A probe force monitoring method and system of a five-axis measuring machine

Abstract

The application discloses a five-axis measuring machine probe force monitoring method and system, and relates to the technical field of probe force monitoring. The method collects multi-dimensional force signals of the probe through a mechanical sensor, decomposes an axial component, dynamically maps the probe attitude to a workpiece coordinate system, performs smooth processing on a force vector with an excessive change rate, matches a historical database to identify key feature points of complex geometric contact, converts to a local coordinate system of a contact point to obtain a force state, and adjusts a motion path if the components are unbalanced. The system is provided with multi-dimensional force collection and decomposition, attitude transformation, feature analysis, adjustment control and evaluation report modules, realizes the above functions, and improves measurement accuracy and equipment reliability through monitoring, adjustment and stability evaluation.

Description

Technical Field

[0001] This application relates to the field of probe force monitoring technology, and in particular to a method and system for monitoring probe force on a five-axis measuring machine. Background Technology

[0002] In modern manufacturing, five-axis measuring machines (WCMs), as high-precision inspection equipment, are widely used for quality verification after the machining of complex parts. Their probe force monitoring technology is directly related to measurement accuracy and equipment safety. Research in this field has undeniable value in improving industrial production efficiency and product quality, especially in industries with extremely high precision requirements such as aerospace and automotive manufacturing, where accurate perception and analysis of probe force are crucial.

[0003] Chinese Patent, Publication No. CN103419083B, Publication Date: October 2, 2015, discloses a method for monitoring the force on a CNC machine tool feed system. This method monitors the force on the CNC machine tool feed system and includes the following steps: Step 1: Acquiring the current of the position controller and the signal data of the encoder (or grating ruler) of the CNC machine tool feed system, and calculating the displacement and rotational speed measurements of the CNC machine tool feed system based on the signal data; Step 2: Obtaining the constant gain coefficient based on a Kalman filter state observer through iterative calculation; Step 3: Introducing the displacement and rotational speed measurements, along with the constant gain coefficient, into the Kalman filter state observer to obtain the load force observation, displacement observation, and rotational speed observation of the CNC machine tool feed system, thereby monitoring the force on the system.

[0004] The shortcomings of the above technical solutions are as follows: 1. Indirectly estimating the load force (friction force + cutting force) through motor current and position data relies on the linear system assumption and Kalman filter iterative calculation, making it difficult to accurately reflect the multidimensional force coupling effect at complex geometric contact points, especially prone to cumulative errors under dynamic cutting or vibration conditions. 2. Not designed for the spatial motion characteristics of the five-axis measuring machine probe, unable to analyze the spatial distribution characteristics of multidimensional force signals, and lacking the ability to identify peak forces and extract key features at complex curved surface contact points, making it difficult to meet the needs of high-precision measurement scenarios. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this application is to provide a method and system for monitoring the force on the probe of a five-axis measuring machine, which can monitor the force state of the probe of the five-axis measuring machine at contact points with complex geometries in real time and accurately, and optimize the measurement accuracy and equipment stability through dynamic adjustment.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] This application provides a method for monitoring the force on the probe of a five-axis measuring machine, the method comprising the following steps:

[0008] S101 uses a mechanical sensor to collect multi-dimensional force signals from the probe of a five-axis measuring machine during spatial motion, and uses a general force vector decomposition algorithm to decompose the multi-dimensional force signals into force components along each axis.

[0009] S102: Collect the current posture parameters of the probe, construct an angle change matrix based on the current posture parameters of the probe, and dynamically map the force components of each axis from the sensor coordinate system to the workpiece coordinate system through the standard coordinate transformation algorithm to obtain the transformed force vector.

[0010] S103, if the rate of change of the converted force vector exceeds the preset rate of change threshold, the converted force vector is smoothed to obtain a smooth force characteristic curve.

[0011] S104. Match and analyze the smooth force characteristic curve with the historical measurement path database to identify the peak position of the smooth force characteristic curve. If the peak position corresponds to the contact point of a complex geometry, mark the peak position as a key feature point.

[0012] S105: Based on key feature points, fuse the current posture parameters of the probe, and transform the force vector from the workpiece coordinate system to the local coordinate system of the probe contact point through a secondary coordinate transformation to obtain the final contact force state.

[0013] S106, if the final contact force state shows an unbalanced component, then calculate the adjustment amount based on the force deviation, send the adjustment command to the control system of the five-axis measuring machine, and optimize the motion path parameters according to the adjustment amount;

[0014] S107. Update the multidimensional force signal collected by the mechanical sensor using the optimized motion path parameters. Perform the above steps S102-S104 on the updated multidimensional force signal to obtain the updated force vector and the peak position of the key feature point. Analyze the variance of the updated force vector and the consistency index between the peak position of the key feature point and the peak value of the historical measurement path database to determine the overall force stability of the five-axis measuring machine and generate a measurement accuracy evaluation report.

[0015] As a preferred technical solution, in S101, the step of using a mechanical sensor to collect multidimensional force signals from the probe of a five-axis measuring machine during spatial motion includes:

[0016] A six-dimensional force sensor is rigidly integrated into the connection interface between the spindle and the probe of a five-axis measuring machine. The six-dimensional force sensor simultaneously measures the force and torque signals of the probe in six degrees of freedom during spatial motion, obtaining the original multidimensional force signal. When the probe is stationary under no load, the multidimensional force signal is collected for several seconds, and the average value of the multidimensional force signal over several seconds is taken as the zero drift value. The zero drift value is subtracted from the original multidimensional force signal to obtain the zero-drift processed multidimensional force signal. The zero-drift processed multidimensional force signal is corrected based on the calibrated temperature-drift coefficient matrix to obtain the preprocessed multidimensional force signal.

[0017] In S101, the step of decomposing the multidimensional force signal into force components along each axis using a general force vector decomposition algorithm includes:

[0018] A six-dimensional force sensor is fixed on a six-degree-of-freedom motion platform, and known standard forces or torques are applied sequentially in the six degrees of freedom directions. Several forces or torques of different amplitudes are applied in each degree of freedom direction, and the corresponding multi-dimensional force signals and standard forces or torques are recorded. The optimal decoupling matrix is ​​solved by fitting using the least squares method, and a decoupling model is established based on the optimal decoupling matrix. During the measurement process, the six-dimensional force sensor acquires the pre-processed multi-dimensional force signals in real time, and the force components in each axis are calculated through the decoupling model.

[0019] As a preferred technical solution, in S102, the current attitude parameters of the probe are collected, an angle change matrix is ​​constructed based on the current attitude parameters of the probe, and the force components of each axis are dynamically mapped from the sensor coordinate system to the workpiece coordinate system through a standard coordinate transformation algorithm to obtain the transformed force vector, including:

[0020] The encoder of the five-axis measuring machine outputs the absolute angle values ​​of the two rotary axes of the machine tool to obtain the current posture parameters of the probe. Based on the current posture parameters of the probe, the angle transformation matrix from the sensor coordinate system to the machine tool coordinate system is obtained by combining the rotation matrices of the fixed axes. The tool setter or workpiece reference point is pre-calibrated, and the transformation relationship between the workpiece coordinate system and the machine tool coordinate system is described by the workpiece calibration matrix. The dot product of the rotation matrix and the workpiece calibration matrix is ​​calculated to obtain the complete angle transformation matrix from the sensor coordinate system to the workpiece coordinate system. After obtaining the force components and the complete rotation matrix in the X, Y, and Z axes of the sensor coordinate system, the coordinate mapping of the force vector is realized through the homogeneous transformation matrix to obtain the transformed force vector.

[0021] As a preferred technical solution, in S103, if the rate of change of the converted force vector exceeds a preset rate of change threshold, the converted force vector is smoothed to obtain a smooth force characteristic curve, including:

[0022] The converted force vector is low-pass filtered to obtain the filtered force vector. Based on the filtered force vector, the rate of change of force components in each axis is calculated using the central difference method. When the rate of change of any axial force component exceeds a preset rate of change threshold, smoothing is triggered. For planar measurements, a sliding window weighted average method and a Kalman filter method are used for smoothing. For measurements of curved transition zones or thin-walled parts, a wavelet threshold denoising method and a Kalman filter method are used for smoothing. A smoothed force characteristic curve is generated with the sampling timestamp of the force sensor as the horizontal axis and the smoothed force components as the vertical axis.

[0023] S103 also includes:

[0024] Perform smoothing verification, calculate the signal-to-noise ratio and root mean square error of the force components before and after smoothing, ensure that the signal-to-noise ratio of the smoothed force components is greater than the preset signal-to-noise ratio threshold and the root mean square error is less than the preset root mean square error threshold, and ensure that the peak value of the contact point is not over-smoothed; store the smoothed force characteristic curve that has passed the smoothing verification as time series data, and record the reason for triggering smoothing.

[0025] As a preferred technical solution, in S104, the step of matching and analyzing the smoothing force characteristic curve with the historical measurement path database to identify the peak position of the smoothing force characteristic curve, and marking the peak position as a key feature point if the peak position corresponds to a contact point with a complex geometry, includes:

[0026] A historical measurement path database is constructed, storing typical geometric features, force curve templates for these features, and a peak-feature mapping table. The variance of the smoothed force characteristic curves in each degree of freedom direction is calculated, and the force component with the largest variance is selected as the principal force component. The zero-crossing point of the first derivative and the negative maxima of the second derivative of the principal force component are calculated to obtain the peak value of the smoothed force characteristic curve of the principal force component. Statistical, frequency domain, and morphological features of the smoothed force characteristic curve of the principal force component are extracted to initially screen the force curve templates in the historical measurement path database. If the peak amplitude is greater than the average peak value of the force curve templates in the historical measurement path database, a preliminary screening is performed. If a proportional threshold is set, the set of peak positions is output. Local features of each peak are extracted, and statistical features, frequency domain features, and morphological features are concatenated with the local features of the peak to form a curve feature vector. Based on the curve feature vector, force curve templates in the historical measurement path database are matched. Geometric features are inferred based on the peak-feature mapping table of the current peak's local features and the force curve template. Based on the probe motion trajectory recorded by the five-axis measuring machine, the probe coordinates corresponding to the peak moment are obtained. The workpiece model is queried to determine whether the probe coordinates fall within the predefined geometric feature area. If they are within the predefined geometric feature area, the association is confirmed, and the peak position is marked as a key feature point.

[0027] As a preferred technical solution, in S105, the step of fusing the current posture parameters of the probe based on key feature points and transforming the force vector from the workpiece coordinate system to the local coordinate system of the probe contact point through a quadratic coordinate transformation to obtain the final contact force state includes:

[0028] Extract the set of key feature points within the current measurement cycle. Each key feature point includes the contact point coordinates and contact time in the workpiece coordinate system, geometric features, and the associated workpiece surface normal direction. Align the key feature points with the current probe attitude parameters using timestamps. Construct a core transformation matrix between the workpiece coordinate system and the local contact point coordinate system to transform the force vectors of the key feature points from the workpiece coordinate system to the local contact point coordinate system, or transform the force vectors of the key feature points in the workpiece coordinate system to the local contact point coordinate system through homogeneous transformation. Based on the force vectors of the key feature points in the local contact point coordinate system, perform force component balance and torque balance assessments, and generate state labels based on the assessment results. Output the force vectors and state labels of the key feature points in the local contact point coordinate system to obtain the final contact force state.

[0029] As a preferred technical solution, in S106, if the final contact force state shows an uneven component, an adjustment amount is calculated based on the force deviation, an adjustment command is sent to the control system of the five-axis measuring machine, and the motion path parameters are optimized according to the adjustment amount, including:

[0030] Based on historical data and mechanical experience, a mapping rule base for imbalance type, characteristic index, and possible causes is constructed. Using this rule base and real-time operating data, possible causes are filtered to determine the imbalance type and dominant cause. Based on the diagnosed imbalance type and dominant cause, and combined with a force-motion parameter mechanical model, the motion parameters to be adjusted and their adjustment amounts are calculated. Based on the required motion parameters and their adjustment amounts, the original motion path is locally optimized to generate optimized motion path parameters.

[0031] As a preferred technical solution, in S107, the process of updating the multidimensional force signal collected by the mechanical sensor using the optimized motion path parameters, performing steps S102-S104 on the updated multidimensional force signal, obtaining the updated force vector and the peak positions of key feature points, analyzing the variance of the updated force vector and the consistency index between the peak positions of key feature points and the peak values ​​in the historical measurement path database, judging the overall force stability of the five-axis measuring machine, and generating a measurement accuracy evaluation report includes:

[0032] The optimized motion path parameters are converted into guiding rules for force signal acquisition, and a path feature-acquisition strategy mapping table is established. Based on the time axis of the motion path parameters, the force signal and probe movement are synchronized. Using the optimized path parameters and the updated acquisition strategy, steps S102, S103, and S104 are re-executed to obtain the updated force vector and key feature points. The variance of the force vector is calculated to evaluate the time-domain stability of the five-axis measuring machine. For repeated measurement path segments, the differences in force vectors between different path segments are analyzed to evaluate the stability between path segments of the five-axis measuring machine. By comparing the peak position, amplitude, and width of key feature points in the current measurement with those in the historical measurement path database, the consistency of probe-workpiece contact is evaluated. A measurement accuracy evaluation report is generated by combining stability analysis and consistency analysis.

[0033] This application provides a probe force monitoring system for a five-axis measuring machine, the system comprising:

[0034] The multidimensional force signal acquisition and decomposition module includes a mechanical sensor for acquiring multidimensional force signals during probe spatial motion and decomposing the multidimensional force signals into force components along each axis using a general force vector decomposition algorithm.

[0035] The attitude sensing and coordinate transformation unit collects the current attitude parameters of the probe, constructs an angle change matrix, and dynamically maps each axial force component from the sensor coordinate system to the workpiece coordinate system based on the standard coordinate transformation algorithm to generate the transformed force vector.

[0036] The force feature analysis and key point identification module monitors the rate of change of the force vector after conversion. If it exceeds a preset threshold, it is smoothed to obtain a smooth force feature curve. The curve is then matched and analyzed with the historical measurement path database to identify peak positions and mark the peak positions of the contact points of the corresponding complex geometric shapes as key feature points.

[0037] The contact force state calculation and adjustment control module, based on key feature points and the current posture parameters of the probe, transforms the force vector from the workpiece coordinate system to the local coordinate system of the probe contact point through a secondary coordinate transformation to obtain the final contact force state; if the final force state shows unbalanced components, it calculates the adjustment amount based on the force deviation and sends instructions to the control system to optimize the motion path parameters;

[0038] The stability assessment and accuracy report generation module updates the multidimensional force signals collected by the mechanical sensor using optimized motion path parameters, repeatedly performs coordinate transformation, feature analysis, and key point identification steps to obtain updated force vectors, smooth force characteristic curves, and key feature points; analyzes the variance of the updated force vectors, the peak consistency index of key feature points and historical databases, judges the overall force stability, and generates a measurement accuracy assessment report.

[0039] Compared with the prior art, the beneficial effects of this application are as follows:

[0040] This application directly acquires multidimensional force signals through a mechanical sensor and combines it with a general force vector decomposition algorithm to solve the accuracy loss problem caused by indirect estimation, and can accurately analyze the multidimensional force state of the probe in spatial motion.

[0041] This application constructs an angle change matrix based on the probe posture parameters to realize the dynamic mapping of the force vector from the sensor coordinate system to the workpiece coordinate system and the local coordinate system of the contact point, adapting to the complex motion trajectory of the five-axis measuring machine and ensuring the spatial consistency of the force signal.

[0042] This application introduces smoothing processing and historical database matching technology to identify the peak position of contact points with complex geometries as key feature points, thus solving the problem of insufficient sensitivity of traditional methods to dynamic contact features.

[0043] This application adjusts motion path parameters by calculating force deviation, reduces component imbalance, optimizes probe motion trajectory, reduces measurement errors caused by abnormal force, and improves equipment stability.

[0044] This application combines stability analysis and accuracy assessment reports to form a closed loop of "monitoring-adjustment-verification," providing data support for equipment maintenance and process optimization. It is applicable to high-precision scenarios such as aerospace and automotive manufacturing. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the steps of a method for monitoring the force on the probe of a five-axis measuring machine according to this application. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present application, the technical solutions in specific embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0047] like Figure 1 As shown, this application provides a method for monitoring the force on the probe of a five-axis measuring machine, the method comprising the following steps:

[0048] S101 uses a mechanical sensor to collect multidimensional force signals from the probe of a five-axis measuring machine during spatial motion, and uses a general force vector decomposition algorithm to decompose the multidimensional force signals into force components along each axis.

[0049] S102: Collect the current posture parameters of the probe, construct an angle change matrix based on the current posture parameters of the probe, and dynamically map the force components of each axis from the sensor coordinate system to the workpiece coordinate system through the standard coordinate transformation algorithm to obtain the transformed force vector.

[0050] S103, if the rate of change of the converted force vector exceeds the preset rate of change threshold, the converted force vector is smoothed to obtain a smooth force characteristic curve.

[0051] S104. Match and analyze the smooth force characteristic curve with the historical measurement path database to identify the peak position of the smooth force characteristic curve. If the peak position corresponds to the contact point of a complex geometry, mark the peak position as a key feature point.

[0052] S105: Based on key feature points, the current posture parameters of the probe are fused, and the force vector is transformed from the workpiece coordinate system to the local coordinate system of the probe contact point through a secondary coordinate transformation to obtain the final contact force state.

[0053] S106 If the final contact force state shows an imbalance of components, the adjustment amount is calculated based on the force deviation, and an adjustment command is sent to the control system of the five-axis measuring machine to optimize the motion path parameters according to the adjustment amount.

[0054] S107. Update the multidimensional force signal collected by the mechanical sensor using the optimized motion path parameters. Perform the above steps S102-S104 on the updated multidimensional force signal to obtain the updated force vector and the peak position of the key feature point. Analyze the variance of the updated force vector and the consistency index between the peak position of the key feature point and the peak value of the historical measurement path database to determine the overall force stability of the five-axis measuring machine and generate a measurement accuracy evaluation report.

[0055] Furthermore, in S101, the multidimensional force signals of the probe of the five-axis measuring machine during spatial motion are acquired using a mechanical sensor, including:

[0056] The six-dimensional force sensor (DAQ) is rigidly integrated into the connection interface between the spindle and the probe of the five-axis measuring machine. This ensures that the sensor directly senses the force transmission when the probe contacts the workpiece, avoiding additional coupling errors introduced by the elastic deformation of intermediate transmission chains (such as ball screws and guide rails).

[0057] The six-dimensional force sensor simultaneously measures the force and torque signals (3 translational forces) of the probe of a five-axis measuring machine in six degrees of freedom during its spatial motion. ; 3 torques: ), to obtain the original multidimensional force signal (denoted as ). ).

[0058] It should be noted that for complex curved surface measurement scenarios (such as thin-walled parts and deep grooves), a miniature triaxial piezoresistive force sensor array (such as FlexiForce thin film sensor) can be installed near the contact point at the front end of the probe for high-precision supplementary measurement of local contact force, solving the problem of force transmission lag caused by the installation position of the six-dimensional force sensor.

[0059] When the probe is stationary under no load, a multidimensional force signal is collected for several seconds. The average value of the multidimensional force signal for several seconds is taken as the zero drift value. The zero drift value is subtracted from the original multidimensional force signal to obtain the multidimensional force signal after zero drift processing.

[0060] The multidimensional force signal after zero-drift processing is corrected based on the calibrated temperature-drift coefficient matrix to obtain the preprocessed multidimensional force signal.

[0061] Furthermore, in S101, a general force vector decomposition algorithm is used to decompose the multidimensional force signal into force components along each axis. The goal is to couple the original signals using a pre-calibrated coupling coefficient matrix. Decomposed into independent force components in each axis .include:

[0062] Through static loading experiments, the coupling relationship between the various axes of the sensor was determined, and a decoupling model was established. Where C is a 6×6 decoupling matrix, and F is the decoupled force component. This is the original signal after preprocessing.

[0063] Specifically, the six-dimensional force sensor is fixed to a six-degree-of-freedom motion platform, and then applied in sequence in the six degrees of freedom directions ( Apply a known standard force or torque, such as by using calibrated weights or a torque wrench, with an accuracy of ±0.1%FS.

[0064] Several (e.g., 10) forces or torques of different amplitudes are applied in each degree of freedom direction (covering 20% ​​to 100% of the range), and the corresponding acquired multidimensional force signals and standard forces or torques are recorded. The optimal decoupling matrix is ​​solved by fitting using the least squares method, and a decoupling model is established based on the optimal decoupling matrix.

[0065] During the measurement process, the six-dimensional force sensor acquires the pre-processed multi-dimensional force signals in real time. The force components along each axis are calculated using a decoupled model:

[0066] .

[0067] Furthermore, in step S102, the current attitude parameters of the probe are collected, and an angle change matrix is ​​constructed based on these parameters. A standard coordinate transformation algorithm is then used to dynamically map the force components along each axis from the sensor coordinate system to the workpiece coordinate system, resulting in a transformed force vector. The goal is to construct a rotation matrix based on the probe's real-time attitude parameters to describe the orientation relationship between the sensor and workpiece coordinate systems. A homogeneous transformation algorithm is then used to dynamically map the force components from the sensor coordinate system to the workpiece coordinate system, thereby analyzing the force distribution in the workpiece's body coordinate system and providing a unified reference benchmark for subsequent anomaly detection and feature matching.

[0068] To achieve cross-coordinate system mapping of force vectors, it is necessary to clarify the four key coordinate systems involved in the five-axis measuring machine system and their relative relationships:

[0069] Coordinate system type definition Key role Sensor coordinate system (SCS) With the geometric center of the mechanical sensor array as the origin, the X / Y / Z axes are aligned with the physical installation direction of the sensors (e.g., X-axis to the right, Y-axis forward, Z-axis upward). Initial reference frame of the original force components Probe Coordinate System (PCS) With the center of the probe end face as the origin, it is bound to the probe's own geometry (e.g., the probe axis of a trigger-type probe is the Z-axis). Describe the contact posture between the probe and the workpiece. Machine coordinate system (MCS) Using the fixed origin of the machine tool bed as a reference, the machine tool controller provides real-time feedback on the positions of each linear axis (X / Y / Z) and rotary axis (A / B / C). An intermediate reference system connecting the sensor, probe, and workpiece. Workpiece coordinate system (WCS) Using the reference point set after workpiece clamping (such as the upper left corner of the workpiece) as the origin, the transformation relationship between the workpiece coordinate system and the MCS is determined through workpiece coordinate system calibration (such as a tool setter). The target reference frame for the final force analysis

[0070] S102 includes:

[0071] The encoders of a five-axis measuring machine output the absolute angle values ​​of two rotary axes of the machine tool, providing the current attitude parameters of the probe. A five-axis measuring machine is typically equipped with three linear axes (X / Y / Z) and two rotary axes (e.g., A-axis: rotation around the X-axis, C-axis: rotation around the Z-axis). The rotary axis encoders directly output absolute angle values ​​(unit: rad or °): A-axis angle. (Reflects the probe's pitch attitude around the X-axis); C-axis angle (Reflects the yaw attitude of the probe around the Z-axis).

[0072] Based on the current attitude parameters of the probe, the angle transformation matrix from the sensor coordinate system to the machine tool coordinate system is obtained by combining the fixed-axis rotation matrices:

[0073] Rotate around the Z-axis (C-axis angle): ;

[0074] Rotate around the X-axis (A-axis angle): ;

[0075] Combined rotation matrix (C-axis first, then A-axis): .

[0076] Pre-calibration of the tool setter or workpiece reference point measurement, using the workpiece calibration matrix. Describe the transformation relationship between the workpiece coordinate system and the machine tool coordinate system. Calculate the dot product of the rotation matrix and the workpiece calibration matrix to obtain the complete angle transformation matrix from the sensor coordinate system to the workpiece coordinate system: Obtain the force components along the X, Y, and Z axes in the sensor coordinate system. (Based on the decoupling results of S101) and the complete rotation matrix Then, the coordinate mapping of the force vector is achieved through a homogeneous transformation matrix, resulting in the transformed force vector:

[0077] Extend the rotation matrix into a 4×4 homogeneous transformation matrix. Used for unified processing of vector transformations (force vectors are 3-dimensional, and homogeneous coordinates have an additional 4th row [0,0,0,1]):

[0078] ;

[0079] Extend the force vector in the sensor coordinate system to homogeneous coordinates: The force vector in the workpiece coordinate system is obtained through matrix multiplication:

[0080] ;

[0081] The first three rows represent the transformed force vectors in the workpiece coordinate system:

[0082] .

[0083] S102 achieves dynamic transformation of force components from the probe coordinate system to the workpiece coordinate system through coordinate system hierarchy definition, attitude parameter acquisition, rotation matrix construction, homogeneous transformation matrix combination, and force vector coordinate mapping. This method solves the problem of inconsistent force signal reference systems caused by probe attitude changes in five-axis measuring machines, ensuring that force analysis is carried out in the workpiece body coordinate system. It provides a unified mechanical reference benchmark for subsequent anomaly detection and accuracy evaluation, and its real-time performance and transformation accuracy meet the requirements of precision measurement.

[0084] Furthermore, in S103, if the rate of change of the converted force vector exceeds a preset rate of change threshold, the converted force vector is smoothed to obtain a smoothed force characteristic curve. This includes:

[0085] The converted force vector is low-pass filtered to obtain the filtered force vector. The original converted force vector may contain high-frequency noise (such as electromagnetic interference), and directly calculating the rate of change will amplify the noise. Therefore, the converted force vector needs to be low-pass filtered first. Specifically, a second-order Butterworth low-pass filter is used for low-pass filtering, and the cutoff frequency is set according to the measurement scenario (e.g., cutoff frequency = 500Hz for fine measurement, cutoff frequency = 1kHz for coarse measurement). Filtered signal: .

[0086] Based on the filtered force vector, the rate of change of force components along each axis is calculated using the central difference method:

[0087] ;

[0088] in, The sampling time interval (determined by the DAQ sampling rate, e.g., at a sampling rate of 2kHz), =0.5ms).

[0089] When the rate of change of any axial force component exceeds the preset rate of change threshold This triggers smoothing processing.

[0090] Preset rate of change threshold To balance anomaly detection sensitivity with avoiding false triggers, a dynamic adjustment mechanism based on operating conditions is adopted.

[0091] The maximum permissible rate of change of each axial force component was determined through offline experiments:

[0092] A uniform scanning motion (speed v = 0.5 m / s) is performed on a standard workpiece (such as a granite platform), and the rate of force change during normal contact is collected. ;

[0093] Pick , where k=2~3 (confidence coefficient, adjusted according to the measurement accuracy requirements; k=3 for fine measurement and k=2 for coarse measurement).

[0094] Furthermore, the threshold is dynamically adjusted based on real-time operating conditions to avoid the limitations of fixed thresholds in different scenarios.

[0095] Material Adaptive: When contacting hard and brittle materials (such as ceramics), the threshold is lowered (k=2) to capture minute impacts; when contacting soft materials (such as aluminum alloys), the threshold is increased (k=3) to avoid normal force abrupt changes caused by elastic deformation of the material.

[0096] Probe attitude adaptation: When the probe tilt angle is large (e.g., the angle with the workpiece surface is <10°), the contact force is easily affected by attitude fluctuations. The threshold should be appropriately increased (k=2.5).

[0097] Historical data driven: Based on the statistical data of the rate of change over the past 10 seconds (mean μ, standard deviation σ), the threshold is dynamically adjusted. (μ,σ are updated after outliers are removed in real time).

[0098] When performing planar measurements, a sliding window weighted average method and a Kalman filter method are used for smoothing. When measuring curved transition zones or thin-walled parts, a wavelet thresholding denoising method and a Kalman filter method are used for smoothing.

[0099] The sliding window weighted averaging method is suitable for high-frequency random noise scenarios (such as electromagnetic interference) where a fast response is required. The window length L in the sliding window weighted averaging method is set according to the noise frequency (e.g., when the noise frequency is 1kHz, L=10 points, corresponding to a 5ms time window); the weighting coefficients use a Hanning window to reduce signal distortion at the window edges.

[0100] ;

[0101] in Hanning window function .

[0102] Kalman filtering is suitable for non-stationary signal scenarios (such as force fluctuations when a probe scans a curved surface), requiring tracking of the actual force signal and noise suppression. It offers strong real-time performance (low computational cost per step), adaptively adjusts the noise covariance, and preserves the abrupt changes in the force signal (such as contact point peaks). The state equation for Kalman filtering is: (Assuming the force components change at a constant rate); Kalman filter observation equations: ( To observe the noise, the covariance Q is adaptively adjusted by the filter.

[0103] Wavelet thresholding denoising includes non-stationary noise (such as servo system vibration and probe cantilever resonance), requiring the preservation of abrupt change features. Specifically, a db4 wavelet basis is selected, with a decomposition level of n=3; soft thresholding is applied to the detail coefficients of each level. , where the threshold (σ is the noise standard deviation, N is the signal length); a smooth force characteristic curve is generated with the sampling timestamp of the force sensor as the horizontal axis and the smoothed force component as the vertical axis.

[0104] Furthermore, S103 also includes:

[0105] Perform smoothing verification and calculate the signal-to-noise ratio (SNR) and root mean square error (RMSE) of the force components before and after smoothing:

[0106] ;

[0107] Ensure that the signal-to-noise ratio (SNR) of the smoothed force component is greater than a preset SNR threshold and the root mean square error (RMSE) is less than a preset RMSE threshold, and ensure that the peak value at the contact point is not over-smoothed. Specifically, the requirements are SNR ≥ 30 dB, RMSE ≤ 0.02 N (precise measurement) or 0.05 N (coarse measurement).

[0108] Store the smoothed force characteristic curve that has passed the smoothing verification as time series data (including timestamps and smoothed force components for each axis), and record the reason for triggering smoothing (such as "Fx rate of change exceeds limit").

[0109] S103 achieves accurate detection and dynamic smoothing of abrupt changes in the transformed force vector through rate of change calculation, adaptive threshold setting, multi-mode smoothing, and feature curve generation and verification. This method suppresses noise interference while preserving true mechanical characteristics (such as contact point peaks), providing reliable smoothed force data for subsequent identification of complex geometric features and measurement accuracy evaluation. Its real-time performance and robustness meet the high-speed dynamic measurement requirements of a five-axis measuring machine.

[0110] Furthermore, in S104, the smoothing force characteristic curve is matched and analyzed with the historical measurement path database to identify the peak position of the smoothing force characteristic curve. If the peak position corresponds to a contact point with a complex geometry, the peak position is marked as a key feature point, including:

[0111] Construct a historical measurement path database, which stores typical geometric features, force curve templates of geometric features, and peak-feature mapping tables.

[0112] Specifically, the historical measurement path database includes:

[0113]

[0114] Initial filling of the historical measurement path database: An initial template is constructed using measurement data from standard workpieces (such as calibration blocks containing typical geometric features), and feature types and peak-feature mapping relationships are manually labeled; Online learning: In actual measurement, if a new feature is matched (similarity ≥ threshold but no corresponding template), its feature parameters and geometric position are automatically extracted and added to the database to achieve self-evolution.

[0115] Calculate the variance of the smooth force characteristic curves for each degree of freedom, and select the force component with the largest variance (e.g., Fz at contact as the main force component) as the principal force component. Calculate the zero-crossing point of the first derivative and the negative maxima of the second derivative of the principal force component to obtain the peak value of the smooth force characteristic curve of the principal force component. Specifically, calculate the first derivative of the principal force component; the peak value corresponds to the zero-crossing point where the first derivative changes from positive to negative; calculate the second derivative; the second derivative at the peak value is less than the negative gradient threshold.

[0116] The statistical, frequency domain, and morphological characteristics of the smooth force characteristic curves of the principal force components are extracted to preliminarily screen force curve templates in the historical measurement path database. The statistical characteristics include: mean, standard deviation, maximum, and minimum values; frequency domain characteristics: the principal frequency and the proportion of high-frequency components are extracted using FFT (reflecting contact stability; for example, a low principal frequency during surface scanning indicates a high proportion of high-frequency components during vibration); morphological characteristics: the number of curve slope changes and the number of inflection points (reflecting the complexity of the geometric features).

[0117] If the peak amplitude is greater than the preset percentage threshold of the average peak value of the force curve template in the historical measurement path database, then the set of peak positions will be output. Specifically, the peak amplitude must be ≥ 50% of the average peak value of the historical template (to avoid misjudgment due to minor fluctuations).

[0118] Extract the local features of each peak (the local features of the peak include the peak amplitude, width, rising slope, the time proportion of the peak in the curve, and the interval between adjacent peaks), and concatenate the statistical features, frequency domain features, and morphological features with the local features of the peak to form a curve feature vector.

[0119] Based on curve feature vectors, force curve templates are matched against historical measurement path databases. Geometric features are inferred based on the local features of the current peak and the peak-feature mapping table of the force curve templates. Specifically,

[0120]

[0121] Based on the probe motion trajectory recorded by the five-axis measuring machine, obtain the probe coordinates corresponding to the peak moment, query the workpiece model, and determine whether the probe coordinates fall within the predefined geometric feature area. If they are within the geometric feature area, confirm the association and mark the peak position as a key feature point.

[0122] S104 achieves precise correspondence between smooth force characteristic curves and complex geometric contact points through historical database construction, smooth curve peak detection, multi-dimensional feature extraction, two-level database matching, peak-geometric feature association, and key feature point marking. This method deeply integrates mechanical signals and geometric features, providing crucial geometric anchors for subsequent force state analysis and measurement accuracy evaluation. Its real-time performance and matching accuracy meet the dynamic measurement requirements of a five-axis measuring machine.

[0123] Furthermore, in S105, based on key feature points, the current attitude parameters of the probe are fused, and the force vector is transformed from the workpiece coordinate system to the local coordinate system of the probe contact point through a quadratic coordinate transformation, resulting in the final contact force state, including:

[0124] Extract the set of key feature points within the current measurement cycle. Each key feature point includes the contact point coordinates and contact time in the workpiece coordinate system, geometric features (such as "sharp corners" and "curved transition areas"), and the associated workpiece surface normal direction (which can be pre-stored from the CAD model or estimated by the contact force direction, with the default assumption that the normal direction is consistent with the direction of the main force component).

[0125] Align key feature points with the current attitude parameters of the probe using timestamps.

[0126] The Local Coordinate System (LCS) takes the contact point of the key feature points as its origin. The coordinate axes are strongly correlated with the probe-workpiece contact state and need to be dynamically defined in conjunction with geometric semantics and probe posture. In this application, the LCS is defined as follows (taking the contact in the curved surface transition zone as an example):

[0127] LCS origin: directly defined as the contact point coordinates of key feature points in the workpiece coordinate system.

[0128] LCS coordinate axis directions: Z-axis (normal direction): Points to the outward normal direction of the workpiece surface, reflecting the surface orientation of the contact point. If the CAD model has pre-stored surface normals, the pre-stored normal vector is directly taken, normalized, and used as the Z-axis of the LCS coordinate axis. If the CAD normal cannot be obtained, the direction of the principal force component is used as the normal by default. X-axis (tangential direction): In the tangential plane of the workpiece surface, along the probe movement direction or characteristic geometric direction (such as the generatrix direction of the curved transition area). The tangential direction of the probe is extracted through the probe rotation matrix (the rotation matrix from the probe coordinate system to the sensor coordinate system constructed in S102); if there is a lateral force at the contact point, the direction of the lateral force is taken as the tangential direction. Y-axis (bi-normal direction): Determined by the right-hand rule to ensure that the LCS is a right-handed orthogonal coordinate system.

[0129] Construct the core transformation matrix between the workpiece coordinate system and the local coordinate system of the contact point to realize the transformation of the force vector of key feature points from the workpiece coordinate system to the local coordinate system of the contact point:

[0130] The relationship between LCS and WCS can be understood through translation. and rotation describe:

[0131] ;

[0132] Among them, translation Let LCS origin be the coordinates under WCS, rotate Composed of the LCS coordinate axis direction matrix:

[0133] ( (This is a concatenation of the column vectors of the LCS coordinate axis unit vectors under the WCS).

[0134] Alternatively, the force vectors of key feature points in the workpiece coordinate system can be used. (Smoothed results from S103), transformed to the local coordinate system of the contact point via homogeneous transformation:

[0135] Extended to homogeneous coordinates: ; Applying the transformation matrix: Extracting the force vector under LCS: Ignoring the 4th row of homogeneous coordinates, we get: Ft (tangential force), Fb (binormal force), and Fn (normal force) are the force components in the local coordinate system.

[0136] Obtain the sensor coordinate system position of the probe contact point (The vector from the origin of the sensor coordinate system to the contact point can be calibrated using the probe's geometric model, such as the probe length L corresponding to...) Torque By force With lever arm Cross product calculation (for the rotation matrix from LCS to sensor coordinate system): .

[0137] Based on the force vectors of key feature points in the local coordinate system of the contact point, force component balance and torque balance are evaluated, and status labels are generated based on the evaluation results.

[0138] Output the force vectors and state labels of key feature points in the local coordinate system of the contact point to obtain the final contact force state.

[0139] Specifically, force component balance assessment: normal force ratio: calculate the proportion of normal force in the total force. Ideal contact (such as planar positive pressure) should be >90%. If <70%, it indicates the presence of large lateral force. Tangential force threshold: tangential force should be ≤ the safety threshold (such as 0.1N to avoid probe slippage or wear). Exceeding the standard indicates surface roughness or posture deviation.

[0140] Torque balance assessment: Torque amplitude: If the torque amplitude is greater than the threshold (e.g., 0.05 N·mm), it indicates that there is bending moment at the contact point (e.g., probe tilt or uneven workpiece surface); Torque direction: Analyze the components of the torque on each axis of the LCS to determine the source of the bending moment (e.g., a large tangential torque indicates tangential tilt, a large subnormal torque indicates normal deviation).

[0141] Based on the above assessment, contact stress state labels are generated, such as "stable contact", "excessive lateral force", and "excessive bending moment".

[0142] The S105 achieves a precise mapping from the global force vector of the workpiece to the local mechanical state of the contact point through key feature point information extraction, local coordinate system definition at the contact point, secondary coordinate transformation from WCS to LCS, local force / torque calculation, and force state description. This method combines geometric semantics with real-time attitude, directly reflecting the microscopic contact mechanical characteristics between the probe and the workpiece. It provides a core basis for subsequent force imbalance judgment and feedback control, meeting the need for refined evaluation of the mechanical state of the contact point in complex geometric measurements using five-axis measuring machines.

[0143] Furthermore, in S106, if the final contact force state shows an imbalance of components, an adjustment amount is calculated based on the force deviation, and an adjustment command is sent to the control system of the five-axis measuring machine. The motion path parameters are optimized based on the adjustment amount, including:

[0144] Based on historical data and mechanical experience, a mapping rule base for imbalance type, characteristic index, and possible causes is constructed. Using this mapping rule base, and combined with real-time operating condition data, possible causes are filtered to determine the imbalance type and dominant cause.

[0145] Imbalance types include: excessive lateral force, insufficient normal force, excessive bending moment, and multi-component coupling imbalance.

[0146] Calculate the angle between the current probe normal and the CAD normal of the workpiece surface (by the dot product of the LCS normal and the CAD normal in S105). If the angle between the current probe normal and the CAD normal of the workpiece surface is greater than the preset angle threshold, it is determined to be "attitude deviation dominant". Query the current feed speed. If the current feed speed is greater than the safe speed, it is determined to be "speed too fast dominant". Calculate the radius of curvature of the path at this point. If the radius of curvature is less than the minimum allowable radius of curvature, it is determined to be "path sharp bend dominant".

[0147] Based on the diagnosed type of imbalance and its dominant cause, and combined with the force-motion parametric mechanical model, the motion parameters (velocity, attitude, path curvature, etc.) that need to be adjusted and the amount of adjustment for the motion parameters are calculated.

[0148] For example, a larger tangential force reduces friction and attitude correction. The goal is to... The value should be controlled within ≤0.3. Degree adjustment: based on the Coulomb friction model. ( The coefficient of friction, The speed sensitivity coefficient (calibrated experimentally) is used to deduce the maximum allowable speed and calculate the adjustment amount; if the attitude deviation... The main task is to calculate the required adjustment angle of the rotation axis (such as the A-axis or C-axis) to make the probe normal close to the workpiece CAD normal, and then adjust the amount of adjustment. ( This is the attitude control proportional coefficient, such as 0.8.

[0149] For example, if the normal force is insufficient, the normal component force and gap compensation are increased. The goal is to... Increase the force to ≥0.7 × total force. Increase the contact angle between the probe and the workpiece surface (reduce tilt), and adjust this by rotating the axis angle (e.g., rotating the A-axis). To reduce the angle between the normal direction and the workpiece surface, calculate the adjusted normal force. If there is a gap on the workpiece surface (such as uneven clamping), increase the Z-axis downward amount ΔZ in the path planning to move the actual contact point of the probe downward and increase the normal contact pressure.

[0150] For example, if the bending moment exceeds the limit, the lever arm and path smoothing are reduced. The goal is to... The bending moment should be controlled within ≤0.05 N·m. If the bending moment is caused by an excessively long probe (large lever arm L), the length of the virtual probe should be temporarily shortened. For sharp bends, a transition arc point should be inserted to increase the radius of curvature and reduce the bending moment.

[0151] For example, in the case of multi-component coupling imbalance, a composite adjustment strategy is adopted. A weighted comprehensive adjustment is used to prioritize addressing the dominant factors (such as correcting the attitude to reduce lateral force when both lateral force and bending moment exceed the limit, and then optimizing the path curvature to reduce bending moment). The adjustment amount is the vector sum of the adjustment amounts of each component.

[0152] Finally, based on the motion parameters that need to be adjusted and the amount of adjustment, the original motion path is locally optimized to generate optimized motion path parameters (interpolation point coordinates, velocity, acceleration, etc.).

[0153] The S106 achieves precise adjustment and motion path optimization for force imbalance problems through imbalance state diagnosis, adjustment calculation, path replanning, command issuance, and closed-loop verification. This method combines mechanical mechanisms and control engineering, which can reduce the risk of contact forces from the source and continuously optimize path parameters through closed-loop feedback, significantly improving the measurement stability and accuracy consistency of the five-axis measuring machine.

[0154] Furthermore, in S107, the multidimensional force signal collected by the mechanical sensor is updated using the optimized motion path parameters. Steps S102-S104 are then performed on the updated multidimensional force signal to obtain the updated force vector and the peak positions of key feature points. The variance of the updated force vector and the consistency index between the peak positions of key feature points and the peak values ​​in the historical measurement path database are analyzed to determine the overall force stability of the five-axis measuring machine and generate a measurement accuracy evaluation report, including:

[0155] The optimized motion path parameters are converted into guiding rules for force signal acquisition, and a path feature-acquisition strategy mapping table is established:

[0156]

[0157] Based on the time axis of motion path parameters, the force signal and probe movement are synchronized.

[0158] Using the optimized path parameters and the updated acquisition strategy, steps S102, S103, and S104 are re-executed to obtain the updated force vector and key feature points.

[0159] The variance of the force vector was calculated to evaluate the time-domain stability of the five-axis measuring machine.

[0160] For repeated measurement path segments, the differences in force vectors between different path segments are analyzed to evaluate the stability between path segments of the five-axis measuring machine. Specifically, the mean and variance of the force vectors for the same feature in different path segments are calculated to reflect the influence of path repeatability on the force; the maximum relative deviation of the force vector amplitude in different path segments is calculated.

[0161] The consistency of probe-workpiece contact is evaluated by comparing the peak position, amplitude, and width of key feature points in the current measurement with those in the historical measurement path database. Specifically, the coordinate deviation and timestamp deviation of the peak position are calculated, as well as the amplitude similarity and morphological similarity of the peak. The position, amplitude, and morphological similarity are then fused to generate a consistency score.

[0162] A measurement accuracy assessment report is generated by combining stability and consistency analyses. For example,

[0163]

[0164] This application provides a probe force monitoring system for a five-axis measuring machine, the system comprising:

[0165] The multidimensional force signal acquisition and decomposition module includes a mechanical sensor for acquiring multidimensional force signals during probe spatial motion and decomposing the multidimensional force signals into force components along each axis using a general force vector decomposition algorithm.

[0166] The attitude sensing and coordinate transformation unit collects the current attitude parameters of the probe, constructs an angle change matrix, and dynamically maps each axial force component from the sensor coordinate system to the workpiece coordinate system based on the standard coordinate transformation algorithm, generating the transformed force vector.

[0167] The force feature analysis and key point identification module monitors the rate of change of the transformed force vector. If it exceeds a preset threshold, it smooths the force vector to obtain a smoothed force feature curve. This curve is then matched and analyzed against a historical measurement path database to identify peak positions. The peak positions of the corresponding complex geometric contact points are marked as key feature points.

[0168] The contact force state calculation and adjustment control module, based on key feature points and the probe's current posture parameters, transforms the force vector from the workpiece coordinate system to the probe contact point's local coordinate system through a quadratic coordinate transformation to obtain the final contact force state. If the final force state shows unbalanced components, the module calculates the adjustment amount based on the force deviation and sends instructions to the control system to optimize the motion path parameters.

[0169] The stability assessment and accuracy report generation module updates the multidimensional force signals collected by the mechanical sensor using optimized motion path parameters. It repeatedly performs coordinate transformation, feature analysis, and key point identification steps to obtain updated force vectors, smoothed force characteristic curves, and key feature points. It analyzes the variance of the updated force vector, the peak consistency index between the key feature points and the historical database, determines the overall force stability, and generates a measurement accuracy assessment report.

[0170] It should be noted that the terms "first," "second," and similar terms used in this application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, "a" or "one," and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. "A plurality" or "several" indicates at least two. Unless otherwise stated, terms such as "front," "back," "left," "right," "lower," and / or "upper" are for illustrative purposes only and are not limited to a location or spatial orientation. Terms such as "comprising" or "including" indicate that the elements or objects preceding "comprising" encompass the elements or objects listed following "comprising" or "including" and their equivalents, and do not exclude other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0171] The singular forms “a,” “the,” and “the” used in this application specification and appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0172] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for monitoring the force on the probe of a five-axis measuring machine, characterized in that, The method includes the following steps: S101 uses a mechanical sensor to collect multi-dimensional force signals from the probe of a five-axis measuring machine during spatial motion, and uses a general force vector decomposition algorithm to decompose the multi-dimensional force signals into force components along each axis. S102: Collect the current posture parameters of the probe, construct an angle change matrix based on the current posture parameters of the probe, and dynamically map the force components of each axis from the sensor coordinate system to the workpiece coordinate system through the standard coordinate transformation algorithm to obtain the transformed force vector; S103, if the rate of change of the converted force vector exceeds the preset rate of change threshold, the converted force vector is smoothed to obtain a smooth force characteristic curve. S104. Match and analyze the smooth force characteristic curve with the historical measurement path database to identify the peak position of the smooth force characteristic curve. If the peak position corresponds to the contact point of a complex geometry, mark the peak position as a key feature point. S105: Based on key feature points, fuse the current posture parameters of the probe, and transform the force vector from the workpiece coordinate system to the local coordinate system of the probe contact point through a secondary coordinate transformation to obtain the final contact force state. S106, if the final contact force state shows an unbalanced component, then the adjustment amount is calculated based on the force deviation, and an adjustment command is sent to the control system of the five-axis measuring machine to optimize the motion path parameters according to the adjustment amount; S107. Update the multidimensional force signal collected by the mechanical sensor using the optimized motion path parameters. Perform the above steps S102-S104 on the updated multidimensional force signal to obtain the updated force vector and the peak position of the key feature point. Analyze the variance of the updated force vector and the consistency index between the peak position of the key feature point and the peak value of the historical measurement path database to determine the overall force stability of the five-axis measuring machine and generate a measurement accuracy evaluation report.

2. The method for monitoring the force on the probe of a five-axis measuring machine according to claim 1, characterized in that, In S101, the method of using a mechanical sensor to collect multidimensional force signals of the probe of a five-axis measuring machine during spatial motion includes: A six-dimensional force sensor is rigidly integrated into the connection interface between the spindle and the probe of a five-axis measuring machine. The six-dimensional force sensor simultaneously measures the force and torque signals of the probe in six degrees of freedom during spatial motion, obtaining the original multidimensional force signal. When the probe is stationary under no load, the multidimensional force signal is collected for several seconds, and the average value of the multidimensional force signal over several seconds is taken as the zero drift value. The zero drift value is subtracted from the original multidimensional force signal to obtain the zero-drift processed multidimensional force signal. The zero-drift processed multidimensional force signal is corrected based on the calibrated temperature-drift coefficient matrix to obtain the preprocessed multidimensional force signal. In S101, the step of decomposing the multidimensional force signal into force components along each axis using a general force vector decomposition algorithm includes: A six-dimensional force sensor is fixed on a six-degree-of-freedom motion platform, and known standard forces or torques are applied sequentially in the six degrees of freedom directions. Several forces or torques of different amplitudes are applied in each degree of freedom direction, and the corresponding multi-dimensional force signals and standard forces or torques are recorded. The optimal decoupling matrix is ​​solved by fitting using the least squares method, and a decoupling model is established based on the optimal decoupling matrix. During the measurement process, the six-dimensional force sensor acquires the pre-processed multi-dimensional force signals in real time, and the force components in each axis are calculated through the decoupling model.

3. The method for monitoring the force on the probe of a five-axis measuring machine according to claim 2, characterized in that, In step S102, the current attitude parameters of the acquisition probe are collected, an angle change matrix is ​​constructed based on the current attitude parameters, and the force components of each axis are dynamically mapped from the sensor coordinate system to the workpiece coordinate system using a standard coordinate transformation algorithm to obtain the transformed force vector, which includes: The encoder of the five-axis measuring machine outputs the absolute angle values ​​of the two rotary axes of the machine tool to obtain the current posture parameters of the probe. Based on the current posture parameters of the probe, the angle transformation matrix from the sensor coordinate system to the machine tool coordinate system is obtained by combining the rotation matrices of the fixed axes. The tool setter or workpiece reference point is pre-calibrated, and the transformation relationship between the workpiece coordinate system and the machine tool coordinate system is described by the workpiece calibration matrix. The dot product of the rotation matrix and the workpiece calibration matrix is ​​calculated to obtain the complete angle transformation matrix from the sensor coordinate system to the workpiece coordinate system. After obtaining the force components and the complete rotation matrix in the X, Y, and Z axes of the sensor coordinate system, the coordinate mapping of the force vector is realized through the homogeneous transformation matrix to obtain the transformed force vector.

4. The method for monitoring the force on the probe of a five-axis measuring machine according to claim 1, characterized in that, In S103, if the rate of change of the converted force vector exceeds a preset rate of change threshold, the converted force vector is smoothed to obtain a smooth force characteristic curve, including: The converted force vector is low-pass filtered to obtain the filtered force vector. Based on the filtered force vector, the rate of change of force components in each axis is calculated using the central difference method. When the rate of change of any axial force component exceeds a preset rate of change threshold, smoothing is triggered. For planar measurements, a sliding window weighted average method and a Kalman filter method are used for smoothing. For measurements of curved transition zones or thin-walled parts, a wavelet threshold denoising method and a Kalman filter method are used for smoothing. A smoothed force characteristic curve is generated with the sampling timestamp of the force sensor as the horizontal axis and the smoothed force components as the vertical axis. S103 also includes: Perform smoothing verification, calculate the signal-to-noise ratio and root mean square error of the force components before and after smoothing, ensure that the signal-to-noise ratio of the smoothed force components is greater than the preset signal-to-noise ratio threshold and the root mean square error is less than the preset root mean square error threshold, and ensure that the peak value of the contact point is not over-smoothed; store the smoothed force characteristic curve that has passed the smoothing verification as time series data, and record the reason for triggering smoothing.

5. A method for monitoring the force on the probe of a five-axis measuring machine according to claim 1 or 4, characterized in that, In S104, the step of matching and analyzing the smoothing force characteristic curve with the historical measurement path database to identify the peak position of the smoothing force characteristic curve, and marking the peak position as a key feature point if the peak position corresponds to a contact point with a complex geometry, includes: A historical measurement path database is constructed, storing typical geometric features, force curve templates for these features, and a peak-feature mapping table. The variance of the smoothed force characteristic curves in each degree of freedom direction is calculated, and the force component with the largest variance is selected as the principal force component. The zero-crossing point of the first derivative and the negative maxima of the second derivative of the principal force component are calculated to obtain the peak value of the smoothed force characteristic curve of the principal force component. Statistical, frequency domain, and morphological features of the smoothed force characteristic curve of the principal force component are extracted to initially screen the force curve templates in the historical measurement path database. If the peak amplitude is greater than the average peak value of the force curve templates in the historical measurement path database, a preliminary screening is performed. If a proportional threshold is set, the set of peak positions is output. Local features of each peak are extracted, and statistical features, frequency domain features, and morphological features are concatenated with the local features of the peak to form a curve feature vector. Based on the curve feature vector, force curve templates in the historical measurement path database are matched. Geometric features are inferred based on the peak-feature mapping table of the current peak's local features and the force curve template. Based on the probe motion trajectory recorded by the five-axis measuring machine, the probe coordinates corresponding to the peak moment are obtained. The workpiece model is queried to determine whether the probe coordinates fall within the predefined geometric feature area. If they are within the predefined geometric feature area, the association is confirmed, and the peak position is marked as a key feature point.

6. The method for monitoring the force on the probe of a five-axis measuring machine according to claim 1, characterized in that, In S105, the step of fusing the current posture parameters of the probe based on key feature points and transforming the force vector from the workpiece coordinate system to the local coordinate system of the probe contact point through a quadratic coordinate transformation to obtain the final contact force state includes: Extract the set of key feature points within the current measurement cycle. Each key feature point includes the contact point coordinates and contact time in the workpiece coordinate system, geometric features, and the associated workpiece surface normal direction. Align the key feature points with the current probe attitude parameters using timestamps. Construct a core transformation matrix between the workpiece coordinate system and the local contact point coordinate system to transform the force vectors of the key feature points from the workpiece coordinate system to the local contact point coordinate system, or transform the force vectors of the key feature points in the workpiece coordinate system to the local contact point coordinate system through homogeneous transformation. Based on the force vectors of the key feature points in the local contact point coordinate system, perform force component balance and torque balance assessments, and generate state labels based on the assessment results. Output the force vectors and state labels of the key feature points in the local contact point coordinate system to obtain the final contact force state.

7. The method for monitoring the force on the probe of a five-axis measuring machine according to claim 1, characterized in that, In S106, if the final contact force state shows an imbalance of components, an adjustment amount is calculated based on the force deviation, an adjustment command is sent to the control system of the five-axis measuring machine, and the motion path parameters are optimized according to the adjustment amount, including: Based on historical data and mechanical experience, a mapping rule base for imbalance type, characteristic index, and possible causes is constructed. Using this rule base and real-time operating data, possible causes are filtered to determine the imbalance type and dominant cause. Based on the diagnosed imbalance type and dominant cause, and combined with a force-motion parameter mechanical model, the motion parameters to be adjusted and their adjustment amounts are calculated. Based on the required motion parameters and their adjustment amounts, the original motion path is locally optimized to generate optimized motion path parameters.

8. The method for monitoring the force on the probe of a five-axis measuring machine according to claim 1, characterized in that, S107, the process of updating the multidimensional force signal collected by the mechanical sensor using the optimized motion path parameters, performing steps S102-S104 on the updated multidimensional force signal to obtain the updated force vector and the peak positions of key feature points, analyzing the variance of the updated force vector and the consistency index between the peak positions of key feature points and the peak values ​​in the historical measurement path database, judging the overall force stability of the five-axis measuring machine, and generating a measurement accuracy evaluation report includes: The optimized motion path parameters are converted into guiding rules for force signal acquisition, and a path feature-acquisition strategy mapping table is established. Based on the time axis of the motion path parameters, the force signal and probe movement are synchronized. Using the optimized path parameters and the updated acquisition strategy, steps S102, S103, and S104 are re-executed to obtain the updated force vector and key feature points. The variance of the force vector is calculated to evaluate the time-domain stability of the five-axis measuring machine. For repeated measurement path segments, the differences in force vectors between different path segments are analyzed to evaluate the stability between path segments of the five-axis measuring machine. By comparing the peak position, amplitude, and width of key feature points in the current measurement with those in the historical measurement path database, the consistency of probe-workpiece contact is evaluated. A measurement accuracy evaluation report is generated by combining stability analysis and consistency analysis.

9. A probe force monitoring system for a five-axis measuring machine, characterized in that, The system includes: The multidimensional force signal acquisition and decomposition module includes a mechanical sensor for acquiring multidimensional force signals during probe spatial motion and decomposing the multidimensional force signals into force components along each axis using a general force vector decomposition algorithm. The attitude sensing and coordinate transformation unit collects the current attitude parameters of the probe, constructs an angle change matrix, and dynamically maps each axial force component from the sensor coordinate system to the workpiece coordinate system based on the standard coordinate transformation algorithm to generate the transformed force vector. The force feature analysis and key point identification module monitors the rate of change of the force vector after conversion. If it exceeds a preset threshold, it is smoothed to obtain a smooth force feature curve. The curve is then matched and analyzed with the historical measurement path database to identify peak positions and mark the peak positions of the corresponding complex geometric contact points as key feature points. The contact force state calculation and adjustment control module, based on key feature points and the current posture parameters of the probe, transforms the force vector from the workpiece coordinate system to the local coordinate system of the probe contact point through a secondary coordinate transformation to obtain the final contact force state; if the final force state shows unbalanced components, it calculates the adjustment amount based on the force deviation and sends instructions to the control system to optimize the motion path parameters; The stability assessment and accuracy report generation module updates the multidimensional force signals collected by the mechanical sensor using optimized motion path parameters, repeatedly performs coordinate transformation, feature analysis, and key point identification steps to obtain updated force vectors, smooth force characteristic curves, and key feature points; analyzes the variance of the updated force vectors, the peak consistency index of key feature points and historical databases, judges the overall force stability, and generates a measurement accuracy assessment report.

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