter data group are averaged to form a single set of processing feature conditional relationships corresponding to the object splitting historical extraction parameter data group. The processing feature condition correlation formulas corresponding to the single-group processing feature condition data groups extracted from the historical data of different objects are averaged to form the object processing feature condition correlation formulas corresponding to the historical clustering data of the object processing feature conditions.

6. The method for measuring temperature using a galvanometer reflector with environmental temperature control and compensation according to claim 1, characterized in that, The process environment parameter prediction function, which determines different process environment parameters based on current environmental parameters and combined with historical clustering data of the corresponding object processing characteristics, for different ordered processing steps, includes: For the historical clustering data of the object processing feature conditions corresponding to different ordered processing steps, the following process environment parameter feature extraction is performed: For different process environment parameters, obtain the process environment parameter change function of the corresponding splitting item in the historical splitting data of different object processing features under the historical clustering data of the object processing feature conditions. v represents the number of different historical split data of the object processing features in the historical clustering data of the object processing features; Set the standard time consumption for processing characteristic conditions, and change the environmental parameters of different split items as functions. The process is scaled according to the ratio of the time consumed to the standard time consumed under the aforementioned processing characteristics, forming a corresponding decomposed item process environment standard function. And according to the standard function of the process environment of the split item. Determine the corresponding process environmental standard change rate function for the split item. ; For different process environment parameters, according to the corresponding process environment standard variation function for all the decomposed items. A homogenization process is performed to generate a prediction function for the rate of change of the process environment parameters corresponding to the process environment parameters. ; Based on the order of the ordered processing steps data, a prediction function for the rate of change of the process environmental parameters corresponding to the ordered processing steps data with the current environmental parameters as the first order is used. The processing parameter values ​​are input, and process environment parameter prediction functions for different process environment parameters under the ordered processing steps data of different sequences are determined sequentially.

7. A galvanometer reflector temperature measurement system with environmental temperature control and compensation, employing the galvanometer reflector temperature measurement method with environmental temperature control and compensation as described in any one of claims 1-6, characterized in that, include: The data acquisition unit is used to collect historical processing data, data to be processed, and real-time machining data. The feature extraction unit is used to perform cluster feature analysis on the historical processing data acquired by the data acquisition unit for the processing object, and form object processing temperature control data; The compensation analysis unit is used to perform temperature control compensation analysis based on the data to be processed collected by the data acquisition unit and the object processing temperature control data formed by the feature extraction unit, so as to form target processing prediction compensation temperature control data. The real-time monitoring unit is used to perform deviation monitoring and analysis based on the real-time processing data obtained by the data acquisition unit and the target processing prediction compensation temperature control data formed by the compensation analysis unit, and to form deviation adjustment compensation temperature control data. The output execution unit is used to process the deviation adjustment and compensation temperature control data generated by the real-time monitoring unit.

Images