Intelligent control method and system for a tool machining process

Abstract

The application provides an intelligent control method and system for a tool machining process. The method comprises: collecting tool operation data of a machining device and processing the tool operation data into a feature data set representing a tool state; generating a state mapping matrix of the tool according to the feature data set, and determining a trajectory deviation quantization result of the tool according to the state mapping matrix, to determine a cutting path offset corresponding to the trajectory deviation quantization result; optimizing a cutting parameter of the tool according to the cutting path offset, and calibrating a position of the tool by using the optimized cutting parameter to obtain calibrated position data; generating a stability compensation strategy for the machining device according to the position data and the cutting parameter, performing device performance evaluation according to the stability compensation strategy to obtain a device performance evaluation result; and updating the state mapping matrix and a parameter optimization logic of the machining device according to the device performance evaluation result. The application optimizes the intelligent control mode of the tool machining process.

Description

Technical Field

[0001] This application relates to the field of intelligent industry, and more specifically, to an intelligent control method and system for tool processing. Background Technology

[0002] In modern manufacturing, the precision and stability of processing equipment are core pillars for improving production efficiency and product quality, and their importance is self-evident. Especially in the field of high-precision machining, any slight deviation can lead to product defects or even production accidents. Research in this area is directly related to the level of intelligence and economic benefits of industrial production, becoming a key direction for driving technological progress. However, many current machining control methods based on industrial control systems still have significant shortcomings. They generally rely on preset parameters and human experience, making it difficult to adapt to dynamically changing data in complex machining environments. In particular, when faced with fluctuations in equipment status or sudden changes in processing conditions, they often cannot adjust in time, leading to decreased machining accuracy or accelerated equipment wear.

[0003] Looking deeper, the core challenges in this field lie primarily in how to monitor equipment status in real time and precisely adjust the machining process. Firstly, because the dynamic changes in tool status during machining are difficult to capture accurately, traditional monitoring methods are often lagging and cannot reflect the actual performance of the tool in a timely manner. This directly leads to the accumulation of deviations in the machining trajectory. As trajectory deviations occur, the mismatch between cutting parameters and actual requirements is further exacerbated, severely impacting the accuracy of tool position adjustments. Ultimately, this results in a decline in both machining quality and efficiency. This chain reaction, from insufficient status monitoring to trajectory deviation, and then to inaccurate parameter adjustments, constitutes the main bottleneck for current technological advancements.

[0004] Therefore, this application provides an intelligent control method and system for tool machining processes to solve one of the aforementioned technical problems. Summary of the Invention

[0005] The purpose of this application is to provide an intelligent control method and system for tool machining processes, which can solve at least one of the technical problems mentioned above. The specific solution is as follows:

[0006] According to specific embodiments of this application, in a first aspect, this application provides an intelligent control method for a tool machining process, comprising:

[0007] The process involves collecting tool operation data from a machining equipment and processing it into a feature dataset characterizing the tool's state. A tool state mapping matrix is ​​generated based on the feature dataset, and the tool trajectory deviation quantization result is determined based on the state mapping matrix to identify the cutting path offset corresponding to the trajectory deviation quantization result. The cutting parameters of the tool are optimized according to the cutting path offset, and the tool's position is calibrated using the optimized cutting parameters to obtain calibrated position data. A stability compensation strategy for the machining equipment is generated based on the position data and the cutting parameters. Equipment performance is evaluated according to the stability compensation strategy to obtain an equipment performance evaluation result. Finally, the state mapping matrix and the parameter optimization logic of the machining equipment are updated based on the equipment performance evaluation result.

[0008] According to a specific embodiment of this application, in a second aspect, this application provides an intelligent control system for a tool machining process, comprising:

[0009] The system includes a data acquisition unit for acquiring tool operation data from the machining equipment; a processing unit for processing the tool operation data into a feature dataset characterizing the tool's state; generating a tool state mapping matrix based on the feature dataset, and determining the tool trajectory deviation quantization result based on the state mapping matrix to determine the cutting path offset corresponding to the trajectory deviation quantization result; optimizing the tool's cutting parameters according to the cutting path offset, and using the optimized cutting parameters to perform position calibration on the tool, obtaining calibrated position data; generating a stability compensation strategy for the machining equipment based on the position data and the cutting parameters, evaluating the equipment performance according to the stability compensation strategy, and obtaining an equipment performance evaluation result; and updating the state mapping matrix and the parameter optimization logic of the machining equipment based on the equipment performance evaluation result.

[0010] Compared with the prior art, the above-described solution of this application has at least the following beneficial effects: This application provides an intelligent control method for tool machining process. By collecting and processing tool operation data in real time to form a feature dataset, a state mapping matrix is ​​generated to determine the trajectory deviation and cutting path offset. Based on this, cutting parameters are optimized and tool position is calibrated. Then, a stability compensation strategy is formulated to evaluate equipment performance. Finally, the state mapping matrix and parameter optimization logic are updated based on the evaluation results. This method not only improves machining accuracy and equipment stability, but also extends tool life and reduces production costs through continuous data feedback and adaptive adjustment mechanisms, ensuring an efficient and stable production process and providing reliable quality assurance for intelligent manufacturing. Attached Figure Description

[0011] Figure 1A flowchart of an intelligent control method for tool machining process is shown;

[0012] Figure 2 A flowchart of a method for generating a tool state mapping matrix based on a feature dataset is shown;

[0013] Figure 3 A flowchart of a method for determining the quantization result of tool trajectory deviation based on a state mapping matrix is ​​shown.

[0014] Figure 4 A flowchart of a method for determining the cutting path offset corresponding to the trajectory deviation quantization result is shown.

[0015] Figure 5 A flowchart of a method for optimizing tool cutting parameters according to cutting path offset is shown;

[0016] Figure 6 A flowchart of a method for calibrating tool position using optimized cutting parameters is shown;

[0017] Figure 7 A flowchart of a method for generating a stability compensation strategy for machining equipment based on position data and cutting parameters is shown.

[0018] Figure 8 A flowchart of a method for evaluating equipment performance according to a stability compensation strategy and obtaining equipment performance evaluation results is shown.

[0019] Figure 9 A flowchart of a method for updating a state mapping matrix and optimizing the parameters of a processing device is shown.

[0020] Figure 10 A unit block diagram of an intelligent control system for a tool machining process according to an embodiment of this application is shown. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0023] It should be understood that the term "and / or" used in this article 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 existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0024] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application for description, these descriptions should not be limited to these terms, which are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of this application, first may also be referred to as second, and similarly, second may also be referred to as first.

[0025] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0026] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0027] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0028] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0029] The embodiments provided in this application are embodiments of an intelligent control method for a tool machining process.

[0030] The following is combined with Figure 1 The embodiments of this application will be described in detail.

[0031] Figure 1 A flowchart of an intelligent control method for tool machining process is shown, such as... Figure 1 As shown, it includes the following steps.

[0032] Step S101: Collect the tool operation data of the processing equipment and process it into a feature dataset that characterizes the tool state.

[0033] Step S102: Generate the tool state mapping matrix based on the feature dataset, and determine the tool trajectory deviation quantization result based on the state mapping matrix, so as to determine the cutting path offset corresponding to the trajectory deviation quantization result.

[0034] Step S103: Optimize the cutting parameters of the tool according to the cutting path offset, and use the optimized cutting parameters to calibrate the tool position to obtain the calibrated position data.

[0035] Step S104: Based on the position data and cutting parameters, generate a stability compensation strategy for the machining equipment, evaluate the equipment performance according to the stability compensation strategy, and obtain the equipment performance evaluation result.

[0036] Step S105: Update the state mapping matrix and the parameter optimization logic of the processing equipment based on the equipment performance evaluation results.

[0037] The method provided in this application forms a feature dataset by real-time acquisition and processing of tool operation data, generates a state mapping matrix to determine trajectory deviation and cutting path offset, optimizes cutting parameters and calibrates tool position accordingly, formulates a stability compensation strategy to evaluate equipment performance, and finally updates the state mapping matrix and parameter optimization logic based on the evaluation results. This method not only improves machining accuracy and equipment stability, but also extends tool life and reduces production costs through continuous data feedback and adaptive adjustment mechanisms, ensuring an efficient and stable production process and providing reliable quality assurance for intelligent manufacturing.

[0038] In some embodiments, a sensor network is used to collect tool operation data in the machining equipment in real time and process it into a feature dataset characterizing the tool state. Specifically, on a CNC lathe, sensors monitor the tool operation state at a sampling frequency of 1000 times per second, acquiring raw signals including the acceleration value of vibration signals, the temperature value of the tool cutting area, and the magnitude change of cutting force, forming a complete dataset containing multiple signals. For these raw signals, a low-pass filter is used to remove high-frequency noise above 200 Hz from the vibration signals, and a moving average method is used to smooth the temperature data to eliminate abnormal fluctuations, thereby obtaining a clean signal set. Furthermore, the cleaned vibration signals and cutting force fluctuations are feature decomposed using methods such as short-time Fourier transform to extract frequency components and amplitude features reflecting the tool state, thus determining the feature dataset. If certain feature values ​​in the feature dataset exceed preset thresholds, such as the vibration amplitude increasing from the normal value of 2.5 units to 3.8 units, the system automatically compares historical status monitoring records to determine whether there are abnormal fluctuations in the tool and generates status report data to prompt the operator to replace the tool in time. This multi-dimensional analysis and processing method not only improves monitoring accuracy but also reduces processing risks through real-time feedback, forming a complete tool status monitoring closed loop and providing reliable support for intelligent manufacturing.

[0039] As a feasible embodiment, based on the above, a tool state mapping matrix is ​​generated according to the feature dataset, and the tool trajectory deviation quantification result and corresponding cutting path offset are determined based on this matrix. This step is crucial for identifying minute deviations that may occur during machining, helping to predict future deviation trends, ensuring that the tool cuts according to the predetermined trajectory, and improving machining accuracy. This method can effectively prevent quality problems caused by trajectory deviations, ensuring production efficiency and product quality.

[0040] As a specific embodiment, the cutting parameters of the tool are optimized according to the calculated cutting path offset, and the tool position is calibrated using the optimized parameters to obtain the calibrated position data. This adaptive adjustment mechanism can dynamically optimize cutting parameters, such as cutting speed and feed rate, to adapt to different machining requirements. This not only improves machining efficiency but also extends tool life, reduces replacement frequency, thereby reducing production costs and ensuring an efficient and stable production process.

[0041] For example, based on calibrated position data and optimized cutting parameters, a stability compensation strategy for the machining equipment is generated, and the equipment performance is evaluated accordingly. This process ensures that the machining equipment can operate stably under various working conditions, effectively preventing failures caused by equipment overload or instability. Through continuous monitoring and evaluation of equipment performance, potential problems can be identified and adjusted in a timely manner, ensuring the continuity and stability of production.

[0042] For example, the state mapping matrix and parameter optimization logic of the processing equipment are updated based on the equipment performance evaluation results. This feedback mechanism enables the entire control system to continuously learn and adapt to new processing environments or task changes, improving the system's intelligence and flexibility. Through continuous self-optimization, the system can better cope with complex processing challenges, ensuring optimal results in every processing operation, and significantly improving production efficiency and product quality.

[0043] Figure 2 A flowchart illustrating a method for generating a tool state mapping matrix based on a feature dataset is shown, such as... Figure 2 As shown, it includes the following steps.

[0044] Step S201: Based on the vibration signal and cutting force fluctuation represented by the feature dataset, organize the dynamic change data during tool operation to obtain a set of change patterns that reflect the periodicity of the dynamic change data.

[0045] Step S202: Determine the pattern matching data corresponding to the dynamic change data based on the matching degree between the dynamic change data and the preset change data threshold.

[0046] Step S203: If the pattern matching data exceeds the preset matching data threshold, the set of variation patterns is corrected according to the vibration signal and cutting force fluctuation to obtain the corrected set of variation patterns.

[0047] Step S204: Construct a data mapping relationship using dynamically changing data and pattern matching data, and integrate the multi-dimensional signal data contained in the corrected set of change patterns into a tool state mapping matrix.

[0048] In this embodiment, the feature dataset refers to a representative set of parameters formed after preprocessing and feature extraction of raw data (such as vibration signals, temperature changes, cutting force fluctuations, etc.) collected during the machining process, used to characterize the current operating state of the tool. Dynamic change data refers to the state information that changes continuously over time during the machining process, reflecting the real-time behavior of the tool and equipment under different working conditions, such as the changing trend of vibration amplitude or the rate of temperature rise. The change pattern set is a set of regular data patterns extracted after periodic analysis and trend organization of the dynamic change data, used to reveal the potential trend of tool state changes. Pattern matching data is the result generated by comparing the change pattern set with a preset normal or abnormal state model, used to determine whether the current tool state deviates from the expected pattern, and to provide a basis for subsequent deviation identification and parameter adjustment.

[0049] In this application, when determining the pattern matching data corresponding to dynamically changing data, the extracted set of change patterns is first compared item by item with the preset standard state model in the system. The degree of matching is evaluated by calculating the similarity or difference between the two. If the matching degree is higher than a set threshold, the current state is considered to be within the normal range and no intervention is required; if the matching degree is lower than the threshold, it indicates an abnormal trend. At this time, the system will generate corresponding pattern matching data based on the difference, marking the specific deviation feature dimension and possible risk level. This process is usually combined with data association analysis methods to ensure that subtle changes in the tool state can be accurately captured and to provide reliable data support for subsequent trajectory deviation analysis and control strategy optimization.

[0050] As a feasible implementation, pattern matching data is determined based on the matching degree between dynamically changing data and a preset threshold value for changing data. For example, suppose the preset threshold for vibration signal fluctuation is set to 3.5 units, while the actual variation pattern set shows a fluctuation of 4.2 units over a certain period. Through data association methods, this degree of deviation can be quickly determined, and pattern matching data can be generated. The significance of this method lies in its ability to promptly identify potential anomalies in tool operation, providing a basis for subsequent corrections.

[0051] As a specific implementation, when the pattern matching data exceeds a preset matching data threshold, the set of variation patterns needs to be corrected. For example, if there are short-term abrupt changes in the cutting force fluctuation data, the abrupt value can be smoothly integrated with the preceding and following data using a weighted average method, thereby obtaining a more accurate set of variation patterns. This corrected set of variation patterns can more realistically reflect the actual dynamic change trend of the tool, ensuring the accuracy of subsequent analysis.

[0052] For example, when constructing the final state mapping matrix, the multi-dimensional signal data contained in the corrected set of change patterns are integrated into a comprehensive mapping matrix. This matrix not only reflects the relationship between state trends and tool state change patterns, but also allows operators to intuitively understand whether the tool state conforms to the expected patterns. For instance, if vibration signals and cutting force fluctuations show a synchronous upward trend in the matrix, this may indicate that the tool is experiencing accelerated wear. Through this mapping matrix, adjustment decisions can be made quickly to avoid quality defects or equipment damage caused by tool problems.

[0053] In this embodiment, the steps are interconnected from an overall process perspective. From time series analysis to matrix transformation, the understanding of the tool status is gradually deepened. This multi-level analysis method not only effectively improves the comprehensiveness of status monitoring but also ensures that subtle changes in tool operation are not overlooked, providing strong support for the stable operation of machining equipment. Simultaneously, through this approach, the entire system can better adapt to complex machining environments, guaranteeing an efficient and stable production process.

[0054] Figure 3 A flowchart illustrating a method for determining the quantization result of tool trajectory deviation based on a state mapping matrix is ​​shown, as follows: Figure 3 As shown, it includes the following steps.

[0055] Step S301: Real-time monitoring of vibration signals and cutting force data contained in the state mapping matrix, and determination of abnormal mode distribution in the state mapping matrix based on preset threshold data to obtain abnormal mode characteristics.

[0056] Step S302: Match the abnormal mode features with the ideal trajectory of the tool point by point to obtain the quantified value of the deviation between the abnormal mode features and the ideal trajectory at each time point.

[0057] Step S303: The deviation quantization values ​​obtained at different time points are integrated to obtain the tool trajectory deviation quantization result.

[0058] In this embodiment, the abnormal pattern distribution refers to the distribution of datasets showing deviations of the tool's operating state from the normal range during machining, typically reflecting the unstable performance of the equipment within a specific time period. Abnormal pattern features are key characteristics or indicators extracted from these abnormal pattern distributions, used to specifically describe abnormal behavior during tool operation. The ideal tool trajectory is a pre-set tool movement path based on the machining task, representing the precise route the tool should follow under ideal conditions. The deviation quantification value is a specific numerical expression of the degree of difference between the actual tool trajectory and the ideal trajectory at each point in time. The trajectory deviation quantification result is an overall assessment derived from a comprehensive analysis of all deviation quantification values ​​throughout the entire machining process, demonstrating the actual deviation of the tool relative to the ideal trajectory throughout the entire machining cycle.

[0059] In some embodiments, the process of determining the quantification result of tool trajectory deviation based on the state mapping matrix includes real-time monitoring of vibration signals and cutting force data contained in the state mapping matrix, and identifying abnormal pattern distributions in the state mapping matrix based on preset threshold data to obtain abnormal pattern characteristics. For example, during CNC machine tool machining, by comparing the real-time acquired vibration signal data with preset threshold ranges one by one, abnormal pattern distributions exceeding the threshold can be quickly identified. Suppose that real-time monitoring detects that the vibration signal value reaches 4.0 units within a certain period of time, the anomaly is immediately marked, and its duration and fluctuation amplitude are recorded to form specific abnormal pattern characteristic data, providing a solid foundation for subsequent analysis.

[0060] As a feasible implementation, the aforementioned abnormal pattern characteristics are matched point-by-point with the ideal tool trajectory, and the deviation quantification value between the abnormal pattern characteristics and the ideal trajectory at each time point is calculated. For example, during tool cutting, if the ideal trajectory requires vibration signal fluctuations to be no more than 0.5 units, but actual monitoring reveals a deviation value of 0.8 units for a certain segment of the trajectory, the deviation value at each time point can be quantified through point-by-point processing. This method can accurately capture every potential problem that may affect machining accuracy, thereby taking timely corrective measures to ensure high precision in the machining process.

[0061] As a specific implementation, the quantified deviation values ​​obtained at different time points are integrated to ultimately obtain the quantified result of the tool trajectory deviation. Through systematic analysis and integration of the deviation values ​​at each time point, not only can the changing trend of trajectory deviation throughout the entire machining process be clearly understood, but also potential future deviation accumulation can be effectively predicted. This comprehensive data processing method not only improves the depth of understanding of the dynamic changes in the machining process but also provides a scientific basis for further optimizing machining parameters, ensuring the stable operation of machining equipment under various working conditions and improving production efficiency and product quality. Through such a closed-loop control system, real-time monitoring and dynamic adjustment of the tool status are achieved, providing a reliable quality assurance solution for intelligent manufacturing.

[0062] Figure 4 A flowchart illustrating a method for determining the cutting path offset corresponding to the trajectory deviation quantization result is shown, as follows: Figure 4 As shown, it includes the following steps.

[0063] Step S401: Using the real-time position data of the tool collected by the processing equipment, the cutting path represented by the real-time position data and the trajectory deviation quantification result is compared to obtain the deviation distribution characteristics.

[0064] Step S402: By analyzing the distribution range of the deviation distribution characteristics, the deviation accumulation trend is dynamically extrapolated to obtain the deviation accumulation trend prediction result.

[0065] Step S403: Combine real-time position data with the prediction results of deviation accumulation trend to determine the potential offset value of the cutting path, which is used as the cutting path offset corresponding to the trajectory deviation quantification result.

[0066] In this embodiment, real-time position data refers to the specific position information of the tool or workpiece collected in real time by sensors and other devices during the machining process, reflecting the precise movement trajectory of the tool in three-dimensional space. The trajectory deviation quantification result is a quantitative analysis of the difference between the actual tool trajectory and the preset ideal trajectory, showing the cumulative deviation throughout the machining process. Deviation distribution characteristics are key features extracted from these trajectory deviations, describing the magnitude, frequency, and location of the deviations. Deviation accumulation trend refers to the pattern or tendency of how these deviations gradually change over time as the machining process progresses. The deviation accumulation trend prediction result is a conclusion based on historical data and current deviation distribution characteristics, predicting the potential growth or change of deviations over a future period. The potential offset value of the cutting path is calculated based on the deviation accumulation trend prediction result, representing the maximum offset distance that the actual tool path may produce relative to the ideal path at a future moment without any corrective measures, used to guide subsequent parameter adjustments and control strategy optimization.

[0067] In some embodiments, the method for determining the cutting path offset corresponding to the trajectory deviation quantification result includes using real-time tool position data collected by the machining equipment, comparing the deviations of these real-time position data with the cutting paths represented by the trajectory deviation quantification result, and obtaining the deviation distribution characteristics. Specifically, in the machining task of a CNC machine tool, the preset cutting path requires the tool to be 10.5 units in a certain axis, while the actual collected data shows that the position is 10.8 units. A deviation of 0.3 units is recorded, and the distribution characteristics of this deviation along the entire path are further analyzed to form deviation range data, providing a basis for subsequent processing.

[0068] As a feasible implementation, the deviation accumulation trend is dynamically extrapolated based on the distribution range of the deviation distribution characteristics obtained above, resulting in a prediction of the deviation accumulation trend. For example, in a certain machining path, if historical data shows an average offset of 0.4 units, and combined with the current machining speed and tool status, it can be extrapolated that the deviation may accumulate to 0.7 units in the future. This dynamic extrapolation method based on historical and real-time data helps to identify potential risk points in advance, thereby enabling the implementation of corresponding preventive measures.

[0069] As a specific implementation, by combining real-time position data with the predicted deviation accumulation trend, the potential offset value of the cutting path is determined as the cutting path offset corresponding to the trajectory deviation quantification result. For example, in actual operation, assuming that real-time position data shows that the tool deviates from the predetermined path by 0.3 units, and the deviation accumulation trend predicts that it may accumulate to 0.7 units in the future, the system will calculate this potential offset value of 0.7 units as a reference for adjusting the cutting path. In this way, not only can the deviation at each point in time be accurately captured, but the potential accumulation of deviation in the future can also be effectively predicted, ensuring the accuracy and stability of the machining process. This method not only improves machining accuracy but also reduces product quality problems caused by trajectory deviation, providing a reliable quality assurance solution for intelligent manufacturing.

[0070] In some embodiments, the cutting parameters of the tool include cutting speed and feed rate.

[0071] Figure 5 A flowchart illustrating a method for optimizing tool cutting parameters based on cutting path offset is shown, as follows: Figure 5 As shown, it includes the following steps.

[0072] Step S501: Based on the cutting path offset and combined with environmental variables in the machining environment, record the real-time data of cutting speed and feed rate, and obtain the fluctuation characteristics of the cutting process from the records.

[0073] Step S502: Each fluctuation feature is matched with a preset feature threshold item by item, so as to generate a parameter adjustment instruction when a fluctuation feature exceeds the preset feature threshold.

[0074] Step S503: Determine the adjusted cutting speed and feed rate values ​​according to the parameter adjustment command, and perform a virtual simulation based on the adjusted cutting speed and feed rate values ​​and the machining accuracy requirements to obtain the potential change trend of each cutting parameter from the simulation.

[0075] Step S504: If the potential trend of change meets the machining accuracy requirements, the adjusted cutting speed and feed rate values ​​are transmitted to the control module of the machining equipment, and feedback data characterizing the operating status of the machining equipment are obtained from the control module.

[0076] Step S505: Based on the feedback data, the applicability of the adjusted cutting speed and feed rate values ​​is evaluated. If the applicability evaluation results indicate that the machining accuracy requirements are not met, the adjusted cutting speed and feed rate values ​​are further optimized.

[0077] In this embodiment, the fluctuation characteristics during the cutting process refer to the data characteristics reflecting changes in the tool's operating state collected by sensors during machining, such as vibration frequency, temperature changes, and cutting force fluctuations. These characteristics reveal the dynamic behavior of the tool and equipment under different working conditions. The preset characteristic threshold is a standard range set according to the requirements of the machining task and the equipment performance, used to determine whether the real-time monitored fluctuation characteristics are within the normal range. The parameter adjustment command is a specific instruction automatically generated by the system when the detected fluctuation characteristics exceed the preset threshold, used to guide the adjustment of tool or equipment operating parameters (such as cutting speed and feed rate) to restore stable operation. Machining accuracy requirements refer to the precision required for the machining process to meet product design standards, including specific indicators such as dimensional tolerances and surface finish. The potential change trend of cutting parameters is predicted based on current working conditions and historical data, indicating the possible direction and magnitude of changes in the cutting process after adjusting certain key parameters. Feedback data characterizing the operating state of the machining equipment is actual operating information collected from the equipment's sensors, used to verify the effect of parameter adjustments, ensure that the equipment operates as expected, and support further optimization of control strategies. These concepts together form a closed-loop control system that enables intelligent monitoring and optimization of the processing, thereby improving production efficiency and product quality.

[0078] As a specific example, in a CNC machine tool machining task, the cutting speed is set to 500 units / minute and the feed rate is 0.2 units / revolution. During actual operation, the cutting speed fluctuates to 510 units / minute due to the increase in ambient temperature. This recording method helps to identify the reasons behind the parameter fluctuations and provides a basis for subsequent adjustments.

[0079] As a feasible implementation, each fluctuation characteristic is matched against a preset characteristic threshold to determine whether any situation exceeds the preset threshold. For example, assuming the preset cutting speed threshold range is 490 to 510 units / minute, if the actual speed reaches 515 units / minute, it exceeds the preset range, and the system will generate a parameter adjustment command. This comparison mechanism can promptly detect anomalies and provide a scientific basis for subsequent adjustments.

[0080] As a specific implementation, the adjusted cutting speed and feed rate values ​​are determined based on the generated parameter adjustment instructions, and a virtual simulation is performed in conjunction with machining accuracy requirements to predict potential trends after adjustment. For example, assuming the adjusted cutting speed is reduced to 500 units / minute while the feed rate remains unchanged, simulation based on historical data and current environmental variables may reveal that machining stability improves after the speed reduction, but machining time is slightly extended. This simulation method not only provides a reference direction for dynamic optimization but also helps determine whether the machining accuracy requirements are met.

[0081] For example, to meet the needs of adaptive control, the adjusted cutting speed and feed rate values ​​are transmitted to the control module of the machining equipment, and feedback data characterizing the operating status of the machining equipment is obtained from the control module. For instance, after the adjusted speed of 500 units / minute and feed rate of 0.2 units / revolution are sent to the control module, the feedback data shows that the tool runs smoothly with the fluctuation range reduced to ±0.1 units. This synchronization mechanism ensures the actual execution effect of parameter adjustment, and at the same time, the applicability of the configuration scheme is verified through feedback data, laying the foundation for subsequent optimization.

[0082] Furthermore, the applicability of the adjusted cutting speed and feed rate values ​​is evaluated based on feedback data. If the evaluation results show that they do not fully meet the machining accuracy requirements, the adjusted cutting speed and feed rate values ​​need to be optimized again. For example, if the initial adjustment fails to completely resolve the fluctuation problem during machining, the system will recalculate the optimal parameter configuration based on new feedback data until the machining accuracy requirements are met. The entire process forms a closed-loop control system, from the extraction of fluctuation characteristics to the implementation of parameter adjustments, and finally to the evaluation of the effect, ensuring high precision and stability of the machining process and providing reliable quality assurance for intelligent manufacturing.

[0083] Figure 6 A flowchart illustrating a method for tool position calibration using optimized cutting parameters is shown, such as... Figure 6 As shown, it includes the following steps.

[0084] Step S601: Monitor the real-time position data of the tool.

[0085] In step S602, when the real-time position data indicates that the tool offset value exceeds the preset offset threshold, the real-time position data is compared and processed through a closed-loop feedback mechanism to generate a corresponding deviation correction value for the real-time position data.

[0086] Step S603: Combine the deviation correction value with the optimized cutting parameters to perform tool position calibration.

[0087] In this embodiment, the tool offset value refers to the degree of deviation of the actual tool position from its ideal trajectory or predetermined position during machining, reflecting the positioning error during tool operation. The offset threshold is a maximum allowable deviation range set according to machining accuracy requirements. Once the actual offset value exceeds this threshold, the tool operation is considered abnormal and requires adjustment. The closed-loop feedback mechanism is a control system design method that monitors the actual operating state of the tool in real time, compares this data with a preset ideal state, and automatically adjusts control parameters based on the difference to correct the deviation, ensuring that the system output meets the expected target. The deviation correction value corresponding to the real-time position data is a specific value calculated based on the current position data after detecting that the tool offset exceeds the threshold, used to guide how to adjust the tool position, including the direction and magnitude of adjustment, so as to realign the tool onto the correct machining path.

[0088] As a specific example, in the machining task of a certain CNC machine tool, the ideal position of the tool is set to 100 units on the X-axis and 50 units on the Y-axis. However, actual monitoring found that the X-axis shifted to 102 units. This shift data not only helps to identify static position deviations, but also captures dynamic change trends, such as the shift of the tool caused by vibration during high-speed cutting, thus providing multi-dimensional basis for judging whether the shift is abnormal.

[0089] As a feasible implementation, when the real-time position data indicates that the tool offset exceeds a preset offset threshold, a closed-loop feedback mechanism is used to compare the real-time position data and generate a corresponding deviation correction value. For example, assuming the preset threshold range for the X-axis is 99 to 101 units, when the detected tool position is 102 units, the system will mark it as abnormal and match it against the preset value one by one to quickly identify the problem point. In this case, the system will calculate a correction instruction of 2 units in the negative direction to ensure the accuracy of the adjustment.

[0090] As a specific implementation, tool position calibration is performed by combining deviation correction values ​​with optimized cutting parameters. This process mainly involves the following steps: First, the deviation correction value is obtained by continuously monitoring the real-time position data of the tool during machining and comparing it with the preset ideal position to determine whether the actual position deviates from the ideal position. If the deviation exceeds the preset threshold range, a corresponding deviation correction value is generated, indicating the specific direction and magnitude of adjustment required. Second, based on the offset of the cutting path and complex variables in the current machining environment (such as temperature and humidity), an adaptive control algorithm is used to dynamically optimize the cutting parameters to ensure that optimal cutting conditions are maintained under different working conditions, thereby improving machining efficiency and quality. Finally, the position control module is used to dynamically correct the tool position. For example, assuming the correction command is to adjust the X-axis to 100 units, the position control module will drive the servo motor to perform fine-tuning and monitor in real time whether the corrected position meets the standard, with the error controlled within ±0.1 units, ensuring that the calibration accuracy meets the preset requirements.

[0091] For example, in CNC machine tools, position calibration is not a one-time operation but a continuous process. After the calibrated position data is transmitted to the control module, the system continues to monitor the equipment's operating status and collect feedback data to ensure the tool position remains stable within the ideal range. If a new deviation is detected, the system will restart the position calibration process, forming a closed-loop control system. For instance, after receiving a deviation correction value, the position control module will dynamically correct the tool position. Assuming the correction command is to adjust the X-axis to 100 units, the module will drive the servo motor to perform fine-tuning and monitor in real time whether the corrected position meets the standard. This synchronization mechanism ensures the real-time nature and reliability of parameter adjustments, while verifying the calibration effect through feedback data, providing a reference for subsequent optimization.

[0092] Furthermore, determining whether the calibration has met the preset standards is also crucial. Using the calibrated position information, combined with the needs of dynamic monitoring, the adjusted parameters are transmitted to the control module, and feedback data on the operating status is obtained from the module to determine the final applicability of the calibration accuracy. For example, after calibration, the adjusted parameters are transmitted to the control module, and operating status feedback is obtained. Assuming the tool position stabilizes at 100 units on the X-axis after calibration, the control module feedback shows smooth operation and reduced vibration amplitude. This synchronization mechanism ensures the real-time nature and reliability of parameter adjustments, while verifying the calibration effect through feedback data, providing a reference for subsequent optimization. All of the above steps form a complete closed-loop system, from position monitoring to calibration to feedback, each step is closely linked, ensuring precise control of the tool position, effectively addressing various uncertainties, and improving machining consistency.

[0093] Figure 7A flowchart illustrating a method for generating a stability compensation strategy for machining equipment based on position data and cutting parameters is shown, such as... Figure 7 As shown, it includes the following steps.

[0094] Step S701: Using the calibrated position data and optimized cutting parameters as monitoring standards, the operating status of the machining equipment is continuously monitored to obtain the status fluctuation value corresponding to when the machining equipment does not meet the monitoring standards.

[0095] Step S702: If the state fluctuation value exceeds the preset fluctuation threshold, a temporary instruction correction scheme is generated to adjust the control frequency and control amplitude of the processing equipment.

[0096] Step S703: Adjust the control commands of the processing equipment according to the temporary command correction scheme, and dynamically monitor the operating status of the processing equipment after adjustment to obtain the stability performance data of the processing equipment.

[0097] Step S704: Combining stability performance data and parameter optimization requirements, determine a stability compensation scheme for readjusting control commands, and determine the applicability of the stability compensation scheme under different operating conditions.

[0098] Step S705: The stability compensation scheme and its applicable scope are used as the stability compensation strategy for the processing equipment.

[0099] In some embodiments, to obtain the state fluctuation values ​​corresponding to when the processing equipment fails to meet monitoring standards, it is first necessary to monitor and collect data on the key parameters of the equipment operation in real time. This data is continuously transmitted to the monitoring system via a sensor network and compared with preset normal operating standards or threshold ranges. Once one or more parameters are detected to deviate from the preset standard range, it indicates that the equipment's operating state is abnormal. At this time, the system records the specific parameter values ​​and the degree of deviation at that moment as state fluctuation values. These state fluctuation values ​​not only reflect the current unstable state of the equipment but also provide important basis for subsequent problem diagnosis, parameter adjustment, and optimization of control strategies, ensuring that measures can be taken in a timely manner to restore the equipment to a stable operating state.

[0100] In some embodiments, high-frequency data acquisition can capture subtle fluctuations in equipment status, providing a basis for subsequent analysis. For example, during the machining process of a CNC machine tool, operating parameters such as spindle vibration frequency, rotational speed, and temperature changes are recorded in real time. Suppose that under specific operating conditions, the spindle vibration frequency is detected to rise from the normal value of 10 Hz to 15 Hz, while the preset threshold range is 8 to 12 Hz; this fluctuation will be marked as abnormal.

[0101] As a feasible implementation, if the monitored state fluctuation value exceeds a preset fluctuation threshold, a temporary instruction correction scheme is generated to adjust the control frequency and amplitude of the processing equipment. For example, when the vibration frequency reaches 15 Hz, the system may generate a temporary instruction to reduce the spindle speed from 3000 rpm to 2800 rpm by reducing the spindle speed or adjusting the feed rate. This adjustment aims to alleviate equipment overload or instability, and the feasibility of the adjustment is verified through real-time monitoring. It is important to note that the frequency and amplitude adjustments of the temporary instruction must be combined with the characteristics of the current operating conditions to avoid creating new instability factors due to excessive adjustments.

[0102] As a specific implementation, the control commands of the processing equipment are adjusted according to the temporary command correction scheme, and the operating status of the processing equipment after adjustment is dynamically monitored to obtain stability performance data. For example, after adjusting the rotational speed, the vibration frequency drops to 11 Hz, which is within the threshold range. The system will further record the continuous operating time and temperature change of the equipment in this state to determine whether the stability meets the expected standard. The collection of stability performance data not only focuses on a single indicator but also includes the comprehensive performance of multi-dimensional parameters, such as the correlation between vibration and temperature, thereby providing a comprehensive basis for subsequent optimization.

[0103] For example, based on the monitoring results of stability performance data and the requirements of parameter optimization, a stability compensation scheme is determined for readjusting control commands, and the applicability of the stability compensation scheme under different operating conditions is determined. Assuming that the vibration frequency meets the standard after the initial adjustment, but the equipment temperature rises slightly, the system may further fine-tune the rotational speed to 2850 rpm, while optimizing the operating frequency of the cooling system to balance vibration and temperature control. This secondary adjustment ensures the applicability of the scheme under different operating conditions. The determination of the compensation scheme also needs to consider the characteristics of the processed materials and the task cycle, specifically expanding the scope of application and improving the equipment's adaptability in complex environments.

[0104] For example, the aforementioned monitoring, adjustment, and optimization processes form a complete closed-loop system within the logical progression from the core solution to the extended solution. The core lies in timely detection and correction of equipment status fluctuations through data acquisition and feedback, while the extended solution ensures the stability and reliability of the equipment during long-term operation through comprehensive optimization of multi-dimensional parameters. For instance, based on the monitoring results of stability performance data, control commands are adjusted a second time to form the final stability compensation solution. Suppose that although the vibration frequency meets the standard after the initial adjustment, the equipment temperature slightly increases. The system may further fine-tune the rotation speed to 2850 rpm, while simultaneously optimizing the operating frequency of the cooling system to balance vibration and temperature control. This secondary adjustment ensures the applicability of the solution under different operating conditions.

[0105] In the above embodiments of this application, the stability compensation scheme and its applicable scope are used as a stability compensation strategy for processing equipment. This approach can effectively address uncertainties in the processing process, providing strong support for maintaining processing accuracy and equipment lifespan. Through this closed-loop control system, real-time monitoring and dynamic adjustment of the processing equipment's operating status are achieved, ensuring efficient and stable processing and improving production efficiency and product quality.

[0106] Figure 8 A flowchart illustrating a method for evaluating equipment performance according to a stability compensation strategy and obtaining the evaluation results is shown, as follows: Figure 8 As shown, it includes the following steps.

[0107] Step S801: Adjust the control commands of the machining equipment according to the stability compensation strategy, and monitor the tool status and equipment operation of the machining equipment to obtain state fluctuation data during the machining process.

[0108] Step S802: Integrate the status fluctuation data and the real-time data stream, and use the integrated data stream to evaluate the equipment performance and obtain the equipment performance evaluation result.

[0109] In this embodiment, the control commands for the machining equipment are adjusted according to a stability compensation strategy, and the tool status and equipment operation are continuously monitored to obtain state fluctuation data during the machining process. For example, in a CNC machine tool machining scenario, information such as tool vibration, wear, and temperature is recorded in real time. Assuming the tool vibration frequency is displayed as fluctuating 5 times per minute, while the normal range should be within 3 times, this fluctuation data will be recorded and used for subsequent analysis.

[0110] As a feasible implementation, state fluctuation data and real-time data streams are integrated and processed, and the integrated data stream is used to evaluate equipment performance to obtain the evaluation results. For example, during real-time monitoring, tool vibration data and equipment operating power data are integrated onto a single time axis to facilitate observation of their changing trends. Assuming that an increase in tool vibration frequency is accompanied by abnormal fluctuations in equipment power, this correlation will be further analyzed to determine potential causal relationships, providing a basis for comprehensive performance evaluation. In this way, a comprehensive understanding of the equipment's operating status and its changing trends can be achieved, ensuring data integrity and continuity and avoiding analytical biases caused by missing data.

[0111] As a specific implementation, the system comprehensively evaluates equipment performance based on the integrated data stream. By analyzing the correlations and trends between data points, it determines whether the equipment is operating stably and whether there is a risk of declining processing quality. For example, in a CNC machine tool's machining task, if the tool vibration frequency exceeds the normal range and the equipment power also fluctuates abnormally, the system will further analyze the correlations between these data points to determine if there are any potential problems. If unstable equipment operation or a risk of declining processing quality is detected, the system will generate corresponding evaluation results, providing a scientific basis for subsequent adjustments.

[0112] For example, in a specific application scenario, suppose the tool vibration frequency increases from the normal 3 times / minute to 5 times / minute, while the equipment power also fluctuates abnormally. The system integrates these fluctuation data with the real-time data stream and performs a comprehensive evaluation based on the integrated data. By analyzing the correlation and trends among these data, the system determines that the equipment may be overloaded or the tool wear may be accelerated, thus generating an equipment performance evaluation result. This process not only helps to identify potential problems in a timely manner but also provides important references for subsequent optimization, ensuring that the equipment can operate stably under various working conditions.

[0113] In this embodiment, the various steps form a complete closed-loop system from an overall process perspective. The core lies in the timely detection and correction of equipment status fluctuations through data acquisition and feedback. This technology, based on real-time monitoring and data fusion, effectively addresses uncertainties in the processing, ensuring processing accuracy and equipment lifespan. This closed-loop control system enables dynamic monitoring and intelligent management of the processing equipment's operating status, providing reliable quality assurance for intelligent manufacturing. Ultimately, the equipment performance evaluation results not only reflect the current operating status of the equipment but also provide a scientific basis for subsequent parameter optimization and control strategy adjustments, ensuring efficient and stable processing.

[0114] Figure 9 A flowchart illustrating a method for updating a state mapping matrix and optimizing the parameters of a processing device is shown, as follows: Figure 9 As shown, it includes the following steps.

[0115] Step S901: Classify and process the data of the equipment performance evaluation results, and extract the core feature values ​​related to processing quality and equipment wear.

[0116] Step S902: By comparing the core feature values ​​with the state mapping matrix, the parameters in the state mapping matrix are dynamically adjusted to obtain the state mapping logic corresponding to the adjusted state mapping matrix.

[0117] Step S903: If the state mapping logic meets the performance criteria, then the key parameters in the state mapping logic are calibrated to obtain optimized parameter values ​​suitable for feedback loops of the key parameters.

[0118] Step S904: Extract features from the optimized parameter values ​​and update the feature dataset based on the extracted features.

[0119] In this embodiment, the data from the equipment performance evaluation results are categorized to extract core feature values ​​related to processing quality and equipment wear. For example, in a CNC machine tool processing scenario, feature values ​​related to processing quality may include the surface roughness change rate, while feature values ​​related to equipment wear may involve the spindle bearing temperature rise rate. Through categorization, the surface roughness change rate is recorded as an increase of 0.3 units per hour, while the bearing temperature rise rate is 0.2 degrees per minute. These core feature values ​​will be used for subsequent risk level correlation analysis to simplify complex multi-source data and facilitate subsequent comparison and judgment.

[0120] As a feasible implementation, by comparing core feature values ​​with a state mapping matrix, the parameters in the state mapping matrix are dynamically adjusted to obtain the state mapping logic corresponding to the adjusted state mapping matrix, and it is determined whether the state mapping logic meets the operating performance standards of the processing equipment. For example, assuming that a surface roughness change rate exceeding 0.2 units corresponds to a medium risk, and a bearing temperature rise rate exceeding 0.15 degrees also corresponds to a medium risk, if the current data indicates a medium risk level, the system will dynamically adjust the matrix parameters based on real-time data, such as adjusting the temperature rise rate threshold from 0.15 degrees to 0.18 degrees to adapt to the current operating conditions. This dynamic adjustment ensures that the state mapping logic matches the actual operating performance.

[0121] As a specific implementation, after the state mapping logic meets the operational performance standards, the key parameters in the logic are calibrated to obtain optimized parameter values ​​suitable for the feedback loop. For example, assuming that after calibration, the threshold for the surface roughness change rate is adjusted from 0.2 units to 0.25 units, the optimized parameter value is verified to have a high degree of matching with the prediction of machining quality risks. This improved matching degree helps to more accurately identify potential problems. The state mapping logic converts the operating state of the tool and equipment into an understandable state expression through a series of algorithms or models. It establishes a correspondence between feature values ​​(such as the surface roughness change rate, the spindle bearing temperature rise rate, etc.) and preset risk levels to achieve an intuitive assessment of the equipment status.

[0122] For example, after completing the above steps, further feature extraction is performed on the optimized parameter values, and the feature dataset is updated based on the extracted features. Assuming the original monitoring frequency was once per minute, it is adjusted to once every 30 seconds to more promptly capture changes in equipment wear. The adjusted monitoring scheme can continuously track subtle changes in bearing temperature and surface roughness, providing more reliable data support for risk warning. This dynamic adjustment mechanism can flexibly change according to operating conditions, improving the adaptability of monitoring.

[0123] For example, when assessing whether a monitoring scheme can continuously track changes in equipment wear, the completeness of data before and after adjustments can be compared. Assuming that after adjustment, data coverage increases from 80% to 95%, it indicates that the scheme can better capture dynamic changes in equipment wear. This improvement helps to identify potential problems in a timely manner, ensuring the stability of processing quality and extending equipment life. The entire process not only achieves precise monitoring and intelligent management of tool and equipment status but also ensures efficient and stable processing, improving production efficiency and product quality. In this way, the system can maintain high precision and reliability in complex and ever-changing processing environments, providing strong support for intelligent manufacturing.

[0124] This application also provides system embodiments that follow the above embodiments, for implementing the method steps of the above embodiments. The interpretation of the same names is the same as that of the above embodiments, and they have the same technical effects as those of the above embodiments, so they will not be repeated here.

[0125] like Figure 10 As shown, this application provides an intelligent control system 1000 for a tool machining process, comprising:

[0126] The acquisition unit 1001 is used to acquire the tool operation data of the processing equipment.

[0127] Processing unit 1002 is used to process tool operation data into a feature dataset characterizing the tool state. Based on the feature dataset, a tool state mapping matrix is ​​generated, and the tool trajectory deviation quantization result is determined according to the state mapping matrix to determine the cutting path offset corresponding to the trajectory deviation quantization result. The cutting parameters of the tool are optimized according to the cutting path offset, and the tool position is calibrated using the optimized cutting parameters to obtain calibrated position data. Based on the position data and cutting parameters, a stability compensation strategy for the machining equipment is generated, and the equipment performance is evaluated according to the stability compensation strategy to obtain the equipment performance evaluation result. Based on the equipment performance evaluation result, the state mapping matrix and the parameter optimization logic of the machining equipment are updated.

[0128] Regarding the system in the above embodiments, the specific manner in which each module performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0129] Although the operations are described in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0130] The methods and systems of this application can be implemented using standard programming techniques, utilizing rule-based logic or other logic to implement various method steps. It should also be noted that the terms "system" and "module" as used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0131] Any step, operation, or procedure described herein may be performed or implemented using one or more hardware or software modules, either alone or in combination with other devices. In one embodiment, the software module is implemented using a computer program product comprising a computer-readable medium containing computer program code, which is executable by a computer processor to perform any or all of the described steps, operations, or procedures.

[0132] The foregoing description of implementations of this application has been provided for illustrative and descriptive purposes. The foregoing description is not exhaustive and is not intended to limit this application to the exact forms disclosed. Various modifications and variations may exist in accordance with the foregoing teachings, or may arise from practice of this application. These embodiments were chosen and described to illustrate the principles of this application and its practical application, enabling those skilled in the art to utilize this application in various implementations and modifications to suit the specific purpose of the concept.

[0133] Regarding the system in the above embodiments, the specific manner in which each module performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0134] It can be further understood that, unless otherwise specified, "connection" includes both direct connections where no other components exist between the two parties and indirect connections where other components exist between them.

[0135] It is further understood that although the operations are described in a specific order in the accompanying drawings in the embodiments of this application, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all the operations shown to be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0136] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the field of this application that are not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0137] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

[0138] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An intelligent control method for a cutting tool machining process, characterized in that, include: Collect tool operation data from the machining equipment and process it into a feature dataset characterizing the tool state; The tool's state mapping matrix is ​​generated based on the feature dataset, and the tool's trajectory deviation quantization result is determined based on the state mapping matrix, so as to determine the cutting path offset corresponding to the trajectory deviation quantization result; The cutting parameters of the tool are optimized according to the cutting path offset, and the position of the tool is calibrated using the optimized cutting parameters to obtain the calibrated position data. Based on the position data and the cutting parameters, a stability compensation strategy for the processing equipment is generated, and the equipment performance is evaluated according to the stability compensation strategy to obtain the equipment performance evaluation result. Based on the equipment performance evaluation results, update the state mapping matrix and the parameter optimization logic of the processing equipment; The cutting parameters of the tool include cutting speed and feed rate. The optimization of the cutting parameters of the tool according to the cutting path offset includes: Based on the cutting path offset and combined with environmental variables in the machining environment, real-time data of the cutting speed and feed rate are recorded, and the fluctuation characteristics of the cutting process are obtained from the records. Each of the aforementioned fluctuation features is matched against a preset feature threshold item by item, so that when a fluctuation feature exceeds the preset feature threshold, a parameter adjustment instruction is generated. The adjusted cutting speed and feed rate are determined according to the parameter adjustment instructions. Based on the adjusted cutting speed and feed rate, and combined with the machining accuracy requirements, a virtual simulation is performed to obtain the potential change trend of each cutting parameter from the simulation. If the potential trend of change meets the machining accuracy requirements, the adjusted cutting speed and feed rate values ​​are transmitted to the control module of the machining equipment, and feedback data characterizing the operating status of the machining equipment is obtained from the control module. The applicability of the adjusted cutting speed and feed rate values ​​is evaluated based on the feedback data, so that if the applicability evaluation results indicate that the machining accuracy requirements are not met, the adjusted cutting speed and feed rate values ​​are further optimized. The potential trend of cutting parameters is predicted based on current working conditions and historical data.

2. The method according to claim 1, characterized in that, The step of generating the state mapping matrix of the tool based on the feature dataset includes: Based on the vibration signals and cutting force fluctuations characterized by the feature dataset, the dynamic change data during the operation of the tool is organized to obtain a set of change patterns that reflect the periodic regularity of the dynamic change data; Based on the matching degree between the dynamically changing data and the preset changing data threshold, the pattern matching data corresponding to the dynamically changing data is determined; If the pattern matching data exceeds the preset matching data threshold, the set of variation patterns is corrected according to the vibration signal and the cutting force fluctuation to obtain the corrected set of variation patterns. A data mapping relationship is constructed using the dynamic change data and the pattern matching data, and the multidimensional signal data contained in the corrected change pattern set is integrated into the state mapping matrix of the tool.

3. The method according to claim 1, characterized in that, The step of determining the trajectory deviation quantization result of the tool based on the state mapping matrix includes: The vibration signals and cutting force data contained in the state mapping matrix are monitored in real time, and the distribution of abnormal modes in the state mapping matrix is ​​determined according to the preset threshold data in order to obtain the abnormal mode characteristics. The abnormal pattern features are matched point by point with the ideal trajectory of the tool to obtain the quantified value of the deviation between the abnormal pattern features and the ideal trajectory at each time point. The deviation quantification values ​​obtained at different time points are integrated to obtain the tool trajectory deviation quantification result.

4. The method according to claim 1, characterized in that, Determining the cutting path offset corresponding to the trajectory deviation quantification result includes: Using the real-time position data of the tool collected by the processing equipment, the deviation of the cutting path represented by the real-time position data and the trajectory deviation quantification result is compared to obtain the deviation distribution characteristics. By analyzing the distribution range of the aforementioned deviation distribution characteristics, the deviation accumulation trend is dynamically extrapolated to obtain the deviation accumulation trend prediction result. By combining the real-time position data with the deviation accumulation trend prediction result, the potential offset value of the cutting path is determined, which is used as the cutting path offset corresponding to the trajectory deviation quantification result.

5. The method according to claim 1, characterized in that, The step of calibrating the tool position using optimized cutting parameters includes: The real-time position data of the cutting tool is monitored; When the real-time position data indicates that the offset value of the tool exceeds a preset offset threshold, the real-time position data is compared and processed through a closed-loop feedback mechanism to generate a deviation correction value corresponding to the real-time position data. The tool position is calibrated by combining the deviation correction value with the optimized cutting parameters.

6. The method according to claim 1, characterized in that, The step of generating a stability compensation strategy for the machining equipment based on the position data and the cutting parameters includes: Using the calibrated position data and the optimized cutting parameters as monitoring standards, the operating status of the processing equipment is continuously monitored to obtain the state fluctuation value corresponding to when the processing equipment does not meet the monitoring standards. If the state fluctuation value exceeds the preset fluctuation threshold, a temporary instruction correction scheme is generated to adjust the control frequency and control amplitude of the processing equipment. The control commands of the processing equipment are adjusted according to the temporary command correction scheme, and the operating status of the processing equipment after adjustment is dynamically monitored to obtain the stability performance data of the processing equipment. Based on the stability performance data and parameter optimization requirements, a stability compensation scheme for readjusting the control commands is determined, and the applicability of the stability compensation scheme under different operating conditions is determined. The stability compensation scheme and its applicable scope are used as a stability compensation strategy for the processing equipment.

7. The method according to claim 1, characterized in that, The process of evaluating equipment performance according to the stability compensation strategy to obtain equipment performance evaluation results includes: The control commands of the processing equipment are adjusted according to the stability compensation strategy, and the tool status and equipment operation of the processing equipment are monitored to obtain state fluctuation data during the processing. The state fluctuation data and real-time data stream are integrated and processed, and the integrated data stream is used to evaluate the device performance to obtain the device performance evaluation result.

8. The method according to claim 1 or 7, characterized in that, The step of updating the state mapping matrix and the parameter optimization logic of the processing equipment based on the equipment performance evaluation results includes: The data from the equipment performance evaluation results are classified and processed to extract core feature values ​​related to processing quality and equipment wear. By comparing the core feature values ​​with the state mapping matrix, the parameters in the state mapping matrix are dynamically adjusted to obtain the state mapping logic corresponding to the adjusted state mapping matrix. If the state mapping logic meets the performance criteria, then the key parameters in the state mapping logic are calibrated to obtain optimized parameter values ​​suitable for feedback loops of the key parameters. Feature extraction is performed on the optimized parameter values, and the feature dataset is updated based on the extracted features.

9. An intelligent control system for a cutting tool machining process, characterized in that, A smart control method for implementing the tool machining process as described in any one of claims 1 to 8, comprising: The acquisition unit is used to acquire tool operation data from the processing equipment; The processing unit is configured to process tool operation data into a feature dataset characterizing the tool state; generate a tool state mapping matrix based on the feature dataset, and determine the tool trajectory deviation quantization result based on the state mapping matrix to determine the cutting path offset corresponding to the trajectory deviation quantization result; optimize the tool cutting parameters according to the cutting path offset, and perform position calibration on the tool using the optimized cutting parameters to obtain calibrated position data; generate a stability compensation strategy for the machining equipment based on the position data and the cutting parameters, evaluate the equipment performance according to the stability compensation strategy, and obtain an equipment performance evaluation result; and update the state mapping matrix and the parameter optimization logic of the machining equipment based on the equipment performance evaluation result.

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