Self-adaptive compensation processing technology of high-density cooling fin

Abstract

The invention relates to a self-adaptive compensation machining process for a high-density cooling fin in the technical field of intelligent manufacturing, which comprises the following steps: acquiring position deviation data of a rotating shaft of a machine tool and thermal elongation deformation data of a main shaft through a sensor, and determining overall dynamic error distribution of the machine tool; according to the dynamic error distribution, an error compensation model is adopted to calculate a cutter posture adjustment value, and compensation parameters used for real-time correction are determined; specific area chip flow simulation data are extracted from the cutting path with balanced stress, and whether the chip removal space meets the machining requirement or not is judged; if the chip removal space is insufficient, the feeding rate and cooling liquid flowing parameters are adjusted based on the chip flowing simulation data, and an optimized chip removal strategy is obtained; according to the optimized chip removal strategy, a tool path is updated in combination with a multi-axis linkage control system, and a complete machining sequence is determined; and multi-axis engraving and milling operation is executed through the machining sequence, and a target structural component is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent manufacturing, and particularly relates to a self-adaptive compensation processing technology for high-density heat dissipation fins. BACKGROUND

[0002] With the continuous improvement of the power density of electronic equipment, high-density heat dissipation fins have become the core components for guaranteeing the long-term high-performance operation of CPUs and GPUs. The fin thickness needs to be controlled within 0.2-0.4 mm, and the height needs to reach 15-30 mm, and the fin spacing is less than 1 mm. Such an extremely high aspect ratio structure puts almost extreme requirements on milling processing, and any slight deviation will cause the heat dissipation performance to drop sharply or be directly scrapped. At present, the processing mainly relies on three-axis or ordinary five-axis machine tools. Although five-axis linkage can enable the side edge of the tool to participate in cutting to reduce the cutting force, the phenomenon of uneven fin wall thickness, excessive thinning at the top or even breakage still frequently occurs when the high fins are actually processed. The fundamental reason for these defects is that the tool always circles in the same direction during processing, and only one side of the tool is used to remove material at each layer, resulting in a long-term unchanged direction of the cutting force, continuous accumulation of slight tool setting, and finally forming a clear taper or wavy residue at the top of the fin.

[0003] At the same time, the rotating shaft of the machine tool will produce position deviation at different swing angles, and the high-speed operation of the main shaft will also bring thermal elongation. These errors change in real time with the posture, speed and load, while the traditional compensation only adjusts the static error under the cold state once, and cannot offset the dynamic drift generated during processing, making it difficult to guarantee the size consistency between the same batch of workpieces or even different regions of the same workpiece. More seriously, after the tool feeds downward for dozens of layers in the narrow deep groove, the chip removal space is extremely small, and if the chips cannot be smoothly removed in time, they will be squeezed again, further enlarging the deformation. Simply relying on fixed up-cutting or down-cutting mode is difficult to maintain the best stress and chip removal state at all depths. After these interrelated factors are superimposed, a vicious cycle is formed: unilateral stress accumulation causes tool setting → tool setting aggravates dynamic error performance → dynamic error in turn aggravates uneven cutting force → ultimately leading to thin-walled fins being prone to unstable deformation in the later stage of processing.

[0004] Therefore, how to simultaneously solve the unilateral stress accumulation of the tool, the dynamic spatial error during processing and the stress and chip removal contradiction in the narrow deep groove during multi-axis engraving and milling, so as to realize the stable maintenance of the high aspect ratio fin thickness tolerance within ±0.02 mm and the batch consistency, has become the key problem restricting the localization and mass production of high-density heat dissipation fins. SUMMARY

[0005] The present application provides a self-adaptive compensation processing technology for high-density heat dissipation fins, mainly comprising:

[0006] The position deviation data and the thermal elongation deformation data of the rotating shaft of the machine tool are collected by sensors to determine the overall dynamic error distribution of the machine tool; according to the dynamic error distribution, an error compensation model is used to calculate the tool posture adjustment value to determine the compensation parameters for real-time correction; if the compensation parameters exceed the preset safety threshold range, the cutting direction alternating mode is adjusted by a path optimization algorithm to obtain a balanced stress cutting path; the specific area chip flow simulation data is extracted from the balanced stress cutting path to determine whether the chip removal space meets the processing requirements; if the chip removal space is insufficient, the feed rate and the cooling liquid flow parameters are adjusted based on the chip flow simulation data to obtain an optimized chip removal strategy; for the optimized chip removal strategy, the tool trajectory is updated in combination with the multi-axis linkage control system to determine the complete machining sequence; the multi-axis engraving and milling operation is performed through the machining sequence to obtain the target structure component. Further, the position deviation data and the thermal elongation deformation data of the rotating shaft of the machine tool are collected by sensors to determine the overall dynamic error distribution of the machine tool, including: the position deviation data of the rotating shaft of the machine tool under multiple swing angle conditions is obtained by a precision sensor, and the thermal elongation deformation data of the main shaft during high-speed operation is also obtained; according to the position deviation data and the thermal elongation deformation data, a deviation fusion matrix is determined by coordinate system mapping fusion; for the deviation fusion matrix, a correlation deformation mode between components is calculated using the correlation coefficient between matrix elements; by projecting the correlation deformation mode onto the machine tool coordinate space, the overall dynamic error distribution of the machine tool is obtained. Further, according to the dynamic error distribution, an error compensation model is used to calculate the tool posture adjustment value to determine the compensation parameters for real-time correction, including: the thermal elongation deformation data is obtained from the dynamic error distribution, and a deviation fusion matrix is determined by mapping and fusing the position deviation data and the thermal elongation deformation data; for the deviation fusion matrix, a correlation deformation projection between components is calculated to obtain a correlation deformation mode; according to the correlation deformation mode, a preset error compensation model is used to calculate the tool posture adjustment value to obtain the posture correction calculation result; by fusing the preset vibration influence compensation parameters through the posture correction calculation result, the compensation parameters for real-time correction are determined. Further, if the compensation parameters exceed the preset safety threshold range, the cutting direction alternating mode is adjusted by a path optimization algorithm to obtain a balanced stress cutting path, including: threshold comparison data is obtained from the compensation parameters, and numerical comparison is performed with the preset safety threshold range; if the range is exceeded, a path optimization trigger signal is generated; for the path optimization trigger signal, a path search algorithm is used to obtain a cutting direction transformation sequence to obtain an alternating mode generation result; according to the alternating mode generation result, the stress balance is calculated by fusing deformation influence data to determine the tool load distribution vector; the tool load distribution vector is used to adjust the machining path node position to obtain the balanced stress cutting path.Further, the specific region chip flow simulation data is extracted from the balanced force cutting path, and whether the chip removal space meets the machining requirement is judged, including: obtaining the geometric boundary data of the specific region from the balanced force cutting path, discretizing the boundary data by a grid division method to obtain a region grid model; fusing material thermal deformation influence data for the region grid model, calculating the chip flow trajectory by fluid dynamics simulation to obtain chip flow simulation results; extracting chip removal channel volume parameters from the chip flow simulation results, and comparing the parameters with a preset machining requirement threshold to determine a chip removal space distribution vector; judging whether the chip removal space of the specific region meets the machining requirement through the chip removal space distribution vector. Further, if the chip removal space is insufficient, the feed rate and cooling liquid flow parameters are adjusted based on the chip flow simulation data to obtain an optimized chip removal strategy, including: extracting chip flow simulation data in the specific region from the balanced force cutting path to obtain chip trajectory information; fusing thermal deformation influence data according to the chip trajectory information, calculating chip accumulation density by fluid dynamics simulation to obtain accumulation density parameters; extracting chip removal channel volume data from the accumulation density parameters, and comparing the data with a preset threshold to determine a distribution vector; if the distribution vector exceeds the threshold range, adjusting the feed rate and cooling liquid flow parameters based on the chip flow simulation data by an iterative simulation method to obtain the optimized chip removal strategy. Further, for the optimized chip removal strategy, the tool path is updated in combination with a multi-axis linkage control system to determine a complete machining sequence, including: obtaining tool posture calibration data by the optimized chip removal strategy, adjusting multi-axis linkage control parameters by a coordinate transformation method to obtain a posture calibration result; fusing chip flow simulation information for the posture calibration result, calculating a shortest path to update the tool path by a path planning tool to determine a trajectory update vector; extracting thermal deformation influence data according to the trajectory update vector, calculating a deviation distribution vector by an iterative simulation method to judge sequence completeness; if the sequence completeness meets a preset threshold, integrating cooling liquid flow parameters to adjust the feed rate to obtain the complete machining sequence. Further, the multi-axis engraving and milling operation is performed through the machining sequence to obtain a target structure component, including: obtaining chip thickness monitoring information through the machining sequence, adjusting multi-axis linkage control parameters by a coordinate transformation method to obtain a posture calibration result; fusing chip thickness monitoring information for the posture calibration result, calculating an updated path by a path planning tool to determine a trajectory update vector; extracting thermal deformation influence data according to the trajectory update vector, calculating a distribution vector by an iterative method until the deviation converges to judge sequence completeness; if the sequence completeness meets a preset threshold, integrating cooling liquid flow parameters to adjust the feed rate, and performing the multi-axis engraving and milling operation through the machining sequence to obtain the target structure component.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0008] This invention discloses an adaptive compensation machining process for high-density heat sinks. This method addresses the core challenges in five-axis machining, including rotary axis position deviations caused by swing angle changes, spindle thermal expansion, and difficulties in chip removal in narrow, deep grooves, leading to excessive thickness tolerances and poor consistency. It constructs a dynamic error distribution by real-time acquisition of machine tool multi-swing angle position deviations and high-speed thermal deformation data. Combined with a preset error compensation model, it calculates tool posture adjustment values. When the compensation parameters exceed a safety threshold, it automatically switches to a general path optimization algorithm to generate a balanced force cutting path. It also extracts chip flow simulation data for deep groove areas. If chip removal space is insufficient, it uses fluid dynamics simulation to simultaneously optimize the feed rate and coolant parameters to form an optimized chip removal strategy. Finally, it integrates the updated five-axis linkage trajectory for machining. Simultaneously, it iterative adjustments to the error compensation model parameters through batch consistency comparison achieve the core technical effects of stable control of fin structure thickness tolerance and significant improvement in batch consistency. Attached Figure Description

[0009] Figure 1 This is a flowchart of an adaptive compensation process for a high-density heat sink according to the present invention. Detailed Implementation

[0010] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0011] like Figure 1 The adaptive compensation process for a high-density heat sink in this embodiment may specifically include:

[0012] Step S101: The position deviation data of the machine tool rotary axis under various swing angle conditions and the thermal elongation deformation data when the spindle is running at high speed are collected by precision sensors.

[0013] Precision sensors acquire position deviation data under various swing angle conditions from the machine tool's rotary axis and thermal expansion deformation data during high-speed operation from the spindle. Based on the position deviation data and the thermal expansion deformation data, a deviation fusion matrix is ​​determined by mapping the position deviation data to the coordinate system of the thermal expansion deformation data. Using this deviation fusion matrix, correlation coefficients between elements are calculated to obtain the associated deformation patterns between machine tool components. For these associated deformation patterns, the overall dynamic error distribution of the machine tool is obtained by projecting them onto the machine tool coordinate space.

[0014] In one implementation, by collecting position deviation data of the machine tool's rotating axis using a precision sensor, it is first necessary to set various swing angle conditions.

[0015] For example, the sway angle conditions can include the positioning of the rotating axis at angles such as 0 degrees, 45 degrees, 90 degrees, and 180 degrees. Precision sensors such as laser displacement sensors or encoders are mounted near the rotating axis to monitor the offset of the axis at these sway angles in real time.

[0016] Specifically, the sensor calculates positional deviation by emitting a laser beam and receiving the reflected signal, recording the deviation data in millimeters. This acquisition process ensures comprehensive capture of the dynamic performance of the machine tool's rotary axes under different angular conditions. Furthermore, this data helps in analyzing the sources of geometric errors in the machine tool.

[0017] Preferably, the overall stability of the machine tool is considered while collecting position deviation data.

[0018] It should be noted that precision sensors need to be calibrated to eliminate the effects of environmental factors such as vibration or temperature fluctuations. For example.

[0019] In one possible implementation, the sensor is connected to a data acquisition system that samples the deviation value 100 times per second, thus forming a time-series dataset. In this way, the position deviation data not only reflects static errors but also captures deviations caused by dynamic oscillations, providing a basis for constructing the subsequent error distribution. Furthermore, for data acquisition regarding thermal elongation deformation during high-speed spindle operation, the spindle is set to operate at different speeds, such as 1000 rpm to 5000 rpm. Precision sensors, such as infrared temperature sensors and contact displacement sensors, are used in combination. The infrared sensor monitors temperature changes on the spindle surface, while the displacement sensor measures axial elongation.

[0020] Specifically, thermal elongation deformation is obtained by calculating the product of the material's coefficient of thermal expansion and the temperature difference.

[0021] For example, assuming the spindle material is steel with a coefficient of thermal expansion of approximately 12 × 10⁻⁶ / ℃, the elongation can be estimated based on this when the temperature rises from 20℃ to 50℃. This data acquisition process records in detail the curves of thermal deformation changing with time and rotational speed.

[0022] Understandably, this helps identify deformation patterns caused by heat sources such as bearing friction.

[0023] In one embodiment, positional deviation data and thermal elongation deformation data are integrated to obtain the overall dynamic error distribution of the machine tool. First, the two types of data are imported into error analysis software, which maps the deviations to the three-dimensional model of the machine tool through spatial coordinate transformation.

[0024] For example, positional deviation data is used to correct trajectory errors of the rotary axis, while thermal expansion data adjusts the spindle length parameters. By overlaying these error sources, a dynamic error distribution map is generated, which uses color gradients to represent the error intensity distribution. This integration enables a comprehensive assessment of machine tool machining accuracy and can guide error compensation strategies in actual production.

[0025] Specifically, in another implementation scenario in the field of machine tool processing, for the application of CNC lathes, the data acquisition process can be extended to multi-axis linkage conditions.

[0026] For example, the rotary axis synchronously monitors the linkage deviations of the X, Y, and Z axes when the swing angle changes, while the spindle thermal expansion data is combined with cooling system parameters. Furthermore, in this scenario, the error distribution calculation employs a gridding method, dividing the machine tool workspace into multiple grid points, calculating the comprehensive error value at each point, thus forming a more refined distribution model. This method enhances the versatility of the technical solution, making it applicable to different models of CNC machine tools.

[0027] Preferably, to improve data accuracy, sensor calibration is performed before data acquisition.

[0028] For example, use a standard metrology block to calibrate the laser sensor to ensure that the measurement error is less than 0.01 mm.

[0029] It should be noted that this calibration process includes zero-point setting and linear correction. Furthermore, a vibration suppression device is introduced during high-speed operation to stabilize data acquisition. This preparatory work ensures the reliability of the obtained error distribution.

[0030] In one possible implementation, the error distribution can be output as a three-dimensional visualization graphic, and further, real-time feedback can be achieved by combining it with the machine tool control system.

[0031] For example, when the error exceeds a threshold, the system calculates compensation parameters based on the deviation fusion matrix and associated deformation patterns to compensate for the deformation. This application demonstrates the effectiveness of the technology in improving machine tool performance, enabling higher machining accuracy. Furthermore, in another embodiment, for milling machines, various sway angle conditions are extended to continuous rotation paths, with sensors collecting continuous deviation data. Thermal elongation deformation data is monitored over extended periods of operation, and the integrated dynamic error distribution is used to calculate tool posture adjustment values. This diversified implementation covers different operating modes within the same domain, ensuring the flexibility of the solution.

[0032] Step S102: Based on the dynamic error distribution, calculate the tool posture adjustment value using a preset error compensation model, and determine the compensation parameters used for real-time correction.

[0033] Thermal elongation deformation data is obtained from the dynamic error distribution. A deviation fusion matrix is ​​determined by mapping and fusing the positional deviation data and thermal elongation deformation data in a coordinate system. For the deviation fusion matrix, the correlation coefficient between matrix elements is used to calculate the associated deformation projection between components, obtaining the associated deformation pattern. Based on the associated deformation pattern, a preset error compensation model is used with the associated deformation pattern as input to calculate the tool attitude adjustment value, obtaining the attitude correction calculation result. The attitude correction calculation result is then fused with preset vibration influence compensation parameters to determine the real-time correction parameters.

[0034] In one implementation, the error compensation model is used to calculate the error based on the dynamic error distribution of the machine tool. First, the error distribution data needs to be used as input.

[0035] Specifically, the dynamic error distribution is usually represented in three-dimensional coordinates, covering factors such as rotation axis deviation and spindle thermal expansion. The model processes these data through matrix transformation to generate a preliminary adjustment scheme for the tool posture.

[0036] For example, the error compensation model can be based on inverse kinematics principles to convert the error vector into an attitude offset, ensuring that the calculation process takes into account the machine tool's geometry. Furthermore, the pre-defined error compensation model includes polynomial fitting or neural network structures to simulate the relationship between error and attitude.

[0037] It should be noted that the polynomial fitting model fits the error data using the least squares method.

[0038] For example, by using positional deviation as the independent variable and attitude adjustment value as the dependent variable, a quadratic or cubic polynomial function can be formed. The principle behind this model is to capture nonlinear error patterns, thereby calculating the rotation and displacement adjustment values ​​of the tool on the X, Y, and Z axes.

[0039] Specifically, during the calculation process, the model first extracts key features of the error distribution, such as the maximum deviation point and the distribution gradient, and then applies a transformation matrix to map these features to the tool coordinate system.

[0040] For example, assuming the error distribution shows a Z-axis thermal elongation of 0.05 mm, the model will calculate the corresponding attitude adjustment value to ensure that the tool tip position is corrected to the target trajectory. This method is suitable for precision machining scenarios on CNC machine tools and can handle the superimposed effects of multiple error sources.

[0041] Preferably, when determining the compensation parameters, the adjustment values ​​are further optimized by combining the feedback mechanism of the machine tool control system.

[0042] In one possible implementation, the compensation parameters include position offset and angle correction values, which are extracted from the attitude adjustment values ​​through an iterative algorithm.

[0043] Specifically, the iterative process involves comparing the calculated values ​​with the actual measured values. If the difference exceeds a threshold such as 0.01 mm, the model parameters are adjusted to converge the results.

[0044] Understandably, this method of determination ensures the real-time nature of the parameters and is suitable for high-speed milling operations.

[0045] For example, when milling planar parts, compensation parameters are directly input into the CNC program to achieve dynamic correction of the tool path.

[0046] In one embodiment, for multi-axis linkage scenarios in horizontal machining centers, the error compensation model is extended to include a time dimension, taking into account the influence of machining speed when calculating tool posture adjustment values. Subsequently, in this machining center, the parameter determination process is linked through consecutive steps: first, speed data is collected; then, the time dimension error is calculated; and finally, the posture value is adjusted.

[0047] Specifically, the model integrates dynamic error distribution and rotational speed data, predicts attitude changes through time-series analysis, and thus determines compensation parameters. In this scenario, the parameter determination process employs filtering techniques to remove noise and ensure the accuracy of the correction. Furthermore, to adapt to different machine tool models, the preset model allows for parameterized configuration.

[0048] For example, in a horizontal machining center, compensation calculations focus on errors caused by gravity, while parameter determination emphasizes real-time monitoring cycles. This diverse implementation demonstrates the flexibility of the solution in the machine tool field, supporting a variety of tasks from roughing to finishing.

[0049] Step S103: If the compensation parameter exceeds the preset safety threshold range, the alternation mode of the cutting direction is adjusted by the general path optimization algorithm to obtain a cutting path with balanced force.

[0050] 1. Obtain threshold comparison data from the compensation parameters, and compare the threshold comparison data with a safety threshold range. If the value exceeds the range, generate a path optimization trigger signal. 2. For the path optimization trigger signal, use the A* algorithm to search for possible cutting direction transformation sequences and obtain alternating mode generation results. 3. Based on the alternating mode generation results, fuse deformation influence data and calculate force balance through weighted summation to determine the tool load distribution vector. 4. Adjust the machining path node positions using the tool load distribution vector to obtain a cutting path with balanced force.

[0051] In one implementation, when the compensation parameter exceeds the preset safety threshold range, the parameter first needs to be threshold-determined.

[0052] Specifically, safety threshold ranges are typically set based on the machine tool's structural characteristics. For example, the upper limit for positional offset is 0.1 mm, and the upper limit for angle correction is 0.5 degrees. These thresholds are pre-configured through the machine tool control system. If compensation parameters, such as positional offset, exceed these ranges, a path optimization process is triggered to avoid vibration or accuracy loss caused by uneven force distribution during machining. This judgment mechanism is integrated into the CNC system to ensure real-time monitoring of the compliance of compensation parameters.

[0053] Specifically, the general path optimization algorithm refers to a computational framework based on path planning used to adjust the cutting path to achieve force balance. The algorithm's principle lies in analyzing the force vector distribution during the cutting process and iteratively calculating and modifying the path parameters.

[0054] It should be noted that this algorithm first extracts the mechanical model of the current cutting path, including the components of the cutting force in different directions, and then applies optimization rules such as minimizing force differences to replan the path. This framework is applicable to various machine tool types and can handle machining tasks with complex geometries. Furthermore, when adjusting the alternation pattern of the cutting direction, the algorithm considers the dynamic changes in the cutting force.

[0055] For example, when machining curved parts on a milling machine, if the compensation parameters exceed a threshold, the algorithm calculates the force distribution difference between climb milling and conventional milling in the current path, and then balances the force by alternately switching the cutting direction. Specifically, the process involves dividing the path into multiple segments, evaluating the torque value for each segment, and if the torque in one direction is too large, inserting a cutting segment in the opposite direction to reduce the load on the machine spindle. This adjustment ensures that the path achieves uniform force distribution while maintaining machining efficiency. Preferably...

[0056] In one possible implementation, a general path optimization algorithm integrates sensor data from the machine tool, such as real-time cutting forces acquired by a force sensor.

[0057] Specifically, the algorithm processes this data through iterative loops, first establishing the force balance equations and then solving for the optimal cutting direction sequence.

[0058] For example, assuming the initial path is a straight cutting path, the algorithm simulates force distributions in different alternating patterns and selects the one with the smallest force difference as the output. This method is widely used in precision milling on CNC machine tools and can adapt to the machining requirements of materials with different hardnesses.

[0059] In one embodiment, for a multi-axis linkage scenario of a lathe, when the compensation parameters exceed the safety threshold, the path optimization algorithm is extended to include the adjustment of the rotary axis.

[0060] Specifically, the algorithm analyzes the alternation of the cutting direction of the cutting tool, such as switching from clockwise to counterclockwise, to balance the distribution of axial and radial forces. The process involves mapping compensation parameters to the path coordinate system and then progressively optimizing the pattern using techniques similar to gradient descent to ensure the resulting cutting path remains stable under varying rotational speeds. This implementation demonstrates the algorithm's flexibility in rotary machining.

[0061] Understandably, after obtaining the cutting path with balanced force, this path is directly input into the CNC program to correct the tool movement.

[0062] For example, when processing complex parts in a machining center, an optimized path reduces tool wear and disperses heat buildup through alternating patterns. Furthermore, to adapt to different machine tool models, the algorithm allows for the configuration of optimization parameters, such as the number of iterations or force balance thresholds. This versatility ensures the solution's universal applicability across machine tool applications, supporting a variety of machining tasks from planar to curved surfaces.

[0063] For example, in a horizontal machining center, if the compensation parameter exceeds a threshold, the algorithm focuses on path adjustment under the influence of gravity.

[0064] Specifically, the process first calculates the force offset caused by gravity, and then compensates for it by optimizing the alternation of cutting directions to ensure path balance. This implementation emphasizes real-time calculation cycles to handle dynamic machining environments.

[0065] Step S104: Extract chip flow simulation data in the narrow deep groove area from the cutting path with balanced force, and determine whether the chip removal space in the area meets the processing requirements.

[0066] Geometric boundary data of the narrow deep groove region is obtained from the cutting path under balanced force. The boundary data is discretized using the finite element method to obtain a mesh model of the deep groove region. Material thermal deformation influence data is integrated into the mesh model of the deep groove region, and particle fluid dynamics simulation tools are used to simulate the movement behavior of chips under thermal deformation conditions to calculate the chip flow trajectory, obtaining the chip flow simulation results. The volume parameter V of the chip removal channel is extracted based on the chip flow simulation results. The volume parameter V is compared with a preset processing requirement threshold T. If V is less than T, the requirement is not met, and T is 0.8. This determines the chip removal space distribution vector S. The chip removal space distribution vector S is used to determine whether the chip removal space in the narrow deep groove region meets the processing requirements, resulting in a space satisfaction conclusion.

[0067] In one implementation, to extract chip flow simulation data within a narrow groove region from a cutting path subjected to balanced forces, it is first necessary to identify the narrow groove region within the path.

[0068] Specifically, narrow, deep groove regions refer to groove-shaped structures in machined parts that are narrow in width but deep in depth. These areas tend to accumulate chips during cutting, leading to difficulties in chip removal. This identification process is based on a geometric model of the path, analyzing path coordinate data to locate areas where the groove width is less than a preset value (e.g., 2 mm) and the depth exceeds 5 mm. This method is integrated into the CNC system to ensure accurate positioning for complex parts. Furthermore, extracting chip flow simulation data involves simulating chip generation and movement trajectories during the cutting process.

[0069] It should be noted that the chip flow simulation data includes the chip velocity distribution, density distribution, and flow direction, which are obtained through a finite element analysis framework. Specifically, the process involves inputting a cutting path subjected to balanced forces into the simulation software, which then calculates the dynamic behavior of the chips based on the cutting force vector and material properties.

[0070] For example, in milling, the simulation framework considers tool speed and feed rate to generate a vector field of chips flowing outward from the cutting point. This extraction ensures that the data reflects actual machining conditions, supporting subsequent judgment.

[0071] Preferably, when determining whether the chip removal space in the area meets the processing requirements, the chip removal space is first defined as the remaining space after subtracting the volume occupied by the chips from the available volume in the slot.

[0072] Specifically, the judgment criterion is based on calculating chip removal efficiency using simulated data. If the remaining space is greater than a preset threshold, such as 30% of the slot volume, it is considered to meet the requirements. This process is achieved by comparing the simulated chip volume with the slot geometry to ensure that the risk of clogging is avoided. This judgment mechanism runs in the path verification module of the CNC machine tool.

[0073] In one possible implementation, for a deep groove milling scenario on a vertical machining center, after extracting data from the path, the simulated chip flow shows a high-density area concentrated at the bottom of the groove. If the chip removal space is determined to be insufficient, the system will prompt adjustments to the path parameters. This scenario demonstrates the application of this technology in precision parts machining.

[0074] Understandably, in another embodiment, for narrow slot machining on horizontal machine tools, the coolant flow model is integrated when extracting simulation data. Specifically, the coolant velocity vector is combined with the chip flow to calculate the composite flow path, and then it is determined whether the chip removal space allows for smooth chip removal.

[0075] For example, if the simulation shows a chip retention rate exceeding 20%, the requirement is not met. This extension enhances the adaptability of the solution to different machine tool configurations.

[0076] Specifically, in the chip extraction process, the chip flow simulation framework works by establishing a fluid dynamics model to analyze the force balance of chip particles, including gravity, cutting force, and friction. A data field is generated by iteratively calculating the flow trajectory. This framework is applicable to various material processing applications, ensuring the accuracy of the extracted data. Furthermore, the process for determining the chip removal space includes a quantitative evaluation step: first, the chip volume is integrated from the simulation data, and then compared with the slot space. If the requirements are met, the path is directly used in production; otherwise, the process returns to the optimization step. This logic ensures processing continuity.

[0077] In one embodiment, when extracting data for the deep groove region of a multi-axis linkage machine tool, the influence of axial tilt on the flow is taken into account.

[0078] Specifically, the tilt angle is simulated and adjusted to calculate the tendency of the chips to flow downwards, and then the adequacy of the space is determined.

[0079] For example, in the machining of aerospace parts, this method reduces chip accumulation and improves surface quality.

[0080] It should be noted that this technical solution achieves versatility across various scenarios, such as maintaining objective standards for data extraction and judgment during adjustments to different groove depths. This versatility supports broad protection under the claims.

[0081] Step S105: If the chip removal space is insufficient, then based on the chip flow simulation data, the feed rate and coolant flow parameters are adjusted using a fluid dynamics simulation method to obtain an optimized chip removal strategy.

[0082] Chip trajectory information is obtained by extracting chip flow simulation data within a narrow, deep groove region using a cutting path optimized based on mechanical equilibrium. This chip trajectory information is then fused with thermal deformation impact data from finite element thermal analysis. Fluid dynamics simulation is used to calculate the chip packing density by integrating the fluid mesh elements, yielding a packing density parameter. The volume data of the chip removal channel is extracted based on this packing density parameter, and the distribution vector is determined by comparing the calculated volume of the statistical chip trajectory distribution with a preset threshold. If the distribution vector exceeds the threshold range of 0.8, an iterative simulation method is used to adjust the feed rate and coolant flow parameters based on the chip flow simulation data, resulting in an optimized chip removal strategy. This optimized chip removal strategy is then used to determine whether the chip removal space in the narrow, deep groove region meets the machining requirements, leading to a conclusion that the space is sufficient.

[0083] In one embodiment, if the chip removal space is insufficient, the feed rate and coolant flow parameters are adjusted using a fluid dynamics simulation method based on the chip flow simulation data to obtain an optimized chip removal strategy.

[0084] Specifically, the process first extracts key indicators from the simulation data, such as chip velocity distribution and density distribution, which reflect the flow state within the narrow, deep groove region. Using fluid dynamics simulation methods, a multiphase flow model is established, treating the chips as the particulate phase and the coolant as the continuous phase, to simulate the interaction between the two.

[0085] For example, in CNC milling, the model takes the initial feed rate and coolant flow rate as input and calculates the chip residence time and discharge path within the groove. This method is integrated into the machining path optimization module, ensuring adjustments are based on actual data. Furthermore, the fluid dynamics simulation method applies the Navier-Stokes equations to describe fluid motion while coupling particle dynamics to handle chip behavior. The specific process includes initializing the simulation mesh, importing the geometric model of the narrow, deep groove into the software, and then setting boundary conditions such as tool speed and material properties. The simulation iteratively calculates the flow field, analyzing the balance of chip particles under gravity, viscous forces, and coolant scouring forces. Through multiple iterations, velocity and pressure field data are generated, which are used to evaluate chip removal efficiency.

[0086] For example, when machining deep grooves in precision molds, if simulations show excessively high chip density leading to blockages, the system records these flow vectors as a basis for adjustments. This simulation framework ensures data accuracy and supports subsequent parameter optimization. The business objective of this process is to reduce machining interruptions and improve groove cleanliness, thereby maintaining continuous cutting.

[0087] Preferably, the specific steps for adjusting the feed rate are quantified based on simulation data. First, the average velocity value is extracted from the chip flow simulation. If this value is lower than a threshold such as 0.5 meters per second, the feed rate is reduced to decrease the amount of chips generated.

[0088] Specifically, the adjustment algorithm uses a binary search method to gradually reduce the feed rate while resimulating the flow state until the remaining volume of the chip removal space exceeds 40% of the slot volume.

[0089] For example, when machining narrow slots on a vertical CNC machine tool, the initial feed rate is 200 mm / min. After simulation shows chip retention, the rate is adjusted to 150 mm / min, and the flow path is recalculated. This adjustment process is implemented through a software module, with no more than 10 iterations to control the calculation time. The principle behind this step is to balance cutting efficiency and chip removal capacity, avoiding chip accumulation caused by excessively fast feed.

[0090] Understandably, adjusting coolant flow parameters also relies on fluid dynamics simulations. Specifically, coolant velocity and injection angle are input as variables into the model to simulate their impact on chip scouring. By analyzing the complex flow field, if the chip removal rate is lower than expected, the coolant flow rate is increased or the injection direction is changed.

[0091] For example, when machining deep groove parts on a horizontal machine tool, the initial coolant flow rate is 10 liters per minute. After simulation shows that chips accumulate at the bottom, the flow rate is adjusted to 15 liters per minute, and the spray angle is changed from vertical to a 30-degree angle. The new flow trajectory is then calculated. This method takes into account fluid viscosity and surface tension, ensuring that parameter changes directly improve drainage. The operational process for this adjustment includes verifying the match between the simulation results and actual machining, and calibrating the model through sensor feedback.

[0092] In one possible implementation, the optimized chip removal strategy involves integrating the adjusted parameters to form a complete solution.

[0093] Specifically, the strategy includes recommended feed rate ranges, coolant configurations, and path modification suggestions, which are generated by aggregating simulation data.

[0094] For example, in a deep groove machining scenario on a multi-axis machine tool, if space is limited, the simulated and adjusted strategy specifies a feed rate of 180 mm / min and a coolant flow rate of 12 liters / min, and recommends adding auxiliary air blowing to enhance drainage. This strategy outputs a set of instructions executable by the CNC system, supporting automated applications. The principle behind this process is to optimize multi-variable combinations to ensure the strategy covers different groove widths and depths.

[0095] It should be noted that the application of fluid dynamics simulation methods in the adjustment process has been extended to the processing of various materials.

[0096] For example, in the milling of narrow slots in aluminum alloy parts, the simulation considers the material's low density characteristics and adjusts coolant parameters to increase scouring force; while in steel machining, the emphasis is on reducing the feed rate to control chip size. The specific process involves inputting material property libraries into the simulation to calculate flow behavior under specific conditions. This diversity ensures the versatility of the technical solution within the same field. Furthermore, the business logic of this optimization process includes a feedback loop: if the adjusted simulation still shows insufficient space, the parameters are iteratively adjusted until the requirements are met.

[0097] For example, in the machining of precision instrument parts, after insufficient initial simulation, a strategy was obtained through three iterations of adjustment, which improved chip removal efficiency. This loop ensures high efficiency through threshold judgment control.

[0098] Specifically, in another embodiment, for machining deep grooves, the simulation method integrates the effects of the temperature field, taking into account the interference of thermal expansion on flow when adjusting parameters. By calculating the temperature gradient, the coolant is optimized to cool the chips and reduce adhesion. This extension enhances the adaptability of the strategy.

[0099] For example, in a slotting scenario on a CNC lathe, if space is insufficient, adjusting the feed to 120 mm / min and the coolant to 18 liters / min based on simulation data reduces the risk of clogging. This implementation demonstrates the application of the method in rotary machining.

[0100] Preferably, the entire process relies on an integrated software platform, with simulation data imported into the adjustment module in real time, and the output strategy directly applied to machine tool control. This integration ensures continuity from judgment to optimization.

[0101] Step S106: Based on the optimized chip removal strategy, the tool path is updated in conjunction with the five-axis linkage control system to determine the complete machining sequence.

[0102] Tool posture calibration data is obtained through the optimized chip removal strategy. A coordinate transformation method is used to first calculate the rotation matrix and then apply translation vectors to adjust the five-axis linkage control parameters, resulting in posture calibration results. Based on the posture calibration results and chip flow simulation information, a path planning tool is used with the A* algorithm to input the current trajectory data to calculate the shortest path to update the tool trajectory and determine the trajectory update vector. Thermal deformation impact data is extracted based on the trajectory update vector. An iterative simulation method is used to cyclically calculate the deviation from the initial parameters until convergence and comparison with the distribution vector to determine the sequence completeness. If the sequence completeness meets a preset threshold, the feed rate is adjusted in conjunction with coolant flow parameters to obtain a complete machining sequence.

[0103] In one implementation, the optimized chip removal strategy is combined with a five-axis CNC control system to update the toolpath and determine the complete machining sequence. This process first uses the optimized chip removal strategy as input data, including adjusted feed rates and coolant parameters, and imports it into the five-axis CNC control system. A five-axis CNC control system is a CNC system capable of simultaneously controlling five axial movements, such as X, Y, and Z linear axes and A and B rotary axes, used for machining complex curved surfaces.

[0104] Specifically, the system receives strategy data through a software interface and then calculates the tool's motion path in space based on this data.

[0105] For example, when machining parts with narrow, deep grooves, the system analyzes the parameters specified in the strategy to generate initial trajectory data, ensuring the trajectory avoids chip accumulation areas. The principle behind this integration process is to map strategy parameters to axial control commands, achieving initial trajectory planning. Further, the specific steps for updating the tool trajectory involve the iterative application of a trajectory generation algorithm. This algorithm is a computational method based on geometric models and kinematic constraints. First, a three-dimensional model of the workpiece is established, and then the flow parameters from the chip removal strategy are incorporated into the model. The specific process includes extracting key indicators from the strategy, such as chip removal paths and velocity distributions, which are used to modify the trajectory curve.

[0106] For example, when machining precision parts on a multi-axis CNC machine tool, if the strategy indicates that the feed rate needs to be reduced to improve chip removal, the system will adjust the radius of curvature of the trajectory and calculate the coordinates of the new path. Through multiple iterations, the algorithm balances the trajectory length and chip removal efficiency, ensuring that the updated trajectory covers the entire machining area. The algorithm's operational process relies on a set of kinematic equations, handles inter-axis coupling relationships, and avoids interference.

[0107] Preferably, the function of the five-axis linkage control system is to coordinate the movement of multiple axes in real time.

[0108] Specifically, the system includes a main controller and a servo drive module. The main controller receives updated trajectory data and converts it into pulse signals to drive the servo motor.

[0109] For example, when machining deep groove molds on a vertical five-axis machine tool, the system adjusts the rotation angles of the A and B axes according to a strategy to generate a tilted trajectory to enhance the coolant flushing effect. This coordination process is achieved through a feedback loop, where sensors monitor axis displacement and calibrate trajectory deviations.

[0110] It should be noted that the principle of five-axis linkage is based on inverse kinematics solution, which converts the end tool position into joint angles to ensure accurate trajectory.

[0111] In one possible implementation, determining the complete machining sequence involves serializing the update trajectory with machining steps. The machining sequence refers to a series of ordered cutting operations, including roughing, finishing, and clean-up steps. Specifically, this involves extracting segmented paths from the update trajectory and then arranging these paths in chronological order to form a sequence of instructions.

[0112] For example, when machining complex groove structures on a horizontal five-axis machine tool, the sequence first executes a roughing stage, using a strategy-optimized low-speed trajectory to remove large pieces of material, and then switches to a finishing stage to complete the surface with a high-precision trajectory. This serialization process is implemented through programming software, generating a G-code instruction set that supports machine tool execution.

[0113] Specifically, the detailed mechanism for updating the tool path in conjunction with the five-axis linkage control system includes the integration of a path optimization module. This module is a software component that processes strategy data and geometric constraints. It first imports the workpiece CAD model and then applies an interpolation algorithm to generate a smooth curve.

[0114] Specifically, this module analyzes the coolant parameters in the strategy and adjusts the feed direction of the trajectory to match the flow path.

[0115] For example, when machining deep groove parts from aluminum alloys, the module calculates the acceleration curve of the trajectory to ensure deceleration in high-density chip zones. In this way, the module outputs optimized trajectory data for use by the five-axis system. The module's principle lies in minimizing path error, employing spline curve fitting to handle complex geometries, and further extending to machining scenarios for different materials.

[0116] Understandably, this process extends to a variety of machine tool configurations.

[0117] For example, when machining steel grooved parts on a bridge-type five-axis machine tool, the system updates the toolpath based on a strategy, with the sequence including auxiliary steps such as intermittent pauses to remove chips. Specifically, the strategy parameters are embedded in the sequence logic to generate a machining program containing conditional branches. This extension ensures the applicability of the technical solution within the same domain. Furthermore, the business process for updating the toolpath emphasizes data consistency.

[0118] Specifically, the flow simulation results extracted from the strategy are interpolated with the motion model of the five-axis system to calculate the trajectory deviation. If the deviation exceeds a threshold, the system iteratively adjusts the axis parameters.

[0119] For example, when machining narrow grooves in titanium alloys, the trajectory update takes material hardness into account, generating a multi-layered progressive sequence. The business objective of this process is to maintain machining continuity and reduce interruptions through trajectory adjustments.

[0120] In one embodiment, the generation of a complete machining sequence includes a verification step. Specifically, the sequence is input into a simulation environment, executed in a simulation to check the chip removal effect, and then the trajectory parameters are fine-tuned.

[0121] For example, when machining large mold slots on a gantry five-axis machine tool, the sequence covers the complete path from inlet to outlet, integrating strategies to optimize discharge. This validation enhances the reliability of the sequence.

[0122] Preferably, the application of this technical solution in high-precision machining involves adaptive parameter adjustment.

[0123] Specifically, the five-axis system monitors the machining status in real time and dynamically updates the trajectory according to the strategy to form an adaptive sequence.

[0124] For example, when machining composite grooved parts, the sequence automatically switches trajectory modes to adapt to changes in chip size. This adaptive mechanism, based on sensor data, ensures sequence integrity.

[0125] It should be noted that the entire process relies on an integration platform that connects the strategy, five-axis control, and sequence generation modules.

[0126] Specifically, the platform transmits data through API interfaces, supporting an end-to-end process from strategy optimization to sequence output.

[0127] For example, when applied in CNC machining centers, the machining sequence generation platform generates sequences suitable for different groove depths, demonstrating the versatility of the solution.

[0128] Step S107: Perform multi-axis milling operation through the processing sequence to obtain a fin structure component with stable thickness tolerance.

[0129] Based on a predefined chip removal strategy, chip thickness monitoring information is obtained through an optimized algorithm. Using a coordinate transformation method, the rotation axis angle θ is first determined, and the rotation matrix R(θ) = [cosθ - sinθ; sinθcosθ] is calculated, where θ is the angle. Combined with the translation vector T = [TxTyTz], the five-axis linkage control parameters are adjusted axis-by-axis to obtain the attitude calibration result. The attitude calibration result is then integrated with the chip thickness monitoring information. Using the A* path planning tool, the updated path is calculated by inputting the current trajectory data and obstacle constraints, determining the trajectory update vector. Based on the trajectory update vector, thermal deformation impact data is extracted from the machine tool sensors. An iterative method is used to iteratively calculate the distribution vector starting from the initial deviation D0 until the deviation converges, determining the sequence integrity. If the sequence integrity meets a preset threshold of 0.95, the feed rate is adjusted segment by segment using coolant flow parameters obtained from the cooling system to obtain the machining sequence. Multi-axis milling operations are performed using the machining sequence to obtain a finned structure component with stable thickness tolerance.

[0130] In one implementation, the process of performing multi-axis engraving and milling operations through the machining sequence first involves importing sequence data into the machine tool control system. The machining sequence refers to a series of ordered cutting instructions, including trajectory paths and parameter settings, used to guide multi-axis motion.

[0131] Specifically, this sequence is generated based on a previously optimized chip removal strategy, ensuring efficient chip removal during the engraving and milling process. During execution, the system reads the initial instructions from the sequence and initiates multi-axis linkage. For example, when machining fin structures on a vertical machine tool, it controls the linear movement of the X, Y, and Z axes combined with the rotation of the A-axis to achieve precise engraving of the workpiece surface. The principle behind this execution process is to convert the sequence instructions into servo signals, driving the tool to move along a specified path and avoiding machining interruptions. Furthermore, the specific steps of multi-axis engraving and milling operations involve the interactive coordination between the tool and the workpiece. Multi-axis engraving and milling refers to a milling method that utilizes simultaneous movement along multiple axes, suitable for machining complex geometries, such as the thin-walled features of fin structures.

[0132] When machining aluminum alloy fin components, the sequential guide tool starts from the roughing stage and gradually removes material to form fins with uniform spacing.

[0133] This operation monitors the tool position through a sensor monitoring system, i.e., a feedback mechanism. If a deviation is detected, the system adjusts the axis speed in real time according to calibration parameters in the sequence, such as a preset threshold of 0.1 mm. This coordination reduces vibration by lowering the axis speed, thereby ensuring the continuity of the milling process and maintaining uniform fin thickness.

[0134] Preferably, obtaining a finned structure component with stable thickness tolerance depends on the precision control parameters in the sequence. Thickness tolerance refers to the dimensional deviation range of the finned portion of the component; stable tolerance means that the deviation is controlled within a specified threshold, such as ±0.05mm. The specific process includes performing a finishing step in the later stages of milling, with the sequence specifying a low-speed feed to uniformly remove excess material.

[0135] For example, when machining steel fins on a horizontal machine tool, the system applies a hierarchical path in the sequence, progressively milling from the outermost layer to the innermost layer, while maintaining material stability in conjunction with coolant parameters. The business objective of this process is to achieve geometric consistency of the components and improve the overall structural strength through tolerance stability.

[0136] In one possible implementation, the technical solution is extended to engraving and milling scenarios with different machine tool configurations.

[0137] Specifically, when machining titanium alloy fin structures on a bridge-type machine tool, the sequence execution includes intermittent milling steps to remove accumulated chips and ensure uniform thickness. Furthermore, the system integrates sensor data to verify the tolerance values ​​after each milling layer; if they exceed the range, correction instructions in the sequence are iteratively executed. This extended demonstration of the solution's applicability in precision machining supports stable output across various materials.

[0138] Understandably, the entire execution process emphasizes data consistency and verification.

[0139] Specifically, from sequence import to milling completion, a closed-loop control is formed, with sensors collecting thickness data in real time and comparing it with preset values ​​for the sequence.

[0140] For example, when machining composite material fins on a gantry milling machine, the operation covers the entire path from entry milling to exit finishing, and the sequence parameters are adjusted to optimize the tool tilt angle to improve tolerances. This verification enhances the reliability of the component, ensuring that the final fin structure meets design requirements.

[0141] In one embodiment, obtaining the finned structure component involves a post-processing step. Specifically, the milled component is placed in a measurement environment, and an optical scanner is used to check thickness tolerances and confirm the effectiveness of the sequence execution.

[0142] Preferably, in high-precision machining scenarios, the sequence can adaptively adjust the milling depth and dynamically modify the path based on material feedback. This mechanism, based on the machine tool's integrated platform, enables an end-to-end process from sequence to part, supporting multiple applications within the same field.

[0143] Step S108: Perform a batch production comparison analysis based on the fin structure components to determine whether the consistency index meets the preset standard.

[0144] Batch production sequence data is obtained through the finned structural components. Dimensional measurements, including length, width, and thickness, are extracted from the production logs to determine the structural component inspection results. Based on the structural component inspection results, production process monitoring information is integrated. Error distribution is collected from real-time sensors, and a deviation calibration adjustment vector, i.e., a vector V composed of the mean and standard deviation of the error (V=[μ, σ], where μ is the mean and σ is the standard deviation), is obtained through statistical calculation. Based on the deviation calibration adjustment vector and thermal deformation compensation data, deformation offset is integrated from temperature simulation records. A trajectory vector update path, i.e., the adjusted processing path coordinate sequence P (P=[x1,y1,z1;x2,y2,z2;......]), is obtained through linear interpolation. Consistency index values ​​are extracted using the trajectory vector update path. The average tolerance, i.e., the arithmetic mean of all deviations, is calculated from the path deviation statistics. If the average tolerance is less than 0.05mm, the consistency index value is determined to have reached a preset standard threshold, and the batch production comparison analysis is completed.

[0145] In one embodiment, the process of performing batch production comparative analysis based on the fin structure component first involves collecting processing data from multiple batches.

[0146] Specifically, this process involves measuring the thickness tolerance of each batch of fin components after multi-axis milling operations, for example, by collecting data points using precision calipers or laser scanners. Batch production refers to a mode in which multiple identical parts are produced by continuously executing a machining sequence.

[0147] It should be noted that the consistency index includes the average and standard deviation of thickness deviation, which are calculated using statistical methods.

[0148] For example, during the machining of aluminum alloy fins, data from 50 parts in each of 10 batches are collected to form a dataset for subsequent comparison. This collection ensures that the data covers production variables, such as the impact of machine tool stability. Furthermore, the comparative analysis step performs a difference assessment based on the collected data.

[0149] In one embodiment, the thickness tolerance data of each batch is compared with a reference standard to calculate the percentage deviation. Specifically, the average thickness of each batch is subtracted from a preset value, and then divided by the preset value to obtain the relative error.

[0150] It should be noted that the consistency index refers to the overall coefficient of variation of deviations across all batches. The calculation first calculates the mean of the standard deviations of each batch, and then compares it with the overall mean.

[0151] For example, in the mass production of steel fins, a coefficient of variation of less than 0.02 is considered consistent. This analysis reveals the stability of the production process and avoids the influence of single-batch deviations on judgment.

[0152] Preferably, determining whether the consistency index meets the preset standard depends on the threshold setting.

[0153] In one possible implementation, a preset standard, such as a thickness tolerance not exceeding ±0.03mm, is used to perform verification through a logic judgment module.

[0154] Specifically, if the calculated coefficient of variation is lower than the threshold of 0.01, the output will meet the standard.

[0155] For example, in the production of titanium alloy fins, the system integrates historical data for iterative judgment to ensure batch consistency. The technical goal of this judgment process is to improve manufacturing reliability and reduce scrap rate by stabilizing indicators.

[0156] The comparative analysis was extended to scenarios with different batch sizes.

[0157] Specifically, for small-batch production such as 20 parts, the analysis focuses on randomly sampled data, while for large-batch production such as 500 parts, a full inspection mode is adopted. Furthermore, the system records abnormal batches and adjusts processing sequence parameters based on deviations (i.e., the difference between actual measured values ​​and target values). This extended demonstration of the applicability of this approach in precision component manufacturing supports continuous optimization.

[0158] In one embodiment, batch comparison of finned structural components involves a post-verification step. Specifically, the analysis results are input into a reporting system to generate visual charts, such as bar charts, displaying consistency indicators for each batch.

[0159] Preferably, if the indicator fails to meet the standard, an alarm mechanism is triggered to re-execute the milling sequence. This verification enhances the closed-loop control of the production process, ensuring that the final part meets design requirements.

[0160] Step S109: If the consistency index does not meet the preset standard, the parameters of the error compensation model are adjusted by an iterative method to obtain an improved processing scheme.

[0161] Using the aforementioned batch sequence data, deformation measurements are extracted from the material deformation monitoring system to obtain the component deformation results. The component deformation results are then integrated with processing path optimization information derived from a path planning algorithm. This information is obtained by collecting distributed errors (i.e., deviations between path points) from path sensors to generate a path calibration vector. Based on this path calibration vector and temperature compensation data (from environmental sensors), offset deformation (i.e., thermally induced displacement changes) is integrated from simulation records to determine an updated trajectory path. Deviation indices are extracted using the updated trajectory path, and the average tolerance (i.e., the average deviation value) is calculated from statistical deviations. If the deviation index does not reach a preset threshold of 0.05, the compensation model parameters are adjusted iteratively. This model is an error compensation model that takes distributed error as input and outputs a calibration vector. The parameters include weight w and bias b, and the model formula is v = w * e + b, where v is the calibration vector and e is the distributed error. This determines the improvement of the processing scheme. Through the processing scheme improvement, sequence adjustments are integrated from the optimized path to achieve consistency comparison.

[0162] In one implementation, if the consistency index fails to meet the preset standard, the parameters of the error compensation model are adjusted using an iterative method.

[0163] Specifically, the process first identifies the sources of deviation, such as analyzing errors caused by machine tool vibration or material thermal expansion in the machining of finned structural components. The error compensation model is a mathematical framework based on historical machining data used to predict and correct machining path deviations. Its parameters include compensation coefficients and offsets, which are updated by inputting the current consistency index value.

[0164] It should be noted that the iterative method employs a cyclic feedback mechanism, whereby each iteration calculates the difference between the model output and the actual measurement data, and fine-tunes the parameters accordingly. Furthermore, the adjustment process involves multiple iterations.

[0165] In one possible implementation, starting with initial parameters, the system collects thickness measurement data from non-compliant batches and feeds it into the model as input.

[0166] For example, when machining copper alloy fins, if the coefficient of variation exceeds a threshold, the first round of calculation of the compensation coefficient is performed to correct the milling path by reducing the offset. This method ensures that the model converges gradually, avoiding instability caused by a large one-time adjustment.

[0167] Understandably, the number of iterations is set according to the degree of deviation, usually controlled within 5 to 10 times, in order to balance computational efficiency and accuracy.

[0168] Preferably, the improved processing scheme depends on the adjusted model output. Specifically, the optimized parameters are applied to the processing sequence to form new operation instructions.

[0169] For example, in the mass production of stainless steel fins, after iterative adjustments, the model generates corrected toolpath data, reducing thickness tolerance fluctuations. This approach records intermediate results from each iteration to support subsequent verification.

[0170] In one embodiment, the error compensation model comprises an input layer, a processing layer, and an output layer. The input layer receives consistency index data, such as the standard deviation; the processing layer performs iterative calculations, adjusting parameters based on the gradient direction without involving specific numerical formulas; and the output layer generates specific parameters for the improvement scheme, such as velocity adjustment values.

[0171] It should be noted that this modular design facilitates application in the processing of different fin materials, such as aluminum alloys or titanium alloys, ensuring versatility. Furthermore, a threshold checking mechanism is introduced during iterative adjustments.

[0172] Specifically, the model performance is evaluated after each iteration, and the process is terminated if the deviation is reduced to within a preset range.

[0173] For example, in small-batch fin production, such as 30 parts, adjustments focus on key parameters, while in large-batch production, such as 200 parts, the focus expands to full-parameter optimization. This check enhances the robustness of the process. In one implementation, the improved machining scheme is integrated into the control system. Specifically, the adjusted parameters are uploaded to the machine tool controller, generating a new G-code sequence.

[0174] For example, in precision fin milling operations, the scheme adjusts feed rate and depth parameters to suit continuous production modes. This integration enables a seamless transition and supports real-time applications.

[0175] Understandably, the entire adjustment process extends to multivariate scenarios.

[0176] Specifically, in fin processing that considers the effects of temperature, the iterative method simultaneously optimizes thermal compensation parameters. By analyzing environmental data, the model parameters are progressively adapted to ensure the solution covers production variables. This extended demonstration method demonstrates its applicability in precision manufacturing.

[0177] In one embodiment, subsequent verification steps confirm the effectiveness of the improvement scheme.

[0178] Specifically, a small-scale test batch is conducted to measure the new consistency indicators, and if the indicators are met, the system is officially adopted.

[0179] Preferably, if the condition is still not met, the iteration is restarted. This verification forms a closed-loop control, improving the reliability of the solution.

[0180] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. An adaptive compensation processing technology for high-density heat sinks, characterized in that, include: The overall dynamic error distribution of the machine tool is determined by collecting data on the position deviation of the machine tool's rotating axis and the thermal expansion deformation of the spindle using sensors. Based on the dynamic error distribution, an error compensation model is used to calculate the tool attitude adjustment value and determine the compensation parameters for real-time correction. If the compensation parameters exceed a preset safety threshold range, the cutting direction alternation mode is adjusted using a path optimization algorithm to obtain a balanced force cutting path. Chip flow simulation data for a specific area is extracted from the balanced force cutting path to determine whether the chip removal space meets the machining requirements. If the chip removal space is insufficient, the feed rate and coolant flow parameters are adjusted based on the chip flow simulation data to obtain an optimized chip removal strategy. For the optimized chip removal strategy, the tool trajectory is updated in conjunction with a multi-axis linkage control system to determine a complete machining sequence. Multi-axis engraving and milling operations are performed through the machining sequence to obtain the target structural component.

2. The adaptive compensation processing technology for a high-density heat sink as described in claim 1, characterized in that, The step of collecting machine tool rotary axis position deviation data and spindle thermal expansion deformation data through sensors to determine the overall dynamic error distribution of the machine tool includes: acquiring position deviation data of the machine tool rotary axis under various swing angle conditions using precision sensors, and simultaneously acquiring thermal expansion deformation data of the spindle during high-speed operation; determining a deviation fusion matrix by mapping and fusing the position deviation data and the thermal expansion deformation data through coordinate system mapping; calculating the associated deformation patterns between components using correlation coefficients between matrix elements for the deviation fusion matrix; and obtaining the overall dynamic error distribution of the machine tool by projecting the associated deformation patterns onto the machine tool coordinate space.

3. The adaptive compensation processing technology for a high-density heat sink as described in claim 1, characterized in that, The step of calculating tool posture adjustment values ​​using an error compensation model based on the dynamic error distribution and determining compensation parameters for real-time correction includes: obtaining thermal elongation deformation data from the dynamic error distribution and determining a deviation fusion matrix by mapping and fusing position deviation data with thermal elongation deformation data; calculating the inter-component related deformation projection for the deviation fusion matrix to obtain the related deformation pattern; calculating the tool posture adjustment value using a preset error compensation model based on the related deformation pattern to obtain the posture correction calculation result; and determining the compensation parameters for real-time correction by fusing the posture correction calculation result with preset vibration influence compensation parameters.

4. The adaptive compensation processing technology for a high-density heat sink as described in any one of claims 1-3, characterized in that, If the compensation parameter exceeds a preset safety threshold range, the cutting direction alternation mode is adjusted using a path optimization algorithm to obtain a cutting path with balanced force. This includes: obtaining threshold comparison data from the compensation parameter and comparing it numerically with a preset safety threshold range; if the value exceeds the range, a path optimization trigger signal is generated; for the path optimization trigger signal, a path search algorithm is used to obtain a cutting direction transformation sequence to obtain an alternation mode generation result; based on the alternation mode generation result, deformation influence data is fused to calculate force balance and determine the tool load distribution vector; the machining path node positions are adjusted using the tool load distribution vector to obtain the cutting path with balanced force.

5. The adaptive compensation processing technology for a high-density heat sink as described in any one of claims 1-3, characterized in that, The step of extracting chip flow simulation data from a specific region of the balanced force cutting path and determining whether the chip removal space meets the processing requirements includes: obtaining geometric boundary data of a specific region from the balanced force cutting path; discretizing the boundary data using a mesh generation method to obtain a region mesh model; integrating material thermal deformation influence data into the region mesh model and using fluid dynamics simulation to calculate the chip flow trajectory to obtain chip flow simulation results; extracting chip removal channel volume parameters based on the chip flow simulation results and comparing them numerically with a preset processing requirement threshold to determine the chip removal space distribution vector; and determining whether the chip removal space of the specific region meets the processing requirements based on the chip removal space distribution vector.

6. The adaptive compensation processing technology for a high-density heat sink as described in any one of claims 1-3, characterized in that, If the chip removal space is insufficient, the feed rate and coolant flow parameters are adjusted based on the chip flow simulation data to obtain an optimized chip removal strategy. This includes: extracting chip flow simulation data from a specific region of the balanced force cutting path to obtain chip trajectory information; fusing thermal deformation influence data with the chip trajectory information and using fluid dynamics simulation to calculate the chip packing density to obtain a packing density parameter; extracting chip removal channel volume data for the packing density parameter and comparing it with a preset threshold to determine a distribution vector; if the distribution vector exceeds the threshold range, adjusting the feed rate and coolant flow parameters using an iterative simulation method based on the chip flow simulation data to obtain the optimized chip removal strategy.

7. The adaptive compensation processing technology for a high-density heat sink as described in any one of claims 1-3, characterized in that, The optimized chip removal strategy, combined with the multi-axis linkage control system to update the tool path and determine the complete machining sequence, includes: obtaining tool posture calibration data through the optimized chip removal strategy, adjusting the multi-axis linkage control parameters using a coordinate transformation method to obtain posture calibration results; integrating chip flow simulation information with the posture calibration results, calculating the shortest path to update the tool path using a path planning tool, and determining the trajectory update vector; extracting thermal deformation influence data based on the trajectory update vector, calculating the deviation distribution vector using an iterative simulation method, and determining the sequence completeness; if the sequence completeness meets a preset threshold, integrating coolant flow parameters to adjust the feed rate to obtain the complete machining sequence.

8. The adaptive compensation processing technology for a high-density heat sink as described in any one of claims 1-3, characterized in that, The step of performing multi-axis milling operations through the machining sequence to obtain the target structural component includes: acquiring chip thickness monitoring information through the machining sequence, adjusting multi-axis linkage control parameters using a coordinate transformation method to obtain attitude calibration results; fusing the chip thickness monitoring information with the attitude calibration results, calculating and updating the path using a path planning tool, and determining the trajectory update vector; extracting thermal deformation influence data based on the trajectory update vector, calculating the distribution vector using an iterative method until the deviation converges, and determining the sequence integrity; if the sequence integrity meets a preset threshold, integrating coolant flow parameters to adjust the feed rate, and performing multi-axis milling operations through the machining sequence to obtain the target structural component.

Images