A workpiece trimming machine cutting method and system

Abstract

The application discloses a kind of workpiece trimming machine cutting methods and systems, it is related to workpiece processing technical field, method includes: obtaining workpiece cutting path data;According to the motion characteristic and vibration characteristic of trimming machine, the workpiece cutting path data is analyzed, and path vibration region is identified, and the path vibration region is used to indicate the area that exists driving motor speed fluctuation and tool local vibration;According to the processing tolerance of workpiece, the path vibration region is reconstructed, and the replacement path of kinematic characteristic change controlled is generated;According to the replacement path, the path in original path corresponding to path vibration region is replaced, and target path is obtained;According to the target path, trimming machine is controlled to workpiece cutting.The application can eliminate vibration by path reconstruction, to realize workpiece cutting, improve accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of workpiece processing technology, and in particular to a workpiece trimming machine cutting method and system. Background Technology

[0002] In modern industrial production, rotary workpiece trimming machines are key equipment for processing the edges of circular or near-circular workpieces. These machines are typically used for mass production of conventional workpieces with smooth edge contours. However, as market demands for product design diversification and functional integration increase, processing tasks are gradually shifting towards new types of workpieces with multiple sharp local corners and complex curvature changes on their edges. Existing control systems rely on the coordinate data sequence of the cutting path for cutting, resulting in abnormally dense coordinate data in areas with sharp corners. This leads to a sharp increase in the number of instructions that need to be processed per unit time, easily causing cutting abnormalities, affecting workpiece quality, and resulting in low control accuracy and reliability.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose a workpiece trimming machine cutting method and system that can eliminate vibration through path reconstruction to achieve workpiece cutting, thereby improving accuracy and reliability.

[0005] On one hand, embodiments of the present invention provide a workpiece trimming machine cutting method, including the following steps:

[0006] Obtain workpiece cutting path data;

[0007] Based on the motion and vibration characteristics of the trimming machine, the workpiece cutting path data is analyzed to identify the path vibration area, which is used to represent the area where there are speed fluctuations of the drive motor and local vibrations of the tool.

[0008] Based on the workpiece's machining tolerances, the path vibration region is reconstructed to generate a replacement path with controlled kinematic characteristics.

[0009] Based on the replacement path, the path corresponding to the vibration area in the original path is replaced to obtain the target path;

[0010] According to the target path, the trimming machine is controlled to cut the workpiece.

[0011] In some embodiments, the step of reconstructing the path vibration region based on the workpiece's machining tolerances to generate a replacement path with controlled kinematic changes includes:

[0012] When processing the first batch of workpieces, when the cutting tool is in the vibration area of ​​the path, the first current consumption characteristics of the drive motor of the trimming machine are collected as an initial health benchmark.

[0013] When processing non-first batch workpieces, when the cutting tool is in the vibration area of ​​the path, the second current consumption characteristics of the drive motor of the trimming machine are collected as real-time current characteristics.

[0014] Based on the real-time current characteristics and the initial health benchmark, the tool wear state is identified;

[0015] The cutting feed rate is adjusted according to the tool wear condition.

[0016] The replacement path is generated based on the adjusted cutting feed rate.

[0017] In some embodiments, identifying the tool wear state based on the real-time current characteristics and the initial health benchmark includes:

[0018] When machining the first batch of workpieces, the micro-vibration data of the cutting tool is collected when the cutting tool is outside the vibration area of ​​the path.

[0019] Damping characteristic features are extracted from the micro-vibration data to obtain a damping measurement value;

[0020] The damping measurement value is compared with the preset damping measurement reference value to determine the current deviation threshold;

[0021] Based on the current deviation threshold, the real-time current characteristics are compared with the initial health benchmark to identify the tool wear state.

[0022] In some embodiments, adjusting the cutting feed rate according to the tool wear state includes:

[0023] If the tool wear condition is that wear exists, then the characteristic deviation is calculated based on the real-time current characteristics and the initial health benchmark;

[0024] The adjustment range is determined based on the deviation of the described features;

[0025] The current deviation is calculated based on the real-time current characteristics and the preset current characteristic range;

[0026] The cutting feed rate is adjusted according to the adjustment range and the current deviation to stabilize the real-time current characteristic within the preset current characteristic range.

[0027] In some embodiments, after adjusting the cutting feed rate according to the adjustment magnitude and the current deviation, the method further includes:

[0028] Collect ambient temperature data of the cutting zone and chip accumulation status of the tool load area;

[0029] Extract the frequency components and fluctuation patterns of the real-time current characteristics;

[0030] Based on the ambient temperature data, the chip accumulation state, the frequency components, and the fluctuation pattern, the changes in current characteristics caused by tool wear are identified.

[0031] Calculate the deviation amount based on the current characteristic change and the preset current change range;

[0032] The cutting feed rate is updated based on the change deviation.

[0033] In some embodiments, identifying changes in current characteristics caused by tool wear based on the ambient temperature data, the chip accumulation state, the frequency components, and the fluctuation pattern includes:

[0034] The power spectral density and the initial time-domain signal of the real-time current characteristics within the preset harmonic frequency range in the cutting region are acquired.

[0035] The initial time-domain signal is denoised to obtain the target time-domain signal;

[0036] Based on the ambient temperature data and the chip accumulation state, determine the dynamic monitoring threshold of the power spectral density;

[0037] The target time-domain signal is smoothed.

[0038] Calculate the root mean square value and kurtosis based on the smoothed target time-domain signal;

[0039] If the power spectral density is greater than the dynamic monitoring threshold, the change in current characteristics is identified based on the root mean square value, the kurtosis, the frequency components, and the fluctuation pattern.

[0040] In some embodiments, the acquisition of power spectral density within a preset harmonic frequency range in the cutting region includes:

[0041] Acquire local cutting force fluctuation signals in the cutting area;

[0042] Frequency analysis was performed on the local cutting force fluctuation signal to obtain the signal frequency characteristics;

[0043] Based on the signal frequency characteristics, the power spectral density of the local cutting force fluctuation signal within the preset harmonic frequency range is extracted.

[0044] In some embodiments, the denoising process on the initial time-domain signal to obtain the target time-domain signal includes:

[0045] Acquire power supply voltage fluctuation signals and electromagnetic field intensity signals in the cutting area;

[0046] The initial time-domain signal is subjected to bandpass filtering.

[0047] Wavelet decomposition was performed on the initial time-domain signal after bandpass filtering to obtain the low-frequency and high-frequency components related to tool wear.

[0048] Frequency analysis is performed on the power supply voltage fluctuation signal and the electromagnetic field intensity signal to identify the main interference frequency;

[0049] Based on the main interference frequency, the low-frequency component and the high-frequency component are corrected;

[0050] The corrected low-frequency and high-frequency components are reconstructed to obtain the target time-domain signal.

[0051] In some embodiments, performing wavelet decomposition on the initial time-domain signal after bandpass filtering to obtain low-frequency and high-frequency components related to tool wear includes:

[0052] Statistical analysis is performed on the initial time-domain signal after bandpass filtering to obtain statistical characteristics, including energy distribution, frequency range, and transient impact characteristics.

[0053] Based on the statistical characteristics, determine the wavelet basis functions and the number of wavelet decomposition layers;

[0054] Based on the wavelet basis function and the number of wavelet decomposition levels, wavelet decomposition is performed on the initial time-domain signal after bandpass filtering to obtain the low-frequency component and the high-frequency component.

[0055] On the other hand, embodiments of the present invention provide a workpiece trimming machine cutting system, comprising:

[0056] The data acquisition module is used to acquire workpiece cutting path data;

[0057] The path analysis module is used to analyze the workpiece cutting path data based on the motion and vibration characteristics of the edge trimming machine, and identify the path vibration area. The path vibration area is used to indicate the area where there are speed fluctuations of the drive motor and local vibrations of the tool.

[0058] The path reconstruction module is used to reconstruct the path vibration area according to the workpiece machining tolerance, and generate a replacement path with controlled kinematic characteristics.

[0059] The path replacement module is used to replace the path corresponding to the path vibration area in the original path according to the replacement path to obtain the target path;

[0060] The cutting module is used to control the trimming machine to cut the workpiece according to the target path.

[0061] The embodiments of this application include at least the following beneficial effects: First, the workpiece cutting path data is obtained. Then, based on the motion and vibration characteristics of the trimming machine, the workpiece cutting path data is analyzed to identify the path vibration area. Next, based on the workpiece machining tolerance, the path vibration area is reconstructed to generate a replacement path with controlled kinematic characteristics. Finally, based on the replacement path, the path corresponding to the path vibration area in the original path is replaced to obtain the target path. Based on the target path, the trimming machine is controlled to cut the workpiece. Thus, vibration can be eliminated through path reconstruction to achieve workpiece cutting, thereby improving accuracy and reliability.

[0062] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart of a workpiece trimming machine cutting method according to an embodiment of the present invention;

[0065] Figure 2 This is a schematic diagram of the cutting system of a workpiece trimming machine according to an embodiment of the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements.

[0067] In related technologies, rotary workpiece edge trimming machines are key equipment for processing the edges of circular or near-circular workpieces in modern industrial production. The core of this type of equipment lies in the precise control of its cutting device to ensure that the processing quality of the workpiece edges meets stringent standards. Typically, these machines are used for mass production of workpieces with conventional shapes, such as disc-shaped components with smooth arcs or large-radius curves at the edges. In this stable and predictable processing environment, the control system of the cutting device, including its drive motor and transmission mechanism, can operate efficiently and stably with preset parameter combinations, thereby producing high-quality finished products.

[0068] However, as market demands for product design diversification and functional integration continue to increase, machining tasks are gradually shifting from cutting conventional workpieces with relatively gentle edge contours to cutting a new type of workpiece with multiple local sharp corners and complex curvature changes on its edges. For example, a new type of composite ring structure used in the aerospace field may require the integration of multiple sensor mounting slots or connecting protrusions on its edges. These features mean that its outline is no longer a simple arc, but rather includes geometric features such as concave acute angles, convex sharp angles, and S-shaped transition curves. This change is not accidental, but rather a response to the performance requirements of products in specific application scenarios, such as achieving a more compact structural layout or superior aerodynamic performance.

[0069] To accurately track such complex edge contours, the coordinate data sequence of the target cutting path, upon which existing control systems rely, becomes exceptionally dense in these abrupt corner regions. This is because traditional CNC systems, when processing curved paths, typically decompose them into a series of extremely short straight line segments or tiny arc segments for approximation. When the curvature of the workpiece edge changes drastically, or when there are very sharp corners, to ensure cutting accuracy—that is, to ensure that the deviation between the actual cutting path and the theoretically designed path (often called chord height error or contour error) remains within a very small allowable range—the control program must generate a far greater number of path points in these areas than is conventional. For example, at an abrupt corner only a few millimeters long, hundreds or even thousands of coordinate points may be needed to accurately describe its geometry, leading to a dramatic increase in the number of instructions that need to be processed per unit time.

[0070] When the cutting tool passes through these dense path points at high speed, the drive motor needs to perform continuous and rapid start, braking, and reversing operations. For example, when tracking an S-shaped curve that is concave inward and convex outward, the drive motor may need to accelerate from forward to a certain speed in a very short time, then decelerate rapidly, or even briefly reverse, and then accelerate again. The PID parameters originally set for smooth paths begin to exhibit response delays and slight fluctuations in speed control under this highly dynamic command flow. This is because traditional PID (proportional-integral-derivative) controllers are designed with parameters that balance the system's response speed, overshoot, and stability under specific loads and motion characteristics. When the command frequency and amplitude far exceed their design range—for example, when the motor is frequently required to perform large acceleration, deceleration, or direction changes in a very short time—fixed PID parameters struggle to simultaneously meet the requirements of rapidly tracking commands and suppressing oscillations. This results in the actual motor speed not perfectly following the theoretical command, leading to periodic, high-frequency speed deviations, the so-called "micro-fluctuations." While such fluctuations may not be noticeable on a macroscopic level, at a microscopic level, they mean that the torque and speed output by the motor are not completely stable.

[0071] The high-frequency speed oscillations caused by unstable motor control are directly transmitted to the tool head through the transmission mechanism. Although the entire machine frame is designed with extremely high rigidity to resist vibrations generated during conventional machining, thus preventing overall resonance, the tool clamping and spindle system, as the end effector, has different structural characteristics from the frame. The tool clamping system is typically a relatively cantilevered structure with relatively small mass and its own natural vibration frequency. This continuous, specific-frequency excitation, namely the high-frequency speed fluctuations from the motor, even if its energy is insufficient to cause resonance in the entire frame, may happen to be close to or coincide with a local natural frequency of the tool clamping system. When the excitation frequency is close to the system's natural frequency, even a small excitation amplitude can lead to a significant resonance response in the system, thereby exciting local high-frequency micro-vibrations in the tool clamping system, which is commonly referred to as "tool chatter." This "tool chatter" phenomenon manifests as high-frequency, micro-vibrations perpendicular to the cutting direction superimposed on the normal feed motion of the tool edge during the cutting process.

[0072] The substrate used in this new type of workpiece is a low-toughness engineering plastic, such as certain high-performance polycarbonate or polyamide composites. Under normal, stable cutting forces, such materials can be separated by the cutting tool through shearing or plastic deformation, resulting in a smooth, burr-free cut. However, for cutting edges superimposed with high-frequency chatter, this material exhibits significant brittle characteristics. When the tool "chatters," its cutting edge applies high-frequency, repetitive impact loads to the material while cutting it. This impact load alters the stress state of the material in the cutting zone, causing the fracture mode to shift from controlled plastic shear to brittle fracture due to localized stress concentration. As a result, irregular burrs and micro-notches are left on the cut edge of the workpiece, rather than the expected smooth surface. These defects not only affect the product's aesthetics but may also affect its subsequent assembly accuracy or functional performance; for example, leading to leaks in sealing applications or becoming the starting point of stress concentration in load-bearing components.

[0073] The final application of this workpiece has strict acceptance standards for edge smoothness. For example, in medical devices or precision optical equipment, any visible burrs or chips will be considered defective, directly leading to product scrapping. Simultaneously, to maintain the overall efficiency of the production line, eliminating vibration by significantly reducing the overall processing speed is unacceptable. This is because reducing the overall processing speed to a very low level simply to eliminate tremor would cause the production cycle of this station (i.e., the rotary workpiece trimming machine) to be far lower than other stations on the production line. For example, if other stations can process 10 workpieces per minute, while the trimming station can only process 2, this will create a serious production bottleneck, leading to a significant decrease in the overall production line capacity and a substantial increase in the unit production cost, which is unacceptable in today's highly competitive market environment.

[0074] Therefore, in the above scenario, the technical challenge is how to enable the control method of the cutting device to go beyond passively executing preset coordinate point commands, and instead analyze the entire cutting path data in advance to identify "dangerous sections" where geometric features (such as sharp angles and complex curvature changes) may induce drive motor control fluctuations and local tool chatter. Furthermore, before the tool enters these path vibration zones, the control system needs to be able to proactively and temporarily switch to a special control mode specifically designed to suppress vibration. For example, this might involve real-time, adaptive adjustments to the PID parameter combination for motor speed control, or the use of a feedforward control algorithm to pre-smooth the speed command curve to reduce motor overshoot and oscillation during high-speed dynamic response. After the tool successfully passes through these path vibration zones, the control system needs to seamlessly switch back to the original high-speed machining mode, thereby eliminating machining quality defects in specific sections without sacrificing overall production efficiency, ensuring that the edge finish of new and complex workpieces meets stringent acceptance standards.

[0075] In view of this, the embodiments of this application identify path vibration areas and reconstruct the path in these areas according to the workpiece's machining tolerances to generate alternative paths with controlled kinematic changes. This effectively solves the machining quality problems caused by drive motor speed fluctuations and local tool vibrations when traditional edge trimming machines process complex workpieces, thus improving accuracy and reliability.

[0076] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0077] Figure 1 This is an optional flowchart of a workpiece trimming machine cutting method provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0078] Step S101: Obtain workpiece cutting path data;

[0079] Step S102: Based on the motion and vibration characteristics of the trimming machine, analyze the workpiece cutting path data and identify the path vibration area. The path vibration area is used to represent the area where there are speed fluctuations of the drive motor and local vibrations of the tool.

[0080] Step S103: Based on the workpiece's machining tolerances, reconstruct the path for the vibration area to generate a replacement path with controlled kinematic changes.

[0081] Step S104: Replace the path corresponding to the vibration area in the original path according to the replacement path to obtain the target path;

[0082] Step S105: Control the trimming machine to cut the workpiece according to the target path.

[0083] Steps S101 to S105 as shown in the embodiments of this application can eliminate vibration through path reconstruction to achieve workpiece cutting, thereby improving accuracy and reliability.

[0084] In some embodiments, steps S101-S105 can first acquire workpiece cutting path data. For example, the three-dimensional model data of the workpiece can be directly exported using computer-aided design (CAD) software, and the two-dimensional or three-dimensional coordinate sequence of the edge contour can be extracted as cutting path data. Alternatively, the actual workpiece can be reverse-engineered using an optical scanner or laser measuring device to acquire its edge contour point cloud data, which is then processed and fitted to generate cutting path data. Furthermore, the cutting path can be defined manually by inputting a series of path points based on the dimensions and geometric features on the workpiece drawing through manual programming. It is understood that workpiece cutting path data refers to digital information describing the trajectory of the cutting tool on the workpiece edge, typically presented as a series of coordinate points. This data forms the basis for the precise cutting of the edge trimming machine.

[0085] Then, based on the motion and vibration characteristics of the trimming machine, the workpiece cutting path data is analyzed to identify path vibration regions. These vibration regions represent areas where there are fluctuations in drive motor speed and localized tool vibration. For example, the trimming machine can be pre-modeled kinematically and dynamically. By simulating and analyzing the speed, acceleration, and jerk curves of different path segments, high-dynamic regions that may cause motor speed fluctuations or tool vibration can be identified. In another implementation, during the actual cutting process, acceleration or vibration sensors installed on the trimming machine are used to monitor the vibration of the tool or spindle in real time. Combined with path data, path segments with vibration amplitudes exceeding a preset threshold are marked as path vibration regions. Furthermore, by analyzing the current or voltage signals of the drive motor, regions with severe motor load fluctuations can be identified. These regions are often closely related to speed fluctuations and tool vibration. It is understood that the motion characteristics of the trimming machine refer to the inherent properties of its mechanical components (such as the drive motor, transmission mechanism, and tool) during the execution of cutting tasks, including dynamic response, acceleration, speed limits, and positioning accuracy. The vibration characteristics of an edge trimming machine refer to the vibration modes, frequencies, and amplitudes of the edge trimming machine during operation, caused by its mechanical structure, drive system, or the cutting process itself.

[0086] Then, based on the workpiece's machining tolerances, the path in the vibration region is reconstructed to generate a replacement path with controlled kinematic changes. For example, mathematical methods such as B-spline curves or NURBS curves can be used to smoothly fit the original path points within the vibration region, generating new curve segments. During the fitting process, by adjusting the control points or weights of the curve, the rate of curvature change of the curve can be controlled, thereby limiting the changes in tool speed and acceleration within the allowable range of machining tolerances. Another implementation involves inserting additional path points within the vibration region and adjusting the spacing and position of these points to reduce the angular changes between adjacent path points, thereby reducing the tool's turning speed and acceleration in these regions. Furthermore, optimization algorithms can be used to find a replacement path that minimizes changes in tool kinematic characteristics (such as speed, acceleration, and jerk) while satisfying machining tolerance constraints. It is understood that machining tolerance refers to the allowable deviation range of the workpiece's dimensions, shape, position, and other geometric parameters after machining, and is an important indicator for measuring machining quality.

[0087] Finally, based on the replacement path, the path corresponding to the vibration area in the original path is replaced to obtain the target path. For example, the reconstructed replacement path segment can be directly inserted into the original path data to replace the original vibration area. During the replacement process, it is necessary to ensure the continuity of the replacement path and the original path at the connection point to avoid impact during path switching. Another implementation method is to fuse the original path data and the replacement path data, using methods such as weighted averaging or interpolation to smoothly transition at the boundary of the vibration area, thereby generating a complete and seamless target path. Simultaneously, based on the target path, the trimming machine is controlled to cut the workpiece. The generated target path data can be input into the CNC system of the trimming machine. The CNC system generates corresponding drive commands based on the coordinate points and kinematic parameters of the target path, controlling the drive motor and actuator to cut along the target path. Another implementation method is to use a real-time feedback control system to compare the actual position and speed of the tool with the target path and make real-time corrections based on the deviation to ensure the tool moves precisely along the target path. Furthermore, feedforward control can be combined to pre-adjust the control signal of the drive motor based on the kinematic parameters of the target path to improve the system's response speed and control accuracy.

[0088] Through the above technical solution, this embodiment acquires workpiece cutting path data and performs in-depth analysis of this data based on the motion and vibration characteristics of the trimming machine itself. This allows for the precise identification of vibration areas along the path that are prone to causing speed fluctuations in the drive motor and localized vibrations in the cutting tool. This enables targeted treatment of problem areas, rather than unnecessary modifications to the entire path. This embodiment performs local optimization of the vibration areas by smoothing the path and adjusting curvature, making the tool's movement in these areas more stable and avoiding vibrations caused by abrupt acceleration, deceleration, or turning. By using the replaced target path for cutting, this embodiment significantly reduces burrs and micro-gap on the workpiece edge, improving the product's aesthetics and functionality, while avoiding increased costs due to excessively reduced production speed. This targeted path optimization strategy enables the trimming machine to achieve higher precision and a more stable processing flow when handling complex workpieces, resulting in significant technological advancements.

[0089] In some embodiments, in step S103, the path vibration region is reconstructed according to the workpiece's machining tolerance to generate a replacement path with controlled kinematic characteristics. This may include, but is not limited to, the following steps:

[0090] Step S201: When processing the first batch of workpieces, when the cutting tool is in the path vibration area, the first current consumption characteristics of the drive motor of the edge trimming machine are collected as the initial health benchmark.

[0091] Step S202: When processing non-first batch workpieces, when the cutting tool is in the path vibration area, the second current consumption characteristics of the drive motor of the edge trimming machine are collected as real-time current characteristics.

[0092] Step S203: Identify the tool wear state based on real-time current characteristics and initial health benchmark;

[0093] Step S204: Adjust the cutting feed rate according to the tool wear condition;

[0094] Step S205: Generate a replacement path based on the adjusted cutting feed rate.

[0095] In some embodiments, if the actual wear condition of the cutting tool is not fully considered, it may lead to problems such as excessive tool wear or decreased machining quality that cannot be effectively avoided during actual cutting, even after the path is reconstructed, thereby affecting the machining accuracy of the workpiece and the service life of the trimming machine. To address this, when machining the first batch of workpieces, the first current consumption characteristic of the trimming machine's drive motor can be collected when the cutting tool is within the path vibration area, serving as an initial health baseline. It is understood that the first current consumption characteristic refers to the typical current consumption pattern or numerical range exhibited by the drive motor during cutting operations within the path vibration area when the cutting tool is in a brand-new or near-brand-new condition. This characteristic is used as a reference benchmark for subsequent assessment of the tool's health condition.

[0096] When machining non-first-batch workpieces, the second current consumption characteristic of the drive motor of the trimming machine is collected as a real-time current characteristic when the cutting tool is within the path vibration area. This second current consumption characteristic refers to the real-time current consumption data collected by the drive motor when the cutting tool is within the path vibration area during actual machining of non-first-batch workpieces. This real-time current characteristic reflects the current working state of the tool under actual cutting conditions.

[0097] Then, based on the real-time current characteristics and the initial health benchmark, the tool wear condition can be identified. This can be achieved by comparing the real-time current characteristics with the initial health benchmark. For example, if the real-time current characteristics deviate significantly from the initial health benchmark (such as an increase in current value or a change in fluctuation pattern), it can be determined that the tool may be worn.

[0098] Next, the cutting feed rate is adjusted based on the tool wear condition. For example, if tool wear is detected, the cutting feed rate can be appropriately reduced to lessen the tool load, slow down the wear process, and maintain machining quality. Finally, an alternative path is generated based on the adjusted cutting feed rate. This means that the kinematic characteristics of the alternative path are not only controlled by the workpiece machining tolerances but also dynamically adapt to the actual tool wear, thereby ensuring optimized cutting conditions are maintained throughout the machining process.

[0099] In this embodiment, by introducing a real-time monitoring and feedback mechanism for the tool wear state, the limitation that the path reconstruction in the basic solution does not fully consider the actual condition of the tool is solved. Specifically, when the cutting tool performs cutting within the path vibration region, its wear degree will directly affect the cutting force, which in turn causes a change in the current consumption of the driving motor. By collecting the first current consumption characteristic as the initial health benchmark when the tool is in a healthy state and comparing it with the second current consumption characteristic (real-time current characteristic) collected during the machining of non-first batch workpieces, the wear state of the tool can be effectively identified. Once the tool wear is identified, the system will adjust the cutting feed rate according to the wear degree. The purpose of this adjustment is to reduce the tool load and avoid excessive cutting force, poor machining quality or further tool damage caused by tool wear. Finally, according to the adjusted cutting feed rate, a replacement path is regenerated, so that the replacement path not only meets the machining tolerance requirements of the workpiece, but can also adapt to the actual wear condition of the tool, thereby effectively extending the service life of the tool while ensuring the machining quality.

[0100] To illustrate this technical solution more clearly, a specific example is used for explanation below. Suppose when machining a new batch of workpieces, a brand-new cutting tool is first used to cut the first batch of workpieces. During this process, when the cutting tool enters the preset path vibration region, the first current consumption characteristic of the driving motor of the trimming machine is collected and recorded. For example, its average current value is X amperes and the fluctuation range is Y amperes. This data is set as the initial health benchmark. Subsequently, during the machining of subsequent non-first batch workpieces, the system continuously monitors the second current consumption characteristic of the driving motor within the same path vibration region. For example, when machining the Zth batch of workpieces, the second current consumption characteristic collected in real time shows that its average current value has risen to X' amperes and the fluctuation range has expanded to Y' amperes. By comparing X' and Y' with the initial health benchmark X and Y, the system identifies that the tool is worn. Based on this wear state, the system automatically calculates and determines that the cutting feed rate needs to be adjusted from V mm / s to V' mm / s (V' < V). Finally, according to this adjusted cutting feed rate V', the system regenerates the replacement path corresponding to this path vibration region, and the kinematic parameters of the replacement path are optimized to adapt to the lower feed rate, so as to ensure a stable cutting process and qualified machining quality under the condition of tool wear.

[0101] Through the above technical solution, this embodiment identifies the tool wear state by real-time monitoring of the current consumption characteristics of the drive motor, enabling the path reconstruction process to dynamically adapt to the actual health condition of the tool and avoid fluctuations in machining quality caused by tool wear. Secondly, adjusting the cutting feed rate according to the tool wear state effectively reduces the load on the worn tool, delays further tool wear, and thus significantly extends tool life and reduces production costs. Furthermore, the generated replacement path not only has controlled changes in kinematic characteristics but also fully considers the actual tool wear, ensuring that the trimming machine maintains efficient and stable operation throughout the entire machining process, ultimately improving the machining accuracy and surface quality of the workpiece.

[0102] In some embodiments, step S203, identifying the tool wear state based on real-time current characteristics and an initial health benchmark, may include, but is not limited to, the following steps:

[0103] When machining the first batch of workpieces, microscopic vibration data of the cutting tool are collected when the cutting tool is outside the path vibration zone.

[0104] Damping characteristic features are extracted from micro vibration data to obtain damping measurement values;

[0105] The damping measurement value is compared with the preset damping measurement reference value to determine the current deviation threshold;

[0106] Based on the current deviation threshold, the real-time current characteristics are compared with the initial health benchmark to identify the tool wear status.

[0107] In some embodiments, identifying tool wear status solely through simple current characteristic comparison can be limited. For example, slight variations in workpiece material, processing environment, or the condition of the trimmer itself can cause fluctuations in the current consumption of the drive motor, leading to misjudgments of tool wear status and affecting the accuracy of path reconstruction and processing efficiency. To address this, micro-vibration data of the cutting tool can be collected during the initial workpiece machining, when the cutting tool is outside the path vibration zone. Micro-vibration data refers to the extremely small amplitude, high-frequency vibration signals generated by the cutting tool during the cutting process. These vibration signals can sensitively reflect the interaction state between the tool and the workpiece, including tool sharpness, wear level, and stability during the cutting process. The aim is to provide more direct and refined tool status information than macro-current characteristics. Collecting this micro-vibration data during the initial workpiece machining, when the cutting tool is outside the path vibration zone—that is, under relatively stable cutting conditions—can establish a clean tool health baseline unaffected by path vibration.

[0108] Then, damping characteristic features are extracted from the micro-vibration data to analyze the rate and mode of tool vibration energy decay, obtaining a damping metric. The damping metric is an indicator that quantifies this decay characteristic; for example, it can be the attenuation rate of the vibration signal, the quality factor, or the half-power bandwidth. Its purpose is to reflect changes in the internal structure of the tool material or micro-damage to the cutting edge by quantifying the inherent vibration characteristics of the tool, because tool wear typically leads to changes in its structural stiffness or material damping characteristics.

[0109] The damping measurement value is then compared with a preset damping reference value to determine the current deviation threshold. The preset damping reference value is a standard value pre-set based on the damping characteristics of a brand-new or healthy cutting tool. By comparing the real-time acquired damping measurement value with this reference value, the health condition of the cutting tool can be assessed. When the damping measurement value deviates from the reference value to a certain extent, it indicates that the cutting tool may be worn. Therefore, a current deviation threshold can be dynamically determined. This current deviation threshold is no longer fixed but is adjusted according to the actual micro-vibration characteristics and damping state of the cutting tool. The purpose is to make subsequent current characteristic comparisons more accurate and avoid misjudgments caused by changes in environment or operating conditions.

[0110] Finally, based on the current deviation threshold, the real-time current characteristics are compared with the initial health baseline to identify the tool wear condition. For example, if the deviation of the real-time current characteristics from the initial health baseline exceeds the current deviation threshold, the tool wear condition can be identified more reliably.

[0111] To illustrate this technical solution more clearly, a specific example is used below. Assume that during the initial processing of the first batch of workpieces, the trimming machine performs stable cutting in a non-path vibration zone. At this time, a high-frequency accelerometer collects microscopic vibration data of the cutting tool. Fourier transform and time-domain analysis are performed on the collected vibration signals to extract the damping ratio or quality factor as a damping measure. For example, when the damping ratio of a new tool is 0.02, it is set as the preset damping reference value. As the tool gradually wears down, its damping ratio may increase to 0.03. At this point, the system dynamically adjusts the current deviation threshold based on the degree of change in the damping ratio from 0.02 to 0.03. For example, if a fixed current deviation threshold of 5% is initially set (i.e., a deviation of more than 5% between the real-time current and the reference current is considered wear), the system may now adjust this threshold to 3% or 7% based on the change in the damping ratio to more accurately match the current tool wear state. Subsequently, when processing non-initial workpieces, the current consumption characteristics of the drive motor are collected in real time and compared with the initial healthy reference current. When the deviation of the real-time current characteristic from the initial health reference exceeds a current deviation threshold dynamically determined by the damping characteristics, tool wear can be identified. For example, if the real-time current deviates from the reference current by 6%, and the dynamic threshold is set to 5%, wear is identified; if the dynamic threshold is set to 7%, wear is not identified, thus avoiding false positives.

[0112] Through the above technical solution, this embodiment introduces micro-vibration data and damping characteristic analysis, making the judgment of tool wear more comprehensive and precise. The dynamically determined current deviation threshold can better adapt to various working condition changes that may occur during actual machining, effectively avoiding misjudgments caused by environmental or workpiece differences. This reduces unnecessary tool replacements or prevents continued machining with worn tools, extending tool life, reducing production costs, and ensuring workpiece machining quality. Furthermore, more accurate wear condition identification provides a more reliable basis for subsequent cutting feed rate adjustments, further optimizing the overall performance of path reconstruction and trimming machine control.

[0113] In some embodiments, in step S204, adjusting the cutting feed rate according to the tool wear condition may include, but is not limited to, the following steps:

[0114] If the tool wear condition is present, the characteristic deviation is calculated based on the real-time current characteristics and the initial health benchmark.

[0115] The adjustment range is determined based on the deviation of the characteristic.

[0116] Calculate the current deviation based on the real-time current characteristics and the preset current characteristic range;

[0117] Adjust the cutting feed rate according to the adjustment range and current deviation to keep the real-time current characteristics stable within the preset current characteristic range.

[0118] In some embodiments, simply adjusting based on tool wear conditions may not effectively control dynamic changes during the cutting process, leading to fluctuations in drive motor speed and localized tool vibration, thereby affecting machining stability and workpiece quality. Therefore, the tool wear condition can be assessed first. If wear is present, the characteristic deviation is calculated based on real-time current characteristics and an initial health benchmark. By comparing the real-time current characteristics with the initial health benchmark, it can be determined that the actual wear level of the tool has reached or exceeded a preset wear threshold, requiring intervention in cutting parameters. The characteristic deviation refers to the degree of difference between the real-time current characteristics and the initial health benchmark; this difference quantifies the impact of tool wear on motor current consumption. For example, it can be obtained by calculating the difference, ratio, or more complex statistical indicators between the real-time current characteristics and the initial health benchmark.

[0119] Then, based on the feature deviation, the adjustment range is determined. This adjustment range is an initial adjustment to the cutting feed rate based on the feature deviation, reflecting the initial strategy for correcting the feed rate according to tool wear. This adjustment range can be pre-calibrated using experimental data, empirical formulas, or machine learning models.

[0120] Then, based on the real-time current characteristics and the preset current characteristic range, the current deviation is calculated. The preset current characteristic range refers to the ideal operating range of the drive motor's current consumption characteristics, pre-set to ensure cutting process stability and tool life. When the real-time current characteristics exceed this range, it indicates a possible abnormality in the cutting state. The current deviation refers to the degree of deviation between the real-time current characteristics and the preset current characteristic range; it measures the difference between the current cutting state and the ideal state. For example, when the real-time current characteristics exceed the preset range, its distance from the range boundary can be calculated as the current deviation.

[0121] Finally, based on the adjustment range and current deviation, the cutting feed rate is adjusted to stabilize the real-time current characteristics within the preset current characteristic range. The cutting feed rate refers to the speed at which the cutting tool moves on the workpiece, directly affecting cutting force, cutting temperature, and machining efficiency. By dynamically adjusting the cutting feed rate, the real-time current consumption characteristics of the drive motor can be controlled within a preset ideal operating range, thereby ensuring the smoothness of the cutting process, avoiding overload or underload, and optimizing tool life.

[0122] To illustrate this technical solution more clearly, a specific example is used below. Suppose that during the machining of a batch of workpieces, the system identifies tool wear by comparing real-time current characteristics with an initial health baseline. At this point, the deviation between the real-time current characteristics and the initial health baseline can be calculated first. For example, if the average current of the initial health baseline is 10A, and the average current of the real-time current characteristics is 12A, then the deviation is 2A. According to a pre-set rule, for example, each 1A deviation corresponds to an adjustment range of 0.1mm / s, the initial adjustment range is determined to be 0.2mm / s. Simultaneously, the system monitors whether the real-time current characteristics are within a preset current characteristic range (e.g., 9A to 11A). If the average current of the real-time current characteristics is 12A, it exceeds the upper limit of the preset range of 11A, and the current deviation is calculated as 1A (12A - 11A). Finally, the system comprehensively considers the adjustment range of 0.2mm / s and the current deviation of 1A to adjust the current cutting feed rate. For example, if the current cutting feed rate is 50 mm / s, the system can initially reduce the speed based on the adjustment range and further fine-tune it based on the current deviation to ensure that the adjusted real-time current characteristics can fall back and stabilize within the preset range of 9A to 11A. For example, the final cutting feed rate may be adjusted to 49.5 mm / s, thereby effectively controlling the impact of tool wear and maintaining the stability of the cutting process.

[0123] Through the above technical solution, this embodiment introduces a dual feedback mechanism of characteristic deviation and current deviation, making the adjustment of the cutting feed rate more precise and dynamic. This not only effectively addresses the impact of tool wear on the cutting process but also stabilizes the real-time current characteristics of the drive motor within a preset ideal operating range. This significantly improves the stability of the cutting process, reduces equipment operating risks, extends tool life, and ultimately ensures the machining quality of the workpiece. This refined adjustment strategy allows the trimming machine to maintain efficient and stable operation even when facing tool wear.

[0124] In some embodiments, after adjusting the cutting feed rate based on the adjustment magnitude and current deviation, the method further includes:

[0125] Step S301: Collect ambient temperature data of the cutting area and chip accumulation status of the tool load area;

[0126] Step S302: Extract the frequency components and fluctuation patterns of the real-time current characteristics;

[0127] Step S303: Identify the changes in current characteristics caused by tool wear based on ambient temperature data, chip accumulation state, frequency components, and fluctuation patterns;

[0128] Step S304: Calculate the deviation amount based on the current characteristic change and the preset current change range;

[0129] Step S305: Update the cutting feed rate based on the change in deviation.

[0130] In some embodiments, the identification of tool wear and the precise adjustment of the cutting feed rate can be influenced by a variety of complex factors. For example, variations in ambient temperature, chip accumulation in the tool load area, and more subtle frequency components and fluctuation patterns in the drive motor current characteristics can all significantly affect the actual wear state of the tool and the stability of the cutting process. If adjustments are made solely based on the overall deviation of the current characteristics, the true wear condition of the tool may not be fully and accurately reflected, resulting in insufficiently precise adjustments, or even over-adjustment or under-adjustment, affecting machining quality and tool life.

[0131] To this end, we can first collect ambient temperature data of the cutting zone and chip accumulation status of the tool load area. The ambient temperature data of the cutting zone refers to the temperature information around the area where the tool and workpiece contact during the cutting process. This can be collected in real time by temperature sensors placed near the cutting zone. Its purpose is to reflect the generation and dissipation of cutting heat, and its impact on tool material properties and workpiece material hardness. The chip accumulation status of the tool load area refers to the accumulation of chips near the tool cutting edge or in the chip removal channel during the cutting process. This can be monitored using visual sensors, acoustic sensors, or force sensors. Its purpose is to assess whether chip removal is smooth and whether there is chip blockage leading to an abnormal increase in cutting force.

[0132] Then, the frequency components and fluctuation patterns of the real-time current characteristics are extracted. For example, the energy distribution at different frequencies and its dynamic characteristics over time can be obtained by performing spectral analysis and time-domain analysis on the second current consumption characteristics of the drive motor. For instance, the real-time current characteristics can be decomposed into different frequency components using methods such as Fast Fourier Transform (FFT) or wavelet analysis, and their amplitude, phase, and trends over time can be analyzed. The aim is to reveal specific patterns in the current signal caused by factors such as tool wear, cutting vibration, or workpiece material inhomogeneity.

[0133] Based on ambient temperature data, chip accumulation state, frequency components, and fluctuation patterns, changes in current characteristics caused by tool wear can be identified. A deeper attribution analysis of real-time current characteristic changes can be performed by establishing a multi-parameter model or employing machine learning algorithms. Specific frequency components or fluctuation patterns, such as high-frequency vibrations or periodic impacts, may more directly indicate tool edge chipping or wear. The aim is to improve the accuracy and robustness of tool wear identification and avoid misjudgments.

[0134] Finally, based on the changes in current characteristics and the preset current variation range, the deviation is calculated. The comprehensive identification result can be compared with the preset current characteristic variation range corresponding to a healthy tool or acceptable wear level, quantifying the difference between the current state and the ideal state. The purpose is to provide precise quantitative basis for subsequent updates to the cutting feed rate. Therefore, updating the cutting feed rate based on this deviation allows for refined and adaptive adjustment of cutting parameters, ensuring the cutting process is always in an optimal or acceptable state.

[0135] To illustrate this technical solution more clearly, a specific example is used below. Suppose that during the machining of a batch of special alloy workpieces, the trimming machine is cutting within the path vibration zone. Initially, slight tool wear can be identified based on real-time current characteristics and an initial health baseline, and a preliminary adjustment to the cutting feed rate can be made. However, for a period after the adjustment, the system continues to monitor fluctuations in the real-time current characteristics. At this point, the system collects ambient temperature data for the cutting area and finds it has increased compared to before; simultaneously, visual or acoustic sensors detect signs of light chip accumulation in the tool load area. Furthermore, frequency analysis of the real-time current characteristics reveals an increase in power spectral density at a specific high frequency band, and the fluctuation pattern exhibits periodic impact characteristics. Combining this information, the system can more accurately determine that the current fluctuation is not only related to tool wear but may also be influenced by the combined effects of increased ambient temperature leading to material softening and increased local cutting force caused by light chip accumulation. Based on this more comprehensive identification result, the system calculates a more accurate deviation and accordingly makes further fine adjustments to the cutting feed rate. For example, while slightly reducing the feed rate, it may also suggest adjusting the coolant flow rate or chip removal parameters to ensure the stability of the cutting process and the continued healthy operation of the tool. This refined adjustment avoids over- or under-adjustment based solely on current fluctuations, thus effectively optimizing the machining process.

[0136] Through the above technical solution, this embodiment, by comprehensively considering ambient temperature, chip accumulation, and the frequency and fluctuation patterns of current characteristics, can more effectively eliminate interference from non-wear factors on the current signal, thereby more accurately identifying the true changes in current characteristics caused by tool wear. Consequently, the adjustment of the cutting feed rate will be more precise and adaptive, not only effectively extending tool life and reducing tool consumption costs, but also significantly improving machining quality, reducing scrap rate, and ensuring stable and efficient operation of the trimming machine under various working conditions.

[0137] In some embodiments, in step S303, identifying changes in current characteristics caused by tool wear based on ambient temperature data, chip accumulation state, frequency components, and fluctuation patterns may include, but is not limited to, the following steps:

[0138] Step S401: Acquire the initial time-domain signals of the power spectral density and real-time current characteristics within the preset harmonic frequency range in the cutting region;

[0139] Step S402: Denoise the initial time-domain signal to obtain the target time-domain signal;

[0140] Step S403: Determine the dynamic monitoring threshold of power spectral density based on ambient temperature data and chip accumulation status;

[0141] Step S404: Smooth the target time-domain signal;

[0142] Step S405: Calculate the root mean square value and kurtosis based on the smoothed target time domain signal;

[0143] Step S406: If the power spectral density is greater than the dynamic monitoring threshold, the changes in current characteristics are identified based on the root mean square value, kurtosis, frequency components, and fluctuation patterns.

[0144] In some embodiments, initial time-domain signals of power spectral density and real-time current characteristics within a preset harmonic frequency range in the cutting region can be acquired first. The power spectral density is used to quantify the energy distribution of the signal at different frequencies, and the initial time-domain signal provides the raw time-series data of the current characteristics. Simultaneously, the initial time-domain signal is denoised to obtain the target time-domain signal, aiming to eliminate interference caused by non-tool wear factors, such as power fluctuations or electromagnetic interference, thereby allowing subsequent analysis to more accurately focus on tool wear-related characteristics.

[0145] Then, based on ambient temperature data and chip accumulation state, a dynamic monitoring threshold for power spectral density is determined. Ambient temperature and chip accumulation state are important factors affecting the cutting process and tool wear. By taking these environmental parameters into account, the monitoring threshold can be dynamically adjusted to better adapt to changes in actual working conditions and improve the accuracy of identification. Furthermore, smoothing the target time-domain signal helps remove high-frequency noise and transient spikes, making the signal trend clearer and facilitating subsequent feature extraction and analysis.

[0146] Based on the smoothed target time-domain signal, the root mean square (RMS) value and kurtosis are calculated. The RMS value reflects the effective energy or intensity of the signal, while kurtosis characterizes the sharpness or impact of the signal. These two statistics can effectively capture changes in current characteristics caused by tool wear. If the power spectral density is greater than the dynamic monitoring threshold, changes in current characteristics are identified based on the RMS value, kurtosis, frequency components, and fluctuation patterns. When the power spectral density exceeds the dynamic monitoring threshold, it indicates possible abnormal vibration or energy changes during the cutting process. In this case, combining the RMS value, kurtosis, and pre-extracted frequency components and fluctuation patterns allows for a comprehensive judgment and identification of changes in current characteristics caused by tool wear.

[0147] This embodiment, by comprehensively analyzing the power spectral density of the cutting zone, the time-domain signal of real-time current characteristics, and environmental factors, and introducing a dynamic monitoring threshold, can more accurately identify changes in current characteristics caused by tool wear. Specifically, by denoising and smoothing the initial time-domain signal, interference can be effectively filtered out, highlighting the true characteristics of tool wear. Simultaneously, dynamically adjusting the power spectral density monitoring threshold based on ambient temperature and chip accumulation status makes the identification process more adaptable and robust. Furthermore, the calculation of the root mean square value and kurtosis can quantify the characteristics of the current signal from different dimensions, providing a more comprehensive basis for tool wear identification.

[0148] Through the above technical solution, this embodiment can effectively improve the accuracy and real-time performance of tool wear condition identification. By employing multi-dimensional, multi-stage data processing and feature extraction, environmental interference can be more effectively eliminated, capturing subtle features of tool wear. This provides a more reliable basis for subsequent cutting feed rate adjustments, further optimizing workpiece machining quality and tool life.

[0149] In some embodiments, step S401, acquiring the power spectral density within a preset harmonic frequency range in the cutting region, may include, but is not limited to, the following steps:

[0150] Acquire local cutting force fluctuation signals in the cutting area;

[0151] Frequency analysis was performed on the local cutting force fluctuation signal to obtain the signal frequency characteristics;

[0152] Based on the signal frequency characteristics, the power spectral density of the local cutting force fluctuation signal within the preset harmonic frequency range is extracted.

[0153] In some embodiments, local cutting force fluctuation signals in the cutting area can be acquired first. These local cutting force fluctuation signals refer to the instantaneous mechanical signal changes generated in the cutting area during the workpiece cutting process due to the interaction between the tool and the workpiece, as well as dynamic instabilities during the cutting process (such as chip formation, fracture, friction, etc.). These fluctuation signals can be acquired in real time by force sensors or acceleration sensors installed near the cutting area, with the aim of directly reflecting the dynamic load changes during the cutting process.

[0154] Then, frequency analysis is performed on the local cutting force fluctuation signal to obtain its frequency characteristics. Fourier transform and other spectral analysis methods can be used to convert the acquired time-domain local cutting force fluctuation signal to the frequency domain, revealing the various frequency components contained in the signal and their corresponding intensities. Thus, the signal frequency characteristics can be obtained, specifically manifested as the main frequency components, harmonic frequencies, and the amplitude or power of each frequency component. The purpose is to identify specific frequency patterns related to tool wear or abnormal cutting processes.

[0155] Then, based on the signal frequency characteristics, the power spectral density of the local cutting force fluctuation signal within a preset harmonic frequency range is extracted. The preset harmonic frequency range is one or more frequency intervals pre-defined based on the inherent vibration characteristics of the trimming machine and the cutting tool, cutting parameters, and specific frequency responses that may be caused by tool wear. Power spectral density represents the distribution of signal power at different frequencies and can quantify the energy intensity of specific frequency components. Its purpose is to accurately capture energy changes related to tool wear, because tool wear often causes an increase in cutting force fluctuations at specific harmonic frequencies.

[0156] This embodiment, by acquiring local cutting force fluctuation signals in the cutting area, can more directly and accurately reflect the actual interaction state between the tool and the workpiece. Since tool wear causes changes in cutting force and exhibits enhanced fluctuations at specific frequencies, frequency analysis of this fluctuation signal can yield signal frequency characteristics closely related to tool wear. Furthermore, by extracting the power spectral density of these signals within a preset harmonic frequency range, the energy distribution related to tool wear can be quantified, providing crucial input data for subsequent identification of current characteristic changes caused by tool wear. This method effectively avoids interference from other non-wear factors in current characteristic identification, improving the accuracy of the identification.

[0157] Through the above technical solution, this embodiment can more accurately acquire cutting force fluctuation information related to tool wear, thereby providing a more reliable and direct data basis for identifying current characteristic changes caused by tool wear. This power spectral density extraction method based on local cutting force fluctuation signals helps improve the sensitivity and accuracy of tool wear state identification, thereby optimizing the adjustment of cutting feed rate, ensuring workpiece machining quality, and extending tool life.

[0158] In some embodiments, step S402, denoising the initial time-domain signal to obtain the target time-domain signal, may include, but is not limited to, the following steps:

[0159] Step S501: Acquire power supply voltage fluctuation signals and electromagnetic field intensity signals in the cutting area;

[0160] Step S502: Perform bandpass filtering on the initial time-domain signal;

[0161] Step S503: Perform wavelet decomposition on the initial time-domain signal after bandpass filtering to obtain the low-frequency and high-frequency components related to tool wear;

[0162] Step S504: Perform frequency analysis on the power supply voltage fluctuation signal and electromagnetic field strength signal to identify the main interference frequency;

[0163] Step S505: Correct the low-frequency and high-frequency components according to the main interference frequency;

[0164] Step S506: Reconstruct the corrected low-frequency and high-frequency components to obtain the target time-domain signal.

[0165] In some embodiments, the initial time-domain signal is often affected by various external noise sources such as power supply voltage fluctuations and electromagnetic field interference. If these interferences are not effectively filtered out, the resulting denoised target time-domain signal may still contain noise components unrelated to tool wear, thus affecting the accurate identification of subsequent current characteristic changes and reducing the reliability of tool wear condition judgment. Therefore, power supply voltage fluctuation signals and electromagnetic field intensity signals from the cutting area can be acquired first to obtain information on external noise sources that may interfere with the initial time-domain signal. Specifically, power supply voltage fluctuation signals reflect the instability of the power supply system, and electromagnetic field intensity signals from the cutting area indicate electromagnetic interference generated during the cutting process. These signals provide a basis for subsequent identification and elimination of the main interference frequency.

[0166] The initial time-domain signal is then subjected to bandpass filtering to initially remove excessively high or low frequency components unrelated to tool wear characteristics, limiting the signal to a specific frequency range relevant to tool wear. This reduces the amount of data required for subsequent processing and improves processing efficiency. Simultaneously, wavelet decomposition is applied to the bandpass-filtered initial time-domain signal, decomposing it into components of multiple frequency scales to obtain low-frequency and high-frequency components relevant to tool wear. Wavelet decomposition effectively analyzes the signal simultaneously in both the time and frequency domains, helping to separate signal components with different properties.

[0167] Next, frequency analysis is performed on the power supply voltage fluctuation signal and electromagnetic field strength signal to identify the main interference frequency. This allows for the precise identification of the main frequency components of these external interference sources, i.e., the main interference frequency. These main interference frequencies are usually related to the working environment or power supply characteristics of the workpiece trimming machine. Based on the main interference frequency, the low-frequency and high-frequency components are corrected. The correction process may include attenuating the signal amplitude at specific frequency points or eliminating interference components through adaptive filtering algorithms to ensure that the true signal components related to tool wear are preserved, while external interference is effectively suppressed. Finally, the corrected low-frequency and high-frequency components are reconstructed to obtain a purer and more accurate target time-domain signal. This target time-domain signal removes external noise and interference to the greatest extent, more realistically reflecting the changes in current characteristics caused by tool wear.

[0168] To illustrate this technical solution more clearly, a specific example is used below. Assume that during the operation of the workpiece trimming machine, the power supply voltage fluctuates periodically due to the startup of other large equipment in the workshop or power grid fluctuations. Simultaneously, electromagnetic interference is generated in the cutting area due to electric arc or spark effects. These interference signals are superimposed on the acquired initial time-domain signal of the drive motor, making it difficult to accurately determine the tool wear state by directly analyzing the signal. We can first acquire the real-time power supply voltage fluctuation signal and the electromagnetic field strength signal of the cutting area. For example, this can be done using voltage and electromagnetic field sensors. Then, the initial time-domain signal is bandpass filtered, for example, setting the filtering range to 50Hz to 5000Hz to filter out most of the power frequency noise and high-frequency clutter. Next, the bandpass-filtered signal is decomposed using wavelet decomposition, for example, using the Daubechies wavelet basis function for three-level decomposition to obtain multiple low-frequency and high-frequency components. Simultaneously, the acquired power supply voltage fluctuation signal and electromagnetic field strength signal are analyzed using Fast Fourier Transform (FFT) to identify the main interference frequencies, such as 50Hz and 150Hz. Based on these identified main interference frequencies, the low-frequency and high-frequency components obtained from wavelet decomposition are corrected, for example, by applying notch filters or adaptive noise cancellation algorithms at these interference frequency points. Finally, the corrected components are reconstructed using wavelet decomposition to obtain a target time-domain signal that has significantly removed external interference. This target time-domain signal can clearly show the changes in current characteristics caused by tool wear, such as the mean current drift or enhancement of specific frequency components due to wear, thus providing a reliable data foundation for subsequent tool wear condition identification.

[0169] Through the above technical solution, this embodiment can significantly improve the denoising effect of the initial time-domain signal, effectively suppressing the influence of external noise such as power supply voltage fluctuations and electromagnetic field interference on the signal. Therefore, the obtained target time-domain signal has a higher signal-to-noise ratio and accuracy, and can more realistically and accurately reflect the changes in current characteristics caused by tool wear. This not only enhances the reliability and robustness of tool wear condition identification, but also provides high-quality data input for subsequent calculations of characteristic parameters such as root mean square value and kurtosis. This improves the monitoring accuracy and response speed of the entire workpiece trimming machine cutting system for tool wear, avoids misjudgments or omissions caused by noise interference, extends tool life, and ensures machining quality.

[0170] In some embodiments, step S503 involves performing wavelet decomposition on the initial time-domain signal after bandpass filtering to obtain low-frequency and high-frequency components related to tool wear. This may include, but is not limited to, the following steps:

[0171] Statistical analysis was performed on the initial time-domain signal after bandpass filtering to obtain statistical characteristics, including energy distribution, frequency range, and transient impact characteristics.

[0172] Based on statistical characteristics, determine the wavelet basis functions and the number of wavelet decomposition layers;

[0173] Based on the wavelet basis function and the number of wavelet decomposition levels, wavelet decomposition is performed on the initial time-domain signal after bandpass filtering to obtain low-frequency and high-frequency components.

[0174] In some embodiments, statistical analysis can be performed on the initial time-domain signal after bandpass filtering to obtain statistical characteristics. The characteristics of the signal can be quantified by calculating various statistical quantities. These statistical characteristics include energy distribution, frequency range, and transient impact characteristics. Statistical characteristics describe the signal's behavior in different dimensions. For example, energy distribution refers to the degree of energy concentration of the signal in different time periods or frequency bands, which can be obtained by calculating the root mean square value, energy spectral density, etc., aiming to reveal the variation of signal energy with time or frequency. Frequency range represents the distribution interval of the main frequency components in the signal, which can be determined by spectral analysis methods such as Fourier transform or wavelet transform to identify the dominant vibration modes in the signal. Transient impact characteristics refer to short-duration, high-amplitude sudden events in the signal, such as the impact generated when a tool contacts or separates from a workpiece. These characteristics can be characterized by statistical indices such as kurtosis, impact factor, etc., and are important for identifying tool chipping or abnormal wear.

[0175] Then, based on statistical characteristics, the wavelet basis functions and the number of wavelet decomposition levels are determined. The wavelet basis functions are the core of wavelet decomposition; different basis functions have different time-frequency localization characteristics and regularity. For example, the Haar wavelet is suitable for detecting signal abrupt changes, while the Daubechies wavelet has better smoothness. The number of wavelet decomposition levels determines the fineness of signal decomposition; more levels result in higher frequency resolution but correspondingly lower time resolution. By statistically analyzing the initial time-domain signal, the signal's intrinsic characteristics can be obtained. For example, if there are obvious transient impacts in the signal, a wavelet basis function with good impact signal capture ability can be selected; if the focus is on wear information within a specific frequency range, the appropriate number of decomposition levels can be determined based on the signal's frequency range and the required frequency resolution. Then, based on the wavelet basis functions and the number of wavelet decomposition levels, wavelet decomposition is performed on the initial time-domain signal after bandpass filtering to obtain low-frequency and high-frequency components. This adaptive selection based on signal characteristics ensures that the wavelet decomposition process more effectively separates the low-frequency and high-frequency components related to tool wear.

[0176] Through the above technical solution, this embodiment can adaptively select the most suitable wavelet basis function and decomposition level based on the statistical characteristics of the signal itself. This customized decomposition strategy avoids the problem of poor decomposition results that may be caused by blindly or empirically selecting wavelet parameters, and significantly improves the accuracy and efficiency of wavelet decomposition in separating low-frequency and high-frequency components related to tool wear. Therefore, key information reflecting the tool wear state can be extracted more accurately, providing a more reliable data foundation for subsequent tool wear state identification and cutting feed rate adjustment, thereby improving the robustness and accuracy of the entire workpiece trimming machine cutting method.

[0177] The beneficial effects of implementing the embodiments of the present invention include: First, the workpiece cutting path data is obtained. Then, based on the motion and vibration characteristics of the trimming machine, the workpiece cutting path data is analyzed to identify the path vibration area. Then, based on the workpiece machining tolerance, the path vibration area is reconstructed to generate a replacement path with controlled kinematic characteristics. Finally, based on the replacement path, the path corresponding to the path vibration area in the original path is replaced to obtain the target path. Based on the target path, the trimming machine is controlled to cut the workpiece. Thus, vibration can be eliminated through path reconstruction to achieve workpiece cutting, thereby improving accuracy and reliability.

[0178] like Figure 2 As shown, this embodiment of the invention also provides a workpiece trimming machine cutting system, comprising:

[0179] Data acquisition module 601 is used to acquire workpiece cutting path data;

[0180] The path analysis module 602 is used to analyze the workpiece cutting path data based on the motion and vibration characteristics of the trimming machine, identify the path vibration area, and the path vibration area is used to represent the area where there are speed fluctuations of the drive motor and local vibration of the tool.

[0181] The path reconstruction module 603 is used to reconstruct the path vibration area according to the workpiece machining tolerance, and generate a replacement path with controlled kinematic characteristics.

[0182] The path replacement module 604 is used to replace the path corresponding to the path vibration area in the original path according to the replacement path to obtain the target path;

[0183] The cutting module 605 is used to control the trimming machine to cut the workpiece according to the target path.

[0184] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0185] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A workpiece trimming machine cutting method, characterized in that, Includes the following steps: Obtain workpiece cutting path data; Based on the motion and vibration characteristics of the trimming machine, the workpiece cutting path data is analyzed to identify the path vibration area, which is used to represent the area where there are speed fluctuations of the drive motor and local vibrations of the tool. Based on the workpiece's machining tolerances, the path vibration region is reconstructed to generate a replacement path with controlled kinematic characteristics. Based on the replacement path, the path corresponding to the vibration area in the original path is replaced to obtain the target path; According to the target path, control the trimming machine to cut the workpiece; The process of reconstructing the path in the vibration region to generate a replacement path with controlled changes in kinematic properties includes: When processing the first batch of workpieces, when the cutting tool is in the vibration area of ​​the path, the first current consumption characteristics of the drive motor of the trimming machine are collected as an initial health benchmark. When processing non-first batch workpieces, when the cutting tool is in the vibration area of ​​the path, the second current consumption characteristics of the drive motor of the trimming machine are collected as real-time current characteristics. Based on the real-time current characteristics and the initial health benchmark, the tool wear state is identified; The cutting feed rate is adjusted according to the tool wear condition. The replacement path is generated based on the adjusted cutting feed rate.

2. The method according to claim 1, characterized in that, The step of identifying the tool wear state based on the real-time current characteristics and the initial health benchmark includes: When machining the first batch of workpieces, the micro-vibration data of the cutting tool is collected when the cutting tool is outside the vibration area of ​​the path. Damping characteristic features are extracted from the micro-vibration data to obtain a damping measurement value; The damping measurement value is compared with the preset damping measurement reference value to determine the current deviation threshold; Based on the current deviation threshold, the real-time current characteristics are compared with the initial health benchmark to identify the tool wear state.

3. The method according to claim 1, characterized in that, The step of adjusting the cutting feed rate according to the tool wear condition includes: If the tool wear condition is that wear exists, then the characteristic deviation is calculated based on the real-time current characteristics and the initial health benchmark; The adjustment range is determined based on the deviation of the described features; The current deviation is calculated based on the real-time current characteristics and the preset current characteristic range; The cutting feed rate is adjusted according to the adjustment range and the current deviation to stabilize the real-time current characteristic within the preset current characteristic range.

4. The method according to claim 3, characterized in that, After adjusting the cutting feed rate according to the adjustment range and the current deviation, the method further includes: Collect ambient temperature data of the cutting zone and chip accumulation status of the tool load area; Extract the frequency components and fluctuation patterns of the real-time current characteristics; Based on the ambient temperature data, the chip accumulation state, the frequency components, and the fluctuation pattern, the changes in current characteristics caused by tool wear are identified. Calculate the deviation amount based on the current characteristic change and the preset current change range; The cutting feed rate is updated based on the change deviation.

5. The method according to claim 4, characterized in that, The step of identifying changes in current characteristics caused by tool wear based on the ambient temperature data, the chip accumulation state, the frequency components, and the fluctuation pattern includes: The power spectral density and the initial time-domain signal of the real-time current characteristics within the preset harmonic frequency range in the cutting region are acquired. The initial time-domain signal is denoised to obtain the target time-domain signal; Based on the ambient temperature data and the chip accumulation state, determine the dynamic monitoring threshold of the power spectral density; The target time-domain signal is smoothed. Calculate the root mean square value and kurtosis based on the smoothed target time-domain signal; If the power spectral density is greater than the dynamic monitoring threshold, the change in current characteristics is identified based on the root mean square value, the kurtosis, the frequency components, and the fluctuation pattern.

6. The method according to claim 5, characterized in that, The power spectral density of the preset harmonic frequency range in the acquisition cutting region includes: Acquire local cutting force fluctuation signals in the cutting area; Frequency analysis was performed on the local cutting force fluctuation signal to obtain the signal frequency characteristics; Based on the signal frequency characteristics, the power spectral density of the local cutting force fluctuation signal within the preset harmonic frequency range is extracted.

7. The method according to claim 5, characterized in that, The step of denoising the initial time-domain signal to obtain the target time-domain signal includes: Acquire power supply voltage fluctuation signals and electromagnetic field intensity signals in the cutting area; The initial time-domain signal is subjected to bandpass filtering. Wavelet decomposition was performed on the initial time-domain signal after bandpass filtering to obtain the low-frequency and high-frequency components related to tool wear. Frequency analysis is performed on the power supply voltage fluctuation signal and the electromagnetic field intensity signal to identify the main interference frequency; Based on the main interference frequency, the low-frequency component and the high-frequency component are corrected; The corrected low-frequency and high-frequency components are reconstructed to obtain the target time-domain signal.

8. The method according to claim 7, characterized in that, The initial time-domain signal after bandpass filtering is decomposed into wavelet components to obtain low-frequency and high-frequency components related to tool wear, including: Statistical analysis is performed on the initial time-domain signal after bandpass filtering to obtain statistical characteristics, including energy distribution, frequency range, and transient impact characteristics. Based on the statistical characteristics, determine the wavelet basis functions and the number of wavelet decomposition layers; Based on the wavelet basis function and the number of wavelet decomposition levels, wavelet decomposition is performed on the initial time-domain signal after bandpass filtering to obtain the low-frequency component and the high-frequency component.

9. A workpiece trimming machine cutting system, characterized in that, include: The data acquisition module is used to acquire workpiece cutting path data; The path analysis module is used to analyze the workpiece cutting path data based on the motion and vibration characteristics of the trimming machine, and identify the path vibration area. The path vibration area is used to indicate the area where there are speed fluctuations of the drive motor and local vibrations of the tool. The path reconstruction module is used to reconstruct the path vibration area according to the workpiece machining tolerance, and generate a replacement path with controlled kinematic characteristics. The process of reconstructing the path in the vibration region to generate a replacement path with controlled changes in kinematic properties includes: When processing the first batch of workpieces, when the cutting tool is in the vibration area of ​​the path, the first current consumption characteristics of the drive motor of the trimming machine are collected as an initial health benchmark. When processing non-first batch workpieces, when the cutting tool is in the vibration area of ​​the path, the second current consumption characteristics of the drive motor of the trimming machine are collected as real-time current characteristics. Based on the real-time current characteristics and the initial health benchmark, the tool wear state is identified; The cutting feed rate is adjusted according to the tool wear condition. The replacement path is generated based on the adjusted cutting feed rate; The path replacement module is used to replace the path corresponding to the path vibration area in the original path according to the replacement path to obtain the target path; The cutting module is used to control the trimming machine to cut the workpiece according to the target path.

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