A fine processing method and device for double-sided CNC milling machine

Abstract

The present application provides a fine processing method and device for a double-sided CNC milling machine, which relates to the field of CNC milling machine technology. The method comprises: obtaining a target CNC machining tool, wherein the target CNC machining tool comprises a first tool and a second tool; traversing and matching the characteristic parameters of the target workpiece to obtain a target milling machine control scheme; obtaining a machining monitoring record; obtaining a first project parameter and a second project parameter; forming a target comparison deviation set based on the first project comparison deviation; obtaining a predicted synchronization index through machining synchronization prediction model analysis; and issuing a dynamic adjustment instruction for dynamic adjustment optimization when the predicted synchronization index is not within the synchronization index limit. The method achieves the technical goals of intelligent, automated, and data-based double-sided CNC machining processes, and achieves the technical effect of timely discovering the problem of double-sided machining asynchrony and making timely targeted adjustments, ultimately improving machining accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of CNC milling machines, and in particular to a fine processing method and device for a double-sided CNC milling machine. Background Art

[0002] With the rapid development of modern manufacturing, CNC machining technology is being used more and more widely in industrial production. In the CNC machining process, how to improve machining efficiency and machining accuracy and ensure the stability of the machining process has always been the goal pursued by the manufacturing industry. The traditional CNC machining process mainly relies on the experience and technical level of the operator, and the machining efficiency and machining accuracy are subject to certain limitations. In order to solve this problem, researchers have proposed many solutions based on computer technology and artificial intelligence, such as CNC machining databases and neural network models. A CNC machining database is a system used to store and manage various data and information required in the machining process. It can provide the optimal machining plan and parameter settings based on the characteristics of the workpiece and the machining requirements. The neural network model is a computational model that simulates the neuronal structure of the human brain. It can achieve intelligent control and optimization of the machining process through learning and training.

[0003] However, in the actual CNC machining process, due to the influence of various factors, such as tool wear, cutting heat and cutting vibration, the machining process may become unstable and asynchronous. Summary of the Invention

[0004] The purpose of this application is to provide a fine processing method and device for a double-sided CNC milling machine, so as to solve the technical problem that in the existing double-sided CNC machining process, it is impossible to timely detect the problem of double-sided machining asynchrony and take corrective adjustment measures in time, thereby affecting the machining control accuracy and causing unstable machining quality of the double-sided CNC milling machine.

[0005] In view of the above problems, the present application provides a fine processing method and device for a double-sided CNC milling machine.

[0006] In the first aspect, the present application provides a fine processing method for a double-sided CNC milling machine, which is implemented by a fine processing device for a double-sided CNC milling machine, wherein the fine processing method for a double-sided CNC milling machine includes: obtaining a target CNC processing tool, the target CNC processing tool including a first tool on the first CNC surface and a second tool on the second CNC surface of the target double-sided CNC milling machine; traversing and matching the target workpiece feature parameters obtained by multi-dimensional feature collection of the target workpiece in a fine processing database to obtain a target milling machine control scheme; obtaining a processing monitoring record, the processing monitoring record refers to predetermined monitoring items when the first tool and the second tool process the target workpiece under the target milling machine control scheme. Purpose record, the processing monitoring record includes a first record and a second record; extracting the first monitoring item in the predetermined monitoring item, and performing parameter matching in the first record and the second record in turn to obtain first item parameters and second item parameters respectively; forming a target comparison deviation set based on the first item comparison deviation obtained by comparing the first item parameters and the second item parameters; analyzing the target comparison deviation set through a processing synchronization prediction model to obtain a predicted synchronization index, the processing synchronization prediction model is an intelligent model trained based on a neural network model; when the predicted synchronization index is not within the synchronization index limit, issuing a dynamic adjustment instruction, and dynamically adjusting and optimizing the target milling machine control scheme based on the dynamic adjustment instruction.

[0007] In the second aspect, the present application also provides a fine processing device for a double-sided CNC milling machine, which is used to execute a fine processing method for a double-sided CNC milling machine as described in the first aspect, wherein the fine processing device for a double-sided CNC milling machine includes: a tool acquisition module, the tool acquisition module is used to acquire a target CNC processing tool, the target CNC processing tool includes a first tool for the first CNC surface and a second tool for the second CNC surface of the target double-sided CNC milling machine; a scheme acquisition module, the scheme acquisition module is used to traverse and match the target workpiece feature parameters obtained by multi-dimensional feature collection of the target workpiece in a fine processing database to obtain a target milling machine control scheme; a monitoring and recording module, the monitoring and recording module is used to acquire processing monitoring records, the processing monitoring records refer to records of predetermined monitoring items when the first tool and the second tool process the target workpiece under the target milling machine control scheme, and the processing monitoring records include The processing module comprises a first record and a second record; a parameter matching module, the parameter matching module is used to extract the first monitoring item in the predetermined monitoring item, and perform parameter matching in the first record and the second record in turn to obtain the first item parameter and the second item parameter respectively; a comparative analysis module, the comparative analysis module is used to form a target comparative deviation set based on the first item comparative deviation obtained by comparing the first item parameter and the second item parameter; a synchronization index prediction module, the synchronization index prediction module is used to analyze the target comparative deviation set through a machining synchronization prediction model to obtain a predicted synchronization index, and the machining synchronization prediction model is an intelligent model trained based on a neural network model; a dynamic adjustment module, the dynamic adjustment module is used to issue a dynamic adjustment instruction when the predicted synchronization index is not within the synchronization index limit, and dynamically adjust and optimize the target milling machine control scheme based on the dynamic adjustment instruction.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The method comprises obtaining target numerical control machining tools, the target numerical control machining tools comprising a first tool for a first numerical control surface and a second tool for a second numerical control surface of a target double-sided numerical control milling machine; traversing and matching target workpiece feature parameters obtained by multi-dimensional feature collection of a target workpiece in a fine machining database to obtain a target milling machine control scheme; obtaining machining monitoring records, the machining monitoring records being records of predetermined monitoring items when the first and second tools machine the target workpiece under the target milling machine control scheme, the machining monitoring records comprising a first record and a second record; extracting a first monitoring item from the predetermined monitoring items, and sequentially matching parameters in the first and second records to obtain first item parameters and second item parameters, respectively; forming a target comparison deviation set based on a first item comparison deviation obtained by comparing the first item parameters with the second item parameters; analyzing the target comparison deviation set using a machining synchronization prediction model to obtain a predicted synchronization index, the machining synchronization prediction model being an intelligent model trained based on a neural network model; and issuing a dynamic adjustment instruction when the predicted synchronization index is not within a synchronization index limit, and dynamically adjusting and optimizing the target milling machine control scheme based on the dynamic adjustment instruction. In other words, by integrating advanced monitoring technology, data analysis, intelligent models and automated control technology, the technical goals of intelligent, automated and data-based double-sided CNC machining processes have been achieved, and the problem of double-sided machining asynchrony has been discovered in a timely manner, and timely targeted adjustments have been made, ultimately improving the technical effect of machining accuracy.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 A schematic flow chart of a fine processing method of a double-sided CNC milling machine for this application;

[0013] Figure 2This is a structural schematic diagram of a fine processing device of a double-sided CNC milling machine in this application.

[0014] Description of reference numerals:

[0015] Tool acquisition module 11, solution acquisition module 12, monitoring and recording module 13, parameter matching module 14, comparison and analysis module 15, synchronization index prediction module 16, dynamic adjustment module 17. DETAILED DESCRIPTION

[0016] This application provides a fine machining method and apparatus for a double-sided CNC milling machine, resolving the technical problem of the inability to promptly detect and implement corrective adjustments during existing double-sided CNC machining processes, which affects machining control accuracy and leads to unstable machining quality. By performing real-time analysis and prediction of monitoring data during machining, asynchronous machining can be promptly detected and adjusted, thereby ensuring machining stability and quality.

[0017] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0018] For example, see the attached Figure 1 The present application provides a fine processing method for a double-sided CNC milling machine, wherein the method is implemented by executing a fine processing device of the double-sided CNC milling machine, and the fine processing method for the double-sided CNC milling machine specifically includes the following steps:

[0019] Step 1: Obtain target CNC machining tools, wherein the target CNC machining tools include a first tool on a first CNC surface and a second tool on a second CNC surface of a target double-sided CNC milling machine.

[0020] Specifically, the specific model and processing requirements of the target double-sided CNC milling machine are first determined. This includes a detailed analysis of the processing characteristics and requirements of the first and second CNC surfaces. Based on these characteristics, appropriate tool materials, sizes, and shapes are then selected to accommodate the different processing tasks. Next, the specific structures of the first and second tools are designed to ensure they can respectively adapt to the processing requirements of the first and second CNC surfaces. Furthermore, the tool installation and replacement methods must be considered to ensure ease and safety of operation on the CNC milling machine. Finally, through specialized manufacturing processes, the first and second tools that meet the requirements are produced and quality inspected to ensure they meet the processing requirements of the double-sided CNC milling machine.

[0021] In summary, configuring a double-sided CNC milling machine with specialized first and second cutters allows for customized machining of different CNC surfaces, improving both efficiency and quality. This configuration not only enhances machining accuracy but also helps extend tool life and reduce production costs.

[0022] Step 2: The target workpiece feature parameters obtained by multi-dimensional feature collection of the target workpiece are traversed and matched in the fine processing database to obtain the target milling machine control solution.

[0023] Specifically, first, the characteristic parameters of the target workpiece are obtained through multi-dimensional feature collection, and traversal matching is performed in the fine processing database to obtain the target milling machine control scheme suitable for the workpiece, so as to achieve accurate matching between the workpiece characteristic parameters and the milling machine control scheme, thereby improving processing accuracy and efficiency.

[0024] Specifically, first, multi-dimensional features are collected for the target workpiece, including information such as the workpiece's size, shape, and material properties. These feature parameters are then entered into the fine machining database. Next, the database system performs a traversal match based on these parameters to find the most suitable milling machine control solution. Furthermore, the database may contain a variety of pre-existing milling machine control solutions, which are pre-set based on different types of workpiece characteristics and machining requirements. The matching process takes into account various factors, such as machining speed, cutting depth, and tool path. Finally, the milling machine control solution that best matches the target workpiece characteristics is output.

[0025] In summary, by matching the multi-dimensional feature parameters of the target workpiece with the control scheme in the fine machining database, customized milling machine control for specific workpieces can be achieved.

[0026] Step three: Obtain a processing monitoring record, which refers to a record of predetermined monitoring items when the first tool and the second tool process the target workpiece under the target milling machine control scheme, and the processing monitoring record includes a first record and a second record.

[0027] Specifically, the process involves obtaining machining monitoring records, which are monitoring data for specific tools (first tool and second tool) machining the target workpiece under the target milling machine control scheme, collecting and analyzing key data during the machining process in order to evaluate and optimize the machining quality and efficiency.

[0028] Specifically, first, the predetermined items that need to be monitored are determined. These items may include key indicators such as processing time, cutting force, temperature, and tool wear. Then, under the target milling machine control scheme, the target workpiece is processed using the first tool and the second tool, and the data of the above predetermined monitoring items are recorded. Next, these data are divided into two parts: the first record and the second record, which correspond to the processing conditions of the first tool and the second tool respectively. In addition, these records may also include additional information such as the vibration of the tool and the roughness of the workpiece surface. Finally, the collected processing monitoring records will be used to analyze and evaluate the processing process in order to further optimize the tool and milling machine control scheme.

[0029] In summary, by acquiring and analyzing the machining monitoring records of the first tool and the second tool under the target milling machine control scheme, fine monitoring and evaluation of the machining process can be achieved.

[0030] Step 4: extracting the first monitoring item from the predetermined monitoring items, and performing parameter matching in the first record and the second record in sequence to obtain first item parameters and second item parameters respectively.

[0031] Specifically, a specific first monitoring item is extracted from the predefined monitoring items. Parameters are then matched between the first and second records to obtain the first and second item parameters. Through parameter matching, specific monitoring items during the machining process of two different tools are compared and analyzed to better understand the performance and machining results of each tool.

[0032] Specifically, first, the first monitoring item is identified and extracted from the predetermined monitoring items. This may be a key performance indicator, such as cutting force, processing temperature, etc. Then, in the first record, that is, the processing monitoring record for the first tool, the parameter data related to the first monitoring item is found. Next, in the second record, that is, the processing monitoring record for the second tool, the same parameter matching operation is performed. In this way, the first item parameters and the second item parameters are obtained respectively, representing the performance of the two tools under the same monitoring items. In addition, this process may also include further analysis of the parameter data, such as calculating the mean value, standard deviation, etc., in order to have a deeper understanding of the differences in tool performance.

[0033] In summary, by extracting the first monitoring item from a predetermined set of monitoring items and matching the parameters in the first and second records, this technical solution enables the performance comparison of two different tools under specific monitoring items. This helps evaluate and optimize tool selection and use, thereby improving the overall performance and efficiency of the machining process.

[0034] Step 5: Build a target comparison deviation set based on the first item comparison deviation obtained by comparing the first item parameter and the second item parameter.

[0035] Specifically, first, a target comparison deviation set is constructed by comparing the parameters of the first project and the second project. By comparing the performance parameters of the two tools under the same monitoring items, the differences are identified and integrated into a comparison deviation set for further analysis and optimization of the machining process.

[0036] Specifically, a detailed comparative analysis is first performed on the parameters of the first and second projects. This may include calculating the difference, standard deviation, or other statistical measures between the two parameter sets. Based on the results of this comparative analysis, significant deviations between the two parameters are identified. Next, this deviation data is consolidated into a target comparative deviation set. This set will contain all identified significant deviations, providing the foundational data for subsequent analysis. Furthermore, the development of the target comparative deviation set may include categorizing and ranking the deviation data to better understand the extent to which different deviations affect the machining process.

[0037] In summary, by comparing the first item parameters with the second item parameters and constructing a target comparison deviation set, this technical solution can achieve quantitative analysis of the performance difference between the two tools.

[0038] Step 6: Analyze the target contrast deviation set through a processing synchronization prediction model to obtain a predicted synchronization index. The processing synchronization prediction model is an intelligent model trained based on a neural network model.

[0039] Specifically, first, the target comparison deviation set is analyzed using a machining synchronization prediction model to obtain a predicted synchronization index. Then, an intelligent model based on neural network training is used to evaluate and predict the synchronization of the two tools during the machining process, providing a basis for optimizing machining parameters and improving machining quality.

[0040] Specifically, the target comparison deviation set is first provided as input data to the machining synchronization prediction model. This model, based on neural network training, is capable of processing and analyzing complex parameter relationships. The model then analyzes the input deviation data, including identifying deviation patterns and evaluating their impact on machining synchronization. Based on these analysis results, the model outputs a predicted synchronization index, which reflects the degree of synchronization between the two tools during the machining process. Furthermore, the neural network model training process may also include a large amount of historical machining data and synchronization assessment results to ensure the model's prediction accuracy. The machining synchronization prediction model analyzes the target comparison deviation set and generates a predicted synchronization index, enabling intelligent prediction of the machining synchronization between the two tools.

[0041] Step seven: When the predicted synchronization index is not within the synchronization index limit, a dynamic adjustment instruction is issued, and the target milling machine control scheme is dynamically adjusted and optimized based on the dynamic adjustment instruction.

[0042] Specifically, when the predicted synchronization index falls below the synchronization index limit, a dynamic adjustment command is issued, and based on this command, the target milling machine control scheme is dynamically adjusted and optimized. By monitoring and evaluating machining synchronization in real time, the stability and efficiency of the machining process are ensured, and the milling machine control scheme can be dynamically adjusted when necessary.

[0043] Specifically, first, a synchronization index limit is set, which is the ideal synchronization index range predetermined based on the processing quality and efficiency requirements. Then, the predicted synchronization index is compared with this limit. If the predicted synchronization index is lower or higher than the limit range, it indicates that there is a problem with the processing synchronization. Next, a dynamic adjustment instruction is issued, which contains adjustment suggestions for the milling machine control scheme, such as changing the cutting speed, feed rate or tool path. In addition, the generation of dynamic adjustment instructions may also include a comprehensive analysis of the current processing status to ensure the effectiveness of the adjustment measures. Finally, according to the dynamic adjustment instructions, the target milling machine control scheme is adjusted and optimized in real time to restore and maintain ideal processing synchronization.

[0044] In summary, by monitoring and predicting the synchronization index and issuing dynamic adjustment instructions when it does not reach the synchronization index limit, the target milling machine control solution can be optimized in real time. This helps ensure the stability and efficiency of the machining process and reduces machining errors and tool wear caused by synchronization issues.

[0045] Furthermore, the present application includes: the target workpiece characteristic parameters include workpiece material type, workpiece heat treatment status, machining surface quality requirements, machining accuracy requirements and machining allowance.

[0046] Specifically, first, determine the material type of the workpiece, which is the basis for selecting tools and processing parameters. Different material types have different requirements for the processing process. For example, some materials may require special cutting speeds and feed rates. Then, consider the heat treatment state of the workpiece, which affects the hardness and processing performance of the material. Next, clarify the processing surface quality requirements, which include indicators such as surface roughness and smoothness. These requirements will affect the selection of tools and the setting of processing parameters. In addition, the processing accuracy requirement is also one of the key parameters, which determines the maximum error that can be tolerated during the processing. Finally, the processing allowance also needs to be considered, which refers to the amount of material that needs to be retained during the processing to ensure the accuracy of the final workpiece size.

[0047] In summary, by defining the characteristic parameters of the target workpiece in detail, including the workpiece material type, heat treatment status, machining surface quality requirements, machining accuracy requirements and machining allowance, comprehensive information support can be provided for selecting appropriate machining strategies and tools.

[0048] Furthermore, the present application includes: extracting the first fine machining data from the fine machining database, the first fine machining data including the first fine machining workpiece characteristic parameters and the first fine machining milling machine control scheme; reading the parameter labeling scheme, and based on the parameter labeling scheme, sequentially labeling the target workpiece characteristic parameters and the first fine machining workpiece characteristic parameters to obtain a target label vector and a first label vector, respectively; comparing the target label vector and the first label vector to obtain a first consistency ratio; when the first consistency ratio reaches the consistency ratio limit, recording the first fine machining milling machine control scheme as the target milling machine control scheme.

[0049] Specifically, the system first extracts the first fine machining data from the fine machining database. Using a parameter labeling scheme, the target workpiece characteristic parameters and the first fine machining workpiece characteristic parameters are labeled. A consistency ratio comparison is then used to determine whether the first fine machining milling machine control scheme should be used as the target milling machine control scheme. Through labeling and consistency ratio comparison, the most appropriate milling machine control scheme is automatically selected, improving the automation and intelligence of the machining process.

[0050] Specifically, first, the first fine machining data is extracted from the fine machining database, which includes the first fine machining workpiece feature parameters and the corresponding milling machine control scheme. Then, the parameter labeling scheme is read, which defines how to convert the workpiece feature parameters into label vectors. Next, based on this scheme, the target workpiece feature parameters and the first fine machining workpiece feature parameters are labeled to obtain the target label vector and the first label vector, respectively. These label vectors are quantitative representations of the workpiece feature parameters, which facilitate subsequent comparative analysis. In addition, by comparing the target label vector and the first label vector, the first consistency ratio is calculated, which reflects the degree of similarity between the two workpiece feature parameter sets. Finally, when the first consistency ratio reaches the preset consistency ratio limit, it indicates that the two workpiece feature parameter sets are highly similar, so the first fine machining milling machine control scheme is used as the target milling machine control scheme.

[0051] In summary, by extracting data from the fine machining database, labeling the workpiece feature parameters, and calculating the consistency ratio, it is possible to intelligently select the target milling machine control scheme, which helps to improve the automation level of the machining process, reduce manual intervention, and ensure that the selected milling machine control scheme can meet the machining requirements of the target workpiece, thereby improving machining efficiency and accuracy.

[0052] Furthermore, the present application includes: the target milling machine control scheme includes a target spindle speed, a target feed rate and a target cutting depth.

[0053] Specifically, the target milling machine control scheme consists of a target spindle speed, a target feed rate, and a target depth of cut. By precisely setting the spindle speed, feed rate, and depth of cut of the milling machine, optimal control of the machining process is achieved to meet the machining requirements of the target workpiece.

[0054] Specifically, first, determine the target spindle speed, which refers to the speed at which the milling machine spindle rotates, which directly affects the cutting efficiency and the quality of the workpiece surface. Then, set the target feed rate, which is the speed at which the tool moves during the machining process, which affects the machining time and workpiece accuracy. Next, determine the target cutting depth, which refers to the maximum depth of the tool's penetration into the workpiece, which determines the amount of material removed per cut and the number of machining operations. The setting of these three parameters requires comprehensive consideration of factors such as the material properties of the workpiece, machining accuracy requirements, and tool performance. In addition, the determination of the target milling machine control scheme may also include the optimization of other parameters, such as the cutting path, coolant flow rate, etc.

[0055] In summary, by accurately setting the spindle speed, feed rate, and cutting depth in the target milling machine control scheme, optimal control of the machining process can be achieved.

[0056] Furthermore, the present application includes: the predetermined monitoring items include tool wear rate, cutting heat value and cutting vibration amplitude.

[0057] Specifically, the scheduled monitoring items include tool wear rate, cutting heat value and cutting vibration amplitude. By monitoring these key indicators, the tool status and processing performance during the processing are evaluated, thus providing a basis for optimizing processing parameters and improving processing quality.

[0058] Specifically, the tool wear rate is monitored, which refers to the degree of tool wear during machining and reflects tool durability and machining efficiency. Next, the cutting heat value (heat generated during machining) is measured, which affects the workpiece's thermal state and tool life. Next, the cutting vibration amplitude is recorded, which refers to the vibration of the tool during machining and is related to machining accuracy and workpiece surface quality. The data collection and analysis of these monitoring items helps to promptly identify machining problems such as excessive tool wear, excessive cutting temperatures, or excessive vibration, allowing for timely adjustments to machining strategies.

[0059] In summary, by monitoring the predetermined monitoring items such as tool wear rate, cutting heat value and cutting vibration amplitude, this technical solution can achieve fine monitoring of the machining process.

[0060] Furthermore, the present application includes: generating a first parameter time series of the first monitoring project based on the first project parameter; generating a second parameter time series of the first monitoring project based on the second project parameter; obtaining a scheduled time; performing a trend prediction analysis on the first parameter time series in combination with the scheduled time to obtain a first prediction project parameter; performing a trend prediction analysis on the second parameter time series in combination with the scheduled time to obtain a second prediction project parameter; and recording the parameter difference between the first prediction project parameter and the second prediction project parameter as the first project comparison deviation.

[0061] Specifically, a parameter time series is generated based on the first and second project parameters. Trend forecasting analysis is then performed based on a predetermined timeframe to obtain the first and second predicted project parameters. This is then used to calculate the first project's comparative deviation. By analyzing the trend of the parameter time series, the parameter value at a predetermined future timepoint is predicted. By comparing the two predicted parameter values, the difference between the two projects is assessed.

[0062] Specifically, first, a first parameter time series of the first monitoring project is generated based on the first project parameter, which reflects the changing trend of the first project parameter over time. Then, a second parameter time series is generated based on the second project parameter, which also reflects the changing trend of the second project parameter over time. Next, a predetermined time point is determined, which is the time base for trend forecasting analysis. After the predetermined time is determined, a trend forecasting analysis is performed on the first parameter time series to obtain the first forecast project parameter, which is the predicted value of the first project parameter at the predetermined time point. Similarly, a trend forecasting analysis is performed on the second parameter time series to obtain the second forecast project parameter. Finally, the parameter difference between the first forecast project parameter and the second forecast project parameter is calculated. This difference is the first project comparison deviation, which reflects the performance difference between the two projects at the predetermined time point.

[0063] In summary, by generating parameter time series, performing trend forecast analysis in combination with predetermined time, and calculating the first item comparison deviation, it is possible to predict and evaluate the performance difference between the two items.

[0064] Furthermore, the present application includes: obtaining a first time; matching the first parameter at the first time in the first parameter time series and matching the second parameter at the first time in the second parameter time series in sequence; obtaining a first synchronization index at the first time through a predetermined measuring device, and combining the first parameter and the second parameter to form a first training data group; performing supervised learning on the first training data group to obtain the processing synchronization prediction model.

[0065] Specifically, at a first time, the parameter values of the first parameter time series and the second parameter time series are matched, and a first synchronization index is obtained using a predetermined measurement device. This data is then used to form a first training data set. Finally, a processing synchronization prediction model is obtained through supervised learning. By analyzing the parameter values and synchronization index of the two parameter time series at the first time point, an intelligent model capable of predicting processing synchronization is trained.

[0066] Specifically, first, a first time point is determined, which serves as the time reference for parameter matching and synchronization index measurement. Then, a first parameter value corresponding to the first time point is found in the first parameter time series, and a second parameter value corresponding to the first time point is found in the second parameter time series. Next, a first synchronization index is obtained at the first time point using a predetermined measuring device. This is a quantitative assessment of the synchronization of the two parameters at the first time point. The first parameter, second parameter, and first synchronization index at the first time point are then combined into a first training data set. Furthermore, to improve the predictive power of the model, it may be necessary to collect training data sets at multiple different time points. Finally, supervised learning is performed on the first training data set, a machine learning method that trains a model using known input and output data to obtain a processing synchronization prediction model.

[0067] In summary, by matching the parameter values in the parameter time series at the first time point, obtaining the synchronization index, and forming a training data group, an intelligent model for predicting processing synchronization can be trained, which helps to monitor and predict processing synchronization in real time during the actual processing process, thereby adjusting the processing parameters in time to ensure processing quality and efficiency.

[0068] Furthermore, the present application includes: reading a predetermined dynamic adjustment optimization strategy based on the dynamic adjustment instruction; forming a candidate factor set based on the predetermined dynamic adjustment optimization strategy, and performing a correlation analysis on the candidate factor set and the predicted synchronization index to obtain a correlation analysis result; screening the candidate factor set based on the correlation analysis result to obtain a target factor set; and dynamically adjusting and optimizing the target milling machine control scheme by adjusting the target factor set.

[0069] Specifically, the system reads a predefined dynamic adjustment optimization strategy based on dynamic adjustment instructions. By constructing a candidate factor set and performing correlation analysis, it selects a target factor set. Finally, the target factor set is adjusted to dynamically optimize the target milling machine control scheme. By analyzing the correlation between the predicted synchronization index and the candidate factor set, the factors that most influence machining synchronicity are identified. The milling machine control scheme is then adjusted accordingly to improve the synchronicity and efficiency of the machining process.

[0070] Specifically, first, a predetermined dynamic adjustment optimization strategy is read based on the dynamic adjustment instructions. This refers to the rules and methods for adjusting the milling machine control parameters during the machining process. Then, a candidate factor set is assembled. These factors include various parameters that may affect machining synchronization, such as spindle speed, feed rate, cutting depth, etc. Next, a correlation analysis is performed between the candidate factor set and the predicted synchronization index to determine which factors are most relevant to synchronization. In addition, the correlation analysis results will guide the screening of the candidate factor set to obtain the target factor set, which are the factors that have the greatest impact on machining synchronization. Finally, by adjusting the parameters in the target factor set, the target milling machine control scheme is dynamically adjusted and optimized to improve machining synchronization.

[0071] In summary, by reading the dynamic adjustment optimization strategy, forming and screening the target factor set, the intelligent optimization of the target milling machine control scheme is achieved.

[0072] Furthermore, the present application includes: the candidate factor set includes the target workpiece feature parameters, the target milling machine control scheme and the processing monitoring record.

[0073] Specifically, the candidate factor set consists of the target workpiece characteristic parameters, the target milling machine control scheme, and machining monitoring records. By analyzing the correlation between these candidate factors and the predicted synchronization index, the key factors affecting machining synchronization are identified, providing a basis for dynamically adjusting and optimizing the milling machine control scheme.

[0074] Specifically, first, the candidate factor set includes the target workpiece characteristic parameters, which describe the material type, heat treatment status, machining surface quality requirements, etc. of the workpiece, and have a direct impact on the machining process and synchronization. Then, the candidate factor set also includes the target milling machine control scheme, which refers to the specific parameter settings used to machine the workpiece, such as spindle speed, feed rate, etc. The settings of these parameters directly affect the machining efficiency and synchronization. Next, the machining monitoring records are also part of the candidate factor set. These records contain key data in the actual machining process, such as tool wear rate, cutting heat value, etc., which reflect the actual status and performance of the machining process. Through correlation analysis of these candidate factors, it is possible to determine which factors have the greatest impact on machining synchronization, thereby providing guidance for dynamically adjusting and optimizing the milling machine control scheme.

[0075] In summary, by incorporating the target workpiece characteristic parameters, the target milling machine control scheme, and the processing monitoring records into the candidate factor set, various factors affecting processing synchronization can be comprehensively considered. This helps to identify key factors through correlation analysis and provides a scientific basis for dynamically adjusting and optimizing the milling machine control scheme, thereby improving the synchronization and efficiency of the processing process.

[0076] In summary, the fine processing method of a double-sided CNC milling machine provided by this application has the following technical effects:

[0077] The method comprises obtaining target numerical control machining tools, the target numerical control machining tools comprising a first tool for a first numerical control surface and a second tool for a second numerical control surface of a target double-sided numerical control milling machine; traversing and matching target workpiece feature parameters obtained by multi-dimensional feature collection of a target workpiece in a fine machining database to obtain a target milling machine control scheme; obtaining machining monitoring records, the machining monitoring records being records of predetermined monitoring items when the first and second tools machine the target workpiece under the target milling machine control scheme, the machining monitoring records comprising a first record and a second record; extracting a first monitoring item from the predetermined monitoring items, and sequentially matching parameters in the first and second records to obtain first item parameters and second item parameters, respectively; forming a target comparison deviation set based on a first item comparison deviation obtained by comparing the first item parameters with the second item parameters; analyzing the target comparison deviation set using a machining synchronization prediction model to obtain a predicted synchronization index, the machining synchronization prediction model being an intelligent model trained based on a neural network model; and issuing a dynamic adjustment instruction when the predicted synchronization index is not within a synchronization index limit, and dynamically adjusting and optimizing the target milling machine control scheme based on the dynamic adjustment instruction. In other words, by integrating advanced monitoring technology, data analysis, intelligent models and automated control technology, the technical goals of intelligent, automated and data-based double-sided CNC machining processes have been achieved, and the problem of double-sided machining asynchrony has been discovered in a timely manner, and timely targeted adjustments have been made, ultimately improving the technical effect of machining accuracy.

[0078] Embodiment 2: Based on the same inventive concept as the fine processing method of a double-sided CNC milling machine in the above embodiment, this application also provides a fine processing device for a double-sided CNC milling machine, please refer to the attached Figure 2 , the fine processing device of the double-sided CNC milling machine comprises:

[0079] The tool acquisition module 11 is used to acquire a target CNC machining tool. The target CNC machining tool includes a first tool on a first CNC surface and a second tool on a second CNC surface of a target double-sided CNC milling machine.

[0080] The solution obtaining module 12 is used to traverse and match the target workpiece feature parameters obtained by multi-dimensional feature collection of the target workpiece in the fine processing database to obtain a target milling machine control solution.

[0081] The monitoring and recording module 13 is used to obtain processing monitoring records, which refer to records of predetermined monitoring items when the first tool and the second tool process the target workpiece under the target milling machine control scheme. The processing monitoring records include first records and second records.

[0082] The parameter matching module 14 is used to extract the first monitoring item from the predetermined monitoring items, and perform parameter matching in the first record and the second record in sequence to obtain first item parameters and second item parameters respectively.

[0083] The comparison and analysis module 15 is configured to form a target comparison deviation set based on a first project comparison deviation obtained by comparing the first project parameter and the second project parameter.

[0084] The synchronization index prediction module 16 is used to analyze the target comparison deviation set through a processing synchronization prediction model to obtain a predicted synchronization index. The processing synchronization prediction model is an intelligent model trained based on a neural network model.

[0085] The dynamic adjustment module 17 is configured to issue a dynamic adjustment instruction when the predicted synchronization index is not within the synchronization index limit, and dynamically adjust and optimize the target milling machine control scheme based on the dynamic adjustment instruction.

[0086] Furthermore, the solution obtaining module 12 in the fine processing device of the double-sided CNC milling machine is also used for: the target workpiece characteristic parameters include workpiece material type, workpiece heat treatment status, processing surface quality requirements, processing accuracy requirements and processing allowance.

[0087] Furthermore, the scheme obtaining module 12 in the fine processing device of the double-sided CNC milling machine is also used to: extract the first fine processing data in the fine processing database, the first fine processing data including the first fine processing workpiece characteristic parameters and the first fine processing milling machine control scheme; read the parameter labeling scheme, and based on the parameter labeling scheme, sequentially label the target workpiece characteristic parameters and the first fine processing workpiece characteristic parameters to obtain a target label vector and a first label vector respectively; compare the target label vector and the first label vector to obtain a first consistency ratio; when the first consistency ratio reaches the consistency ratio limit, the first fine processing milling machine control scheme is recorded as the target milling machine control scheme.

[0088] Furthermore, the solution obtaining module 12 in the fine processing device of the double-sided CNC milling machine is also used for: the target milling machine control solution includes a target spindle speed, a target feed speed and a target cutting depth.

[0089] Furthermore, the monitoring and recording module 13 in the fine processing device of the double-sided CNC milling machine is also used for: the predetermined monitoring items include tool wear rate, cutting heat value and cutting vibration amplitude.

[0090] Furthermore, the comparison and analysis module 15 in the fine processing device of the double-sided CNC milling machine is also used to: generate a first parameter time series of the first monitoring project based on the first project parameter; generate a second parameter time series of the first monitoring project based on the second project parameter; obtain a predetermined time; perform a trend prediction analysis on the first parameter time series in combination with the predetermined time to obtain a first prediction project parameter; perform a trend prediction analysis on the second parameter time series in combination with the predetermined time to obtain a second prediction project parameter; and record the parameter difference between the first prediction project parameter and the second prediction project parameter as the first project comparison deviation.

[0091] Furthermore, the synchronization index prediction module 16 in the fine processing device of the double-sided CNC milling machine is also used to: obtain a first time; match the first parameter at the first time in the first parameter time series and the second parameter at the first time in the second parameter time series in sequence; obtain the first synchronization index at the first time through a predetermined measuring device, and combine the first parameter and the second parameter to form a first training data group; perform supervised learning on the first training data group to obtain the processing synchronization prediction model.

[0092] Furthermore, the fine processing device of the double-sided CNC milling machine also includes an adjustment decision module, which is used to: read a predetermined dynamic adjustment optimization strategy based on the dynamic adjustment instruction; form a candidate factor set based on the predetermined dynamic adjustment optimization strategy, and perform correlation analysis on the candidate factor set and the predicted synchronization index to obtain a correlation analysis result; screen the candidate factor set based on the correlation analysis result to obtain a target factor set; and dynamically adjust and optimize the target milling machine control scheme by adjusting the target factor set.

[0093] Furthermore, the adjustment decision module in the fine processing device of the double-sided CNC milling machine is also used for: the candidate factor set includes the target workpiece characteristic parameters, the target milling machine control scheme and the processing monitoring record.

[0094] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1The fine processing method and specific examples of a double-sided CNC milling machine in Example 1 are also applicable to the fine processing device of a double-sided CNC milling machine in this embodiment. Through the detailed description of the fine processing method of a double-sided CNC milling machine, those skilled in the art can clearly understand the fine processing device of a double-sided CNC milling machine in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0095] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in their embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0096] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A fine processing method for a double-sided CNC milling machine, characterized in that: include: Acquire a target CNC machining tool, wherein the target CNC machining tool comprises a first tool on a first CNC surface and a second tool on a second CNC surface of a target double-sided CNC milling machine; The target workpiece feature parameters obtained by multi-dimensional feature collection of the target workpiece are traversed and matched in the fine processing database to obtain the target milling machine control solution; Acquire a processing monitoring record, wherein the processing monitoring record refers to a record of predetermined monitoring items when the first tool and the second tool process the target workpiece under the target milling machine control scheme, and the processing monitoring record includes a first record and a second record; Extracting a first monitoring item from the predetermined monitoring items, and performing parameter matching in the first record and the second record in sequence to obtain first item parameters and second item parameters respectively; forming a target contrast deviation set based on a first item contrast deviation obtained by comparing the first item parameter and the second item parameter; The target contrast deviation set is analyzed by a processing synchronization prediction model to obtain a predicted synchronization index, wherein the processing synchronization prediction model is an intelligent model trained based on a neural network model; When the predicted synchronization index is not within the synchronization index limit, a dynamic adjustment instruction is issued, and the target milling machine control scheme is dynamically adjusted and optimized based on the dynamic adjustment instruction.

2. The fine processing method of a double-sided CNC milling machine according to claim 1, characterized in that: The target workpiece characteristic parameters include workpiece material type, workpiece heat treatment status, machining surface quality requirements, machining accuracy requirements and machining allowance.

3. The fine processing method of a double-sided CNC milling machine according to claim 1, characterized in that: include: Extracting first fine machining data from the fine machining database, wherein the first fine machining data includes first fine machining workpiece characteristic parameters and a first fine machining milling machine control scheme; Reading a parameter labeling scheme, and sequentially labeling the target workpiece feature parameters and the first fine-machining workpiece feature parameters based on the parameter labeling scheme to obtain a target label vector and a first label vector, respectively; Comparing the target label vector and the first label vector to obtain a first consistency ratio; When the first consistency ratio reaches a consistency ratio limit, the first fine processing milling machine control scheme is recorded as the target milling machine control scheme.

4. The fine processing method of a double-sided CNC milling machine according to claim 1, characterized in that: The target milling machine control scheme includes a target spindle speed, a target feed rate and a target cutting depth.

5. The fine processing method of a double-sided CNC milling machine according to claim 1, characterized in that: The predetermined monitoring items include tool wear rate, cutting heat value and cutting vibration amplitude.

6. The fine processing method of a double-sided CNC milling machine according to claim 1, characterized in that: include: generating a first parameter time series of the first monitoring item based on the first item parameter; generating a second parameter time series of the first monitoring item based on the second item parameter; Get the scheduled time; Performing trend forecast analysis on the first parameter time series in combination with the predetermined time to obtain a first forecast item parameter; performing a trend prediction analysis on the second parameter time series in combination with the predetermined time to obtain a second prediction item parameter; The parameter difference between the first prediction item parameter and the second prediction item parameter is recorded as the first item comparison deviation.

7. The fine processing method of a double-sided CNC milling machine according to claim 6, characterized in that: include: Get the first time; sequentially matching the first parameter at the first time in the first parameter sequence and matching the second parameter at the first time in the second parameter sequence; Acquire a first synchronization index at the first time by a predetermined measuring device, and combine the first parameter and the second parameter to form a first training data group; Supervised learning is performed on the first training data set to obtain the processing synchronization prediction model.

8. The fine processing method of a double-sided CNC milling machine according to claim 1, characterized in that: Also includes: Reading a predetermined dynamic adjustment optimization strategy based on the dynamic adjustment instruction; forming a candidate factor set based on the predetermined dynamic adjustment optimization strategy, and performing a correlation analysis between the candidate factor set and the predicted synchronization index to obtain a correlation analysis result; Screening the candidate factor set based on the correlation analysis result to obtain a target factor set; Dynamic adjustment and optimization of the target milling machine control solution is performed by adjusting the target factor set.

9. The fine processing method of a double-sided CNC milling machine according to claim 8, characterized in that: The candidate factor set includes the target workpiece characteristic parameters, the target milling machine control scheme and the processing monitoring records.

10. A fine processing device for a double-sided CNC milling machine, characterized in that: The steps for implementing the fine processing method of a double-sided CNC milling machine according to any one of claims 1 to 9 include: A tool acquisition module, the tool acquisition module is used to acquire a target CNC machining tool, the target CNC machining tool including a first tool on a first CNC surface and a second tool on a second CNC surface of a target double-sided CNC milling machine; A solution obtaining module is used to traverse and match the target workpiece feature parameters obtained by multi-dimensional feature collection of the target workpiece in the fine processing database to obtain a target milling machine control solution; a monitoring and recording module, the monitoring and recording module being configured to obtain a processing monitoring record, the processing monitoring record being a record of predetermined monitoring items when the first tool and the second tool process the target workpiece under the target milling machine control scheme, the processing monitoring record including a first record and a second record; a parameter matching module, the parameter matching module being used to extract a first monitoring item from the predetermined monitoring items, and sequentially perform parameter matching in the first record and the second record to obtain first item parameters and second item parameters respectively; a comparison analysis module, configured to establish a target comparison deviation set based on a first item comparison deviation obtained by comparing the first item parameter and the second item parameter; A synchronization index prediction module, configured to analyze the target contrast deviation set using a processing synchronization prediction model to obtain a predicted synchronization index, wherein the processing synchronization prediction model is an intelligent model trained based on a neural network model; A dynamic adjustment module is used to issue a dynamic adjustment instruction when the predicted synchronization index is not within the synchronization index limit, and dynamically adjust and optimize the target milling machine control scheme based on the dynamic adjustment instruction.

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