Conjugate twin simulation CNC machining path residual error analysis compensation method and system

Abstract

The application relates to the technical field of precision numerical control machining, and particularly discloses a CNC machining path residual error analysis compensation method and system fusing twin simulation, which comprises the following steps: extracting a geometric residual error and a dynamic risk characteristic vector through extended digital twin model synchronous simulation; generating candidate compensation instructions and performing lead simulation based on the same, quantitatively evaluating secondary risks to construct a dynamic compensation risk probability field; utilizing a risk-precision multi-target decision maker to generate adaptive compensation instructions according to the risk field, that is, adopting high-gain compensation in a low-risk area and switching to a conservative strategy in a high-risk area; and reversely optimizing the twin model and the risk calculation model by comparing actual risk events with prediction results; and the system comprises four modules corresponding to functions. The application improves compensation from error driving to risk constraint decision, effectively avoids secondary process problems such as overcutting and chatter, and significantly improves machining precision and adaptive capacity under complex working conditions while ensuring stability.

Description

Technical Field

[0001] This invention relates to the field of precision numerical control machining technology, specifically to a method and system for residual analysis and compensation of CNC machining paths that integrates twin simulation. Background Technology

[0002] In the field of high-end CNC machining, especially in the precision machining of complex curved surfaces and thin-walled parts, traditional path residual compensation methods mainly rely on the measurement of geometric errors and reverse offset. Existing technologies generally suffer from the following bottlenecks: Mainstream methods focus on minimizing geometric residuals, but lack an assessment of the secondary risks that the compensation action itself may cause. For example, in operating conditions with poor rigidity or high dynamic loads, aggressive compensation may induce cutting chatter, cause overcutting of the workpiece, or exacerbate tool wear. This risk of "overcompensation" is ignored in existing compensation logic that focuses solely on accuracy.

[0003] Compensation strategies are often simplistic and static. Most schemes use fixed gain coefficients or error mapping tables for global compensation, which cannot adapt to the drastic changes in dynamic characteristics of different regions along the machining path, such as flat surfaces and steep sidewalls, making it difficult to achieve a dynamic optimal balance between accuracy and process stability.

[0004] Existing methods for integrating digital twins can perform simulation predictions, but the model parameters are usually fixed or rely on offline calibration. They cannot perceive the evolution of machine tool performance during long-term operation, such as guideway wear and component aging, and they lack the ability to perform closed-loop and autonomous optimization of the simulation model and compensation strategy based on actual machining results. This leads to a decrease in prediction accuracy over time and limited system intelligence.

[0005] Therefore, there is an urgent need for an intelligent compensation method that can proactively assess compensation risks, adaptively adjust strategies, and possess continuous learning capabilities, in order to overcome the limitations of existing technologies in terms of security, adaptability, and long-term effectiveness. Summary of the Invention

[0006] The purpose of this invention is to provide a CNC machining path residual analysis and compensation method and system that integrates twin simulation, so as to solve the fundamental problem that it is difficult to balance accuracy and stability in existing CNC machining path compensation technology because the secondary risks such as overcutting and chatter that may be caused by the compensation action itself are not assessed.

[0007] To solve the above-mentioned technical problems, the present invention specifically provides the following technical solution: The CNC machining path residual analysis and compensation method integrating twin simulation includes the following steps: S1. Synchronize the extended digital twin model with the physical processing, and jointly output the geometric residual feature vector and the dynamic risk feature vector; S2. Generate candidate compensation instructions using the geometric residual feature vector, simulate the compensation effect in advance in the extended digital twin model, and generate a dynamic compensation risk probability field based on the dynamic risk feature vector and the simulation response change. S3. The risk-precision multi-objective decision-maker receives the compensated risk probability field, performs high-gain compensation in the low-risk area, and switches to robust compensation instruction sequence in the high-risk area to send to the machine tool. S4. Compare and analyze the potential risk events monitored during physical processing with the predictions of the compensation risk probability field, and then optimize and extend the digital twin model in reverse to improve its risk prediction accuracy.

[0008] As a preferred embodiment of the present invention, S1 specifically includes: S11. Based on the machine tool structure, tool clamping parameters, workpiece 3D model and fixture layout, an extended digital twin model is integrated and constructed, which includes a multibody system geometric kinematic model, a cutting force prediction model, a structural dynamics model and a fixture contact force model; among them, the structural dynamics model is used to characterize the modal characteristics of the machine tool-tool-workpiece system; S12. During the physical machining process, the pre-interpolation commands and real-time sensor data of the physical CNC system are synchronously input into the extended digital twin model to drive it to perform lockstep simulation in a virtual environment. The simulation executes two calculation threads in parallel: the first thread generates geometric residual prediction data by comparing the ideal tool path with the actual tool path calculated by the model; the second thread calculates and outputs multimodal dynamic response data in real time by solving the coupled equations of the cutting force prediction model, structural dynamics model and fixture contact force model, including at least the vibration acceleration of the tool tip, the force distribution of the workpiece surface mesh nodes and the constraint reaction force of the fixture positioning point. S13. Perform statistical analysis on the geometric residual prediction data, extract its mean, variance and spatial frequency domain features to form a geometric residual feature vector; at the same time, perform time-frequency domain analysis on the multimodal dynamic response data, such as calculating the effective value of vibration acceleration, the peak value and gradient of the workpiece force, and the fluctuation of the clamp reaction force, and extract indicators characterizing the vibration intensity, load mutation and clamping stability of the system to form a dynamic risk feature vector.

[0009] As a preferred embodiment of the present invention, S2 specifically includes: S21. Based on the geometric residual feature vector, apply at least two different gain coefficients or different spatial directions of compensation strategies to generate a set of candidate preliminary compensation instructions corresponding to the current processing path segment; S22. In the extended digital twin model, a future processing path after compensation is simulated in advance, and the multimodal dynamic response data output by the advance simulation is recorded simultaneously. S23. From the dynamic risk feature vector, extract the core indicators characterizing the system stability under the current uncompensated state as the current dynamic risk benchmark. The benchmark includes at least the typical vibration intensity, average load level and load fluctuation range under the current working condition. For each candidate preliminary compensation command, analyze the multimodal dynamic response data obtained from its advanced simulation in S22, calculate the increment relative to the current dynamic risk benchmark, and quantify and evaluate the vibration aggravation risk value based on the effective value increment of vibration acceleration, the overcutting or undercutting risk value based on the workpiece force distribution and the change in the relative position of the tool and workpiece, and the force over-limit risk value based on the peak value of the fixture constraint reaction force through the risk value quantification calculation model. S24. Associate the various risk values ​​calculated for all candidate preliminary compensation instructions in S23 with the corresponding coordinate points in the three-dimensional processing path space, and form a dynamically updated compensation risk probability field covering the current and future paths through spatial interpolation; this field is used to characterize the probability or expected severity of various secondary risks when compensation is applied at each point on the path.

[0010] As a preferred embodiment of the present invention, S22 specifically includes: S221. Capture the complete model state corresponding to the current physical machining moment from the real-time synchronous simulation thread of the extended digital twin model, as the initial state of the advanced simulation; the state includes at least the position and speed of each axis of the machine tool, the tool-workpiece contact relationship, and the current vibration and stress state of the system; S222. Superimpose one of the candidate preliminary compensation instructions with a future segment of original processing instructions corresponding to the current moment to generate a compensated instruction sequence to be simulated; use this sequence to drive the extended digital twin model to solve in an independent accelerated simulation thread, starting from the initial state of the advanced simulation. S223. During the operation of the accelerated simulation thread, the model output data set of the advanced three-modal dynamic response corresponding to each future interpolation cycle is recorded synchronously; this data set is a future prediction of the multimodal dynamic response data defined in S12, and also includes at least the tool tip vibration acceleration, the force distribution on the workpiece surface and the fixture constraint reaction force. S224. Repeat operations S221 to S223 until all candidate preliminary compensation instructions generated in S21 have been traversed; archive the advanced three-modal dynamic response datasets corresponding to all instructions according to the instruction identifier and timestamp to provide complete input data for the secondary risk quantification assessment in S23.

[0011] As a preferred embodiment of the present invention, S24 specifically includes: S241. For each candidate preliminary compensation instruction, the vibration aggravation risk value, overcut or undercut risk value and force overlimit risk value calculated at each future path point in S23 are bound to the three-dimensional coordinates of the path point in the machine tool coordinate system or workpiece coordinate system to generate a set of discrete three-dimensional risk point cloud data. S242. Within the spatial range of the processing path to be evaluated, establish a regular three-dimensional mesh; based on the three-dimensional risk point cloud data, perform spatial interpolation calculations for each type of risk value, such as using Kriging interpolation or radial basis function interpolation, to calculate the interpolation results corresponding to each type of risk value at each mesh node; S243. The interpolation results of multiple risk values ​​distributed on the three-dimensional grid nodes calculated in S242 are organized into a structured, real-time queryable dynamic compensation risk probability field. The dynamic compensation risk probability field can output the predicted value or probability distribution of various risks under the compensation strategy based on any input three-dimensional coordinate point and the specified candidate preliminary compensation instruction identifier.

[0012] As a preferred embodiment of the present invention, S3 specifically includes: S31. Construct a risk-precision multi-objective decision maker that synchronously receives the geometric residual eigenvector output by S1 and the compensated risk probability field output by S2. S32. The risk-precision multi-objective decision-maker divides the current and prospective processing paths into low-risk areas and medium-high-risk areas according to the preset risk threshold in the compensated risk probability field, and establishes strategy mapping rules to obtain real-time strategy mapping results. S33. Based on the real-time strategy mapping result of S32, dynamically modulate the compensation requirement of each point on the original path to generate a robust compensation instruction sequence that is non-uniformly distributed in space and time, and send the sequence to the machine tool controller for execution in real time.

[0013] As a preferred embodiment of the present invention, S32 specifically includes: S321. For each type of risk in the compensation risk probability field, based on the process knowledge base and historical data, set a basic risk threshold and a high-risk threshold respectively; S322. In the risk-precision multi-objective decision maker, read the compensated risk probability field data for the current processing point and the path points within its predetermined forward window; if the risk prediction values ​​of all categories are lower than their corresponding basic risk thresholds, then the path segment where the corresponding point is located is classified into the low-risk area; if the risk prediction value of any category reaches or exceeds its high-risk threshold, then the corresponding point is classified into the high-risk area; points between the two are classified into the medium-risk area. S323 is an aggressive compensation strategy for mapping low-risk areas, quantified as using a set of aggressive fixed high-gain coefficients to fully compensate all spatial direction error components indicated by the geometric residual feature vector extracted by S1; and a conservative compensation strategy for mapping medium- and high-risk areas, quantified as at least one of the following computable rules: calling a set of preset conservative gain coefficients with lower values; based on risk source analysis, only selecting to compensate specific direction components in the geometric residual feature vector that have low correlation with the current dominant risk category; and in the path parameter domain, calculating the distance from the current point to the nearest downstream point identified as a low-risk area start point, and outputting the number of interpolation cycles corresponding to the delay of the compensation command. S324. Based on the partitioning results of S322 and the quantization rules of S323, generate a real-time strategy mapping table for each point on the current and look-ahead paths, which includes a region label, a set of gain coefficients to be used, a direction mask to be compensated, and a compensation time delay, as the direct basis for generating a robust compensation instruction sequence.

[0014] As a preferred embodiment of the present invention, S4 specifically includes: S41. The physical machining process is monitored in real time by vibration sensors, acoustic emission sensors or online measuring devices installed on the machine tool or workpiece; when the reading of any sensor or its derived indicators, such as vibration spectrum energy or acoustic emission signal RMS value, exceed the adaptive threshold, the system will mark this moment as a potential risk event and record the timestamp of its occurrence, machine tool coordinates and trigger data. S42. Associate and match the timestamp and machine tool coordinates of each potential risk event with the time-synchronized compensated risk probability field generated in S2, and extract various risk values ​​predicted by the risk field at the time and location of the event to form risk prediction data; at the same time, extract the dynamic risk feature vector output by S1 at that moment from the system cache as environmental state data. S43. Compare the actual occurrence of potential risk events with their corresponding risk prediction data: if the event occurs but the predicted risk value is lower than the occurrence threshold, it is determined as a risk miss; if the event does not occur but the predicted risk value is consistently higher than the false alarm threshold, it is determined as a risk false alarm; for risk misses or risk false alarms, the system initiates a root cause analysis process using the associated environmental status data to analyze whether the dynamic characteristic parameters of the extended digital twin model in S1 are inaccurate, such as damping or stiffness inaccuracy, or whether the weights or thresholds of the risk value quantification calculation model in S2 are improperly set; S4. Based on the root cause analysis conclusions of S43, the identified inaccurate parameters are optimized in reverse. After optimization, the system uses the updated model and algorithm to enter the next round of processing and decision-making, thereby achieving continuous self-enhancement of predictive ability.

[0015] As a preferred embodiment of the present invention, S44 specifically includes: S441. Receive the root cause analysis results from S43 and generate a specific parameter optimization instruction, including the optimization type, target parameter identifier, and suggested adjustment amount and direction calculated based on event-prediction bias. S442. If the optimization type is model parameter optimization, then according to the instruction, the suggested adjustment amount will be applied to the corresponding dynamic characteristic parameters in the extended digital twin model in S1, such as the equivalent damping of the spindle-tool joint and the equivalent stiffness of the machine tool guideway in each direction; the update will be performed in a micro-incremental, iterative manner, and the parameter update log will be recorded. S443. If the optimization type is risk algorithm optimization, then adjust the specific coefficients or thresholds in the risk value quantification calculation model in S2 according to the instructions; for example, for repeated risk misses, increase the weight of the corresponding risk category in the composite risk value calculation; for repeated risk false alarms, increase the threshold for risk judgment accordingly. S444. After completing the parameter adjustment, the system uses recent historical processing data and instructions to drive the updated model and algorithm to perform an offline verification simulation, comparing the prediction accuracy of potential risk events before and after optimization; when the prediction accuracy improvement index reaches the preset requirements, the optimization is confirmed to be effective, and the updated model parameters and algorithm coefficients are synchronized to the online system, officially entering the next self-enhancement cycle.

[0016] A CNC machining path residual analysis and compensation system integrating twin simulation is used to implement a CNC machining path residual analysis and compensation method integrating twin simulation, including: The synchronous simulation and feature extraction module is used to build and run an extended digital twin model that integrates the machine tool-tool-workpiece-fixture system, so that it can be simulated synchronously with the physical machining process, and jointly output geometric residual feature vectors and dynamic risk feature vectors. The risk field online simulation module is connected to the synchronous simulation and feature extraction module. It is used to generate candidate compensation instructions with the geometric residual feature vector and simulate the execution effect in advance in the extended digital twin model. At the same time, it combines the dynamic risk feature vector to quantify and evaluate secondary risks to generate a dynamic compensation risk probability field. The compensation decision and execution module is connected to the online risk field simulation module. It is equipped with a risk-precision multi-objective decision-maker to receive the compensation risk probability field and generate a non-uniform robust compensation instruction sequence according to the risk level difference, and finally send it to the machine tool controller. The risk model optimization module is connected to the synchronous simulation and feature extraction module, the online risk field extrapolation module, and the physical sensor, respectively. It is used to monitor potential risk events in physical processing, compare and analyze them with the predictions of the compensated risk probability field, and optimize the parameters of the extended digital twin model or the calculation model in the risk field extrapolation based on the analysis results.

[0017] Compared with the prior art, the present invention has the following advantages: 1. By constructing a compensation risk probability field, the secondary risks such as vibration, over-shear, and over-force that may be caused by each compensation scheme can be quantitatively assessed in advance in virtual space. This allows for proactive avoidance of process disasters caused by blind compensation, solving the core defect of traditional methods that neglect safety in pursuit of accuracy, and realizing a fundamental shift from indiscriminate compensation to risk-aware decision-making.

[0018] 2. Based on the risk field, the processing area is dynamically divided and a differentiated strategy is implemented: in the low-risk area, high-gain compensation is used to improve accuracy, while in the medium- and high-risk areas, it automatically switches to a conservative mode such as reduced gain, directional or time delay compensation. This non-uniform intelligent decision-making mechanism achieves the optimal balance between accuracy and stability under complex working conditions, and achieves contextualized accurate dynamic compensation.

[0019] 3. By comparing actual risk events with predicted results, the system can automatically diagnose the root causes of errors and optimize the parameters of the twin model or the risk calculation model in reverse. This enables the system's risk prediction and compensation decision-making capabilities to continuously evolve with use, providing a self-evolving technological foundation for achieving predictive process optimization. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the method described in Embodiment 1 of the present invention.

[0022] Figure 2 This is a framework diagram of the system described in Embodiment 2 of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The concepts involved in this application will first be described with reference to the accompanying drawings. It should be noted that the following descriptions of various concepts are only for the purpose of making the content of this application easier to understand and do not constitute a limitation on the scope of protection of this application; furthermore, the embodiments and features in the embodiments of this application can be combined with each other unless otherwise specified. This application will now be described in detail with reference to the accompanying drawings and embodiments. Example 1

[0025] like Figure 1 As shown, this invention provides a CNC machining path residual analysis and compensation method integrating twin simulation, including the following steps: S1. Synchronize the extended digital twin model with the physical processing, and jointly output the geometric residual feature vector and the dynamic risk feature vector; specifically including: S11. Extend the integrated construction of digital twin models, specifically: Based on the structural design parameters of the machine tool, including the span of each axis guideway, the structural dimensions of the column and bed, and the mass distribution of the spindle system, a multibody system geometric kinematic model is established. This model is used to describe the spatial pose transformation relationship of each moving part of the machine tool under the drive of ideal commands.

[0026] Based on the tool's geometric parameters, tool overhang on the spindle, and clamping preload, a cutting force prediction model is established by combining the workpiece's material properties and a three-dimensional model. This model is used to calculate the instantaneous cutting force magnitude and direction under different cutting parameters, such as depth of cut, feed rate, and spindle speed.

[0027] Based on the mass, stiffness, and damping characteristics of the machine tool and its key components, a structural dynamics model is established. This model characterizes the natural frequencies, damping ratios, and mode shapes of the machine tool-tool-workpiece coupling system through finite element modal analysis or experimental modal identification methods, and is used to predict the dynamic response under cutting excitation.

[0028] Based on the type of fixture, such as a hydraulic vise, vacuum chuck, or special fixture, the layout of the positioning points, such as the position of the positioning pins, the coordinates of the clamping points, and the clamping force parameters, a fixture contact force model is established. This model is used to calculate the normal and tangential contact force distribution at the interface between the fixture and the workpiece.

[0029] By integrating the above-mentioned multibody system geometric kinematics model, cutting force prediction model, structural dynamics model and fixture contact force model through multiphysics coupling, an extended digital twin model is constructed. This model has the ability to synchronously reproduce the geometric motion and dynamic mechanical behavior of the physical machining process in a virtual environment.

[0030] S12. Lockstep Simulation and Dual-Thread Parallel Computation: While the physical machine tool performs the actual machining operation, the ideal toolpath instructions generated by the physical CNC system before interpolation calculation (i.e., pre-interpolation instructions), including the position instruction sequence and speed planning curve of each axis, as well as real-time sensor data collected from the physical machining site, including spindle load current, encoder feedback position of each axis servo motor, acoustic emission signals or acceleration sensor signals in the cutting area, are synchronously input into the extended digital twin model. This model is driven to perform lockstep simulation in the virtual environment with the same time step as the physical machining process, ensuring that the virtual simulation clock is synchronized with the physical machining clock. The lockstep simulation internally executes two parallel computation threads: S121. The first thread is the geometric residual prediction thread: This thread is based on the geometric kinematic model of the multibody system. It comprehensively considers the following error of each axis servo system, the thermal elongation error of the lead screw, and the joint clearance error. It calculates the actual motion trajectory of the tool center point in the workpiece coordinate system. By comparing the actual path with the ideal tool path point by point, geometric residual prediction data is generated. This data represents the deviation of the actual position of the tool from the ideal position, including the position deviation and contour error in three-dimensional space.

[0031] S122. The second thread is the multimodal dynamic response calculation thread: This thread achieves multi-physics coupled calculation of cutting force excitation, structural vibration response, and fixture contact state by simultaneously solving the coupled equations of the cutting force prediction model, structural dynamics model, and fixture contact force model. Specifically, the cutting force prediction model outputs the instantaneous cutting force based on the current tool position and cutting parameters. This cutting force is used as an excitation input to the structural dynamics model to obtain the vibration response of the tool-workpiece system. At the same time, the workpiece vibration displacement is fed back to the fixture contact force model to update the contact state and constraint reaction force. The change in the fixture constraint reaction force, in turn, affects the dynamic boundary conditions of the workpiece. This thread calculates and outputs multimodal dynamic response data in real time, including at least: the time history data of the vibration acceleration of the tool tip relative to the workpiece, the force distribution on the discrete grid nodes of the workpiece surface, and the time history data of the constraint reaction force at each positioning point of the fixture.

[0032] S13. Extraction and Construction of Dual Feature Vectors: Multi-dimensional statistical analysis is performed on the geometric residual prediction data output by the first thread: First, the mean and variance of the residual data at each point on the machining path are calculated to characterize the systematic bias and dispersion of the residuals; second, spatial frequency domain analysis is performed on the residual data to extract the spatial frequency characteristics of the residuals and identify specific frequency band residual patterns caused by machine tool periodic errors. The above mean, variance, and spatial frequency domain characteristics are vectorized and combined to construct a geometric residual feature vector, which quantitatively characterizes the amplitude and spectral characteristics of the geometric error under the current machining state.

[0033] Simultaneously, a time-frequency domain comprehensive analysis is performed on the multimodal dynamic response data output by the second thread: the effective value and power spectral density of the vibration acceleration signal at the tool tip are calculated to extract indicators characterizing the overall vibration intensity of the system; peak detection and spatial gradient calculation are performed on the force data of the workpiece surface mesh nodes to identify load abrupt change regions and force concentration locations; the time-domain fluctuation and spectral energy distribution of the constraint reaction force signal at the fixture positioning point are calculated to evaluate the stability of the clamping state. Furthermore, the above analysis results are fused to extract key indicators characterizing the system's vibration intensity, load abrupt changes, and clamping stability, constructing a dynamic risk feature vector. This vector is used to quantitatively assess the dynamic stability and potential risk level of the current machining process.

[0034] S2. Generate candidate compensation instructions using geometric residual feature vectors, simulate the compensation effect in advance within the extended digital twin model, and generate a dynamic compensation risk probability field based on dynamic risk feature vectors and simulation response changes; specifically including: S21. Generation of candidate preliminary compensation instructions: Based on the geometric residual feature vector extracted and constructed in S13, at least two compensation strategies with different compensation characteristics are applied to generate a set of candidate preliminary compensation instructions corresponding to the current machining path segment. This generation process first analyzes the parameters of each dimension of the geometric residual feature vector to identify the main components and distribution characteristics of the current geometric error, including the amplitude and direction of systematic deviations, the intensity of random fluctuations, and the frequency characteristics of periodic errors. Based on this analysis, differentiated compensation strategy combinations are designed: the first strategy uses a high gain coefficient to make a large-scale correction to the geometric residual, aiming to eliminate systematic deviations to the greatest extent, suitable for working conditions with significant geometric errors and stable dynamic responses; the second strategy uses a low gain coefficient to make a conservative correction to the geometric residual, prioritizing machining stability, suitable for working conditions with high dynamic risks or severe error fluctuations; the third strategy implements selective compensation for different spatial directions, for example, compensating only the residual components in the feed direction or normal direction while keeping other directions unchanged, to avoid dynamic coupling risks in specific directions. The aforementioned compensation strategies with different gain coefficients or spatial directions are applied to the geometric residual data of the current path segment. Through coordinate transformation and interpolation operations, a set of candidate preliminary compensation instructions is generated. Each candidate instruction contains a sequence of correction position instructions for each servo axis, a correction speed curve, and an execution timing sequence. The instructions are distinguished by strategy identifiers, forming a set of compensation schemes that can be used for subsequent risk assessment and decision-making.

[0035] S22. Advanced Simulation of Compensated Processing Path: In the extended digital twin model, an advanced simulation of a future processing path after compensation is performed, and the multimodal dynamic response data output by the advanced simulation is recorded simultaneously. This advanced simulation is not a real-time reproduction of the physical processing process, but a predictive simulation of possible future processing processes in a virtual environment. Its timeline is ahead of the physical processing clock, thus providing forward-looking data support for risk prediction; specifically: S221. Pre-simulation capture of initial state: The complete model state corresponding to the current physical processing moment is captured from the real-time synchronous simulation thread of the extended digital twin model, serving as the initial state for the advanced simulation. This capture operation is implemented through an atomic state snapshot mechanism to ensure strict temporal consistency of all state variables.

[0036] The captured initial state includes at least three levels: the first level is the machine tool motion state, including the position feedback value, actual velocity value, and acceleration value of each servo axis at the current moment, as well as the integral error and tracking error state of the servo system; the second level is the tool-workpiece contact state, including the geometric topology of the contact area between the current tool cutting edge and the workpiece material, the instantaneous cutting thickness distribution, and the tool wear state parameters; the third level is the system dynamic response state, including the instantaneous displacement and velocity of each modal coordinate in the structural dynamics model, the current vibration displacement and velocity of the tool tip, and the current normal clearance and tangential slip of each contact point in the fixture contact force model. The above complete model state is output in the form of a structured data package as the initial condition input for subsequent independent accelerated simulation threads.

[0037] S222. Generation and driving of the compensated instruction sequence: A candidate preliminary compensation command is superimposed with a future segment of original machining commands starting from the current moment to generate a compensated command sequence to be simulated. This superposition operation first determines the time window and spatial range of the compensation command's effect, then vector-adds the corrected position increment in the compensation command with the ideal position sequence in the original command to obtain the compensated actual target position sequence. Simultaneously, based on the speed correction parameters in the compensation command, the compensated speed curve is recalculated to ensure that the kinematic constraints of each axis of the machine tool are met. This compensated command sequence drives the extended digital twin model, starting from the advanced simulation initial state captured in S221, and is solved in an independent accelerated simulation thread.

[0038] This accelerated simulation thread runs in parallel with the real-time synchronous simulation thread, but uses a smaller time step and a higher iteration frequency, enabling virtual simulation of a longer future time window to be completed within a finite interval of physical time. The accelerated simulation thread also performs geometric residual prediction calculations and multimodal dynamic response calculations in parallel, but its output is only used for risk assessment and is not fed back to the physical processing control system.

[0039] S223. Synchronous recording of advanced three-modal dynamic response data: During the accelerated simulation thread operation, the model outputs a leading three-modal dynamic response dataset corresponding to each future interpolation cycle, which is recorded synchronously. This dataset is a future prediction of the multimodal dynamic response data defined in S12, and its data structure is consistent with the real-time dynamic response data in S12, so as to facilitate direct comparison and risk increment calculation.

[0040] The specific recorded content includes at least three types of data: the first type is the predicted time history of the tool tip vibration acceleration, including the triaxial acceleration values ​​and their spectral characteristics at future times; the second type is the predicted contour map of the force distribution on the workpiece surface, including the normal pressure and tangential friction vectors on the discrete grid nodes of the workpiece surface at future times; the third type is the predicted time history of the fixture constraint reaction force, including the normal clamping force, tangential friction force, and torque components at each positioning point at future times. The above data is stored in a combination of time series and spatial distribution, with each interpolation cycle corresponding to a complete set of multimodal dynamic response data frames.

[0041] S224. Multi-instruction traversal and data archiving: Repeat steps S221 to S223, performing independent advance simulations on all candidate preliminary compensation commands generated in S21. Before each simulation, the real-time model state at the current physical moment is recaptured as the initial condition to ensure consistency in the evaluation benchmark for each candidate command. During each simulation, the advance three-modal dynamic response dataset corresponding to that command is independently recorded. All advance three-modal dynamic response datasets corresponding to all candidate commands are structured and archived according to command identifier and timestamp, establishing a queryable advance simulation database to provide complete input data support for the secondary risk quantification assessment in S23.

[0042] S23. Quantitative Assessment of Secondary Risks: This step uses the dynamic risk feature vector constructed in S13 as a benchmark and the advanced three-modal dynamic response dataset generated in S22 as the assessment object. Through risk increment calculation and multi-dimensional risk value quantification, the system assesses the secondary risks that each candidate preliminary compensation instruction may cause, providing numerical input for the construction of the risk probability field in S24. Specifically: S231. Extraction and Establishment of Current Dynamic Risk Benchmark: From the dynamic risk feature vector constructed in S13, extract the core indicators characterizing the system stability under the current uncompensated state and establish the current dynamic risk benchmark. This extraction process first identifies key dimensions strongly correlated with processing stability, and determines the benchmark indicator set through preset weights or correlation analysis of historical data.

[0043] a. Extraction of vibration intensity benchmarks: The effective value of the vibration acceleration at the tool tip is selected as a typical indicator from the vibration risk feature subset. This value is obtained by calculating the root mean square of the vibration acceleration sampling data within the previous time window at the current moment, representing the background vibration energy level of the system. Simultaneously, the peak value of the vibration acceleration spectrum at the dominant frequency is extracted as an auxiliary benchmark to represent the intensity of periodic vibration; the proportion of energy in the high-frequency flutter band is extracted as a flutter tendency benchmark to represent the degree to which the system approaches the unstable boundary.

[0044] b. Load level benchmark extraction: The spatial average value of the normal contact pressure of the workpiece surface grid nodes is selected from the load risk feature subset as the average load level index, which is obtained by area-weighted averaging of the force distribution cloud map of the workpiece surface at the current moment. At the same time, the average values ​​of the cutting force components in the feed direction, normal direction, and axial direction are extracted as the directional load benchmark; the force distribution non-uniformity coefficient is extracted as the load balance benchmark.

[0045] c. Extraction of load fluctuation benchmark: The coefficient of variation or peak-to-peak value of the stress history on the workpiece surface is selected from the load risk feature subset as the load fluctuation range index, which is obtained by statistical analysis of the stress data in the previous time window at the current moment. At the same time, the mean and maximum values ​​of the cutting force change rate are extracted as the load change frequency benchmark; the average value of the stress spatial gradient is extracted as the force concentration risk benchmark.

[0046] d. Extraction of clamping stability benchmarks: The effective value and maximum value of the constraint reaction force fluctuation at each positioning point of the clamp are selected from the clamping risk feature subset as clamping stability indicators. At the same time, the difference coefficient between the reaction forces at each positioning point is extracted as the clamping uniformity benchmark; the time mean and variance of the friction coefficient are extracted as the friction state stability benchmark.

[0047] The extracted core indicators are normalized and vectorized to construct the current dynamic risk benchmark vector, which quantitatively characterizes the inherent dynamic stability level of the physical processing system before any compensation measures are applied.

[0048] S232. Analysis and Risk Increment Calculation of Advanced Three-Modal Dynamic Response Data: For the advanced three-modal dynamic response dataset generated for each candidate preliminary compensation instruction through advanced simulation in S22, perform data analysis and index calculation corresponding to the current dynamic risk benchmark, and then calculate the risk increment relative to the benchmark.

[0049] a. Structural Analysis of the Advanced Three-Modal Dynamic Response Data: This dataset contains predictive dynamic responses for a future machining path, with the time coverage determined by the duration of the accelerated simulation thread in the advanced simulation. The three-modal dynamic response data frames at each prediction time are analyzed: RMS values, peak frequencies, and high-frequency energy proportions are extracted from the predicted time history of tool tip vibration acceleration; average pressure, non-uniformity coefficient, and spatial gradient are extracted from the predicted force distribution contour map of the workpiece surface; and fluctuating RMS values, peak values, and positioning point differences are extracted from the predicted time history of fixture constraint reaction forces. The analytical indicators for each prediction time are arranged chronologically to form the future evolution curves of the risk indicators.

[0050] b. Time-series calculation of risk increments: The evolution curves of future risk indicators obtained from advanced simulations are compared point-by-point with the current dynamic risk benchmark to calculate risk increments. Vibration intensity increments are calculated as the absolute increment or relative rate of change of the effective value of vibration acceleration relative to the benchmark effective value at each predicted time; load level increments are calculated as the deviations of the average pressure and directional force components relative to the corresponding benchmarks; load fluctuation increments are calculated as the changes in the coefficient of variation and peak value; clamping stability increments are calculated as the changing trends of reaction force fluctuations and the coefficient of variation. The above increment calculations can be performed using methods such as difference, ratio, or logarithmic ratio, with the appropriate increment definition method selected based on the physical characteristics and sensitivity requirements of each risk indicator.

[0051] c. Spatial Aggregation of Risk Increments: Spatial aggregation of time-series risk increments along the processing path is performed to identify the peak location, cumulative effect, and duration of risk increments. The maximum value, average value, and percentage of duration exceeding a set threshold for each risk increment are calculated to form a spatial aggregation index that can summarize the overall risk change trend of future path segments, providing multi-dimensional input information for subsequent risk value quantification.

[0052] S233. Quantitative Calculation Model for Secondary Risk Values: Based on incremental risk data, a quantitative calculation model is used to calculate the quantitative values ​​of three key secondary risks: vibration exacerbation risk value, over-shear or under-shear risk value, and force exceeding limit risk value. This quantitative calculation model adopts a hybrid modeling method combining physical mechanisms and data-driven approaches, mapping the risk increment to a comparable risk scale.

[0053] a. Quantitative Calculation of Vibration Enhancement Risk Value: This risk value characterizes the likelihood that compensation will lead to increased vibration, surface quality deterioration, or reduced tool life. Using the effective value increment of vibration acceleration as the core input, combined with frequency sensitivity weighting (higher weight is given when the dominant frequency is close to the structural modal frequency), weighting with chatter tendency, and weighting the high-frequency energy percentage increment according to the chatter baseline index, the value is then transformed into a quality deterioration expectation through a surface roughness-vibration mapping model, and finally normalized to output a standard risk value.

[0054] b. Quantitative calculation of overcutting or undercutting risk value: This risk value characterizes the possibility that compensation will cause the material removal amount to deviate from the expectation, resulting in dimensional accuracy deviations. A dynamic cutting depth prediction model is established based on the workpiece force distribution and the relative position of the tool and workpiece to calculate the actual cutting depth deviation; this deviation is vector-compared with the compensation correction amount to identify the compensation-deformation coupling relationship, where the same direction leads to overcutting and the opposite direction leads to undercutting; the instantaneous cutting thickness anomaly is evaluated by combining the superposition effect of vibration displacement and cutting trajectory; and the risk value is output through a failure probability model by comprehensively considering the deviation, coupling relationship, vibration effect, and geometric tolerance requirements.

[0055] c. Quantitative Calculation of Force Exceedance Risk Value: This risk value characterizes the possibility that compensation will cause the clamping force or cutting force to exceed the safety threshold, leading to fixture failure or tool breakage. Based on the fixture constraint reaction force prediction time history, the reciprocal of the safety factor between the peak and effective values ​​and the maximum allowable clamping force is calculated; the drift trend term of the reaction force time history is analyzed to identify dynamic processes tending towards overload or relaxation caused by thermal expansion or force accumulation; extreme load events are assessed by combining cutting resultant force prediction data; and the risk value is output through a stress-strength interference model or extreme value statistics method, taking into account the safety factor, fluctuation intensity, drift trend, and extreme load probability.

[0056] S234. Integration and output of risk quantification results: For each candidate preliminary compensation instruction, the calculation results of the above three types of secondary risk values ​​are integrated to form a risk assessment report for that instruction. This report includes: an instruction identifier and a summary of strategy attributes, used to correlate with the compensation strategy parameters in S21; numerical results and calculation confidence levels for vibration exacerbation risk values, over-shear or under-shear risk values, and force exceedance risk values; spatial distribution characteristics of each risk value on future path segments, including peak locations, lengths of high-risk sections, and trends in risk gradient changes; and correlation analysis between risk values ​​to identify whether there is a trade-off between a reduction in one type of risk and an increase in another.

[0057] The risk quantification results of all candidate preliminary compensation instructions are output to S24 as the raw discrete sampling data for constructing the dynamic compensation risk probability field. This output data is organized in the form of a structured table or object list. Each record corresponds to the risk status of a candidate instruction at a future path point and includes fields such as three-dimensional coordinates, instruction identifier, three types of risk values, and timestamp. It supports spatial interpolation and field construction operations in S24.

[0058] S24. Construction and Update of the Compensation Risk Probability Field: The various risk values ​​calculated for all candidate preliminary compensation commands in S23 are associated with their corresponding coordinate points in the three-dimensional processing path space. Spatial interpolation is used to form a dynamically updated compensation risk probability field covering the current and future paths. This field uses three-dimensional spatial location as an index, candidate compensation strategies as dimensions, and risk type as an attribute to construct a multi-dimensional tensor data structure. This structure characterizes the probability or expected severity of various secondary risks triggered when different compensations are applied at different points along the path. Specifically: S241. Generation of 3D Risk Point Cloud Data: For each candidate preliminary compensation instruction, the vibration aggravation risk value, overcut or undercut risk value and force over-limit risk value calculated at each future path point in S23 are bound to the 3D coordinates of the path point in the machine tool coordinate system or workpiece coordinate system.

[0059] The binding operation first determines the three-dimensional coordinates of the tool center point at each prediction moment in the advanced simulation, and uses these coordinates as the spatial anchor point for the risk value. Then, the three types of risk values ​​calculated at that moment are appended to the anchor point as attribute data, generating a set of discrete three-dimensional risk point cloud data. Each data point in the point cloud contains fields such as spatial location coordinates, candidate command identifier, vibration aggravation risk value, overcut or undercut risk value, and force over-limit risk value, forming the original sampled dataset of the risk spatial distribution.

[0060] S242. Spatial Interpolation and Gridded Calculation: Within the spatial range of the processing path to be evaluated, a regular three-dimensional grid is established. The grid resolution is adaptively adjusted based on the accuracy requirements of risk assessment and computational efficiency constraints. A local densification strategy is adopted in areas with drastic changes in path curvature or large risk gradients. Based on the three-dimensional risk point cloud data generated by S241, spatial interpolation is performed for each type of risk value. The interpolation method can be Kriging interpolation, which considers the spatial autocorrelation structure of risk values ​​and achieves optimal unbiased estimation through variogram modeling; or radial basis function interpolation, which is based on the distance weighting principle and is suitable for areas with relatively smooth risk distribution. The interpolation calculation is performed at each grid node. By integrating the sampled values ​​of neighboring risk point clouds, a continuous estimate of each type of risk value corresponding to that node is calculated, filling the spatial gaps between discrete sampling points and forming a continuous distribution representation of the risk field.

[0061] S243. Structured organization of dynamically compensated risk probability fields: The interpolation results of various risk values ​​calculated by S242 and distributed across 3D grid nodes are organized into a structured, real-time queryable dynamic compensation risk probability field. The internal data structure of this field employs a hierarchical hash table or k-d tree index, supporting efficient spatial range queries and nearest neighbor searches. The dynamic compensation risk probability field provides standardized query interfaces: based on any input 3D coordinate point, it determines the grid cell or neighboring interpolation node through spatial positioning and returns the interpolation results of various risk values ​​at that location; based on the input specified candidate preliminary compensation instruction identifier, it filters and returns the risk distribution subfield corresponding to the strategy; based on the input risk type parameter, it returns the spatial distribution of a single risk or the comprehensive weighted result of various risks. As the physical processing progresses and new advanced simulation results are generated, this probability field is continuously updated through a sliding window mechanism, eliminating outdated risk data for path segments and adding risk predictions for newly entered forward-looking path segments, achieving dynamic real-time updates and providing direct data input for residual compensation decisions and robust execution steps under the risk constraints of S3.

[0062] S3. The risk-precision multi-objective decision-maker receives the compensation risk probability field, performs high-gain compensation in the low-risk region, and switches to robust compensation command sequence in the high-risk region before issuing it to the machine tool; specifically including: S31. Construction and Input Synchronization of Risk-Accuracy Multi-Objective Decision Maker: A risk-precision multi-objective decision-maker is constructed, employing a hierarchical structure: the bottom layer is the data fusion layer, the middle layer is the region determination layer, and the top layer is the policy generation layer. This decision-maker synchronously receives the geometric residual feature vector output by S1 and the compensated risk probability field output by S2.

[0063] In the data fusion layer, a dual-channel parallel input interface is established. The geometric residual feature vector receiving channel analyzes its parameters in various dimensions, including residual statistical moment features, frequency domain features, and correlation features, extracting the amplitude level, spatial distribution pattern, and spectral structure characteristics of the geometric error. The compensation risk probability field receiving channel establishes a real-time data connection with the dynamic compensation risk probability field constructed by S24, supporting risk value retrieval based on three-dimensional coordinate queries to obtain the predicted distribution of various risk values ​​on the current processing point and the look-ahead path segment.

[0064] The dual-channel input data undergoes spatiotemporal alignment and fusion processing within the decision-maker: based on timestamps and path parameters, the extraction time of the geometric residual feature vector is matched with the prediction start time of the compensated risk probability field; based on spatial coordinates, the spatial distribution of the geometric residual is mapped to the grid node positions of the risk probability field, ensuring that the geometric error information and the risk prediction information correspond consistently in spatial location and time node, providing a unified decision data foundation for subsequent regional division and strategy generation.

[0065] S32. Path Region Division and Strategy Mapping Rule Establishment: The risk-precision multi-objective decision-maker divides the current and prospective processing paths into low-risk and medium-to-high-risk regions based on a preset risk threshold in the compensation risk probability field, and establishes strategy mapping rules to obtain real-time strategy mapping results. This process is achieved through two sub-stages: risk classification determination and compensation strategy quantification; specifically: S321. Setting and configuring risk thresholds: For the three types of risks in the compensation risk probability field—vibration exacerbation risk, overcutting or undercutting risk, and force exceeding limit risk—basic risk thresholds and high-risk risk thresholds are set based on the process knowledge base and historical data, respectively. The process knowledge base includes prior knowledge such as the machine tool structure dynamics characteristics, tool material strength limits, and workpiece material machinability; the historical data includes risk events recorded in previous machining processes and their corresponding risk index values.

[0066] The basic risk threshold is determined by combining the recommended safety value of the process with the statistical quantile of historical data, representing the upper limit of the acceptable risk level under normal operating conditions. The high-risk threshold is determined by combining the process limit safety value with historical accident record values, representing the critical level that will lead to serious machining quality accidents or equipment safety accidents. The thresholds for the three types of risks are set independently, taking into account their differences in physical dimensions and risk consequences: the vibration exacerbation risk threshold is calibrated based on the allowable degree of surface roughness deterioration or tool life loss rate; the overcut or undercut risk threshold is calibrated based on the width of the workpiece dimensional tolerance zone; and the force over-limit risk threshold is calibrated based on the fixture safety factor or tool strength limit.

[0067] S322. Three-level division of path regions: In the risk-precision multi-objective decision maker, the compensated risk probability field data for the current machining point and its predetermined path points within the forward view window are read. The time domain length or path length of the forward view window is determined based on the machine tool's dynamic response characteristics and the real-time requirements of the decision, typically covering the machining process for tens to hundreds of milliseconds or the path travel of several millimeters to tens of millimeters.

[0068] For each path point within the forward-looking window, a risk level determination is performed: if the predicted risk values ​​for all categories at that point are lower than their corresponding basic risk thresholds, the path segment containing that point is classified as a low-risk area, indicating that the probability of secondary risks arising from implementing compensation measures in that area is negligible; if the predicted risk value for any category at that point reaches or exceeds its high-risk threshold, the point is classified as a high-risk area, indicating that implementing conventional compensation measures in that area is highly likely to cause serious processing accidents; if the predicted risk value at that point is between the basic risk threshold and the high-risk threshold, or if a single category of risk exceeds the basic threshold but does not reach the high-risk threshold, the point is classified as a medium-risk area, indicating that implementing compensation in that area requires careful consideration of the balance between accuracy benefits and risk costs.

[0069] The regional division results form a segmented and continuous sequence of regional labels along the processing path. Adjacent path points may belong to the same risk area or different areas. The regional boundaries are determined by the risk threshold crossing points.

[0070] S323. Quantitative mapping rules for compensation strategies: Map differentiated compensation strategies to different risk areas and transform strategy parameters into computable quantitative rules.

[0071] An aggressive compensation strategy is adopted for mapping low-risk regions: This strategy is quantified as using a set of aggressive, fixed high-gain coefficients, with gain coefficients close to or equal to 1.0, representing a full correction of the detected geometric residuals; simultaneously, all spatial direction error components indicated by the geometric residual feature vector extracted by S1 are fully compensated, without implementing direction-selective masking; the compensation timing is synchronized with error detection, without introducing additional compensation delay. This strategy aims to eliminate geometric errors to the greatest extent and fully utilize the low-risk window to achieve accuracy optimization.

[0072] A conservative compensation strategy is mapped to medium- and high-risk areas: the quantification rules of this strategy include at least one of the following computable rules, which can be applied independently or in combination: Rule a is the conservative gain coefficient calling rule: it calls a set of preset conservative gain coefficients with lower values. The gain coefficient values ​​are usually in the range of 0.3 to 0.7. It performs linear or nonlinear mapping between medium and high risk based on the risk level: the higher the risk level, the lower the gain coefficient, which indicates that the correction strength of geometric residuals decreases as the risk increases, so as to reduce the disturbance of the system dynamic state by the compensation measures.

[0073] Rule b is a direction-selective compensation rule: Based on risk source analysis, it identifies the correlation between the current dominant risk category and the directional components of the geometric residual: vibration exacerbation risk is usually strongly correlated with normal and axial compensation, over-shear or under-shear risk is strongly correlated with normal compensation, and force over-limit risk is strongly correlated with forward compensation. Only specific directional components in the geometric residual eigenvector with low correlation to the current dominant risk category are compensated, shielding the compensation effect of high-risk directions and implementing a conservative correction of directional decoupling.

[0074] Rule c is the compensation timing delay rule: In the path parameter domain, calculate the distance from the current point to the nearest downstream point identified as a low-risk area starting point. This distance represents the path length from the current position to the safety compensation window. Divide this distance by the current feed speed to convert it into a time quantity, and then discretize it according to the interpolation cycle duration to obtain the number of interpolation cycles for the compensation delay. Output the number of interpolation cycles corresponding to the compensation command delay, so that the compensation effect is postponed until the system enters a low-risk state, thus achieving risk avoidance in the time domain.

[0075] S324. Generation of Real-Time Policy Mapping Table: Based on the region division results of S322 and the strategy quantization rules of S323, a real-time strategy mapping table is generated for each point on the current and look-ahead paths. This mapping table uses path parameters as indexes and records the following decision outputs for each path point: a region label identifying the low-risk, medium-risk, or high-risk region to which the point belongs; a set of gain coefficients to be used, containing the specific gain values ​​for each compensation direction; a mask for the direction to be compensated, indicating whether compensation is enabled for each spatial direction in binary vector form; and a compensation delay, representing the delay output duration in interpolation cycles. This real-time strategy mapping table serves as the direct basis for generating robust compensation instruction sequences and is dynamically updated as the processing progresses and the look-ahead window slides.

[0076] S33. Generation and Real-time Execution of Robust Compensation Instruction Sequence: Based on the real-time strategy mapping result of S32, the compensation requirement of each point on the original path is dynamically modulated to generate a robust compensation instruction sequence that is non-uniformly distributed in space and time, and the sequence is sent to the machine tool controller for execution in real time.

[0077] S331. Dynamic Modulation Process for Compensation Requirements: The residual data sequence from the geometric residual feature vector generated in S13 is read. This sequence contains the three-dimensional positional deviations at each point on the original processing path. Based on the gain coefficient set in the real-time strategy mapping table and the direction mask to be compensated, the residual vector at each path point is modulated point-by-point: each directional residual component is multiplied by the corresponding directional gain coefficient, and then a logical AND operation is performed with the direction mask to mask the directions prohibited from compensation, thus obtaining the effective compensation vector for that point. Based on the compensation time delay in the real-time strategy mapping table, a shift operation is performed on the effective compensation vector at each point in the path parameter domain, distributing the compensation effect to the delayed target position, forming a non-uniformly distributed compensation command sequence in the time domain.

[0078] S332. Geometric Smoothing of Compensation Commands: The modulated compensation command sequence undergoes geometric continuity checks and smoothing to avoid abrupt changes in compensation commands due to region switching or parameter jumps. At the boundaries between low-risk and medium-to-high-risk regions, transition curves are used to gradually change the gain coefficient, avoiding abrupt changes in gain. At the jump points of the compensation time delay, spline curves are used to smooth the compensation vector, ensuring the continuity of displacement, velocity, and acceleration. The smoothed compensation command sequence satisfies the machine tool kinematic constraints, ensuring physical executability.

[0079] S333. Real-time Issuance and Execution of Compensation Commands: The generated robust compensation command sequence is issued to the machine tool controller via a high-speed real-time communication interface. The issuance process employs a look-ahead buffer mechanism. The machine tool controller maintains a buffer containing commands for several future interpolation cycles. Compensation commands are injected into this buffer in timestamp order and superimposed and fused with the original CNC commands. The machine tool controller's interpolator executes servo control according to the synthesized commands in the buffer, driving the movement of each axis of the machine tool, thus achieving residual compensation and risk control during the physical machining process. As the machining process progresses, steps S31-S33 are continuously executed in a loop, the real-time strategy mapping table is dynamically updated, and the compensation command sequence is continuously generated, forming a closed-loop online compensation control process.

[0080] S4. Compare and analyze the potential risk events monitored during physical processing with the predictions of the compensated risk probability field, and then optimize and extend the digital twin model in reverse to improve its risk prediction accuracy; specifically including: S41. Real-time monitoring of physical processing and identification of potential risk events: A multi-source, heterogeneous physical machining process monitoring network is constructed by installing vibration sensors, acoustic emission sensors, and online measuring devices at key structural points of the machine tool, such as the spindle bearing housing, tool post column, worktable guide rail, or workpiece clamping area. Vibration sensors collect triaxial acceleration signals, covering several times the modal frequencies of the machine tool structure; acoustic emission sensors are positioned near the cutting area to collect high-frequency stress wave signals; and online measuring devices employ contact probes or non-contact optical measurement systems to obtain the actual geometric dimensions of the workpiece. Data from all sensors is collected through a unified time synchronization mechanism to ensure temporal consistency of the multi-source data.

[0081] The raw sensor signals are processed in real time to calculate derived indicators: for vibration signals, the energy proportion of a specific frequency band is calculated using Fast Fourier Transform; for acoustic emission signals, the root mean square value is calculated; and for online measurement data, the deviation between the actual and target dimensions is calculated. An adaptive threshold mechanism is used for anomaly detection. The threshold is dynamically updated based on the statistical distribution of recent normal processing data, such as using the mean plus several times the standard deviation as the boundary. When any indicator exceeds the corresponding adaptive threshold, the current moment is marked as a potential risk event, and its timestamp, machine tool coordinates, trigger sensor identifier, and derived indicator value are recorded, forming a structured event log stored in a high-speed cache.

[0082] S42. Temporal-spatial correlation matching between risk events and forecast data: The timestamps and machine tool coordinates of the potential risk events marked in S41 are associated and matched with the time-synchronized compensated risk probability field generated in step S2 to extract risk prediction data at the time and location of the event, and at the same time, environmental state data at the corresponding time are also extracted.

[0083] In the time dimension, the event is located in the historical data sequence of the compensation risk probability field based on the event timestamp, and the latest updated risk field snapshot at that moment is extracted. This snapshot records the risk distribution prediction on the forward path segment. In the spatial dimension, the event is located in the risk field based on the machine tool coordinates. If the coordinates coincide with the grid nodes, the risk value is directly read. If the coordinates are between nodes, trilinear interpolation is used to calculate the vibration aggravation risk value, over-shear or under-shear risk value, and force over-limit risk value for each candidate compensation strategy at that location, as well as the regional affiliation label and risk gradient information, to form the risk prediction data.

[0084] Simultaneously, the dynamic risk feature vector output from step S1 at that moment is extracted from the system's circular buffer as environmental state data. This vector includes vibration intensity, load level, load fluctuation, and clamping stability indicators, reflecting the system's dynamic response state when the event occurs. The aforementioned risk prediction data and environmental state data together construct a ternary correlation dataset of event-prediction-environment, providing data support for the root cause analysis of S43.

[0085] S43. Comparative evaluation and root cause analysis of risk prediction performance: Compare and analyze the actual occurrence of potential risk events with the corresponding risk prediction data to identify false alarms and missed predictions, and initiate the root cause analysis process based on the associated environmental status data to locate the source of prediction deviation.

[0086] S431. Risk Missing Detection and Identification: If a potential risk event actually occurs during physical processing, but the predicted risk value at that location in the risk field at the corresponding moment is lower than a preset occurrence threshold, it is determined as a risk missing. The occurrence threshold is usually set as a basic risk threshold to distinguish between low and medium risk, or a higher alert level set according to the severity of the risk consequences. Risk missing indicates that the system's predictive model failed to identify the potential risk at that location in advance, resulting in insufficient risk avoidance measures being taken in the compensation decision.

[0087] S432. Determination and Identification of False Alarms: If the predicted risk value at a certain location in the risk field is consistently higher than the preset false alarm threshold, but no abnormalities are detected in the physical processing during the corresponding time period, it is determined to be a false alarm. A false alarm indicates that the system's prediction model overestimates the risk level at that location, leading to an unnecessary adoption of a conservative strategy in the compensation decision, sacrificing processing accuracy.

[0088] S433. Root Cause Analysis Process Initiation and Execution: For identified risk misses or false alarms, the root cause analysis process is initiated using the environmental status data extracted in S42 to analyze the root causes of prediction bias. Root cause analysis employs a combination of hypothesis testing and parameter sensitivity analysis. If environmental data accompanying a risk underreporting indicates that the system vibration intensity or load level is significantly higher than the historical average, but the risk prediction value remains low, it is suspected that the dynamic characteristic parameters of the extended digital twin model in S1 are inaccurate, such as the equivalent damping coefficient in the structural dynamics model being set too high or the equivalent stiffness coefficient being set too low.

[0089] If the environmental status data accompanying the risk underreporting is within the normal range, but the risk prediction value is abnormally low, it is suspected that the weight setting of the risk value quantification calculation model in S2 is inappropriate. For example, the weight of a certain type of risk in the calculation of the composite risk value is too low, or the risk threshold is set too leniently.

[0090] If the environmental status data accompanying a false alarm indicates that the system is actually operating smoothly, but the risk prediction value remains high, it is suspected that the dynamic characteristic parameters of the extended digital twin model in S1 are inaccurate, such as the equivalent damping coefficient being set too low or the force coefficient of the cutting force prediction model being set too high.

[0091] If both the environmental status data and the risk prediction value are at a high level when a false alarm occurs, but no anomalies are found in the physical system, it is suspected that the threshold setting of the risk value quantification calculation model in S2 is too conservative, or the sensitivity coefficient in the risk increment calculation is set too high.

[0092] The root cause analysis process outputs specific optimization type determinations and target parameter identifiers, and calculates suggested adjustment amounts and directions based on the magnitude of the event-prediction deviation.

[0093] S4. Backward Optimization and Self-Enhancing Closed Loop of Model and Algorithm: Based on the root cause analysis conclusions in S43, the identified inaccurate parameters are backward optimized. After optimization, the system uses the updated model and algorithm to enter the next round of processing and decision-making, thereby achieving continuous self-enhancing of predictive ability; specifically: S441. Generation and Parsing of Parameter Optimization Instructions: Receives the root cause analysis conclusions from S43 and generates a specific parameter optimization instruction in the optimization control module. This instruction is a structured data record containing the following fields: Optimization Type field, indicating whether this optimization belongs to model parameter optimization or risk algorithm optimization; Target Parameter Identifier field, precisely pointing to a specific dynamic characteristic parameter of the extended digital twin model in S1 or a specific coefficient of the risk value quantification model in S2; Suggested Adjustment Quantity and Direction field, calculated based on the statistical magnitude of the event-prediction bias.

[0094] S442. Update of Dynamic Characteristic Parameters of Extended Digital Twin Model: If the optimization type is determined to be model parameter optimization, the suggested adjustment amount is applied to the corresponding dynamic characteristic parameters within the extended digital twin model in S1 according to the instructions. Parameter updates are performed in micro-increments and iteratively to avoid sudden changes in model behavior caused by a single large adjustment: the difference between the current parameter value and the target adjustment amount is calculated, and this difference is divided into several small steps; after each step, the model self-checking program is run to verify the physical rationality of the parameters. After verification, the update is submitted, and the parameter update log is recorded, including the values ​​before and after the update, the update timestamp, and the trigger event identifier. The updated parameters take effect immediately in the structural dynamics model, cutting force prediction model, or fixture contact force model of the extended digital twin model, changing the dynamic response prediction characteristics of the model.

[0095] S443. Adjustment of Coefficients and Thresholds in the Risk Value Quantification Model: If the optimization type is determined to be risk algorithm optimization, then adjust specific coefficients or thresholds within the risk value quantification model in S2 according to the instructions. For recurring false negative events, if the current model is deemed insufficiently sensitive to specific risk categories, increase the weight coefficient of that risk category in the composite risk value calculation, or decrease the risk occurrence threshold for that category to make the model more vigilant about similar risks in the future. For recurring false alarm events, if the current model is deemed to be overly conservative in its estimation of specific risk categories, correspondingly decrease the weight coefficient of that risk category in the composite risk value calculation, or increase the risk judgment threshold for that category to reduce unnecessary conservative decisions. The adjustment of weights and thresholds also adopts a micro-incremental approach and is recorded in the adjustment log.

[0096] S444. Offline Verification and Online Synchronization of Optimization Results: After parameter adjustment, the system uses recent historical processing data and instructions to drive the updated model and algorithm for an offline verification simulation. In the verification simulation, the same historical input data is used to run both the pre-optimization and post-optimization model versions, comparing their prediction outputs for known potential risk events. Prediction consistency indicators before and after optimization are calculated, such as the reduction in false alarm rate, the reduction in false alarm rate, and the root mean square error of risk value prediction. When the prediction consistency improvement indicators meet preset requirements, the optimization is confirmed to be effective. The updated model parameters and algorithm coefficients are synchronized to the online system, replacing the running old version, and the system officially enters the next self-enhancement cycle. If the optimization effect does not meet preset requirements, parameter adjustments are rolled back, root cause analysis is retried, or the optimization strategy is adjusted to avoid ineffective updates affecting the quality of online decision-making. Example 2

[0097] like Figure 2 As shown, the CNC machining path residual analysis and compensation system integrating twin simulation is used to implement the CNC machining path residual analysis and compensation method integrating twin simulation, including: The synchronous simulation and feature extraction module is used to build and run an extended digital twin model integrating the machine tool-tool-workpiece-fixture system, simulating it synchronously with the physical machining process, and jointly outputting geometric residual feature vectors and dynamic risk feature vectors; specifically including: The model building unit is used to integrate the geometric kinematics, cutting mechanics, structural dynamics, and fixture contact model of the multibody system to build the extended digital twin model; The parallel simulation unit is used to drive the model to perform lockstep synchronous simulation and to compute geometric residual prediction data and multimodal dynamic response data in parallel. Feature construction units are used to perform statistical and time-frequency analysis on simulation data, and construct geometric residual feature vectors and dynamic risk feature vectors, respectively.

[0098] The online risk field simulation module, connected to the synchronous simulation and feature extraction module, is used to generate candidate compensation instructions using geometric residual feature vectors and to simulate the execution effect in advance within an extended digital twin model. Simultaneously, it combines dynamic risk feature vectors to quantitatively evaluate secondary risks and generate a dynamic compensation risk probability field. Specifically, it includes: The instruction generation unit is used to generate a set of candidate preliminary compensation instructions based on the geometric residual feature vector according to different strategies. The advanced simulation unit is used to accelerate simulation by sequentially injecting candidate instructions into the extended digital twin model with the current processing state as the initial condition. The risk assessment unit has a built-in risk value quantification calculation model, which is used to calculate three types of risk values: vibration aggravation, over-shearing / under-shearing, and force over-limit based on dynamic risk feature vectors and compared with advanced simulation data. The field construction unit is used to interpolate and map discrete risk values ​​in the three-dimensional processing path space to form the compensated risk probability field.

[0099] The compensation decision and execution module is connected to the online risk field simulation module. Internally, it contains a risk-precision multi-objective decision-maker that receives the compensation risk probability field and generates a non-uniform robust compensation instruction sequence based on risk level differences, ultimately sending it to the machine tool controller. Specifically, it includes: The region division unit is used to divide the processing path into low-risk areas and medium-to-high-risk areas in real time based on the threshold in the compensation risk probability field. The strategy mapping unit, with an embedded quantization rule base, is used to map aggressive high-gain compensation strategies for low-risk areas and conservative strategies that include gain reduction, selective directional compensation, or time delay compensation for medium- and high-risk areas. The instruction synthesis unit is used to modulate and generate robust compensation instruction sequences based on the real-time policy mapping results.

[0100] The risk model optimization module, connected to the synchronous simulation and feature extraction module, the online risk field extrapolation module, and physical sensors, is used to monitor potential risk events in physical processing, compare and analyze these events with the predictions of the compensated risk probability field, and optimize the parameters of the extended digital twin model or the calculation model in the risk field extrapolation based on the analysis results; specifically including: The event monitoring and correlation unit is used to identify potential risk events through sensors and correlate them with the predicted data and corresponding dynamic risk feature vectors in the compensation risk probability field. The root cause analysis unit is used to compare actual events with predicted results to diagnose whether the root cause of missed risk reports or false alarms is inaccurate model parameters or defects in the risk calculation model. The parameter optimization unit is used to adjust the dynamic characteristic parameters of the extended digital twin model or the coefficients and thresholds of the risk value quantification model based on the root cause analysis conclusions. The verification unit is used to review and verify the optimization effect through historical data, and update the online system parameters after confirmation to complete the self-enhancement cycle.

[0101] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects: This invention represents a paradigm shift from error-driven compensation to risk-constrained decision-making, fundamentally eliminating secondary process risks. By using a compensation risk probability field to proactively predict the consequences of each compensation strategy in a virtual space, it provides spatial and probabilistic quantitative assessments of various secondary risks, such as increased vibration, overcutting / undercutting, and force exceeding limits. This transforms compensation from a deterministic action into an intelligent decision-making process based on panoramic risk prediction, proactively avoiding processing defects and equipment damage caused by blind compensation. It is particularly suitable for high-risk precision machining scenarios involving thin-walled parts, complex curved surfaces, and other easily deformable and chatter-prone materials.

[0102] A context-aware, differentiated intelligent compensation mechanism was constructed, achieving an optimal balance between accuracy and stability. This invention utilizes a risk-accuracy multi-objective decision-maker to divide the processing path into regions of different risk levels in real time based on a dynamic compensation risk probability field, and executes differentiated compensation strategies: a high-gain aggressive strategy is used in low-risk regions to maximize accuracy; in medium- and high-risk regions, a conservative strategy including gain reduction, selective directional compensation, or time-delay compensation is automatically switched. This non-uniform, robust compensation command sequence output achieves adaptive matching between compensation intensity and local risk levels. Like an experienced technician, it can accurately eliminate errors while ensuring the stability of the overall process system, thereby significantly enhancing the reliability and robustness of the process while improving processing accuracy.

[0103] This system forms a closed-loop, self-reinforcing system encompassing perception, decision-making, and evolution, enabling continuously improving predictive maintenance capabilities. By monitoring potential risk events during actual machining and comparing them with the prediction results of digital twin simulations, the system automatically diagnoses the root causes of missed or false alarms. Based on this analysis, the system can reverse-engineer and optimize the core dynamic parameters of the extended digital twin model or the weighting coefficients of the risk value quantification model. This closed loop allows the system not only to adapt to the instantaneous state of the machine tool in real time but also to continuously learn and track the long-term evolution trend of machine tool performance, constantly accumulating process knowledge. Consequently, its risk prediction and compensation decision-making capabilities continuously enhance with usage time, laying the core technological foundation for achieving truly predictive intelligent maintenance and autonomous process optimization.

[0104] The embodiments and / or implementation methods described above are merely preferred embodiments and / or implementation methods for implementing the technology of the present invention, and are not intended to limit the implementation methods of the technology of the present invention in any way. Any person skilled in the art may make some modifications or alterations to other equivalent embodiments without departing from the scope of the technical means disclosed in the present invention, but these should still be regarded as the technology or embodiments that are substantially the same as the present invention.

[0105] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are only preferred embodiments of this application. It should be noted that due to the limitations of written expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A CNC machining path residual analysis and compensation method integrating twin simulation, characterized in that, include: The extended digital twin model is synchronized with the physical processing, and the geometric residual feature vector and dynamic risk feature vector are jointly output. Candidate compensation instructions are generated using the geometric residual feature vector, and the compensation effect is simulated in advance in the extended digital twin model. Based on the dynamic risk feature vector and the changes in the simulated response, a dynamic compensation risk probability field is generated. The risk-precision multi-objective decision-maker receives the compensated risk probability field, performs high-gain compensation in the low-risk area, and switches to robust compensation command sequence in the high-risk area to send to the machine tool. By comparing and analyzing the potential risk events monitored during physical processing with the predictions of the compensating risk probability field, the digital twin model is optimized and extended in reverse.

2. The CNC machining path residual analysis and compensation method integrating twin simulation according to claim 1, characterized in that, The process of synchronizing the extended digital twin model with the physical processing and jointly outputting geometric residual feature vectors and dynamic risk feature vectors specifically includes: Based on machine tool structure, tool clamping parameters, workpiece 3D model and fixture layout, an extended digital twin model is integrated and constructed, which includes a multibody system geometric kinematic model, a cutting force prediction model, a structural dynamics model and a fixture contact force model. During the physical machining process, the interpolation pre-input instructions and real-time sensor data of the physical numerical control system are synchronously input into the extended digital twin model, driving it to perform lockstep simulation in a virtual environment and execute two calculation threads in parallel: the first thread generates geometric residual prediction data, and the second thread calculates and outputs multimodal dynamic response data in real time. Statistical analysis is performed on the geometric residual prediction data to extract its mean, variance, and spatial frequency domain features, forming a geometric residual feature vector. Simultaneously, time-frequency domain analysis is performed on the multimodal dynamic response data to extract indicators characterizing system vibration intensity, load mutation, and clamping stability, forming a dynamic risk feature vector.

3. The CNC machining path residual analysis and compensation method integrating twin simulation according to claim 2, characterized in that, Candidate compensation instructions are generated using the geometric residual feature vector. The compensation effect is simulated in advance in the extended digital twin model. Based on the dynamic risk feature vector and the changes in the simulated response, a dynamic compensation risk probability field is generated, specifically including: Based on the geometric residual feature vector, at least two different gain coefficients or different spatial directions of compensation strategy are applied to generate a set of candidate preliminary compensation instructions corresponding to the current processing path segment. In the extended digital twin model, a future segment of the processing path after compensation is simulated in advance, and the multimodal dynamic response data output by the advance simulation is recorded simultaneously. From the dynamic risk feature vector, the core indicators characterizing the system stability under the current uncompensated state are extracted as the current dynamic risk benchmark. For each candidate preliminary compensation command, the multimodal dynamic response data obtained from the advanced simulation are analyzed, the increment relative to the current dynamic risk benchmark is calculated, and the vibration aggravation risk value, over-shear or under-shear risk value and force over-limit risk value are quantitatively evaluated through the risk value quantification calculation model. The calculated risk values ​​are associated with the corresponding coordinate points in the three-dimensional processing path space, and a risk compensation probability field is formed through spatial interpolation.

4. The CNC machining path residual analysis and compensation method integrating twin simulation according to claim 3, characterized in that, In the extended digital twin model, a forward simulation is performed on a future segment of the processing path after compensation, and the multimodal dynamic response data output by the forward simulation is recorded simultaneously, specifically including: The complete model state corresponding to the current physical processing moment is captured from the real-time synchronous simulation thread of the extended digital twin model and used as the initial state for advanced simulation. A candidate preliminary compensation instruction is superimposed with a future segment of original processing instructions starting from the current moment to generate a compensated instruction sequence to be simulated; this sequence drives the extended digital twin model to solve the problem in an independent accelerated simulation thread, starting from the initial state of the advanced simulation. During the operation of the accelerated simulation thread, the model output dataset of the leading three-modal dynamic response corresponding to each future interpolation cycle is recorded synchronously. Repeat the operation until all candidate preliminary compensation instructions have been traversed; archive the advanced three-modal dynamic response datasets corresponding to all instructions according to the instruction identifier and timestamp to provide complete input data for secondary risk quantitative assessment.

5. The CNC machining path residual analysis and compensation method integrating twin simulation according to claim 4, characterized in that, The process of associating the calculated risk values ​​with corresponding coordinate points in the three-dimensional processing path space, and forming a compensated risk probability field through spatial interpolation, specifically includes: For each candidate preliminary compensation instruction, the vibration aggravation risk value, overcut or undercut risk value, and force overlimit risk value are bound to the three-dimensional coordinates of the path point in the machine tool coordinate system or workpiece coordinate system to generate a set of discrete three-dimensional risk point cloud data. Within the spatial range of the processing path to be evaluated, a regular three-dimensional grid is established; based on the three-dimensional risk point cloud data, spatial interpolation calculation is performed for each type of risk value to obtain the interpolation result corresponding to each type of risk value at each grid node; The interpolation results of multiple risk values ​​are organized into a structured, real-time queryable dynamic compensation risk probability field. The dynamic compensation risk probability field can output the predicted value or probability distribution of various risks under the compensation strategy based on any input three-dimensional coordinate point and the specified candidate preliminary compensation instruction identifier.

6. The CNC machining path residual analysis and compensation method integrating twin simulation according to claim 5, characterized in that, The risk-precision multi-objective decision-maker receives the compensated risk probability field, performs high-gain compensation in the low-risk region, and switches to robust compensation command sequence to issue to the machine tool in the high-risk region, specifically including: Construct a risk-precision multi-objective decision-maker that simultaneously receives geometric residual eigenvectors and compensated risk probability fields; The risk-precision multi-objective decision-maker divides the current and prospective processing paths into low-risk and medium-to-high-risk regions based on the preset risk threshold in the compensated risk probability field, and establishes strategy mapping rules to obtain real-time strategy mapping results. Based on the real-time strategy mapping results, the compensation requirements of each point on the original path are dynamically modulated to generate a robust compensation instruction sequence that is non-uniformly distributed in space and time, and the sequence is sent to the machine tool controller for execution in real time.

7. The CNC machining path residual analysis and compensation method integrating twin simulation according to claim 6, characterized in that, The risk-precision multi-objective decision-maker divides the current and prospective processing paths into low-risk and medium-to-high-risk regions based on a preset risk threshold in the compensated risk probability field, and establishes policy mapping rules to obtain real-time policy mapping results, specifically including: For each type of risk in the compensation risk probability field, a basic risk threshold and a high-risk threshold are set based on the process knowledge base and historical data. In the risk-precision multi-objective decision maker, the compensated risk probability field data for the current processing point and the path points within its predetermined forward window are read; if the risk prediction values ​​of all categories are lower than their corresponding basic risk thresholds, the path segment where the corresponding point is located is classified into the low-risk area; if the risk prediction value of any category reaches or exceeds its high-risk threshold, the corresponding point is classified into the high-risk area; points in between are classified into the medium-risk area. The aggressive compensation strategy for mapping low-risk areas is quantified as employing a set of aggressive fixed high-gain coefficients and fully compensating for all spatial direction error components indicated by the geometric residual feature vector. The conservative compensation strategy for mapping medium- and high-risk areas is quantified as at least one of the following computable rules: calling a set of preset conservative gain coefficients with lower values; based on risk source analysis, only selecting to compensate for specific direction components in the geometric residual feature vector that have low correlation with the current dominant risk category; and in the path parameter domain, calculating the distance from the current point to the nearest downstream point identified as the starting point of a low-risk area and outputting the number of interpolation cycles corresponding to the delay of the compensation command. Based on the partitioning results and quantization rules, a real-time strategy mapping table is generated for each point on the current and look-ahead paths. This table includes a region label, a set of gain coefficients to be used, a mask for the direction to be compensated, and a compensation time delay. This table serves as the direct basis for generating a robust compensation instruction sequence.

8. The CNC machining path residual analysis and compensation method integrating twin simulation according to claim 7, characterized in that, The process of comparing and analyzing potential risk events detected during physical processing with predictions from the compensating risk probability field to inversely optimize and extend the digital twin model specifically includes: The physical machining process is monitored in real time by vibration sensors, acoustic emission sensors, or online measuring devices installed on machine tools or workpieces; when the reading of any sensor or its derived index exceeds the adaptive threshold, the system marks this moment as a potential risk event and records the timestamp of its occurrence, machine tool coordinates, and trigger data. The timestamp of each potential risk event is associated with the machine tool coordinates and matched with the compensation risk probability field. Various risk values ​​predicted by the risk field at the time and location of the event are extracted to form risk prediction data. At the same time, dynamic risk feature vectors are extracted from the system cache as environmental state data. The actual occurrence of potential risk events is compared with the corresponding risk prediction data. For risks that are missed or false alarms, the system initiates a root cause analysis process using the associated environmental status data. Based on the root cause analysis results, the identified inaccurate parameters are optimized in reverse. After optimization, the system uses the updated model and algorithm to enter the next round of processing and decision-making cycle.

9. The CNC machining path residual analysis and compensation method integrating twin simulation according to claim 8, characterized in that, Based on the root cause analysis results, the identified inaccurate parameters are optimized in reverse. After optimization, the system uses the updated model and algorithm to enter the next round of processing and decision-making loop, specifically including: Receive the root cause analysis results and generate a specific parameter optimization instruction, including the optimization type, target parameter identifier, and suggested adjustment amount and direction calculated based on event-prediction bias; If the optimization type is model parameter optimization, the suggested adjustment amount will be applied to the corresponding dynamic characteristic parameters within the extended digital twin model according to the instructions. If the optimization type is risk algorithm optimization, then adjust the specific coefficients or thresholds within the risk value quantification calculation model according to the instructions; After parameter adjustment, the system uses recent historical processing data and instructions to drive the updated model and algorithm to perform an offline verification simulation, comparing the prediction accuracy of potential risk events before and after optimization. When the prediction accuracy improvement index reaches the preset requirements, the optimization is confirmed to be effective, and the updated model parameters and algorithm coefficients are synchronized to the online system, officially entering the next self-enhancement cycle.

10. A CNC machining path residual analysis and compensation system integrating twin simulation, characterized in that, The CNC machining path residual analysis and compensation method for implementing the fusion twin simulation as described in any one of claims 1-9 includes: The synchronous simulation and feature extraction module is used to build and run an extended digital twin model that integrates the machine tool-tool-workpiece-fixture system, so that it can be simulated synchronously with the physical machining process, and jointly output geometric residual feature vectors and dynamic risk feature vectors. The risk field online simulation module is connected to the synchronous simulation and feature extraction module. It is used to generate candidate compensation instructions with the geometric residual feature vector and simulate the execution effect in advance in the extended digital twin model. At the same time, it combines the dynamic risk feature vector to quantify and evaluate secondary risks to generate a dynamic compensation risk probability field. The compensation decision and execution module is connected to the online risk field simulation module. It is equipped with a risk-precision multi-objective decision-maker to receive the compensation risk probability field and generate a non-uniform robust compensation instruction sequence according to the risk level difference, and finally send it to the machine tool controller. The risk model optimization module is connected to the synchronous simulation and feature extraction module, the online risk field extrapolation module, and the physical sensor, respectively. It is used to monitor potential risk events in physical processing, compare and analyze them with the predictions of the compensated risk probability field, and optimize the parameters of the extended digital twin model or the calculation model in the risk field extrapolation based on the analysis results.

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