Control method, device and equipment of numerical control five-face drill and storage medium

Abstract

The invention provides a control method, device and equipment for a numerical control five-face drill and a storage medium. The method comprises the steps that sensing data in the machining process are obtained, workpiece material characteristics and machining state information are processed, and a multi-dimensional data matrix containing material hardness and machining parameters is generated; features are extracted based on the multi-dimensional data matrix, historical optimal working conditions are matched, and optimal control parameters matched with the current machining conditions are generated; planning a multi-axis cooperative motion track, and ensuring accurate positioning of each drill bit unit through synchronous control and error compensation; machining state fluctuation is monitored in real time, and operation parameters are dynamically adjusted to maintain drilling precision and movement coordination of all units; and a self-adaptive control parameter library adaptive to the current working condition is established by combining a processing efficiency and quality data iterative optimization control strategy. The machining efficiency is improved while the drilling precision is guaranteed, cost waste caused by parameter mismatching is reduced, and technical support is provided for efficient and high-precision machining in an intelligent manufacturing scene.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to control methods, devices, equipment and storage media for CNC five-sided drilling. Background Technology

[0002] As a core processing equipment in the panel furniture and building decoration industries of modern manufacturing, the CNC five-sided drilling machine directly determines product quality and production costs through its control precision and processing efficiency, playing a pivotal role in the transformation and upgrading of intelligent manufacturing. With the rapid growth of market demand for personalized customization and the continuous improvement of production cycle requirements, the performance optimization of CNC five-sided drilling machines has become a key technological support for manufacturing enterprises to enhance their competitiveness.

[0003] Current mainstream CNC five-sided drilling control solutions have revealed significant limitations in adaptability in practical applications. Traditional control systems often employ fixed parameter configurations, making it difficult to dynamically adjust according to different workpiece materials, thicknesses, and machining requirements, leading to accuracy fluctuations during processing. Furthermore, existing solutions lack effective synchronization mechanisms for multi-axis coordinated control, resulting in poor motion coordination between individual drill bit units and impacting overall machining quality. More importantly, most solutions neglect real-time monitoring and feedback adjustment capabilities during machining, failing to respond promptly to changes in machining status.

[0004] In-depth analysis reveals that the core technological bottleneck restricting the performance improvement of CNC five-sided drilling lies primarily in the insufficient adaptive capability of the control algorithm. Due to the lack of an intelligent parameter optimization mechanism, the system cannot automatically adjust its control strategy according to actual machining conditions, resulting in unstable drilling accuracy under different working conditions. This lack of adaptive capability further triggers coordination problems in multi-axis motion control. When individual drill units cannot obtain optimal motion parameters, their synchronization and coordination are inevitably affected, leading to technical challenges such as low machining efficiency and uneven surface quality.

[0005] How to build an intelligent control system with adaptive parameter optimization capabilities to achieve dynamic response to different machining conditions, and ensure high-precision synchronous movement of each drill bit unit through optimized multi-axis coordinated control strategies, thereby significantly improving overall machining efficiency while ensuring machining accuracy, has become a key issue that needs to be addressed in the current development of CNC five-sided drilling technology. Summary of the Invention

[0006] This invention provides a control method, apparatus, equipment, and storage medium for CNC five-sided drilling, aiming to solve the above-mentioned technical problems.

[0007] The first invention provides a CNC five-sided drilling control method, including: Acquire sensor data during the processing, process workpiece material characteristics and processing status information, and generate a multi-dimensional data matrix containing material hardness and processing parameters; Based on the extraction of features from the multidimensional data matrix and matching of historical best working conditions, optimized control parameters adapted to the current processing conditions are generated. Plan the multi-axis coordinated motion trajectory and ensure the precise positioning of each drill bit unit through synchronous control and error compensation; Real-time monitoring of processing status fluctuations and dynamic adjustment of operating parameters to maintain drilling accuracy and coordination of movement of each unit; By combining processing efficiency and quality data to iteratively optimize the control strategy, an adaptive control parameter library adapted to the current working conditions is established.

[0008] Furthermore, the acquisition of sensor data during the processing, processing of workpiece material characteristics and processing status information, and generation of a multi-dimensional data matrix containing material hardness and processing parameters include: Real-time data is obtained from the position and force sensors of each drill bit unit in the CNC five-sided drilling system; The workpiece material hardness sensor signal is sampled and processed by a pre-established data acquisition module to generate an initial dataset containing hardness features. If the workpiece hardness value exceeds the preset threshold, the high-precision machining mode parameter configuration is activated, the drilling depth, feed rate and spindle speed are recorded, and a multi-dimensional data matrix containing material hardness and machining status is generated.

[0009] Furthermore, the step of extracting features based on a multi-dimensional data matrix and matching them with historical optimal working conditions to generate optimized control parameters adapted to the current processing conditions includes: Feature extraction and pattern recognition are performed using a convolutional neural network algorithm on a multidimensional data matrix to generate feature vectors for workpiece hardness and processing status; Based on the feature vector and workpiece thickness parameters, an adaptive parameter optimization model is generated through gradient descent optimization. If the Euclidean distance between the feature vector and the historical best working condition feature vector is lower than the preset threshold, the historical best parameters are directly obtained as optimization control parameters to adapt to the current processing conditions. Otherwise, the network weights are updated using the backpropagation algorithm to generate optimized control parameters that fit the current processing conditions.

[0010] Furthermore, the planned multi-axis cooperative motion trajectory, through synchronous control and error compensation, ensures the precise positioning of each drill bit unit, including: A multi-axis coordinated control command sequence is generated from the optimized control parameters. The motion trajectory of each drill bit unit is synchronously planned using a distributed motion planning algorithm based on the CANopen protocol, generating precise positioning coordinates and motion timing data in three-dimensional space. If the positional deviation of adjacent drill bit units exceeds the preset synchronization error threshold, the motion speed and acceleration of each axis are adjusted through the error compensation mechanism to update the precise positioning coordinates and motion timing data.

[0011] Furthermore, the real-time monitoring of processing status fluctuations and dynamic adjustment of operating parameters to maintain drilling accuracy and the coordination of movement of each unit includes: From the updated precise positioning coordinates and motion timing data, vibration signals and cutting force data during the machining process are collected through the real-time monitoring module, and the fluctuation trend data of drilling accuracy is calculated by using a sliding window combined with statistical analysis algorithms. If the absolute value of the fluctuation trend data exceeds the preset threshold, the feed rate and spindle speed are adjusted through the feedback adjustment mechanism to generate synchronous motion coordination evaluation data for each drill bit unit.

[0012] Furthermore, the iterative optimization control strategy based on processing efficiency and quality data, and the establishment of an adaptive control parameter library adapted to the current working conditions, include: A dynamic parameter update mechanism is established from synchronous motion coordination evaluation data. Combined with processing efficiency data and surface quality detection data, a reinforcement learning algorithm is used to iteratively optimize the control strategy and generate an optimized set of control parameters. If the drilling accuracy data for multiple consecutive processing cycles are all within the target accuracy range, the optimized control parameter set will be stored as the adaptive control parameter library for the current working condition.

[0013] Secondly, this application provides a CNC five-sided drilling control device, comprising: The acquisition unit is used to acquire sensor data during the processing, process workpiece material characteristics and processing status information, and generate a multi-dimensional data matrix containing material hardness and processing parameters. The generation unit is used to extract features based on a multidimensional data matrix and match historical best working conditions to generate optimized control parameters that are suitable for the current processing conditions. The control compensation unit is used to plan the multi-axis coordinated motion trajectory and ensure the precise positioning of each drill bit unit through synchronous control and error compensation. The adjustment unit is used to monitor the fluctuations in the processing status in real time and dynamically adjust the operating parameters to maintain drilling accuracy and the coordination of the movement of each unit; Establish a unit to iteratively optimize the control strategy by combining processing efficiency and quality data, and establish an adaptive control parameter library adapted to the current working conditions.

[0014] Furthermore, the acquisition unit is used to acquire real-time data from the position sensors and force sensors of each drill bit unit in the CNC five-sided drilling system; to sample and process the workpiece material hardness sensor signal through a pre-established data acquisition module to generate an initial dataset containing hardness features; if the workpiece hardness value exceeds a preset threshold, the high-precision machining mode parameter configuration is activated, the drilling depth, feed rate and spindle speed are recorded, and a multi-dimensional data matrix containing material hardness and machining status is generated.

[0015] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the preceding claims.

[0017] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a control method, device, equipment, and storage medium for a CNC five-sided drill. The method acquires sensor data during the machining process and processes material characteristics and machining states, achieving dynamic response to different workpiece materials, thicknesses, and other conditions. This avoids accuracy fluctuations caused by fixed parameters and improves adaptability to diverse machining needs. By planning multi-axis collaborative motion trajectories and employing synchronous control and error compensation mechanisms, the method effectively solves the problem of poor motion coordination among drill bit units in existing solutions, reducing positional deviations and ensuring precise positioning and synchronous movement of each drill bit unit in three-dimensional space, thus improving the consistency of overall machining quality. By monitoring machining state fluctuations in real time and dynamically adjusting operating parameters, the method overcomes the shortcomings of traditional solutions that neglect real-time feedback. It can promptly respond to state fluctuations such as vibration and cutting force changes during machining, maintaining stable drilling accuracy and reducing machining defects caused by state changes. By combining processing efficiency and quality data to iteratively optimize the control strategy and establish an adaptive parameter library, the core bottleneck of insufficient adaptive capability of the control algorithm is solved. This enables the system to continuously optimize control parameters, improve processing efficiency while ensuring drilling accuracy, and reduce cost waste caused by parameter mismatch. This provides technical support for efficient and high-precision processing in intelligent manufacturing scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart of the CNC five-sided drilling control method of the present invention.

[0019] Figure 2 This is a schematic block diagram of the computer device of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0021] like Figure 1 The CNC five-sided drilling control method in this embodiment may specifically include: S101. Acquire sensor data during the processing, process workpiece material characteristics and processing status information, and generate a multi-dimensional data matrix containing material hardness and processing parameters.

[0022] In this embodiment, real-time data is acquired from the position and force sensors of each drill bit unit in the CNC five-sided drilling system. The workpiece material hardness sensor signal is sampled and processed by a pre-established data acquisition module to generate an initial dataset containing hardness characteristics. If the workpiece hardness value exceeds a preset threshold, the high-precision machining mode parameter configuration is activated, and the drilling depth, feed rate, and spindle speed are recorded to generate a multi-dimensional data matrix containing material hardness and machining status.

[0023] Specifically, based on real-time data acquired by the position and force sensors of each drill bit unit in the CNC five-sided drill, the workpiece material hardness sensor signal is sampled and processed through a pre-established data acquisition module. If the sampling frequency is lower than a preset value, the sampling interval is adjusted to the millisecond level and filtered to obtain an initial dataset containing hardness features. A hardness threshold comparison algorithm is used to judge the hardness feature values ​​in the initial dataset one by one. If the workpiece hardness value exceeds the preset threshold, the high-precision machining mode parameter configuration is activated and the drilling depth monitoring device, feed speed controller, and spindle speed regulator are triggered to obtain a multi-dimensional data matrix containing material hardness and machining status. Using the drilling depth, feed rate, and spindle speed parameters in the multidimensional data matrix, a linear regression algorithm is employed to calculate the correlation coefficient between the machining parameters and the hardness characteristics: \(R=\frac{\sum{(x_i-\bar{x})(y_i-\bar{y})}}{\sqrt{\sum{(x_i-\bar{x})^2}\sum{(y_i-\bar{y})^2}}}\), where \(x_i\) is the machining parameter value, \(y_i\) is the hardness characteristic value, and \(\bar{x}\) and \(\bar{y}\) are the parameter mean values, respectively. If the correlation coefficient is greater than a preset value, an optimized combination of machining parameters is generated to determine a drilling machining scheme suitable for workpieces with different hardness. Based on the drilling machining scheme, a real-time feedback control mechanism dynamically adjusts the position sensor and force sensor data of each drill bit unit. If abnormal fluctuations occur in the force sensor values, the feed rate and spindle speed are corrected to obtain stable five-sided drilling control commands.

[0024] For example, in the actual machining scenario of a CNC five-sided drill, real-time monitoring of the workpiece material hardness is crucial to ensuring machining quality. The data acquisition module obtains real-time data from the drill bit unit through position and force sensors. Assuming the workpiece is an aluminum alloy, the hardness sensor signal displays an initial hardness value of HB80. The sampling frequency is set to 100Hz. If the detected frequency is lower than the preset value of 80Hz, the system automatically adjusts the sampling interval to 5 milliseconds and applies a low-pass filter to remove high-frequency noise, forming an initial dataset containing hardness characteristics. This filtering process effectively reduces the interference of environmental vibration on the data and improves the accuracy of hardness characteristics.

[0025] In one possible implementation, a hardness threshold comparison algorithm analyzes the initial dataset, with a preset hardness threshold of HB100. If the hardness value of a workpiece region reaches HB120, the system automatically activates a high-precision machining mode, adjusts the drilling depth monitoring device to 0.01 mm accuracy, reduces the feed rate controller to 50 mm / min, and adjusts the spindle speed to 8000 rpm. This parameter configuration effectively addresses the machining requirements of high-hardness workpieces, reduces drill bit wear, and improves hole wall finish.

[0026] Specifically, the multidimensional data matrix includes parameters such as drilling depth, feed rate, and spindle speed. Assuming a data set of 2 mm depth, 50 mm / min feed rate, and 8000 rpm spindle speed, a linear regression algorithm is used to calculate the correlation coefficient between the machining parameters and hardness characteristics. The assumed result is 0.85, higher than the preset value of 0.8, indicating a high correlation between the machining parameters and hardness. Based on this, the system generates optimized parameter combinations, such as adjusting the feed rate to 45 mm / min and increasing the spindle speed to 8500 rpm, to accommodate areas with higher workpiece hardness. This optimization scheme improves machining efficiency while reducing energy consumption and tool wear.

[0027] For example, the real-time feedback control mechanism monitors the force applied to the drill bit using force sensors, with a normal range of 100-150 Newtons. If the force on a drill bit unit suddenly increases to 200 Newtons, it indicates that an abnormally hard area may have been encountered. The system will automatically reduce the feed rate to 40 mm / min and adjust the spindle speed to 7500 rpm to stabilize the machining process. This dynamic adjustment effectively avoids drill bit overload or workpiece surface damage, ensuring machining accuracy and equipment safety.

[0028] In one embodiment, for high-hardness workpieces such as stainless steel (HB200), the system can be expanded to a multi-drill-bit collaborative machining mode, optimizing the feed rate and rotational speed distribution of each drill bit based on real-time feedback data. This expansion scheme can improve the machining consistency of complex workpieces, significantly increasing production efficiency and product quality.

[0029] S102. Based on the multidimensional data matrix, extract features and match historical best working conditions to generate optimized control parameters that are suitable for the current processing conditions.

[0030] In this embodiment, a convolutional neural network algorithm is used to extract features and recognize patterns from a multidimensional data matrix, generating feature vectors for workpiece hardness and processing status. Based on the feature vectors and workpiece thickness parameters, an adaptive parameter optimization model is generated through gradient descent. If the Euclidean distance between the feature vector and the historical best working condition feature vector is less than a preset threshold, the historical best parameters are directly obtained; otherwise, the network weights are updated through backpropagation to generate the optimized control parameter combination for the current working condition.

[0031] Specifically, workpiece hardness and processing status data are obtained from the multidimensional data matrix, and feature vectors are extracted using a convolutional neural network algorithm. If the feature vector lacks a workpiece thickness parameter, the workpiece thickness parameter is added as an additional input. The Euclidean distance is calculated between the feature vector and the historical best working condition feature vector. If the Euclidean distance is lower than a preset threshold, the historical best parameter combination is invoked. If the Euclidean distance exceeds the preset threshold, the network weights are adjusted using gradient descent to generate a new control parameter combination, and the historical best working condition feature vector library is updated. The backpropagation algorithm is used to correct the drilling depth and spindle speed deviations in the control parameter combination to obtain the final optimized parameters.

[0032] For example, in the CNC five-sided drilling process, a convolutional neural network can be used to analyze the hardness and machining state characteristics in a multidimensional data matrix.

[0033] For example, when the input feature vector includes the hardness value HB90 of an aluminum alloy workpiece, a drilling depth of 1.5 mm, a feed rate of 60 mm / min, and a spindle speed of 7000 rpm, the network extracts locally correlated features through convolutional layers, such as the nonlinear relationship between hardness and feed rate. If the feature vector does not include the workpiece thickness, the system can supplement the thickness parameter (e.g., 10 mm) to improve the model's prediction accuracy of machining load.

[0034] In one possible implementation, Euclidean distance calculation is used to match the current working condition with historical optimal parameters. Assume there exists a set of feature vectors in the historical database with a hardness of HB95, a feed rate of 55 mm / min, and a rotation speed of 7500 rpm, whose Euclidean distance to the current working condition is 12. If the preset threshold is 10, gradient descent optimization is triggered, adjusting the network weights to generate new parameters, such as reducing the feed rate to 52 mm / min while increasing the rotation speed to 7800 rpm. The optimized parameters are then used to correct drilling depth deviations through backpropagation, for example, calibrating the initial depth setting from 1.8 mm to 1.78 mm to adapt to actual machining requirements.

[0035] Specifically, the backpropagation algorithm adjusts the parameter combination using the error signal.

[0036] For example, when the actual drilling depth differs from the target value by 0.05 mm, the algorithm gradually adjusts the weighting of the spindle speed and feed rate based on the resistance changes fed back by the force sensor, ultimately outputting an optimized speed of 7850 rpm and a depth of 1.77 mm. This process reduces drill bit wobble caused by fluctuations in material hardness, improving hole positioning accuracy.

[0037] In one embodiment, the system is extended to multi-drill bit collaborative scenarios.

[0038] For example, when machining stainless steel workpieces, the main drill bit's feature vector shows a hardness of HB210, while the auxiliary drill bit shows HB205. By sharing parameter combinations from the historical best library (such as a feed rate of 40 mm / min and a rotation speed of 9000 rpm), the system dynamically allocates the load to each drill bit to ensure consistent machining.

[0039] Understandably, the above method effectively reduces the risk of tool wear in the machining of high-hardness workpieces through real-time feature matching and parameter optimization, while avoiding machining vibration caused by parameter mismatch.

[0040] S103. Plan the multi-axis collaborative motion trajectory and ensure the precise positioning of each drill bit unit through synchronous control and error compensation.

[0041] In this embodiment, a multi-axis coordinated control command sequence is generated from the optimized control parameter combination. A distributed motion planning algorithm based on the CANopen protocol is used to synchronously plan the motion trajectories of each drill bit unit, generating precise positioning coordinates and motion timing data in three-dimensional space. If the positional deviation between adjacent drill bit units exceeds a preset synchronization error threshold, an error compensation mechanism is used to adjust the motion speed and acceleration of each axis, updating the precise positioning coordinates and motion timing data.

[0042] Specifically, the target position and velocity of each drill bit unit are extracted from the optimized control parameter combination to generate initial three-dimensional spatial positioning coordinates and motion timing data. According to the CANopen protocol, the positioning coordinates and timing data are distributed to each drill bit unit controller, and a distributed motion planning algorithm is executed to obtain the actual motion trajectory. If the actual position deviation of adjacent drill bit units exceeds a preset synchronization error threshold, the velocity compensation and acceleration compensation for each axis are calculated. The velocity and acceleration parameters in the motion timing data are adjusted using the compensation amounts to update the precise positioning coordinates and regenerate the multi-axis coordinated control command sequence.

[0043] For example, in a multi-drill collaborative machining scenario of a CNC five-sided drill, the generation of the initial three-dimensional spatial positioning coordinates needs to be combined with the workpiece geometric features and machining path planning.

[0044] For example, when machining an aluminum alloy frame, the target position of the main drill bit is set to X=120.5 mm, Y=80.3 mm, and Z=15.0 mm, while the auxiliary drill bit is synchronously positioned to X=125.0 mm, Y=75.8 mm, and Z=15.0 mm, with both moving at a speed of 60 mm / s. After distributing the coordinate data to each controller via the CANopen protocol, the system calculates the actual trajectory in real time based on a distributed motion planning algorithm. If the actual position deviation of the main drill bit reaches 0.1 mm due to load fluctuations (the preset threshold is 0.05 mm), a synchronization compensation mechanism is triggered.

[0045] Specifically, the calculation of the velocity compensation amount needs to take into account the differences in the dynamic response of adjacent drill bits.

[0046] For example, if the auxiliary drill bit lags due to a local increase in material hardness, the system detects that its actual Y-axis position is 0.08 mm slower than the target value. At this time, an acceleration compensation of 0.5 mm / s² is generated, and the speed of the main drill bit is reduced from 60 mm / s to 58 mm / s, so that the two can be re-aligned in the next motion cycle.

[0047] In one embodiment, the update of precise positioning coordinates relies on iterative optimization of multi-axis cooperative control commands.

[0048] For example, when machining stainless steel workpieces, the initial Z-axis coordinate is set to 12.0 mm, but during actual drilling, tool wear causes a depth deviation of 0.03 mm. The system dynamically adjusts the Z-axis pressing speed (increasing from 30 mm / s to 32 mm / s) and the spindle speed (increasing from 8000 rpm to 8200 rpm) to ultimately lock the corrected coordinate to 12.03 mm, ensuring consistent hole depth.

[0049] Understandably, the aforementioned synchronization error compensation mechanism can effectively suppress multi-axis synchronization problems caused by material inhomogeneity or mechanical vibration.

[0050] For example, when machining high-hardness titanium alloys, by adjusting the feed acceleration in real time (from 2.0 mm / s² to 1.8 mm / s²), the radial position deviation between drill bits can be controlled within 0.02 mm, thereby avoiding the deterioration of the machined surface roughness caused by asynchrony.

[0051] Preferably, the generation of multi-axis coordinated control commands also needs to take into account tool life factors.

[0052] For example, when the system detects that the cumulative working time of a drill bit exceeds a threshold, it automatically reduces its movement speed by 5% while increasing the speed of adjacent drill bits by 3% to balance the load and extend the overall tool life.

[0053] S104. Monitor the fluctuations in processing status in real time and dynamically adjust the operating parameters to maintain drilling accuracy and coordination of movement of each unit.

[0054] In this embodiment, vibration signals and cutting force data during the machining process are collected by a real-time monitoring module from the updated precise positioning coordinates and motion timing data. A sliding window combined with a statistical analysis algorithm is used to calculate the fluctuation trend data of drilling accuracy. If the absolute value of the fluctuation trend data exceeds a preset threshold, the feed rate and spindle speed are adjusted through a feedback adjustment mechanism to generate synchronous motion coordination evaluation data for each drill bit unit.

[0055] Specifically, the real-time position and velocity parameters of each drill bit unit are obtained from precise positioning coordinates and motion timing data. Vibration signals and cutting force data are collected through a real-time monitoring module. The vibration signals and cutting force data are segmented using a sliding window method to obtain a first fluctuation dataset. For the first fluctuation dataset, a statistical analysis algorithm is used to calculate the mean and variance within each time window to generate fluctuation trend data. If the absolute value of the fluctuation trend data exceeds a preset threshold, a first adjustment parameter set is determined. Based on the first adjustment parameter set, the compensation amount for feed rate and spindle speed is calculated through a feedback adjustment mechanism to update the motion timing data of each drill bit unit and generate a first coordinated motion command set. The updated motion trajectory of each drill bit unit is obtained from the first coordinated motion command set. A synchronization evaluation algorithm is used to calculate the position deviation and velocity deviation between each drill bit unit to generate synchronization motion coordination evaluation data.

[0056] For example, in CNC multi-drill machining scenarios, for the processing of precise positioning coordinates and motion timing data, a real-time monitoring module can acquire the position and velocity parameters of each drill unit, while simultaneously collecting vibration signals and cutting force data. The vibration signal reflects the stability of the drill during machining, while the cutting force data indicates the intensity of the interaction between the tool and the workpiece. Using a sliding window method to segment these data allows the continuous signal to be decomposed into multiple time-segmented fluctuation datasets, facilitating subsequent analysis. For instance, setting the time window to 0.5 seconds allows for the acquisition of vibration frequency and cutting force changes of the main drill bit during aluminum alloy workpiece machining, forming a preliminary dataset.

[0057] For example, when processing the first fluctuation dataset, statistical analysis algorithms can be used to calculate the mean and variance within each time window, generating fluctuation trend data. Assuming the main drill bit's vibration mean is 0.3 mm and the variance is 0.05 within a certain window, while the preset threshold is 0.2 mm, the absolute value of the fluctuation trend data exceeds the threshold, requiring the determination of the first set of adjustment parameters. This method can promptly capture abnormal fluctuations during the processing, providing a basis for subsequent adjustments.

[0058] For example, the feedback adjustment mechanism plays a crucial role in calculating the feed rate and spindle speed compensation based on the first set of adjustment parameters. Suppose the main drill bit's feed rate was originally 50 mm / s and the spindle speed was 7000 rpm, but due to abnormal vibration, the feed rate needs to be reduced to 48 mm / s while the spindle speed is increased to 7200 rpm to balance machining stability. The updated motion timing data generates the first coordinated motion command set, ensuring the consistency of action across all drill bit units.

[0059] For example, for the updated motion trajectory in the first coordinated motion command set, the synchronization evaluation algorithm can calculate the position and velocity deviations between each drill bit unit. Assuming the position deviation between the main drill bit and the auxiliary drill bit on the Y-axis is 0.06 mm and the velocity deviation is 0.4 mm / s, the system generates synchronization motion coordination evaluation data for subsequent optimization. This evaluation method helps identify potential out-of-synchronization risks.

[0060] For example, in practical implementation, the segmented processing of vibration signals and cutting force data can be combined with multi-dimensional analysis using different window sizes. For instance, a short window of 0.2 seconds is suitable for capturing instantaneous anomalies, while a long window of 1 second is used to analyze overall trends. This multi-level analysis can more comprehensively reflect the machining status and improve the reliability of data processing.

[0061] For example, time delay factors can be introduced for supplementary analysis when generating synchronous motion coordination evaluation data. Assuming that the auxiliary drill bit's positional deviation persists due to mechanical response delay, the system can control the deviation within 0.02 mm by adjusting the timing of command transmission. This approach can further optimize multi-axis coordination.

[0062] Preferably, when actually machining stainless steel workpieces, the threshold for fluctuation trend data can be dynamically adjusted according to the material characteristics. If the material has high hardness, the threshold can be set to 0.25 mm to avoid frequent adjustments, ensuring machining continuity while also meeting accuracy requirements. This flexibility is particularly important for complex working conditions.

[0063] S105. Combine processing efficiency and quality data to iteratively optimize the control strategy and establish an adaptive control parameter library that is suitable for the current working conditions.

[0064] In this embodiment, a dynamic parameter update mechanism is established from the synchronous motion coordination evaluation data. Combined with processing efficiency data and surface quality detection data, a reinforcement learning algorithm is used to iteratively optimize the control strategy, generating an optimized control parameter set. If the drilling accuracy data for multiple consecutive processing cycles are all within the target accuracy range, the optimized control parameter set is stored as an adaptive control parameter library for the current working condition.

[0065] Specifically, the position and velocity deviations of each drill bit unit are extracted from the synchronous motion coordination evaluation data. Combined with processing efficiency data and surface quality inspection data, a preset weighting coefficient is used to calculate the dynamic parameter adjustment priority, resulting in a quantified priority. For the quantified priority, if it exceeds a preset threshold, the control strategies for feed rate and spindle speed are iteratively optimized to generate the optimized control parameter set. The feedback adjustment logic of the real-time monitoring module is updated based on the optimized control parameter set, and drilling accuracy data for multiple consecutive processing cycles is collected to determine if the accuracy data is within the target range. If the accuracy data continuously meets the target range, the optimized control parameter set is stored in the adaptive control parameter library, overwriting the parameter records corresponding to the original working condition.

[0066] For example, in CNC multi-drill machining scenarios, extracting the positional and velocity deviations of each drill unit from synchronous motion coordination evaluation data is a crucial step in optimizing the machining process. Positional deviation reflects the offset of the drill unit in spatial coordinates, while velocity deviation reflects the inconsistency in the motion rhythm of each unit. Assuming that when machining an aluminum alloy workpiece, the positional deviation between the main drill and the auxiliary drill on the X-axis is 0.05 mm, and the velocity deviation is 0.3 mm / s, these data provide the basis for subsequent analysis.

[0067] For example, combining processing efficiency data and surface quality inspection data allows for a more comprehensive assessment of the processing status. Processing efficiency data might include the number of holes drilled per minute, while surface quality inspection data focuses on the surface roughness or defect rate of the workpiece. Assuming a processing efficiency of 10 holes per minute and a surface roughness of 0.8 micrometers, using preset weighting coefficients such as 30% for positional deviation, 20% for speed deviation, 25% for efficiency, and 25% for quality, the quantified result for the dynamic parameter adjustment priority is calculated to be 75, exceeding the preset threshold of 60, indicating that the control strategy needs optimization.

[0068] For example, iterative optimization of control strategies for feed rate and spindle speed allows for parameter adjustments based on quantification results. Assuming the original feed rate was 45 mm / s and the spindle speed was 6500 rpm, the optimized parameters could be adjusted to 43 mm / s and 6700 rpm, forming a new set of control parameters. This adjustment aims to balance machining speed and stability.

[0069] For example, after updating the feedback adjustment logic of the real-time monitoring module, drilling accuracy data for multiple processing cycles needs to be collected. Assuming the accuracy data for five consecutive cycles are 0.02 mm, 0.03 mm, 0.01 mm, 0.02 mm, and 0.02 mm respectively, all within the target range of 0.01-0.03 mm, it indicates that the optimized parameters are effective. At this point, this parameter set is stored in the adaptive control parameter library, overwriting the original records, for reuse in subsequent operating conditions.

[0070] For example, when storing parameters, they can be categorized and managed according to workpiece material or processing conditions. Parameter sets for machining thin aluminum alloy sheets and thick stainless steel sheets can be stored separately to ensure that parameter retrieval is targeted. This approach helps to quickly adapt to different processing needs.

[0071] For example, in calculating priority quantification results, more dimensions of data, such as tool wear or ambient temperature changes, can be incorporated as supplementary data for weighting coefficients. If tool wear data indicates a current wear rate of 15%, the adjustment priority can be appropriately increased to ensure machining accuracy is not affected. This multi-dimensional analysis improves the rationality of parameter adjustments.

[0072] For example, a dynamic target range can be set to determine drilling accuracy data. For example, the target range can be narrowed to 0.01-0.02 mm for workpieces requiring high precision, while it can be widened to 0.01-0.04 mm for ordinary workpieces. This flexibility helps achieve more precise control in different processing scenarios.

[0073] This application embodiment also provides a CNC five-sided drilling control device, including: The acquisition unit is used to acquire sensor data during the processing, process workpiece material characteristics and processing status information, and generate a multi-dimensional data matrix containing material hardness and processing parameters. The generation unit is used to extract features based on a multidimensional data matrix and match historical best working conditions to generate optimized control parameters that are suitable for the current processing conditions. The control compensation unit is used to plan the multi-axis coordinated motion trajectory and ensure the precise positioning of each drill bit unit through synchronous control and error compensation. The adjustment unit is used to monitor the fluctuations in the processing status in real time and dynamically adjust the operating parameters to maintain drilling accuracy and the coordination of the movement of each unit; Establish a unit to iteratively optimize the control strategy by combining processing efficiency and quality data, and establish an adaptive control parameter library adapted to the current working conditions.

[0074] Furthermore, the acquisition unit is used to acquire real-time data from the position sensors and force sensors of each drill bit unit in the CNC five-sided drilling system; to sample and process the workpiece material hardness sensor signal through a pre-established data acquisition module to generate an initial dataset containing hardness features; if the workpiece hardness value exceeds a preset threshold, the high-precision machining mode parameter configuration is activated, the drilling depth, feed rate and spindle speed are recorded, and a multi-dimensional data matrix containing material hardness and machining status is generated.

[0075] The aforementioned CNC five-sided drilling control device is used to execute the aforementioned CNC five-sided drilling control method, and its execution steps will not be repeated here.

[0076] Reference Figure 2 This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 2 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data such as control parameters. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a CNC five-sided drilling control method.

[0077] The processor described above executes a CNC five-sided drilling control method, the method comprising: Acquire sensor data during the processing, process workpiece material characteristics and processing status information, and generate a multi-dimensional data matrix containing material hardness and processing parameters; Based on the extraction of features from the multidimensional data matrix and matching of historical best working conditions, optimized control parameters adapted to the current processing conditions are generated. Plan the multi-axis coordinated motion trajectory and ensure the precise positioning of each drill bit unit through synchronous control and error compensation; Real-time monitoring of processing status fluctuations and dynamic adjustment of operating parameters to maintain drilling accuracy and coordination of movement of each unit; By combining processing efficiency and quality data to iteratively optimize the control strategy, an adaptive control parameter library adapted to the current working conditions is established.

[0078] An embodiment of this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a CNC five-sided drilling control method.

[0079] The processor described above executes a CNC five-sided drilling control method, the method comprising: Acquire sensor data during the processing, process workpiece material characteristics and processing status information, and generate a multi-dimensional data matrix containing material hardness and processing parameters; Based on the extraction of features from the multidimensional data matrix and matching of historical best working conditions, optimized control parameters adapted to the current processing conditions are generated. Plan the multi-axis coordinated motion trajectory and ensure the precise positioning of each drill bit unit through synchronous control and error compensation; Real-time monitoring of processing status fluctuations and dynamic adjustment of operating parameters to maintain drilling accuracy and coordination of movement of each unit; By combining processing efficiency and quality data to iteratively optimize the control strategy, an adaptive control parameter library adapted to the current working conditions is established.

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0081] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, first object, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, first object, or method. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, first object, or method that includes that element.

[0082] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A CNC five-sided drilling machining control method, characterized in that, The method includes: Acquire sensor data during the processing, process workpiece material characteristics and processing status information, and generate a multi-dimensional data matrix containing material hardness and processing parameters; Based on the extraction of features from the multidimensional data matrix and matching of historical best working conditions, optimized control parameters adapted to the current processing conditions are generated. Plan the multi-axis coordinated motion trajectory and ensure the precise positioning of each drill bit unit through synchronous control and error compensation; Real-time monitoring of processing status fluctuations and dynamic adjustment of operating parameters to maintain drilling accuracy and coordination of movement of each unit; By combining processing efficiency and quality data to iteratively optimize the control strategy, an adaptive control parameter library adapted to the current working conditions is established.

2. The CNC five-sided drilling control method according to claim 1, characterized in that, The process of acquiring sensor data during processing, processing workpiece material characteristics and processing status information, and generating a multi-dimensional data matrix containing material hardness and processing parameters includes: Real-time data is obtained from the position and force sensors of each drill bit unit in the CNC five-sided drilling system; The workpiece material hardness sensor signal is sampled and processed by a pre-established data acquisition module to generate an initial dataset containing hardness features. If the workpiece hardness value exceeds the preset threshold, the high-precision machining mode parameter configuration is activated, the drilling depth, feed rate and spindle speed are recorded, and a multi-dimensional data matrix containing material hardness and machining status is generated.

3. The CNC five-sided drilling control method according to claim 1, characterized in that, The process of extracting features based on a multidimensional data matrix and matching them with historical optimal working conditions to generate optimized control parameters adapted to the current processing conditions includes: Feature extraction and pattern recognition are performed using a convolutional neural network algorithm on a multidimensional data matrix to generate feature vectors for workpiece hardness and processing status; Based on the feature vector and workpiece thickness parameters, an adaptive parameter optimization model is generated through gradient descent optimization. If the Euclidean distance between the feature vector and the historical best working condition feature vector is lower than the preset threshold, the historical best parameters are directly obtained as optimization control parameters to adapt to the current processing conditions. Otherwise, the network weights are updated using the backpropagation algorithm to generate optimized control parameters that fit the current processing conditions.

4. The CNC five-sided drilling control method according to claim 1, characterized in that, The planned multi-axis coordinated motion trajectory ensures precise positioning of each drill bit unit through synchronous control and error compensation, including: A multi-axis coordinated control command sequence is generated from the optimized control parameters. The motion trajectory of each drill bit unit is synchronously planned using a distributed motion planning algorithm based on the CANopen protocol, generating precise positioning coordinates and motion timing data in three-dimensional space. If the positional deviation of adjacent drill bit units exceeds the preset synchronization error threshold, the motion speed and acceleration of each axis are adjusted through the error compensation mechanism to update the precise positioning coordinates and motion timing data.

5. The CNC five-sided drilling control method according to claim 1, characterized in that, The real-time monitoring of processing status fluctuations and dynamic adjustment of operating parameters to maintain drilling accuracy and coordination of unit movements include: From the updated precise positioning coordinates and motion timing data, vibration signals and cutting force data during the machining process are collected through the real-time monitoring module, and the fluctuation trend data of drilling accuracy is calculated by using a sliding window combined with statistical analysis algorithms. If the absolute value of the fluctuation trend data exceeds the preset threshold, the feed rate and spindle speed are adjusted through the feedback adjustment mechanism to generate synchronous motion coordination evaluation data for each drill bit unit.

6. The CNC five-sided drilling control method according to claim 1, characterized in that, The aforementioned iterative optimization control strategy, combining processing efficiency and quality data, establishes an adaptive control parameter library adapted to the current operating conditions, including: A dynamic parameter update mechanism is established from synchronous motion coordination evaluation data. Combined with processing efficiency data and surface quality detection data, a reinforcement learning algorithm is used to iteratively optimize the control strategy and generate an optimized set of control parameters. If the drilling accuracy data for multiple consecutive processing cycles are all within the target accuracy range, the optimized control parameter set will be stored as the adaptive control parameter library for the current working condition.

7. A CNC five-sided drilling machining control device, characterized in that, include: The acquisition unit is used to acquire sensor data during the processing, process workpiece material characteristics and processing status information, and generate a multi-dimensional data matrix containing material hardness and processing parameters. The generation unit is used to extract features based on a multidimensional data matrix and match historical best working conditions to generate optimized control parameters that are suitable for the current processing conditions. The control compensation unit is used to plan the multi-axis coordinated motion trajectory and ensure the precise positioning of each drill bit unit through synchronous control and error compensation. The adjustment unit is used to monitor the fluctuations in the processing status in real time and dynamically adjust the operating parameters to maintain drilling accuracy and the coordination of the movement of each unit; Establish a unit to iteratively optimize the control strategy by combining processing efficiency and quality data, and establish an adaptive control parameter library adapted to the current working conditions.

8. The CNC five-sided drilling control device according to claim 7, characterized in that, The acquisition unit is used to acquire real-time data from the position sensors and force sensors of each drill bit unit in the CNC five-sided drilling system; to sample and process the workpiece material hardness sensor signal through a pre-established data acquisition module to generate an initial dataset containing hardness features; if the workpiece hardness value exceeds a preset threshold, the high-precision machining mode parameter configuration is activated to record the drilling depth, feed rate and spindle speed, and to generate a multi-dimensional data matrix containing material hardness and machining status.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein... When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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