Rapid calibration method for CNC machine tool tool bit

Abstract

The invention relates to the field of detection and calibration of numerical control machining equipment, and discloses a quick calibration method for a CNC machine tool cutter bit, which comprises the following steps: S1, collecting state data in the operation process of the cutter bit; s2, carrying out preprocessing on the state data; s3, inputting the preprocessed state data into a tool wear identification model, and outputting tool wear state information; s4, acquiring spatial point cloud data of the end part of the tool bit, and calculating spatial error information between the point cloud and a preset tool reference model; and S5, calculating the position offset and the attitude error of the tool bit in the three-dimensional space based on the space error information. By collecting the physical state data in the operation process of the tool bit and combining with the wear recognition model of the time sequence modeling mechanism, the wear state of the tool can be accurately recognized in real time, so that the problem of unstable recognition precision caused by environmental interference in a traditional method is solved, and the recognition accuracy and real-time performance are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of detection and calibration of numerical control (CNC) machining equipment, and in particular to a rapid calibration method for a CNC machine tool cutter head. Background Art

[0002] In modern CNC machining, the tool is a core component directly involved in material cutting, and its wear state has a decisive impact on machining accuracy. To ensure product quality, traditional methods often rely on manual periodic downtime inspections or use fixed-cycle replacement strategies. Although simple, these methods lack real-time performance and can easily lead to excessive maintenance or machining errors. In recent years, some studies have begun to attempt to introduce sensor monitoring and machine learning models to identify tool wear, but these methods mostly rely on a single physical parameter, such as vibration or cutting force, and ignore changes in spatial geometric morphology, resulting in limited recognition accuracy.

[0003] Current wear detection systems typically focus solely on the changing trends of characteristic signals and fail to effectively correlate with the actual three-dimensional tool topography. Even the slightest deviation in tool geometry is difficult for existing systems to detect quickly. Furthermore, many solutions only output wear status but lack subsequent compensation mechanisms. This disconnect between detection and compensation prevents a closed-loop information system and hinders a truly adaptive adjustment process.

[0004] Other methods rely on fixed model parameters and have limited adaptability to complex processing conditions. Once the environment or the object being processed changes, the model's prediction accuracy rapidly declines. These systems lack self-learning capabilities and can only rely on static training during the development phase, failing to continuously optimize based on actual usage. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a rapid calibration method for a CNC machine tool head, which solves the problems of the existing technology that relies on fixed model parameters and has poor detection effect.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for quickly calibrating a CNC machine tool cutter head, comprising the following steps: S1, collecting status data of the cutter head during operation; S2. Preprocessing the status data; S3, inputting the preprocessed state data into the tool wear recognition model and outputting tool wear state information; S4, obtaining spatial point cloud data of the end of the tool head, and calculating spatial error information between the point cloud and a preset tool reference model; S5. Calculating the position offset and posture error of the tool head in three-dimensional space based on the spatial error information; S6. Generate a tool calibration compensation amount based on the tool wear state information and the spatial error information; S7, converting the compensation amount into a numerical control system control instruction, and driving the CNC machine tool to adjust the position and angle; S8. Perform online updating of the recognition model based on the adjusted feedback data, where the updating includes structural adjustment or parameter optimization.

[0007] Preferably, the S1 state data includes at least one physical parameter collected by a sensor, and the physical parameter is one or more of force response, temperature change, and vibration signal.

[0008] Preferably, the S2 data preprocessing step includes the following sub-steps: S21, performing denoising processing on the collected data; S22, normalizing the denoised data; S23. Align the normalized data into the same time series format.

[0009] Preferably, the S3 tool wear identification model is constructed based on a time series modeling mechanism, and the model outputs the wear status of the tool according to the input time series data.

[0010] Preferably, the S4 spatial point cloud data is acquired through structured light scanning, laser ranging, image reconstruction or multi-angle projection technology.

[0011] Preferably, the S5 spatial error information is calculated by comparing the currently acquired point cloud data with a preset tool reference model, and outputting the spatial error of the tool head.

[0012] Preferably, the S6 position offset and posture error are obtained by comparing point cloud data with a standard three-dimensional model, and the offset includes linear offsets of three axes, and the posture error includes rotation angles around three axes.

[0013] Preferably, the S7 compensation amount is obtained by comprehensive calculation based on the wear state information and the spatial error information, and a compensation amount parameter suitable for the CNC machine tool is generated.

[0014] Preferably, the sensor is one or more of a strain gauge, a temperature sensor, an acceleration sensor, and a vibration sensor.

[0015] A rapid calibration system for a CNC machine tool tool head, comprising: A state acquisition unit is used to collect the physical state data of the cutter head during operation; Data preprocessing module, used to denoise, normalize and time-align the collected data; Wear identification module, used to identify tool wear status based on time series modeling mechanism; A three-dimensional acquisition unit is used to obtain point cloud topography data of the end of the tool head; The spatial registration module is used to compare the point cloud data with the standard model and output the position offset and posture error; A compensation amount generation module is used to calculate the tool calibration compensation amount based on wear state information and spatial error information; An instruction generation module is used to convert the compensation amount into a motion control instruction that can be recognized by the CNC system; The model updating module is used to dynamically update the structure or parameters of the wear identification model according to the feedback data.

[0016] The present invention provides a method for quickly calibrating a CNC machine tool head. It has the following beneficial effects: 1. The present invention collects the physical state data of the tool head during operation and combines it with a wear identification model based on a time series modeling mechanism to accurately identify the wear state of the tool in real time. This solves the problem of unstable recognition accuracy caused by environmental interference in traditional methods, thereby significantly improving the accuracy and real-time performance of recognition.

[0017] 2. This invention compares spatial point cloud data with a pre-set tool reference model and, through spatial registration technology, accurately calculates the tool head's displacement and posture errors. This technological breakthrough addresses the inability of traditional methods to accurately reflect deviations in complex geometric shapes, enabling more precise tool head calibration and effectively improving machining accuracy.

[0018] 3. This invention integrates wear status and spatial error information to generate efficient tool calibration compensation. Compared to traditional compensation methods, this invention can simultaneously consider wear and spatial error to accurately calculate compensation, effectively improving calibration efficiency and processing quality.

[0019] 4. This invention addresses the limited adaptability of traditional methods to dynamic changes by updating the wear identification model online and optimizing it based on real-time feedback data. This technological innovation enables dynamic adaptation of CNC machine tools to varying operating conditions, significantly improving the accuracy and stability of the machining process. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the method steps of the present invention; Figure 2 Schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 The embodiment of the present invention provides a method for quickly calibrating a CNC machine tool head, comprising the following steps: S1. Collecting status data of the cutter head during operation. The S1 status data includes at least one physical parameter collected by a sensor, wherein the physical parameter is one or more of a force response, a temperature change, and a vibration signal. The sensor is one or more of a strain gauge, a temperature sensor, an acceleration sensor, and a vibration sensor.

[0023] Specifically, in actual use, in order to obtain the operating status of the cutter head, the present invention sets various types of sensors on the tool holder structure or in its vicinity. It does not require unified deployment, and an appropriate sensing scheme can be selected for each type of processing condition. The strain gauge usually uses a resistance strain gauge, which is attached to the force path of the tool body and outputs voltage through a micro-deformation bridge to reflect the size and change of the cutting force. It has a fast response, is stable, and is suitable for continuous sampling. When the force on the tool fluctuates greatly during processing, the strain value suddenly changes, which can sensitively reflect the abnormal load and chipping risk of the cutter head. The temperature sensor is generally a thermocouple, mainly K type, which is installed near the cutting edge or near the chuck. It does not directly contact the cutting edge, but is close enough to the heat source.

[0024] Cutting temperature is a key indicator of wear. High-speed machining can cause dramatic temperature fluctuations, and the thermocouple response curve can indicate whether the tool head is within the normal thermal load range. Accelerometers are typically three-axis MEMS devices, magnetically or screwed to the tool holder or spindle housing. They detect subtle changes in vibration frequency to determine the tool's rigidity. Low-frequency amplification or high-frequency tailing in the spectrum often indicates blunt tool wear and unstable cutting. Laser vibration sensors are also used in some scenarios; their non-contact method is suitable for high-precision applications. Vibration sensor data can also be cross-validated with force signals to improve overall reliability.

[0025] Various sensor signals are sampled in real time via the A / D module. The sampling rate is set according to the operating conditions, typically above 5kHz to preserve key changes. This data is not used directly for judgment but serves as an important raw input for subsequent wear identification models. Due to the limited location of the sensors, data sources may not be consistent, but the system supports the coordinated operation of any one or more sensors, providing fault tolerance. Actual processing tests have revealed that relying solely on strain or temperature data can lead to recognition bias. However, multi-source fusion can effectively improve the sensitivity of state recognition and the stability of the judgment boundary.

[0026] S2. Preprocessing the status data; including the following sub-steps: S21, performing denoising processing on the collected data; S22, normalizing the denoised data; S23. Align the normalized data into the same time series format.

[0027] Specifically, after collecting raw data on the tool head's operating status, the system enters the preprocessing process. Unlike the traditional approach of directly feeding the data into the model, the data undergoes three steps of processing to remove interference, unify the scale, and ensure time synchronization. Let's start with denoising. Raw signals, especially those from strain gauges and accelerometers, contain a significant amount of high-frequency jitter, mechanical resonance, and inherent noise from the electronic system. If this noise is not removed, subsequent analysis will be largely in vain.

[0028] In actual processing, we use a modified Savitzky-Golay filter, with a window length dynamically set based on the sampling frequency, typically ranging from 11 to 21 points. This filtering method smooths the signal while preserving data trends, unlike the sliding average method, which tends to lose detail. For sudden signal fluctuations, such as the impact of chipping, a median filter is also used to protect against local outliers. Next is normalization. This seemingly simple step is actually crucial to the stability of the model during training.

[0029] Different sensors have different units, with dimensions varying by tens of times. Feeding the data directly into the model without normalization will only result in one type of data dominating the output. We use min-max normalization to scale each data set to the range [-1, 1]. We avoid using the Z-score due to real-time performance and computational cost considerations, as standard deviation calculations are too resource-intensive at the edge. Normalization eliminates the influence of absolute values on the data, making it more suitable for subsequent time series analysis using structure recognition. Finally, alignment is required. Different sensors may have slightly different sampling frequencies. Even with the same frequency, timeline misalignment can occur due to transmission delays or buffer misalignment.

[0030] The solution is to perform time series registration. We use a lightweight method called DTW (Dynamic Time Warping) to pair data segments during key periods. Although DTW is a dynamic programming algorithm, it is efficient for small sample sizes and can tolerate nonlinear misalignment.

[0031] Interpolation is also helpful. The system has built-in linear and cubic spline interpolation methods, which automatically switch based on the degree of curve change. Ultimately, each sensor data stream is organized into a vector sequence of consistent length and corresponding start and end times, which serves as the basic format for subsequent model input.

[0032] S3. Input the preprocessed status data into the tool wear recognition model and output the tool wear status information; the tool wear recognition model is constructed based on the time series modeling mechanism, and the model outputs the tool wear status according to the input time series data.

[0033] Specifically, after the pre-processed state data is input into the tool wear recognition model, the system analyzes the data through the time series modeling mechanism to output the tool wear state. The model is built based on the bidirectional gated recurrent unit (Bi-GRU) network to capture the temporal dependencies in time series data. The input data is the pre-processed time series data, represented as Where T is the number of time steps and n is the number of sensor channels.

[0034] For each time t, the calculation formula of the GRU model is as follows: Update gate: z t =σ(W z x t +U z h t-1 +b z ); Among them, x t is the input of the current time step, h t-1 is the hidden state at the previous moment, W z and U z is the weight of the update gate, b z is the bias term, and σ represents the Sigmoid activation function.

[0035] Reset gate: r t =σ(W r x t +U r h t-1 +b r ); Among them, W r and U r is the weight of the reset gate, b r is the bias term.

[0036] Candidate status: Among them, r t is the output of the reset gate, ⊙ represents the element-wise product, W h and U h is the weight of the candidate state, b h is the bias term, and tanh is the hyperbolic tangent activation function.

[0037] Current hidden state: Among them, z t is the output of the update gate, h t-1 is the hidden state at the previous moment, ht is the hidden state at the current moment. Through the bidirectional GRU structure, the model not only utilizes information from past moments but also incorporates information from future moments. The hidden state at each moment depends not only on the hidden state at the previous moment but also on data from future moments, obtaining complete temporal information through forward and backward bidirectional computation.

[0038] Finally, the forward and backward hidden state vectors h f and h b are concatenated to obtain the final hidden state vector Finally, the hidden state h t It will be sent to the fully connected layer for linear transformation and output the predicted value of tool wear status This value indicates the degree of tool wear and ranges from [0,1], where 0 indicates no tool wear and 1 indicates complete tool wear.

[0039] The loss function uses the mean square error (MSE), and the calculation formula is: Among them, w i is the true wear label of the i-th sample, is the predicted wear value of the i-th sample, and N is the total number of samples.

[0040] During the training process, the model parameters are optimized through the gradient descent algorithm (such as the Adam optimizer), with the goal of minimizing the loss function L.

[0041] During training, hyperparameters (such as learning rate, number of hidden layer units, etc.) can be adjusted through cross-validation to obtain the best model performance.

[0042] After training, the model can predict the wear status of the tool in real time based on the new sensor data. If the error exceeds a set threshold (e.g., 0.7), the system triggers tool compensation or tool replacement. To avoid misjudgments, the system incorporates an additional judgment mechanism that requires multiple (e.g., three) consecutive predictions to exceed the threshold before compensation is executed, thereby improving system stability and accuracy.

[0043] S4. Acquire spatial point cloud data of the tool head end and calculate spatial error information between the point cloud and a preset tool reference model; the spatial point cloud data is acquired through structured light scanning, laser ranging, image reconstruction or multi-angle projection technology.

[0044] Specifically, in this step, the system acquires spatial point cloud data of the tool tip and calculates the spatial error between this data and a pre-set tool reference model to determine the tool's geometric deviation and wear. The point cloud data provides high-precision information on the tool's surface topography, reflecting the tool tip's true shape and position.

[0045] Spatial point cloud data is acquired using a variety of high-precision measurement technologies, including structured light scanning, laser ranging, image reconstruction, and multi-angle projection. Structured light scanning generates a 3D surface point cloud by projecting a known light spot pattern and capturing its deformation. This technology can generate high-density point cloud data in a short time, making it suitable for applications with complex tool surface features.

[0046] Laser ranging technology calculates distance by using the time difference between laser beam reflections to generate precise point cloud data. This technology is typically used in high-precision measurement environments. Image reconstruction technology captures multiple images from different angles and uses computer vision algorithms to perform three-dimensional reconstruction to form a surface point cloud. Multi-angle projection technology relies on projections from multiple angles to increase the density and accuracy of point cloud data and is suitable for measuring large surfaces.

[0047] Based on the specific processing needs and accuracy requirements, the system selects the appropriate point cloud acquisition method. When the acquired point cloud data has sufficient density and resolution, it can clearly depict the surface topography of the tool head and provide a basis for subsequent error calculations.

[0048] The acquired point cloud data of the tool tip typically contains millions of 3D coordinate points representing the tool tip's surface geometry. To analyze the tool's shape deviations, the system compares this point cloud data with a pre-set tool reference model. This pre-set reference model, typically generated by CAD software, represents the ideal geometry of the tool in its unworn state.

[0049] The core of the calculation process lies in comparing the spatial errors between the point cloud data and the reference model. First, the system needs to register the point cloud data. Because actual point cloud data may contain errors such as offset and rotation during measurement, the goal of registration is to align the acquired tool head point cloud with the point cloud of the reference model.

[0050] The specific calculation process is: The reference model point cloud is set to P = {pi}, and the obtained tool tip point cloud is set to Q = {qi}, where pi and qi represent points on the reference model and the actual tool tip surface, respectively. The ICP algorithm calculates the minimum distance between P and Q to obtain the rotation matrix R and translation vector t, so that the two sets of point clouds are optimally matched: Once the point cloud data is successfully registered, the spatial error of each point can be calculated. The error calculation method is usually based on the Euclidean distance between the point cloud data and the reference model. For each matching point p i and q i , the error is: e i =||p i -q i ||; where ei is the spatial error of the i-th point, p i and q i are the spatial coordinates of the benchmark model and the actual measurement point respectively. By calculating the errors of all points, the overall error distribution is obtained.

[0051] Once the spatial error information is calculated, the system uses this data to assess tool wear. If the error exceeds a preset tolerance, the system triggers tool compensation or tool replacement. This error information not only identifies tool wear but also serves as input into the tool compensation algorithm to correct for tool position deviations caused by wear during machining, thereby improving machining accuracy.

[0052] S5. Based on the spatial error information, the position offset and posture error of the tool head in three-dimensional space are calculated; the spatial error information is calculated by comparing the currently acquired point cloud data with the preset tool reference model, and the spatial error of the tool head is output.

[0053] Specifically, in this step, the system further calculates the tool head's positional offset and attitude error in three-dimensional space based on the previously calculated spatial error information. By comparing the spatial error between the currently acquired point cloud data and the preset tool reference model, the tool head's offset and attitude change are determined, providing a basis for subsequent compensation and adjustment.

[0054] In this step, the system further calculates the position offset and posture error of the tool head in three-dimensional space based on the spatial error information calculated previously. By comparing the spatial error between the currently acquired point cloud data and the preset tool reference model, the offset and posture change of the tool head can be obtained, thus providing a basis for subsequent compensation and adjustment. The rotation matrix R and translation vector t obtained by the ICF algorithm can describe the position offset of the tool head in three-dimensional space. The relationship between the rotation matrix R and the translation vector t is as follows: P transformed =R·P original +t; Among them, P transformed is the transformed point cloud, P original is the original reference model point cloud, R is the rotation matrix, and t is the translation vector.

[0055] Using these parameters, the system can calculate the spatial position offset of the tool head relative to the reference model. The specific translation t represents the position offset of the tool head in the X, Y and Z directions, which can be calculated as follows: Among them, q i and p iare the corresponding points in the measured point cloud and the reference model, respectively, with n being the total number of points. This translation vector represents the spatial offset of the tool head relative to its ideal position and provides information about any positional changes. The attitude error reflects the change in the tool head's rotation angle relative to the preset reference model. The rotation matrix R describes the rotational transformation of the tool head in three-dimensional space. By solving R, we can determine the tool head's attitude error, i.e., the tool head's rotation angle around each axis.

[0056] The rotation matrix R can be represented by decomposing it into three Euler angles: the angles of rotation about the X, Y, and Z axes. By calculating the tool head's spatial offset and attitude error, the system can adjust the tool head's position and angle in real time to ensure tool accuracy during machining. If the position offset and attitude error exceed the preset tolerance range, the system triggers compensation.

[0057] The compensation process updates the tool path instructions in the CNC system, adjusting the position and posture of the tool head to ensure machining accuracy. By comparing the calculated spatial error and position offset, the system can also correct for tool wear. For example, if the tool deflects during machining, the system can make appropriate corrections to the tool path based on the calculated translation and rotation angle, thereby reducing machining errors caused by wear or deformation.

[0058] S6. Generate tool calibration compensation based on tool wear status information and spatial error information; position offset and posture error are obtained by comparing point cloud data with the standard three-dimensional model, and the offset includes the linear offset of the three axes, and the posture error includes the rotation angle around the three axes.

[0059] Specifically, in this step, the system generates tool calibration compensation based on tool wear status and spatial error information. By comparing the current tool's point cloud data with a standard 3D model, the system calculates the tool's spatial error, including position offset and attitude error. Position offset includes linear offsets along the three axes, while attitude error includes rotation angles around the three axes. Based on this error information, the system generates corresponding compensation to correct tool errors during machining and ensure machining accuracy.

[0060] First, the system obtains the position offset and posture error of the tool head in three-dimensional space through the spatial error information obtained previously.

[0061] The position offset is usually expressed as a translation vector t = [t x ,t y ,t z ], where t x , t y and t zRespectively represent the position offset of the tool in the X, Y and Z axis directions.

[0062] The attitude error is expressed as a rotation angle, usually expressed as the rotation angle θ around the X, Y and Z axes x ,θ y and θ z These errors are calculated by comparing the spatial differences between the actual point cloud data of the tool head and a standard tool benchmark model.

[0063] The core of generating tool calibration compensation is to convert these error information into actual NC compensation instructions. The compensation is divided into two parts: position compensation and posture compensation.

[0064] The position compensation is calculated based on the linear offset of the tool head and is expressed as the translation of the tool head in the three coordinate axes (X, Y, Z). Based on the previously calculated translation vector t = [t x ,t y ,t z ], the compensation amount will adjust the position of the tool head accordingly in the CNC system. These adjustments are transmitted to the CNC machine tool through CNC codes (such as G43.4 or G68.2 commands) to align the tool position with the reference model.

[0065] The posture compensation is calculated based on the rotation error of the tool head and is expressed as the rotation angle deviation around the X, Y and Z axes. x ,θ y and θ z , the attitude compensation adjusts the angle of the cutter head to eliminate rotation errors.

[0066] By adjusting the tool posture in the CNC system, the tool angle is made to meet the preset standard reference. The steps for generating the compensation amount are as follows: First, the system calculates the position offset t = [t x ,t y ,t z ] and attitude error θ x ,θ y ,θ z Generate tool compensation. The calculation formula of compensation is: Among them, t x , t y , t z is the position compensation, θ x ,θ y ,θ z The generated compensation amount will be transmitted to the CNC system, and the instruction will be converted into the corresponding tool path adjustment command to ensure the accurate positioning and posture of the tool.

[0067] Through this compensation mechanism, the system can dynamically calibrate the tool in real time based on tool wear and spatial errors. This process not only compensates for tool wear but also effectively addresses geometric changes caused by factors such as tool force and temperature variations, improving machining quality and efficiency.

[0068] S7. Convert the compensation amount into a CNC system control instruction and drive the CNC machine tool to adjust the position and angle; the compensation amount is calculated based on the wear state information and the spatial error information, and a compensation amount parameter suitable for the CNC machine tool is generated.

[0069] Specifically, in this step, the system converts the calculated compensation amount into a numerical control system control instruction, which drives the CNC machine tool to adjust the position and angle. The compensation amount is calculated based on the tool wear status information and spatial error information.

[0070] Using previously calculated compensation values (including position offsets and posture errors), the system generates compensation parameters appropriate for the CNC machine tool, ensuring tool accuracy during machining. These compensation parameters are converted into control instructions for the CNC system, typically using standard CNC languages such as G-code and M-code.

[0071] Compensation commands are transmitted to the CNC machine tool via the numerical control system, and the corresponding adjustments are executed in the machine control system. Position compensation modifies the tool's current coordinates to adjust the tool's position in three-dimensional space to eliminate offsets caused by wear or errors. Attitude compensation corrects the tool's attitude by adjusting the tool's rotation angle, ensuring that the tool angle is consistent with the preset standard.

[0072] Specifically, the position compensation amount can be transmitted through CNC code instructions such as G43.4 or G68.2, and the posture compensation can be processed through corresponding angle adjustment instructions (such as G68 or M19) to ensure that each operating position and posture of the tool are accurately corrected.

[0073] S8. Based on the adjusted feedback data, the recognition model is updated online, and the update includes structural adjustment or parameter optimization.

[0074] Specifically, in this step, the system updates the recognition model online based on the adjusted feedback data, ensuring that the model continuously adapts to changes in tool wear and the machining environment. The update process involves both model structure adjustment and parameter optimization. First, the online update involves collecting feedback data from the CNC machine after compensation. This data includes the actual position and posture of the tool after compensation, as well as changes in machining accuracy.

[0075] By comparing it with the previously calculated compensation amount, the system can identify the model's prediction error, thus providing a basis for subsequent optimization. Structural adjustments usually involve modifying the model architecture to adapt to new processing conditions or improve the model's ability to identify specific wear types. Parameter optimization is to reduce the model's prediction error and improve its prediction accuracy of future wear conditions by training or fine-tuning the model's parameters. In terms of parameter optimization, the system will adjust the model's weights and biases through gradient descent algorithms or other optimization methods to reduce the difference between the model output and the actual feedback data.

[0076] Please see the attached Figure 2 , a rapid calibration system for a CNC machine tool head, comprising: A state acquisition unit is used to collect the physical state data of the cutter head during operation; Data preprocessing module, used to denoise, normalize and time-align the collected data; Wear identification module, used to identify tool wear status based on time series modeling mechanism; A three-dimensional acquisition unit is used to obtain point cloud topography data of the end of the tool head; The spatial registration module is used to compare the point cloud data with the standard model and output the position offset and posture error; A compensation amount generation module is used to calculate the tool calibration compensation amount based on wear state information and spatial error information; An instruction generation module is used to convert the compensation amount into a motion control instruction that can be recognized by the CNC system; The model updating module is used to dynamically update the structure or parameters of the wear identification model according to the feedback data.

[0077] Specifically, the rapid calibration system for CNC machine tool heads consists of multiple modular units to ensure accurate and real-time calibration of the tool during machining. The system's workflow begins with the state acquisition unit, which uses a series of sensors to monitor the physical state data of the tool head, such as cutting force, temperature, and vibration signals, in real time. This data is denoised, normalized, and time-aligned in the data preprocessing module to ensure data quality and consistency. The preprocessed data is then fed into the wear identification module, which uses a time series modeling mechanism to identify the wear state of the tool based on historical data, thereby determining the current degree of wear.

[0078] On this basis, the 3D acquisition unit obtains high-precision point cloud topography data from the end of the tool head and captures the geometric features of the tool surface. The spatial registration module is responsible for comparing these point cloud data with the standard tool model and calculating the position offset and posture error of the tool in three-dimensional space. These position offsets and posture errors, as well as wear status information, are input into the compensation generation module, which calculates the calibration compensation of the tool based on this information to ensure the precise position and posture of the tool during the processing process. Next, the compensation amount is converted into control instructions suitable for the CNC system through the instruction generation module. These instructions will directly drive the CNC machine tool to perform the corresponding compensation operations and adjust the tool's motion trajectory and posture.

[0079] To improve the system's adaptability and accuracy, the model update module dynamically updates the wear identification model based on feedback data from actual machining, optimizing its structure and parameters to better adapt to varying machining conditions and wear patterns. Ultimately, this enables accurate identification and real-time calibration of tool wear. Through modular collaboration, the entire system achieves rapid, automated calibration and compensation of tools, effectively improving machining quality and production efficiency.

[0080] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for rapid calibration of a CNC machine tool head, characterized in that: The steps include: S1, collecting status data of the cutter head during operation; S2. Preprocessing the status data; S3, inputting the preprocessed state data into the tool wear recognition model and outputting tool wear state information; S4, obtaining spatial point cloud data of the end of the tool head, and calculating spatial error information between the point cloud and a preset tool reference model; S5. Calculating the position offset and posture error of the tool head in three-dimensional space based on the spatial error information; S6. Generate a tool calibration compensation amount based on the tool wear state information and the spatial error information; S7, converting the compensation amount into a numerical control system control instruction, and driving the CNC machine tool to adjust the position and angle; S8. Perform online updating of the recognition model based on the adjusted feedback data, where the updating includes structural adjustment or parameter optimization.

2. The method for rapid calibration of a CNC machine tool cutter head according to claim 1, wherein: The S1 state data includes at least one physical parameter collected by a sensor, and the physical parameter is one or more of a force response, a temperature change, and a vibration signal.

3. The rapid calibration method for a CNC machine tool tool head according to claim 1, characterized in that: The S2 data preprocessing step includes the following sub-steps: S21, performing denoising processing on the collected data; S22, normalizing the denoised data; S23. Align the normalized data into the same time series format.

4. The rapid calibration method for a CNC machine tool tool head according to claim 1, characterized in that: The S3 tool wear identification model is constructed based on a time series modeling mechanism, and the model outputs the tool wear status according to the input time series data.

5. The rapid calibration method for a CNC machine tool cutter head according to claim 1, wherein: The S4 spatial point cloud data is acquired through structured light scanning, laser ranging, image reconstruction or multi-angle projection technology.

6. The rapid calibration method for a CNC machine tool cutter head according to claim 1, wherein: The S5 spatial error information is calculated by comparing the currently acquired point cloud data with a preset tool reference model, and outputting the spatial error of the tool head.

7. The rapid calibration method for a CNC machine tool cutter head according to claim 1, characterized in that: The S6 position offset and posture error are obtained by comparing point cloud data with a standard three-dimensional model, and the offset includes the linear offset of the three axes, and the posture error includes the rotation angle around the three axes.

8. The rapid calibration method for a CNC machine tool cutter head according to claim 1, wherein: The S7 compensation amount is calculated based on the wear state information and the spatial error information, and generates compensation amount parameters that are suitable for the CNC machine tool.

9. The method for rapid calibration of a CNC machine tool tool head according to claim 2, wherein: The sensor is one or more of a strain gauge, a temperature sensor, an acceleration sensor, and a vibration sensor.

10. A rapid calibration system for a CNC machine tool cutter head, according to a rapid calibration method for a CNC machine tool cutter head according to any one of claims 1 to 9, characterized in that: include: A state acquisition unit is used to collect the physical state data of the cutter head during operation; Data preprocessing module, used to denoise, normalize and time-align the collected data; Wear identification module, used to identify tool wear status based on time series modeling mechanism; A three-dimensional acquisition unit is used to obtain point cloud topography data of the end of the tool head; The spatial registration module is used to compare the point cloud data with the standard model and output the position offset and posture error; A compensation amount generation module is used to calculate the tool calibration compensation amount based on wear state information and spatial error information; An instruction generation module is used to convert the compensation amount into a motion control instruction that can be recognized by the CNC system; The model updating module is used to dynamically update the structure or parameters of the wear identification model according to the feedback data.

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