A pneumatic control-based cylinder machining precision optimization method

Abstract

The application relates to the technical field of cylinder machining precision, and discloses a cylinder machining precision optimization method based on pneumatic control. The method comprises the following steps: acquiring pneumatic control parameters (such as air pressure and airflow speed) and mechanical vibration spectrum data in the cylinder machining process, comprehensively analyzing the two types of data, and determining a dynamic precision influence factor of the cylinder machining system; combining the factor with a pre-stored cylinder design tolerance threshold value, and formulating a precision compensation strategy suitable for an initial machining condition. The response time sequence curve of a pneumatic actuator of a machining device and workpiece surface topography data are synchronously collected, the time sequence curve is subjected to time-frequency conversion processing, key information is extracted, and a pneumatic energy efficiency feature set is generated; the feature set is aligned with the spatial coordinates of the workpiece surface topography data, a mapping correlation model of the pneumatic control parameters and the machining topography is established, and finally, the initial precision compensation strategy is corrected based on the model, and dynamic precision compensation parameters are generated, so that the cylinder machining precision can be effectively stabilized.

Description

Technical Field

[0001] This invention relates to the field of cylinder machining accuracy technology, specifically a method for optimizing cylinder machining accuracy based on pneumatic control. Background Technology

[0002] In the field of cylinder manufacturing, machining accuracy directly affects the subsequent performance and service life of the cylinder. Pneumatic control, as a key control method in cylinder machining, has a particularly significant impact on machining accuracy due to its parameter settings and system operating status. Currently, traditional cylinder machining accuracy control methods largely rely on experience-based parameter settings. Operators determine pneumatic control parameters, such as air pressure and airflow velocity, based on past machining cases. This approach lacks consideration of dynamically changing factors during machining and is difficult to adapt to complex and varied machining conditions.

[0003] As the precision requirements of cylinder applications continue to increase, the limitations of traditional methods are becoming increasingly apparent. During actual machining, the operation of machining equipment generates mechanical vibrations, which are transmitted to the workpiece machining area through the equipment's transmission system, leading to increased machining errors. However, existing precision control methods often neglect the correlation between mechanical vibration spectrum data and pneumatic control parameters, failing to accurately identify the dynamic factors affecting cylinder machining accuracy, and thus making it difficult to formulate effective precision compensation strategies.

[0004] Existing technologies often employ single data acquisition methods when obtaining processing data, such as collecting only the operating parameters of pneumatic actuators or only the surface morphology data of the workpiece, failing to achieve simultaneous acquisition and integrated analysis of multi-source data. This makes it impossible for technicians to establish a direct correlation between pneumatic control parameters and the processed morphology of the workpiece, and makes it difficult to accurately determine which factors in the pneumatic control process affect the surface quality of the workpiece, and to what extent.

[0005] Traditional precision compensation strategies are mostly fixed patterns, which are continuously applied to subsequent processing once established, and cannot be dynamically adjusted based on real-time processing data. When processing equipment wears down, the performance of pneumatic components deteriorates, or the properties of the processed materials change, the fixed compensation strategy gradually loses its effectiveness, leading to large fluctuations in cylinder machining accuracy, even exceeding the design tolerance threshold. This increases the probability of producing defective products, raises production costs for enterprises, and hinders the high-quality development of the cylinder manufacturing industry. Summary of the Invention

[0006] The purpose of this invention is to provide a method for optimizing cylinder machining accuracy based on pneumatic control, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a method for optimizing cylinder machining accuracy based on pneumatic control, the method comprising:

[0008] Acquire pneumatic control parameters and mechanical vibration spectrum data during cylinder machining, and determine the dynamic accuracy influencing factor of the cylinder machining system based on the pneumatic control parameters and mechanical vibration spectrum data.

[0009] Based on the dynamic accuracy influencing factor and the pre-stored cylinder design tolerance threshold, an initial accuracy compensation strategy is formulated.

[0010] The pneumatic actuator response time-series curve and workpiece surface morphology data of the processing equipment are collected synchronously. The pneumatic actuator response time-series curve is processed by time-frequency conversion to generate a set of pneumatic energy efficiency characteristics.

[0011] Align the aerodynamic energy efficiency feature set with the workpiece surface morphology data in spatial coordinates to establish a mapping relationship model between aerodynamic control and machining morphology.

[0012] Based on the mapping association model, the initial precision compensation strategy is corrected to generate dynamic precision compensation parameters;

[0013] Preferably, the initial accuracy compensation strategy, which combines the dynamic accuracy influence factor and the pre-stored cylinder design tolerance threshold, includes:

[0014] Principal component decomposition is performed on the dynamic accuracy influencing factors to extract the core fluctuation feature vector;

[0015] Match the deviation distribution pattern between the core fluctuation feature vector and the cylinder design tolerance threshold;

[0016] Based on the aforementioned deviation distribution pattern, a priority sequence for machining accuracy compensation is defined;

[0017] Based on the machining accuracy compensation priority sequence, the compensation step size and compensation scope of the initial accuracy compensation strategy are generated;

[0018] Preferably, the step of performing time-frequency conversion processing on the response timing curve of the pneumatic actuator to generate a set of pneumatic energy efficiency characteristics includes:

[0019] Extract the pressure gradient change nodes from the response time-series curve of the pneumatic actuator;

[0020] Calculate the energy attenuation coefficient of adjacent pressure gradient change nodes;

[0021] By aggregating the energy decay coefficients of multiple processing cycles, a cycle performance decay spectrum is generated.

[0022] Key energy efficiency characteristic bands are selected from the periodic energy efficiency decay spectrum to form the aerodynamic energy efficiency characteristic set.

[0023] Preferably, aligning the aerodynamic efficiency feature set with the workpiece surface topography data in spatial coordinates includes:

[0024] Identify the set of geometric topological feature points of the workpiece surface morphology data;

[0025] Establish the timestamp index relationship between the set of geometric topological feature points and the set of aerodynamic energy efficiency features;

[0026] The spatial coupling degree between aerodynamic energy efficiency characteristics and morphological characteristics is calculated using the timestamp index relationship.

[0027] Based on the spatial coupling degree, coordinate alignment is completed, and the aligned feature mapping matrix is ​​output.

[0028] Preferably, the establishment of the mapping relationship model between pneumatic control and machining morphology includes:

[0029] Singular value decomposition is performed on the feature mapping matrix to extract the principal mapping correlation components;

[0030] Calculate the weight contribution rate of the associated components of the main mapping;

[0031] A multi-dimensional mapping correlation equation is constructed based on the weighted contribution rate;

[0032] The parameter configuration of the mapping association model is generated through the multi-dimensional mapping association equation;

[0033] Preferably, the correction of the initial accuracy compensation strategy includes:

[0034] The parameter configuration of the mapping association model is input into the initial precision compensation strategy;

[0035] Analyze the conflicting nodes between the parameter configuration and the compensation priority sequence;

[0036] Reconstruct the compensation step size and compensation scope corresponding to the conflicting nodes;

[0037] Output the reconstructed dynamic precision compensation parameters;

[0038] Preferably, the method further includes:

[0039] Real-time monitoring of node position offsets along the processing path;

[0040] Calculate the cumulative error between the node position offset and the dynamic accuracy compensation parameter;

[0041] When the accumulated error exceeds the preset tolerance threshold, a recalibration command for aerodynamic parameters is triggered.

[0042] Preferably, the triggering of the aerodynamic parameter recalibration command includes:

[0043] Capture the pneumatic pressure pulsation waveform of the current processing cycle;

[0044] Decompose the high-frequency interference components of the pneumatic pressure pulsation waveform;

[0045] The high-frequency interference component is superimposed onto the dynamic accuracy compensation parameter;

[0046] Generate the duty cycle correction coefficient for the pneumatic servo valve;

[0047] Preferably, the method further includes:

[0048] Collect residual stress distribution cloud map of the cylinder inner wall;

[0049] Identify the gradient difference region between the residual stress distribution cloud map and the target stress distribution;

[0050] Map the coordinates of the gradient difference region to the dynamic accuracy compensation parameters;

[0051] Generate gradient compensation values ​​for the axial feed force;

[0052] Preferably, the method further includes:

[0053] Construct a closed-loop verification mechanism for optimizing machining accuracy:

[0054] The dynamic accuracy compensation parameters, the duty cycle correction coefficient of the pneumatic servo valve, and the gradient compensation value of the axial feed force are input synchronously.

[0055] A comprehensive compensation verification vector is generated through a multi-source data stream fusion engine;

[0056] The comprehensive compensation verification vector is fed back to the calculation node of the dynamic accuracy influence factor to form a closed-loop iterative optimization link.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] By acquiring pneumatic control parameters and mechanical vibration spectrum data during cylinder machining, and determining the dynamic accuracy influencing factors of the cylinder machining system based on these data, this method overcomes the limitation of traditional methods that ignore the correlation of multi-source data. It can comprehensively capture the dynamic factors affecting accuracy during machining, no longer limited to the consideration of a single parameter. This allows technicians to clearly understand the combined effect of pneumatic control and mechanical vibration on machining accuracy, thus providing a more comprehensive and practical basis for formulating subsequent accuracy compensation strategies.

[0059] When formulating the initial accuracy compensation strategy, the dynamic accuracy influencing factor and the pre-stored cylinder design tolerance threshold are combined. This ensures that the initial strategy is not blindly set based on experience, but is based on a dual consideration of the dynamic influencing factors of the machining system and the design requirements. Compared with the traditional fixed compensation strategy, this initial strategy is more targeted and can effectively intervene in accuracy deviations in the early stages of machining, reducing early machining errors caused by unreasonable strategies and laying a good foundation for the stability of subsequent machining accuracy.

[0060] The system synchronously acquires the response time-series curves of the pneumatic actuators and the surface morphology data of the workpiece from the processing equipment. It then performs time-frequency conversion on the pneumatic actuator response time-series curves to generate a set of pneumatic energy efficiency characteristics, achieving real-time synchronous integration of multi-source data. This data acquisition and processing method avoids the problem of traditional single-data acquisition failing to comprehensively reflect the processing process. It directly correlates the dynamic operating state of the pneumatic actuator with the actual processing results of the workpiece, allowing technicians to intuitively see the correspondence between the pneumatic execution process and the workpiece surface quality. This eliminates the inability to accurately determine the root cause of accuracy issues due to fragmented data.

[0061] By aligning the aerodynamic efficiency feature set with the workpiece surface morphology data using spatial coordinates, a mapping and correlation model between aerodynamic control and machining morphology is established, further deepening the understanding of the relationship between aerodynamic control and machining accuracy. This model clearly reveals the variation patterns of workpiece surface morphology corresponding to different aerodynamic efficiency features, and clarifies which changes in aerodynamic control parameters lead to morphology deviations on the workpiece surface. This provides a clear direction for correcting accuracy compensation strategies, unlike traditional methods which lack clear basis for compensation adjustments and can only rely on blind experimentation.

[0062] The initial accuracy compensation strategy is corrected based on a mapping-related model, generating dynamic accuracy compensation parameters and enabling dynamic adjustment of the compensation strategy. It can promptly identify deviations between the initial strategy and actual processing requirements based on real-time collected processing data and the established mapping relationship, and make targeted corrections. When processing equipment experiences wear, pneumatic component performance changes, or processing material properties change, the dynamic accuracy compensation parameters can adjust accordingly, always maintaining adaptability to the current processing conditions. This effectively avoids the failure of traditional fixed compensation strategies after changes in operating conditions, ensuring that the cylinder processing accuracy remains consistently stable within the design tolerance range. This reduces the production of defective products, lowers enterprise production costs, enhances the market competitiveness of cylinder products, and drives the cylinder manufacturing industry towards greater efficiency and precision. Attached Figure Description

[0063] Figure 1 This is a schematic diagram illustrating the working principle of the cylinder machining accuracy optimization method based on pneumatic control described in this invention.

[0064] Figure 2A flowchart illustrating the initial accuracy compensation strategy;

[0065] Figure 3 A flowchart for generating a set of aerodynamic energy efficiency characteristics;

[0066] Figure 4 A flowchart for establishing a mapping association model;

[0067] Figure 5 This is a flowchart for recalibrating aerodynamic parameters. Detailed Implementation

[0068] 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.

[0069] Please see Figure 1 This invention provides a method for optimizing cylinder machining accuracy based on pneumatic control, the method comprising:

[0070] The process involves acquiring pneumatic control parameters and mechanical vibration spectrum data during cylinder machining, and determining the dynamic accuracy influencing factor of the cylinder machining system based on this data. Combining this dynamic accuracy influencing factor with pre-stored cylinder design tolerance thresholds, an initial accuracy compensation strategy is formulated. Simultaneously, the response time-series curves of the pneumatic actuators and workpiece surface topography data of the machining equipment are collected. The pneumatic actuator response time-series curves undergo time-frequency conversion processing to generate a set of pneumatic energy efficiency characteristics. The set of pneumatic energy efficiency characteristics is then spatially aligned with the workpiece surface topography data to establish a mapping relationship model between pneumatic control and machining topography. Based on this mapping relationship model, the initial accuracy compensation strategy is corrected, dynamic accuracy compensation parameters are generated, and closed-loop optimization of machining accuracy is achieved through real-time data acquisition and model construction.

[0071] Example 1: See Figure 2 The core of this process lies in transforming abstract dynamic precision influencing factors into executable initial precision compensation strategies. The inputs to this process are the dynamic precision influencing factor matrix calculated in previous steps and the cylinder design tolerance threshold data pre-stored in the system. The dynamic precision influencing factor matrix is ​​a multidimensional dataset, where rows typically represent different processing cycles or time series, and columns represent different influencing dimensions, such as air pressure fluctuations, vibration frequency, and displacement deviations. The pre-stored cylinder design tolerance thresholds are a structured data table that specifies the allowable dimensional deviation ranges for key cylinder components, such as cylinder bore diameter, piston rod diameter, and end cap mounting hole positions.

[0072] Principal component decomposition (PCD) is performed on the dynamic accuracy influencing factors. The purpose of this step is to extract a few core dimensions from numerous potentially correlated dimensions that can represent the majority of the variation information. The data processing system calls the PCD algorithm to calculate the covariance matrix of this matrix, along with its eigenvalues ​​and eigenvectors. The algorithm automatically sorts the eigenvectors according to the magnitude of the eigenvalues, selecting the top few eigenvectors with the highest cumulative contribution as the core fluctuation eigenvectors. These vectors are essentially a set of linear combination coefficients, mapping the original high-dimensional data to a new low-dimensional space, with each principal component dimension representing a fluctuation pattern in the original data. For example, the first principal component might primarily reflect the comprehensive impact of gas source pressure fluctuations, while the second principal component might be more related to the radial vibration of the principal axis.

[0073] The extracted core fluctuation feature vector is compared with the cylinder design tolerance threshold to identify the deviation distribution pattern. Since the core fluctuation feature vector is an abstract feature, while the tolerance threshold is a specific dimensional requirement, the two need to be physically correlated. The system uses a pre-defined mapping table to associate each principal component dimension with one or more specific tolerance items. For example, the principal component dimension strongly correlated with air pressure fluctuations will primarily be matched with the cylinder's roundness and cylindricity tolerances. The matching process calculates the difference between the actual fluctuation feature vector and the ideal zero-deviation state for each principal component dimension. This difference manifests as a distribution pattern in multidimensional space, i.e., a deviation distribution pattern. This pattern may indicate a systematic deviation in certain tolerance items in a specific direction.

[0074] Based on the calculated deviation distribution pattern, the system begins to prioritize machining accuracy compensation. This is a logical judgment process. The priority depends on two main factors: first, the magnitude of the deviation, i.e., how close or far the actual deviation of a tolerance item is from its threshold limit; and second, the importance of that tolerance item in the cylinder function, the weight of which is defined in a pre-stored tolerance threshold database. The system calculates a comprehensive priority score, placing tolerance items with large deviations and high importance at the forefront of the sequence, indicating that these areas require priority and focused compensation. Conversely, items with small deviations or low importance are placed at the end of the sequence. This priority sequence is an ordered list that guides the subsequent allocation of compensation resources.

[0075] Based on the established machining accuracy compensation priority sequence, the system generates specific parameters for the initial accuracy compensation strategy, namely the compensation step size and the compensation scope. The division of the compensation scope is directly based on the priority sequence. The cylinder machining area corresponding to high-priority tolerance items is designated as the core scope, and compensation will be concentrated on these areas. Medium and low-priority areas are designated as secondary or observation scopes. The compensation step size is set according to the principle of "high precision, small step size". For high-priority items within the core scope, the system allocates a smaller compensation step size to allow for fine and gradual adjustments, avoiding overcompensation. For secondary scopes, a relatively larger step size can be used to improve adjustment efficiency. The generated initial accuracy compensation strategy is finally formatted into a parameter file containing specific coordinate ranges, compensation directions, and step size values. This file is output and passed to subsequent process modules to prepare for subsequent dynamic corrections. The entire implementation process is automatically completed by the system's control unit, ensuring efficiency and objectivity from data analysis to strategy generation.

[0076] Example 2: See Figure 3 This study focuses on extracting pneumatic energy features and establishing spatial mapping relationships. The inputs for this stage include real-time acquired pneumatic actuator response time-series curves and workpiece surface topography data. The pneumatic actuator response time-series curves are obtained from a high-frequency pressure sensor installed in the pneumatic system, recording air pressure fluctuations at millisecond intervals. The workpiece surface topography data is acquired using a non-contact laser profilometer, forming a point cloud dataset containing three-dimensional coordinate points. First, the pneumatic actuator response time-series curves are processed. The system identifies pressure gradient change nodes in the curves, corresponding to key action moments such as pneumatic control valve opening / closing and cylinder reversing. A sliding window differential algorithm is used to detect abrupt pressure changes; for example, when the pressure difference between adjacent sampling points exceeds a set threshold, this time point is marked as a gradient change node. Each node records a precise timestamp and corresponding pressure value, forming a node sequence. Subsequently, the energy attenuation coefficient between adjacent nodes is calculated, reflecting the rate of pneumatic energy loss during the action interval. The system subtracts the pressure value of the next node from the pressure value of the previous node, and then divides by the time difference between the two nodes to obtain the energy attenuation per unit time. This process is repeated across multiple consecutive processing cycles, vertically aggregating the attenuation coefficients at the same phase position in each cycle. The system automatically generates a two-dimensional heatmap with the processing cycle as the horizontal axis and the attenuation coefficient as the vertical axis, where different color depths represent attenuation intensity, ultimately generating a cycle performance attenuation spectrum covering the entire processing process. When extracting feature bands from this spectrum, the system uses spectral peak detection technology to identify high-intensity attenuation regions that continuously appear across multiple cycles. For example, recurring red bands in the spectrum correspond to aerodynamic energy abrupt changes in specific process stages, and these bands are extracted as a set of aerodynamic performance features.

[0077] The system performs feature recognition on the point cloud data acquired by laser scanning, and locates the geometric topological feature points of the surface morphology through curvature analysis and region growing algorithms. These feature points include: peaks and troughs of machining marks, edge turning points at end face joints, and curvature abrupt change points at the bottom of sealing grooves. Each feature point records three-dimensional coordinate information and a timestamp attribute. The timestamp is synchronized with the encoder signal of the machining spindle, forming a time-stamped feature point set. When establishing a spatiotemporal mapping relationship, the system aligns the data based on the timestamp index. Each feature band in the aerodynamic efficiency feature set is matched with the morphological feature point set in time. For example, if the timestamp of a pressure gradient change node is T1, then the morphological feature point corresponding to the workpiece surface area being processed at that moment is matched. The system constructs a timestamp mapping table, so that each aerodynamic efficiency feature is associated with a morphological feature point at a specific spatial location. Based on this correspondence, spatial coupling parameters are calculated: aerodynamic efficiency characteristic values ​​(such as energy attenuation coefficients) and their associated morphological characteristic values ​​(such as surface roughness Ra values) are paired, and the Euclidean distance between each pair is calculated in a three-dimensional coordinate system. Simultaneously, the correlation of numerical change trends across multiple pairs is analyzed; for example, whether the surface waviness of the corresponding region increases synchronously when the energy attenuation coefficient increases. A coupling matrix is ​​generated through distance calculation and trend correlation analysis. The rows of this matrix correspond to aerodynamic efficiency characteristics, the columns correspond to morphological characteristic parameters, and the matrix element values ​​contain a composite index of spatial distance and trend correlation coefficient.

[0078] The final spatial coordinate alignment process involves data structure reconstruction. The system adjusts the spatial coordinate offsets of feature points based on the coupling degree matrix to maintain positional consistency between aerodynamic and topographic features in the virtual coordinate system. For example, a high-pressure pulsation feature originally corresponded to timestamp T2, but coupling degree calculations revealed that its actual impact was delayed. Time, as reflected in the shift of the deformation position on the workpiece Distance. Based on this, the system establishes a new coordinate correspondence in the feature mapping matrix, outputting a feature mapping matrix containing the adjusted spatial coordinates. This matrix becomes the basic data structure for subsequent modeling; its column vectors represent spatial location indices, row vectors record aerodynamic efficiency characteristic values, and matrix elements store morphological feature parameters, forming a complete spatiotemporal correspondence table. The entire process achieves millisecond-level response through a distributed computing framework, ensuring real-time updates of the mapping relationship during continuous processing.

[0079] Example 3: See Figure 4 The focus is on establishing an accurate mapping model and correcting the compensation strategy accordingly. The input to this stage is the feature mapping matrix output from Example 2, which is a... A real matrix of dimension, where The number representing aerodynamic efficiency characteristics. Represents the number of morphological feature points, matrix elements Indicates the first The first aerodynamic feature and the second The spatial coupling degree between morphological features is calculated. The feature mapping matrix undergoes singular value decomposition (SVD), a mathematical process that decomposes the matrix into the product of three specific matrices: a left singular vector matrix, a singular value diagonal matrix, and a right singular vector matrix. The singular values ​​are arranged in descending order in the diagonal matrix, and their magnitudes reflect the importance of each component to the information in the original matrix. (Before system extraction...) The largest singular value and its corresponding left and right singular vectors constitute the principal mapping correlation components. These components capture the most significant correlation patterns between aerodynamic control and machining topology. For example, the first principal component may represent the overall correlation between pressure stability and surface roughness, while the second principal component may reflect the specific relationship between pressure pulsation frequency and waviness.

[0080] The system then calculates the weighted contribution rate of each principal mapping component. Each singular value is divided by the sum of all singular values ​​to obtain the proportion of variance explained by that component. These contribution rates are arranged in descending order to form a weight sequence. Based on the weighted contribution rates, the system constructs a multi-dimensional mapping correlation equation. This equation adopts a weighted linear combination form:

[0081]

[0082] in: This represents the feature vector of the processed morphology. This represents the aerodynamic energy efficiency eigenvector. It is the first The weight contribution rate of each principal mapping associated component. It is the first The nonlinear mapping function corresponding to each component This represents the number of principal components retained. The equation establishes a multi-dimensional transformation relationship from aerodynamic features to morphological features.

[0083] The system generates a complete parameter configuration for the mapping association model using a multi-dimensional mapping association equation. These parameters include: the number of principal components to be retained. Weight contribution rate of each component Mapping function for each component The specific form of the parameters (such as multinomial coefficients or neural network weights) and the effective range of each variable are defined. These parameters are encapsulated in a structured configuration file for instantiating the mapping-association model. After obtaining the mapping-association model, the system begins to correct the initial accuracy compensation strategy. First, the model's parameter configuration is input into the initial strategy, a process implemented through a parameter injection interface. The fusion of model parameters and strategy parameters generates a new parameter space, and the system analyzes the conflicting nodes in this space. The identification of conflicting nodes is based on parameter sensitivity analysis: calculating the degree of influence of changes in model parameters on the compensation effect; when a small change in parameters in a certain region causes a large fluctuation in the compensation result, that region is marked as a conflicting node.

[0084] For identified conflict nodes, the system performs parameter reconstruction. The reconstruction process employs constrained optimization methods, adjusting the compensation step size and compensation scope while maintaining model prediction accuracy. The compensation step size reconstruction is based on parameter stability indices; the step size for conflict nodes is reduced to increase adjustment accuracy. The compensation scope reconstruction uses cluster analysis to divide high-conflict regions into finer subdomains. The reconstructed parameters maintain consistency with the mapping model. Finally, the system outputs the reconstructed dynamic accuracy compensation parameters. These parameters include the updated compensation step size matrix and scope boundary coordinates, stored in binary data format and transmitted to the actuator control system via a data bus. The entire implementation process employs an iterative optimization mechanism; when the mapping model is updated, a new round of correction is automatically triggered, ensuring continuous adaptation of the compensation strategy to the actual situation.

[0085] The computational load during implementation is distributed through a distributed processing framework. The decomposition of the feature mapping matrix employs an incremental singular value decomposition algorithm, supporting streaming data updates. The mapping correlation equation is solved using regularized least squares to avoid overfitting. Conflict node analysis utilizes the Nash equilibrium concept from game theory to find the optimal balance point for parameter configuration. Robust control theory is introduced during the parameter reconstruction phase to ensure system stability under parameter disturbances. All these mathematical tools and algorithms are integrated into a unified software module, providing operators with intuitive monitoring tools by displaying parameter changes and conflict resolution processes through a graphical interface.

[0086] Example 4: See Figure 5Based on the dynamically generated accuracy compensation parameters, the system maintains machining accuracy through real-time monitoring and adaptive recalibration mechanisms. Inputs to this stage include the real-time coordinate data stream of the machining path trajectory, an array of dynamic accuracy compensation parameters, and preset tolerance threshold parameters. The system acquires the actual tool position coordinates with a sampling period of 5 milliseconds using a high-resolution optical scale and laser interferometer mounted on the machine tool's motion axes. The theoretical coordinates of each machining path node (such as linear interpolation endpoints and circular arc turning points) are extracted from the CNC program, and the three-dimensional position offset of each node is calculated in real time. The offset calculation uses the spatial vector difference method, recording the deviation values ​​in the X, Y, and Z directions. The system maintains a real-time updated offset data queue, storing continuous offset records of the most recent 30 machining nodes.

[0087] Based on this offset data queue, the system calculates the cumulative error using a weighted sliding window algorithm: the weight coefficient for the current node's offset is 1.0, the previous node is 0.8, the one before that is 0.6, and so on, forming a decay weight sequence. The absolute values ​​of the offsets in the three directions of each node within the window are multiplied by their corresponding weights and then summed to generate a comprehensive cumulative error index. This index reflects the degree of accumulation of short-term machining errors, and its value is directly related to the quality of the machined surface. Preset tolerance thresholds are dynamically set according to the cylinder's accuracy level. The system's built-in threshold configuration library contains tolerance parameters corresponding to different accuracy levels (such as IT6 and IT7), which are automatically loaded during the machining initialization phase. When the real-time calculated cumulative error exceeds the current tolerance threshold, the system triggers a three-level response mechanism: the primary response suspends the machining feed and initiates a data snapshot; the intermediate response activates historical error pattern analysis; and the advanced response executes a pneumatic parameter recalibration command.

[0088] The execution of the pneumatic parameter recalibration command involves four standardized steps. First, the pneumatic pressure pulsation waveform of the current processing cycle is captured. A piezoelectric sensor installed in the main pneumatic pipeline captures the pressure fluctuation curve for the entire working cycle (typically 0.5-2 seconds) at a sampling frequency of 100kHz. After preprocessing, the waveform data enters the high-frequency interference component decomposition stage. The system employs discrete wavelet transform technology, using the db8 wavelet basis function for a 6-level decomposition, extracting signal components with frequencies higher than 1kHz as high-frequency interference components. These components are typically caused by factors such as solenoid valve switching transients and gas turbulence.

[0089] The extracted high-frequency interference components are superimposed with dynamic accuracy compensation parameters. The system establishes a frequency-amplitude mapping table to convert the interference amplitude of different frequency bands into correction coefficients for the compensation parameters. For example, when a significant interference peak is detected in the 5kHz frequency band, the compensation gain coefficient for that frequency band is increased accordingly. The superposition process is performed in the frequency domain: the frequency response curve of the compensation parameters and the spectrum of the interference components are vector-added to generate a new composite compensation spectrum. The duty cycle correction coefficient of the pneumatic servo valve is generated based on the superposition result. The system constructs a transfer function model of the servo valve and converts the frequency domain compensation amount into a time domain control signal. The duty cycle adjustment waveform is obtained through inverse Fourier transform, and then the characteristic parameters of the waveform (such as rise slope, peak position, and pulse width ratio) are extracted to generate a set of correction coefficients. Referring to Table 1, these coefficients are written to the servo valve controller in real time via the fieldbus to realize dynamic updates of the control parameters.

[0090] Table 1: Monitoring data on the offset of processing nodes.

[0091]

[0092] The inner loop is responsible for real-time offset monitoring and error accumulation calculation, with a control cycle of 5 milliseconds; the outer loop performs parameter recalibration, with a trigger cycle of 50 milliseconds. The system features a status monitoring dashboard that provides real-time visualization of offset trends for each axis, error accumulation curves, tolerance threshold lines, and changes in servo valve control parameters. A historical data storage module records key parameters before and after each recalibration event, forming a precision control log for traceability and analysis. Special attention was paid to signal synchronization during implementation: the PTP precision time protocol ensures microsecond-level synchronization between the position sensor, pressure sensor, and control system clock, eliminating errors caused by measurement delays. For multi-axis linkage machining, the system uses a spatial vector synthesis algorithm to calculate the comprehensive offset, avoiding coupling errors caused by single-axis compensation. A safety interlock mechanism is implemented for the execution of recalibration commands; if the accumulated error fails to fall below the threshold after three consecutive calibrations, a system shutdown and inspection process is triggered.

[0093] Example 5: This stage focuses on the residual stress control and closed-loop verification mechanism. Inputs include residual stress distribution data of the cylinder inner wall, dynamic accuracy compensation parameters, and previously generated control parameters. The residual stress distribution cloud map is acquired using an X-ray diffraction analyzer. The equipment is set with 32 scanning sections along the cylinder axis, and 36 circumferential measurements are performed on each section, forming a two-dimensional stress distribution matrix with a resolution of 0.5mm × 0.5mm. The measurement process strictly follows the ASTM E915 standard, recording the triaxial principal stress values ​​and azimuth angles for each data point.

[0094] The measured cloud map is spatially registered with the pre-stored target stress distribution template, and a feature point matching algorithm is used to align the positions of key structures such as cylinder bores and oil ports. After registration, pixel-level difference operations are performed to generate a stress deviation field. This deviation field is divided into 5mm×5mm grid cells, and the modulus and direction of the stress gradient within each cell are calculated. The identification of gradient difference regions is based on threshold segmentation technology: regions with tensile stress deviations exceeding +50MPa or compressive stress deviations below -80MPa are defined as significant difference regions, and the system automatically marks the geometric center coordinates and boundary range of these regions.

[0095] The coordinate mapping process establishes the transformation relationship between the machining coordinate system and the stress field. The center point of each difference region is converted into tool path coordinates through the machine tool coordinate transformation matrix, with mapping accuracy controlled within ±0.1mm. The system constructs a three-dimensional mapping index table to record the difference region number, spatial coordinates, stress gradient value, and corresponding compensation parameter storage address. The overlapping problem of stress concentration regions is specifically addressed during the mapping process: when multiple difference regions intersect on the projection plane, the equivalent action point is calculated using the stress field superposition principle. When generating the gradient compensation value of the axial feed force, the system establishes a stress-cutting force conversion model that considers the coupling effect of material removal rate, tool geometry, and residual stress. For each difference region, the component of its stress gradient in the tool feed direction is calculated, and combined with the current cutting depth and feed rate, the compensation force value to offset the stress effect is derived. The compensation value is output in the form of a command sequence, including three parameters: axial position, compensation force magnitude, and duration of action. These commands are transmitted to the machine tool servo system via real-time Ethernet, triggering the compensation action at the specified machining position.

[0096] The closed-loop verification mechanism employs a multi-channel data fusion architecture. The system allocates an independent data buffer to synchronously receive the dynamic accuracy compensation parameter array, the pneumatic servo valve duty cycle correction coefficient matrix, and the axial feed force gradient compensation command stream. The multi-source data stream fusion engine performs time alignment processing: using the machine tool spindle encoder signal as a reference, data from different sources are resampled to a unified 200μs time grid. The fusion algorithm uses an adaptive weighting method, dynamically adjusting the fusion weights based on the real-time change rate of each parameter. For example, when a drastic fluctuation in pneumatic parameters is detected, its weight coefficient is increased to 0.6, while the relatively stable feed force compensation weight is decreased to 0.3. The generated comprehensive compensation verification vector contains 128-dimensional feature data, covering spatial location index, parameter values, change trends, and confidence indices. This vector is fed back to the dynamic accuracy influence factor calculation node via a high-speed data bus. The feedback loop is equipped with a priority arbitration mechanism: when a new vector arrives, the system compares its timestamp with the current calculation cycle; if it is within the allowable delay range, recalculation is triggered immediately; if it exceeds the delay window, it is stored in the processing queue. The recalculation process uses an incremental update algorithm, which only modifies local parameters affected by the new data, thus maintaining overall computational efficiency.

[0097] The closed-loop iterative optimization process follows state machine logic, defining three operating modes: initialization mode loads basic parameters and establishes data channels; steady-state mode performs routine monitoring and compensation; and optimization mode activates closed-loop iteration. Upon feedback of the comprehensive compensation verification vector, the system automatically switches to optimization mode, recalculates the dynamic accuracy influence factor, and initiates a new round of compensation strategy generation. Each iteration records the parameter change trajectory. When the change in compensation parameters is less than a set threshold for three consecutive iterations, the system is considered to have entered a stable state and switches back to steady-state mode. The entire implementation process is visualized through a monitoring interface, displaying key information such as stress cloud map differences, compensation force application locations, and the number of closed-loop iterations. Operators can observe the system adjustment process in real time.

[0098] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0099] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing cylinder machining accuracy based on pneumatic control, characterized in that, include: Acquire pneumatic control parameters and mechanical vibration spectrum data during cylinder machining, and determine the dynamic accuracy influencing factor of the cylinder machining system based on the pneumatic control parameters and the mechanical vibration spectrum data. Based on the dynamic accuracy influencing factor and the pre-stored cylinder design tolerance threshold, an initial accuracy compensation strategy is formulated. The pneumatic actuator response time-series curve and workpiece surface morphology data of the processing equipment are collected synchronously. The pneumatic actuator response time-series curve is processed by time-frequency conversion to generate a set of pneumatic energy efficiency characteristics. Align the aerodynamic energy efficiency feature set with the workpiece surface morphology data in spatial coordinates to establish a mapping relationship model between aerodynamic control and machining morphology. Based on the mapping association model, the initial precision compensation strategy is modified to generate dynamic precision compensation parameters.

2. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 1, characterized in that, The initial accuracy compensation strategy is formulated by combining the dynamic accuracy influence factor and the pre-stored cylinder design tolerance threshold, including: Principal component decomposition is performed on the dynamic accuracy influencing factors to extract the core fluctuation feature vector; Match the deviation distribution pattern between the core fluctuation feature vector and the cylinder design tolerance threshold; Based on the aforementioned deviation distribution pattern, a priority sequence for machining accuracy compensation is defined; Based on the machining accuracy compensation priority sequence, the compensation step size and compensation scope of the initial accuracy compensation strategy are generated.

3. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 2, characterized in that, The process of performing time-frequency conversion on the response timing curve of the pneumatic actuator to generate a set of pneumatic energy efficiency characteristics includes: Extract the pressure gradient change nodes from the response time-series curve of the pneumatic actuator; Calculate the energy attenuation coefficient of adjacent pressure gradient change nodes; By aggregating the energy decay coefficients of multiple processing cycles, a cycle performance decay spectrum is generated. Key energy efficiency characteristic bands are selected from the periodic energy efficiency decay spectrum to form the aerodynamic energy efficiency characteristic set.

4. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 3, characterized in that, Aligning the aerodynamic efficiency feature set with the workpiece surface topography data in spatial coordinates includes: Identify the set of geometric topological feature points of the workpiece surface morphology data; Establish the timestamp index relationship between the set of geometric topological feature points and the set of aerodynamic energy efficiency features; The spatial coupling degree between aerodynamic energy efficiency characteristics and morphological characteristics is calculated using the timestamp index relationship. Based on the spatial coupling degree, coordinate alignment is completed, and the aligned feature mapping matrix is ​​output.

5. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 4, characterized in that, The establishment of the mapping relationship model between pneumatic control and machining morphology includes: Singular value decomposition is performed on the feature mapping matrix to extract the principal mapping correlation components; Calculate the weight contribution rate of the associated components of the main mapping; A multi-dimensional mapping correlation equation is constructed based on the weighted contribution rate; The parameter configuration of the mapping association model is generated through the multi-dimensional mapping association equation.

6. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 5, characterized in that, The revised initial accuracy compensation strategy includes: The parameter configuration of the mapping association model is input into the initial precision compensation strategy; Analyze the conflicting nodes between the parameter configuration and the compensation priority sequence; Reconstruct the compensation step size and compensation scope corresponding to the conflicting nodes; Output the reconstructed dynamic precision compensation parameters.

7. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 6, characterized in that, Also includes: Real-time monitoring of node position offsets along the processing path; Calculate the cumulative error between the node position offset and the dynamic accuracy compensation parameter; When the accumulated error exceeds the preset tolerance threshold, a recalibration command for aerodynamic parameters is triggered.

8. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 7, characterized in that, The triggering command for recalibrating pneumatic parameters includes: Capture the pneumatic pressure pulsation waveform of the current processing cycle; Decompose the high-frequency interference components of the pneumatic pressure pulsation waveform; The high-frequency interference component is superimposed onto the dynamic accuracy compensation parameter; Generate the duty cycle correction coefficient for the pneumatic servo valve.

9. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 8, characterized in that, Also includes: Collect residual stress distribution cloud map of the cylinder inner wall; Identify the gradient difference region between the residual stress distribution cloud map and the target stress distribution; Map the coordinates of the gradient difference region to the dynamic accuracy compensation parameters; Generate gradient compensation values ​​for the axial feed force.

10. The method for optimizing cylinder machining accuracy based on pneumatic control as described in claim 9, characterized in that, Also includes: Construct a closed-loop verification mechanism for optimizing machining accuracy: The dynamic accuracy compensation parameters, the duty cycle correction coefficient of the pneumatic servo valve, and the gradient compensation value of the axial feed force are input synchronously. A comprehensive compensation verification vector is generated through a multi-source data stream fusion engine; The comprehensive compensation verification vector is fed back to the calculation node of the dynamic accuracy influence factor to form a closed-loop iterative optimization link.

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