Intelligent optimization system for machining process parameters of hanger wheel assembly

Abstract

The application relates to the technical field of industrial automation control, and discloses an intelligent optimization system for machining process parameters of a hanger wheel assembly, which comprises a multi-dimensional data synchronous acquisition unit, an angle domain static atlas construction unit, a phase advance constraint control unit and an angle trigger execution unit. The application establishes a static angle-load topology atlas indexed by a main shaft angle, calculates a phase advance angle in combination with inherent physical response lag time of a servo system, directly injects a parameter adjustment instruction sequence into a servo current loop at a preset angle window before physical load impact occurs, and the feedforward control mechanism based on angle domain reconstruction and phase advance constraint avoids the physical lag problem of traditional time domain feedback control when coping with high-frequency intermittent cutting, and realizes zero-time-difference and even predictive torque compensation for periodic load impact.

Description

Technical Field

[0001] This invention relates to an intelligent optimization system for the processing parameters of a gear train assembly, belonging to the field of industrial automation control technology. Background Technology

[0002] In current CNC machining of change gear components, maintaining a balanced cutting load is crucial to ensuring part quality and tool life. Existing process parameter control generally adopts an adaptive mode based on real-time load feedback, which monitors the spindle load and compares it with a threshold to adjust the feed rate and stabilize the cutting power. This control logic is widely used in continuous and stable cutting conditions. However, change gear machining exhibits typical high-frequency intermittent cutting characteristics, with load changes being both abrupt and periodic. Under these conditions, time-domain feedback strategies face the challenge of physical response lag. Limited by signal filtering, CNC system interpolation cycle, and servo motor mechanical inertia, there is a phase delay from the system sensing a sudden load change to the actuator completing speed adjustment. Under the high-frequency impact of intermittent cutting, this phase lag leads to a mismatch between the control action and the actual cutting state.

[0003] To overcome the physical limitations of hardware response, the industry has seen attempts to optimize parameters using digital means. For example, Chinese invention patent CN120542897A discloses an automated optimization method for production line process parameters based on digital twin technology. This solution establishes a production line rotation model and performs multi-weight simulation to solve the problem of decreased transmission efficiency caused by equipment aging. Such solutions are based on global parameter optimization using quasi-static models. The simulation correction logic focuses on long-cycle macro-efficiency matching and trend adjustment. The calculation and response rhythm is slower than the transient changes in the cutting process. When facing millisecond-level load steps in gear machining, the lack of a phase advance prediction mechanism makes it difficult to overcome the inherent electromechanical inertia bottleneck of servo systems and achieve zero-time-difference torque compensation within the extremely short window of tool entry.

[0004] Therefore, how to avoid the limitations of time-domain feedback in a control system with physical lag and achieve accurate compensation for high-frequency intermittent cutting impact has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: an intelligent optimization system for the processing parameters of a gear assembly, comprising: a multi-dimensional data synchronous acquisition unit, connected to a servo driver and a position encoder, using a hardware interrupt signal to trigger parallel latching logic, and establishing a hard synchronous mapping interface between the spindle load current time series and the spindle angular position series.

[0006] The angular domain static map construction unit performs a dimensional transformation from the time domain to the angular domain. By aligning the phases of multi-cycle synchronous data and performing statistical filtering, it generates a static angle-load topology map with the principal axis angle as the unique index and the load current characteristic value as the mapping object. This map is used to eliminate the time dimension variable of servo control.

[0007] The phase lead constraint control unit calculates the phase lead angle based on the product of the inherent physical response lag time of the servo system and the real-time angular velocity of the spindle, and generates a parameter adjustment command sequence with negative time axis offset attribute according to the distribution position of the load change feature area in the static angle-load topology map.

[0008] The angle-triggered execution unit locks the current angular position of the spindle in real time during the control operation and is controlled by the phase lead angle. It directly injects a parameter adjustment command sequence into the servo current loop within a preset angle window before the physical load impact occurs, so as to achieve zero-time-difference torque compensation for periodic intermittent cutting loads at the physical level.

[0009] Preferably, the angle domain static map construction unit further includes map dynamic migration logic to handle individual differences in the workpiece, including: a feature anchor point capture module, which monitors the first derivative of the load current in the first waveform of the control cycle, locks the instantaneous phase angle and the actual peak load at the rising edge of the actual waveform; a deviation operator solution module, which calculates the phase offset between the instantaneous phase angle and the reference phase angle in the static angle-load topology map, and calculates the amplitude scaling factor between the actual peak load and the reference peak load; and an execution map correction module, which performs a translation transformation on the index axis of the static angle-load topology map using the phase offset, and performs linear gain scaling on the map load value using the amplitude scaling factor to generate a temporary execution map adapted to the current controlled object.

[0010] Preferably, the angle domain static map construction unit further includes a dynamic zeroing logic for the idle window to eliminate environmental thermal drift interference, including: a window locking module, which identifies non-operating angle intervals in the static angle-load topology map where both the load current and its rate of change are below a preset threshold, and marks these intervals as zero-point calibration windows; a dynamic bias update module, which collects real-time load current and calculates the DC component drift value when the spindle rotates to the zero-point calibration window; and a real-time correction module, which subtracts the most recently calculated DC component drift value from the real-time collected load current signal when the spindle rotates to the operating angle interval, and outputs the corrected load feedback data.

[0011] Preferably, the system further includes a virtual stiffness compensation unit to offset mechanical elastic deformation errors. This unit includes: a stiffness model storage module that stores the equivalent stiffness coefficient of the controlled object; a deformation estimation module that calculates the expected elastic deformation of the controlled object at the current angle in real time based on the load current value and the equivalent stiffness coefficient in the static angle-load topology diagram; and a superposition control injection module that generates a micro-displacement compensation command with the same amplitude and opposite direction as the expected elastic deformation, and directly superimposes the command onto the position loop control node of the feed axis, maintaining the geometric position accuracy of the system independently of the velocity loop control command.

[0012] Preferably, the phase lead constraint control unit further includes a gain adaptive evolution module for compensating for resistance changes caused by tool wear. This module includes: a residual extraction submodule, which calculates the control residual sequence between the measured load current and the preset target load value in real time during each machining cycle; an energy integration submodule, which performs integration on the control residual sequence within the cutting angle range to generate a periodic residual energy index characterizing the degree of mismatch in the feedforward model; and a gain scheduling submodule, which updates the global feedforward gain coefficient based on the periodic residual energy index. The phase lead constraint control unit uses this coefficient to dynamically scale the amplitude of the parameter adjustment command sequence.

[0013] Preferably, the gain scheduling submodule updates the global feedforward gain coefficient using a cumulative step algorithm based on residual energy, following the following update rule: ,in, This refers to the updated global feedforward gain coefficient. The global feedforward gain coefficient for the current cycle. The preset learning rate step size, The energy index for the periodic residual is obtained by integrating the absolute value of the control residual sequence over the cutting interval.

[0014] Preferably, the phase lead angle in the phase lead constraint control unit is determined by multiplying the sum of the current loop filtering delay time of the servo system, the calculation interpolation cycle, and the inertial hysteresis constant of the mechanical transmission chain with the real-time angular velocity of the spindle. This phase lead angle defines the advance of the control command relative to the position of the physical load in the angular domain coordinate system.

[0015] Preferably, the multi-dimensional data synchronous acquisition unit uses a field-programmable gate array (FPGA) as the hardware carrier, and triggers parallel data latching through hardware interrupt signals to ensure that the time deviation between the load current sampling time and the position encoder counting time is less than one-tenth of the servo control cycle.

[0016] Preferably, the parameter adjustment command sequence includes a feed rate adjustment command, and the phase lead constraint control unit is configured with inverse mapping logic. This logic establishes an inverse proportional gain function between the feed rate and the cutting load, generates a low feed rate command in the high load characteristic region of the static angle-load topology map, and generates a high feed rate command in the low load characteristic region.

[0017] Preferably, the angular domain static map construction unit includes a periodic overlay submodule, which is used to acquire the raw cutting data stream of multiple consecutive rotation cycles and to use a synchronous averaging algorithm to overlay and average the load data at the same angular index position to eliminate non-periodic random noise interference.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. During the machining of the gear train assembly, the spindle angle position and load current sequence are collected synchronously to construct a load mapping spectrum indexed by angle, which serves as the time reference for feedforward control. By utilizing the periodic characteristics of gear train machining, the time-domain load fluctuation is transformed into the angular-domain spatial characteristics. Combined with the inherent response lag time of the servo system, the phase lead angle is calculated, and the feed adjustment command is issued before the physical moment of load change, thereby achieving predictive compensation for high-frequency intermittent cutting impact. This eliminates the response lag caused by signal filtering delay and servo mechanical inertia in traditional feedback control, ensuring that the feed axis completes speed adjustment the instant the tool enters the workpiece.

[0019] 2. By utilizing the non-cutting angle range of the intermittent cutting process, a dynamic zero-point calibration logic based on process characteristics is established. In each rotation cycle, the idle window in the load spectrum is identified and the current reference value under the window is collected. The thermal drift of the sensor and amplification circuit is obtained in real time and subtracted in the subsequent cutting data processing. This self-calibration mechanism enables the control system to maintain the absolute stability of the cutting load judgment threshold throughout the entire process from cold start to hot equilibrium of the equipment, avoiding interference with control accuracy from changes in ambient temperature.

[0020] 3. To address the issue of nonlinear growth in cutting resistance caused by tool wear, a gain adaptive evolution mechanism based on residual energy integration is introduced. This mechanism calculates the control residual between the actual load and the target load in real time, extracts the energy integral of the residual sequence within the cutting interval as an evaluation index, characterizing the degree of mismatch between the current feedforward model and the actual physical object. Based on the evaluation index, the global gain coefficient of the feedforward command is dynamically corrected, enabling the control system to automatically sense and compensate for the drift of process system characteristics caused by tool aging, ensuring consistent machining quality throughout the entire tool lifecycle without the need for manual adjustment of control parameters. An inverse model of the elastic modulus of the process system is established, converting the current signal in the angle domain load spectrum into elastic deformation, generating micro-displacement compensation commands opposite to the deformation direction, and superimposing these commands into the feed axis position loop control flow, independent of the speed loop power regulation logic. This dual-channel decoupled control strategy maintains constant cutting power through speed variation and uses virtual stiffness injection to offset mechanical tool deflection errors caused by cutting force fluctuations, maintaining the geometric accuracy of the tooth profile when machining high-hardness or large-module gears on rigid machine tools. Attached Figure Description

[0021] Figure 1 This is a block diagram illustrating the system control principle of the angular domain mapping and phase lead constraint of the present invention.

[0022] Figure 2 This is a static angular load topology curve diagram of the processing feature area distribution in this invention.

[0023] Figure 3This is a diagram showing the hardware hierarchy architecture and zero-time-difference closed-loop data interaction topology of the system of this invention. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] The intelligent optimization system for the machining process parameters of the gear train assembly provided by this invention consists of four core functional modules: a multi-dimensional data synchronous acquisition unit, an angle domain static map construction unit, a phase advance constraint control unit, and an angle trigger execution unit. These modules interact via a high-speed fieldbus or shared memory mechanism, forming a closed-loop feedforward control topology with the spindle angle as the absolute reference and the load current as the controlled object. When controlling the high-frequency load impact during gear train machining, the inherent physical time constants of the servo motor, mechanical transmission chain, and current loop filtering in the CNC system prevent traditional real-time feedback-based control strategies from achieving millisecond-level control. The system responds instantly to intermittent inputs. It adopts a periodic load feedforward compensation mechanism based on angular domain mapping. The multi-dimensional data synchronous acquisition unit is connected to the current monitoring port of the machine tool servo drive and the high-speed pulse output port of the spindle position encoder. This unit is equipped with hardware parallel latch logic based on field-programmable gate array (FPGA). It uses hardware interrupt signals to synchronously trigger the acquisition of the spindle load current time series and spindle angular position series, ensuring that the time deviation between the load current sampling time and the position encoder counting time is strictly controlled within one-tenth of the servo control cycle, thereby providing a high-fidelity synchronous data source for subsequent signal reconstruction.

[0026] The angular domain static map construction unit performs dimensional transformation from the time domain to the angular domain. To address the non-periodic random noise in the original acquired data, this unit employs a periodic superposition synchronous averaging algorithm. Specifically, the system acquires the original cutting data stream from multiple consecutive spindle rotation cycles, accumulates the load data at the same angular position using the spindle angle as an index, and takes the arithmetic mean. This process eliminates random disturbances during the cutting process, generating a static angle-load topology map that accurately characterizes the load variation patterns at the tool entry point, exit point, and hard spot positions. This map is based on the spindle angle... As a unique independent variable index, based on the load current characteristic value As the mapping object, time-dimensional variables are eliminated in the control logic, establishing a deterministic space-load mapping relationship; the phase lead constraint control unit is responsible for generating predictive control commands. Addressing the physical delay between the servo system receiving the command and outputting the torque, this unit pre-stores the system's inherent total lag time, including the current loop filtering delay time, the interpolation cycle, and the mechanical transmission chain inertial lag constant. During the control cycle, this unit reads the current angular velocity of the spindle in real time. And according to the formula Real-time calculation of phase lead angle Based on the static angle-load topology map, this unit identifies the characteristic region of load abrupt change and generates corresponding parameter adjustment commands, such as feed rate adjustment or torque compensation commands, for this region. The unit shifts the execution trigger time of the above commands forward by a phase lead angle in the angle domain coordinate system. This generates a sequence of parameter adjustment instructions with a negative time axis offset, ensuring that the control action is initiated before the physical load impact occurs, thus offsetting the response lag of the physical system.

[0027] For the generation of feed rate adjustment commands in the parameter adjustment command sequence, the phase lead constraint control unit is equipped with inverse mapping logic based on the constant power cutting principle. This aims to establish a nonlinear inverse proportional relationship between the feed rate and load characteristic values ​​in the static angle-load topology map. The system pre-sets the reference cutting load target value. and maximum permissible feed rate For each discrete angle index in the map Calculate the corresponding feed rate adjustment command Its operation follows the inverse proportional gain function. ,in The characteristic value of the load current at this angle is shown in the graph. The power balance coefficient, used to adjust response sensitivity, is automatically generated when the spectrum shows a high load peak in a certain angular range. The feed rate command is used to limit the cutting force; when in the low load range, a higher feed rate command is generated to improve machining efficiency. The generated feed rate command sequence is phase-shifted to... The index is stored in the buffer to ensure that the CNC system stores the index before the tool enters the high-load area. The deceleration action begins immediately, thereby maintaining the dynamic constancy of instantaneous cutting power. The angle triggering execution unit acts as a real-time scheduler during machining. This unit locks the current angular position of the spindle in real time and compares it with the instruction sequence index generated by the phase advance constraint control unit. When the spindle rotates to the preset angle window before the physical load impact occurs, that is, when the actual angle reaches the target characteristic angle minus the phase advance angle, this unit directly bypasses the conventional speed loop feedback logic and injects the parameter adjustment instruction sequence into the current loop summation point of the servo system. Through this procedure, the servo motor has completed the establishment of pre-acceleration or pre-deceleration electromagnetic torque at the moment when the tool actually contacts the workpiece tooth groove, realizing zero-time-difference torque compensation for periodic intermittent cutting loads.

[0028] To address individual workpiece variations caused by installation eccentricity or batch-wise fluctuations in material hardness in the gear blank, the angular domain static map construction unit is further configured with dynamic map migration logic. During the first cutting cycle of each new workpiece machining process, this logic activates the feature anchor point capture module, which monitors the first derivative of the load current with respect to the angle in real time. It also locks the moment when the derivative crosses the preset trigger threshold at the first rising edge, and records the actual instantaneous phase angle at that moment. and actual peak load The deviation operator solution module calls the corresponding reference ingress phase angle in the static angle-load topology map. and benchmark peak load Calculate the phase offset and amplitude ratio coefficient The execution map correction module, based on the affine transformation principle, performs a translation transformation on the angle index axis of the original map using a phase offset, and performs linear gain scaling on the load values ​​in the map using an amplitude scaling factor, generating a temporary execution map adapted to the current specific workpiece. This procedure eliminates the need for full-cycle relearning for each workpiece, enabling the control model to achieve transient adaptation to individual differences. To avoid the problem of zero-point drift of the current sensor caused by changes in workshop ambient temperature, the angle domain static map construction unit incorporates dynamic zeroing logic for the idle stroke window. Utilizing the non-cutting idle stroke that inevitably exists during the intermittent cutting process of the change wheel, it identifies load currents and their rates of change that are low in the static angle-load topology map. Within a continuous angular range of a preset no-load threshold, this range is marked as a zero-point calibration window. In each subsequent spindle rotation cycle, when the spindle angle enters this zero-point calibration window, the dynamic offset update module triggers high-frequency sampling and calculates the arithmetic mean of the current data within the window, using it as the current DC component drift value. When the spindle rotation angle enters the cutting operation range, the real-time correction module subtracts the most recently updated DC component drift value from the real-time acquired load current signal. This dynamic calibration mechanism eliminates the influence of thermal drift on the cutting load measurement accuracy without the need for an external constant temperature reference source, ensuring the consistency of the control system's judgment throughout the entire process of cold start-up and hot balance.

[0029] To address the issue of elastic deformation in the machining system caused by cutting force fluctuations, which in turn affects tooth profile accuracy, the system integrates a virtual stiffness compensation unit. This unit pre-stores the equivalent stiffness coefficients of the controlled object obtained through static stiffness experiments. During the processing, the deformation estimation module uses the load current value in the static angle-load topology diagram. Using the inverse model of Hooke's Law ,in The current-to-force conversion coefficient is used to calculate the expected elastic deformation at the current angle in real time. The superposition control injection module generates micro-displacement compensation commands with equal amplitude but opposite direction based on this. The position loop control node of the feed axis servo drive is superimposed on the position loop control node. The direct intervention of this position loop is independent of the power regulation logic of the speed loop. Through microscale reverse displacement injection, it offsets the mechanical tool deflection error caused by physical cutting force and maintains the geometric position accuracy of the system. To solve the problem of insufficient feedforward compensation caused by nonlinear increase in cutting resistance due to tool wear, the phase lead constraint control unit is equipped with a gain adaptive evolution module. The residual extraction submodule in this module calculates the difference between the measured load current and the preset target load value in real time in each machining cycle to generate a control residual sequence. The energy integration submodule selects the control residual sequence within the cutting angle range, calculates the integral of the absolute value, and generates a periodic residual energy index. This index quantifies the degree of mismatch between the current feedforward model gain and the actual cutting state. The gain scheduling submodule uses a cumulative step algorithm to update the global feedforward gain coefficient, and the update rule follows the formula. ,in, This is the updated gain coefficient for the next cycle. This is the current cycle coefficient. With a preset learning rate step size, the phase lead constraint control unit uses the real-time updated global feedforward gain coefficient to dynamically scale the amplitude of the parameter adjustment command sequence, thereby automatically compensating for the increase in cutting resistance caused by tool wear and maintaining constant control performance throughout the entire life cycle.

[0030] Example 1: In a high-speed intermittent cutting application scenario for large-module gear components, the workpiece material is high-strength alloy steel. The machining process is accompanied by periodic and severe cutting force impacts, and the spindle speed is set within the range of 200 rpm to 500 rpm, resulting in the time interval between adjacent tooth cutting actions being shortened to the millisecond level. Under this condition, conventional servo control systems are limited by current loop filtering delay and mechanical transmission chain inertia, and cannot output compensation torque in time at the moment of tool entry, causing a momentary drop in spindle speed and inducing machining vibration. This example utilizes a multi-dimensional data synchronous acquisition unit connected to the servo driver and position encoder. In the first trial cutting stage, through the hardware parallel latching logic of the field-programmable gate array (FPGA), the spindle load current time series and spindle angular position series are synchronously acquired with a high time resolution of one-tenth of the servo control cycle. The angular domain static map construction unit then performs a time-domain to angular domain transformation on the synchronous data, and uses a periodic superposition synchronous averaging algorithm to process the data of 5 to 10 consecutive rotation cycles, filtering out random noise, and generating a sequence based on the spindle angle. To uniquely index and characterize the static angle-load topology map representing the variation law of the cutting force of a specific change gear, the phase lead constraint control unit reads the real-time angular velocity of the spindle. And call the pre-calibrated stored system inherent total lag time (This time is obtained by summing the current loop filtering delay time, the interpolation period, and the inertial hysteresis constant of the mechanical transmission chain), according to the formula The system calculates the phase lead angle in real time, identifies the entry characteristic region of a sharp rise in load current based on the static angle-load topology map, generates the corresponding torque compensation command, and shifts the trigger position of the command forward in the angle coordinate system. The angle-triggered execution unit locks the spindle position in real time and injects the torque compensation command directly into the servo current loop at the preset angle window before the physical cutting impact occurs. This feedforward control mechanism enables the servo motor to establish an electromagnetic torque that matches the cutting resistance at the physical moment when the tool actually contacts the workpiece, controlling the spindle speed fluctuation within 0.1% of the rated speed and avoiding tooth profile errors caused by response lag.

[0031] Example 2: To verify the effectiveness of the technical solution of this invention in a real engineering environment, an experimental verification platform containing real physical loads and environmental interference was built. This platform uses a high-speed gear hobbing machine equipped with a Fanuc0i-MF CNC system as its core. The spindle drive motor has a rated power of 15kW and a maximum speed of 6000 rpm. The experimental workpiece is a 20CrMnTi alloy steel gear blank with a module of 4 and 48 teeth. To simulate electromagnetic interference in a real industrial environment, a 50Hz power frequency interference signal with an amplitude of 0.5A is actively superimposed on the current feedback loop of the servo driver, and Gaussian white noise with a signal-to-noise ratio of 20dB is introduced. Simultaneously, to verify the system's stability against environmental thermal drift, the temperature in the experimental workshop is controlled by an environmental control system at 15°C for 24 hours. Up to 35 They fluctuate periodically between each other.

[0032] The experimental process is as follows: In the comparative sample group, the multi-dimensional data synchronous acquisition unit and phase advance constraint control unit of the present invention were turned off, and cutting was performed solely based on the speed loop and current loop feedback logic built into the CNC system. In the sample group of the present invention, the entire intelligent optimization system was activated. The system performed no-load and trial cutting data acquisition for the first rotation cycle, constructed a static angular domain spectrum, and the phase advance constraint control unit based on the real-time rotation speed and the calibrated total lag time. The phase lead angle is calculated, and a feedforward torque command is generated. To quantify and evaluate the control effect, the angular velocity fluctuation of the spindle is monitored in real time using a high-precision laser vibrometer, and the actual q-axis current response of the servo motor is recorded using a current probe. The comparative analysis of key intermediate data reveals the inherent operating mechanism of the present invention. As shown in Table 1, in the comparative sample group, when the tool cuts into the workpiece (time point...) Due to physical response lag, the establishment of the electromagnetic torque lags behind the load change by approximately 12ms, causing a momentary drop in spindle speed with a peak-to-peak speed fluctuation of 18.5 rpm. However, in the prototype of this invention, thanks to the introduction of a phase lead angle, the electromagnetic torque is established before the tool enters the spindle. The process begins at the moment of entry ( The torque has reached the load balance point.

[0033] Table 1: Comparison of Key Performance Indicators under Different Control Strategies

[0034]

[0035] Furthermore, to verify the effectiveness of the dynamic migration logic of the graph and the adaptive evolution module of the gain, a long-cycle continuous machining test was conducted. When machining the 50th workpiece, a workpiece eccentricity error of 0.05 mm was artificially introduced. The rotational speed fluctuation of the comparison sample group deteriorated to more than 25 rpm, while the sample group of the present invention, through feature anchor point capture and graph correction, brought the rotational speed fluctuation back to within 0.5 rpm within 2 cutting cycles. As tool wear increased (to the 150th piece), the machining error of the comparison sample group showed a linear divergence trend, while the sample group of the present invention used residual energy integral feedback to dynamically increase the feedforward gain coefficient, so that the tooth profile error of the 150th piece was still kept within the accuracy range of 4.0 μm, verifying the adaptive capability of the system throughout its entire life cycle.

[0036] Example 3: This example combines Figures 1 to 3 The explanation of the intelligent optimization system for the processing parameters of the train roller assembly is as follows: Figure 1 As shown, the system architecture demonstrates a complete closed-loop logic flow from signal acquisition to command execution. The starting end consists of a servo driver and a position encoder, responsible for outputting the original current sequence, spindle angular position, and real-time angular velocity signals. These signals are input to a multi-dimensional data synchronous acquisition unit, which transmits the data to the angular domain static map construction unit through an FPGA hardware parallel latch and hard synchronization mapping interface. Here, the transformation from the time domain to the angular domain and the generation of the static angle-load topology are completed. The data flows to the phase lead constraint control unit, which is responsible for calculating the phase lead angle and generating a feedforward command sequence. Finally, the angle trigger execution unit injects commands into the preset angle window to achieve zero-time-difference torque compensation. At the same time, the system is supplemented by map dynamic migration logic to handle individual differences in the workpiece and feature anchor point capture, configured idle window dynamic zeroing logic to eliminate environmental thermal drift, set up a gain adaptive evolution module to compensate for tool wear and perform residual energy integration, and integrate a virtual stiffness compensation unit to offset elastic deformation and inject micro-displacement compensation. All modules work together to achieve feed rate adjustment and physical load impact compensation at the servo current loop control node.

[0037] like Figure 2 As shown, this graph is built in a two-dimensional coordinate system. The horizontal axis represents the spindle angle, covering a complete rotation cycle from 0° to 360°. The vertical axis represents the load current, with a scale range from 0A to 20A. The core of the graph is a static angle trajectory curve formed by a dashed line, vividly illustrating the load variation during the gear cutting process. The entry feature zone near 80° is clearly marked, where the load current shows an upward trend. It reaches a hard point at 170°, corresponding to the peak load current region. The curve enters the exit feature zone at 260°, where the load current decreases accordingly. Figure 3As shown, the entire system is divided into three core parts: the physical processing and driving end, the high-speed hardware synchronization end, and the intelligent optimization calculation center. The physical processing and driving end includes servo motors, encoders, and intelligent servo drivers. Its output position and current signal sequences are transmitted to the high-speed hardware synchronization end through the dotted line. This end is equipped with an FPGA parallel latch terminal to process the hard synchronization mapping data stream. The data is sent to the intelligent optimization calculation center, i.e., the industrial control host, which includes an angle domain static map construction engine and a phase advance constraint control core, for processing. The calculated feedforward torque compensation command with preset angle window injection attributes is fed back to the physical processing and driving end, thereby constructing a zero-time-difference closed-loop control loop.

[0038] Example 4: Addressing the problem that traditional PI control parameter tuning relies on manual experience and is difficult to adapt to time-varying operating conditions during gear machining, this example constructs a gain adaptive evolution procedure based on residual energy index. Instead of relying on complex mathematical models for identification, it utilizes the error signals naturally generated during system operation to achieve online self-optimization of the feedforward gain. In the initial stage of system operation, a parameter initialization step is performed, and the system adjusts the global feedforward gain coefficients... The initial value is set to and adjust the gain correction step size Set as The initial value is chosen based on the theoretical stiffness model of the servo system, while the step size is set to balance convergence speed and stability, avoiding system oscillations caused by excessively large step sizes. The system enters a periodic iterative optimization process, and at the 1st... Within each processing cycle, the phase lead constraint control unit adjusts its control based on the current gain coefficient. Generate feedforward commands and collect the actual load current in real time during the cutting process. With target load current The system calculates the difference between the two, i.e., the control residual. To quantitatively assess the matching degree of the current gain coefficient, the system calculates the cutting angle range for that cycle. Residual energy index within The calculation formula is: This indicator directly reflects the total deviation between the feedforward compensation and the actual cutting resistance. A large value indicates that the current feedforward gain is insufficient to offset the cutting force or is too large, leading to overcompensation.

[0039] Based on the calculated residual energy index, the gain scheduling submodule updates the gain coefficient according to the gradient descent principle, with the update rule being: ,in The sign function of the average residual for this period is given. If the average residual is positive (i.e., the actual load is greater than the target load), it indicates insufficient compensation, and the gain coefficient will increase; conversely, it will decrease. The system continues to execute the above iterative process until continuous... If the rate of change of the residual energy index over a given period is less than a preset convergence threshold (e.g., 2%), it is determined that the gain coefficient has converged to the optimal value under the current operating conditions. However, if a change in the operating conditions is detected, such as a change in tooling or a change in material batch, the convergence is considered complete. In the event of a sudden change, the system automatically resets the step size. And restart the fast search process.

[0040] Example 5: Before deploying the intelligent optimization system for the machining process parameters of this gear train assembly to the actual production line, a standardized system initialization calibration and parameter calibration procedure is executed to ensure accurate matching between the control strategy and the specific physical characteristics of the machine tool. This procedure aims to quantitatively determine the key physical parameters in the control model, including the total lag time of the servo system. and the equivalent stiffness coefficient of the process system In the machine tool's no-load state, the spindle is controlled to rotate at a series of stepped speeds, while a sweep frequency current signal ranging from 10Hz to 1000Hz is injected into the servo driver. The time difference between the current command and the actual current response is recorded using a multi-dimensional data synchronous acquisition unit. By weighted averaging the phase lag at different frequency points, the inherent total lag time of the system, including the current loop filtering delay and the inertia of the mechanical transmission chain, is measured. .

[0041] Static calibration of the machining system stiffness is performed by applying a series of known static radial forces to the spindle with the spindle stationary using a force sensor, and measuring the elastic deformation of the spindle in the force direction using a high-precision displacement sensor. By recording multiple sets of force-displacement data points, a linear regression curve is fitted using the least squares method, and the slope is the equivalent stiffness coefficient of the machining system. The coefficient is stored in the virtual stiffness compensation unit, which is used to convert the load current value into the expected elastic deformation in real time during the cutting process. In addition, before machining a specific type of change gear for the first time, the system performs an idle feature learning process. The spindle rotates slowly for one revolution under no load. The angular domain static map construction unit records the no-load current reference curve during this process, identifies the inherent load fluctuation characteristics caused by mechanical assembly errors or uneven friction, and deducts them in the subsequent cutting load monitoring.

[0042] Example 6: In the engineering configuration stage before batch processing of specific model change gears in this system, to eliminate the uncertainty in the control model introduced by differences in machine tool motor characteristics and workpiece geometry, a standardized identification procedure including current-force conversion coefficient calibration and angle resolution definition is executed. This procedure targets the core current-force conversion coefficient in the virtual stiffness compensation unit. With the spindle servo locked, the system guides an external standard force gauge to apply a stepped, increasing static load covering the rated cutting force range along the tangent of the workpiece's pitch circle. The q-axis current value fed back by the synchronous parallel latch servo driver The least squares linear regression operation was performed on multiple sets of load-current data obtained, and the slope of the resulting regression line was solidified as a dedicated conversion coefficient for the machine tool. This establishes a deterministic mapping benchmark between the current signal and the physical cutting force.

[0043] Meanwhile, to ensure that the maps generated by the angular domain static map construction unit can accurately reproduce the microscopic load characteristics during the intermittent cutting process, the system determines the number of teeth on the workpiece to be processed. In addition to the feature fidelity constraints determined by Shannon's sampling theorem, the formula is used. Calculate the optimal angle discretization resolution ,in To determine the minimum number of sampling points required to analyze the load waveform within a single tooth pitch, with a value not less than 20, the system calculates the result and divides the corner domain index address in the controller's storage space to establish a high-precision data container adapted to the current workpiece geometry.

[0044] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A hanging wheel assembly processing process parameter intelligent optimization system, characterized in that, The application relates to a servo control system for a workpiece cutting machine, which comprises the following units: a multi-dimensional data synchronous acquisition unit, which is connected to a servo driver and a position encoder, uses a hardware interrupt signal to trigger parallel latching logic, and establishes a hard-synchronous mapping interface between a spindle load current time sequence and a spindle angle position sequence; an angle-domain static atlas construction unit, which performs dimension transformation from a time domain to an angle domain, generates a static angle-load topology atlas with a spindle angle as a unique index and a load current characteristic value as a mapping object through phase alignment and statistical filtering of multi-cycle synchronous data, and uses the load topology atlas to eliminate time dimension variables of servo control; a phase lead constraint control unit, which calculates a phase lead angle based on a product of inherent physical response lag time of the servo system and real-time angular velocity of the spindle, and generates a parameter adjustment instruction sequence with a negative time axis offset attribute according to a distribution position of a load mutation feature area in the static angle-load topology atlas; and an angle trigger execution unit, which locks a current angle position of the spindle in real time during control operation, is controlled by the phase lead angle, directly injects the parameter adjustment instruction sequence into a servo current loop in a preset angle window before a physical load impact occurs, and realizes zero-time-difference torque compensation for a periodic intermittent cutting load at a physical level. The angle-domain static atlas construction unit further comprises atlas dynamic migration logic for processing workpiece individual differences, which comprises a feature anchor point capture module, a deviation operator calculation module, and an execution atlas correction module. The feature anchor point capture module monitors a load current first-order derivative in a first waveform of a control cycle, locks a transient phase angle of an actual waveform rising edge and an actual peak load; the deviation operator calculation module calculates a phase offset between the transient phase angle and a reference phase angle in the static angle-load topology atlas, and calculates an amplitude proportional coefficient between the actual peak load and a reference peak load; and the execution atlas correction module performs a translation transformation on an index axis of the static angle-load topology atlas by using the phase offset, and performs a linear gain scaling on atlas load values by using the amplitude proportional coefficient, to generate a temporary execution atlas adapted to a current control object.

2. The system of claim 1, wherein, The angle-domain static atlas construction unit further comprises an idle stroke window dynamic zeroing logic for eliminating environmental thermal drift interference, which comprises a window locking module, a dynamic bias updating module, and a real-time correction module. The window locking module identifies a non-working angle interval with a load current and a change rate of the load current both lower than a preset threshold in the static angle-load topology atlas, and marks the interval as a zero-point calibration window; the dynamic bias updating module collects real-time load current and calculates a direct current component drift value when the spindle rotates to the zero-point calibration window; and the real-time correction module subtracts the direct current component drift value calculated last time from the real-time collected load current signal when the spindle rotates to a working angle interval, and outputs corrected load feedback data.

3. The system of claim 1, wherein, ​ 4. The system of claim 1, wherein, The system further comprises a virtual stiffness compensation unit for offsetting the mechanical elastic deformation error, the unit comprising: a stiffness model storage module storing equivalent stiffness coefficients of the controlled object; a deformation amount estimation module for calculating the expected elastic deformation amount of the controlled object at the current angle based on the load current value in the static angle-load topological atlas and the equivalent stiffness coefficients; and a superimposed control injection module for generating a micro-displacement compensation instruction with the same magnitude and opposite direction of the expected elastic deformation amount, and directly superimposing the instruction to the position loop control node of the feed shaft, independent of the speed loop control instruction to maintain the geometric position accuracy of the system.

5. The system of claim 1, wherein, The phase lead constraint control unit further comprises a gain adaptive evolution module for compensating for the resistance change caused by tool wear, the module comprising: a residual extraction submodule for calculating the control residual sequence between the measured load current and the preset target load value in each machining period; an energy integration submodule for performing integral operation on the control residual sequence in the cutting angle interval to generate a period residual energy index representing the mismatch degree of the feedforward model; and a gain scheduling submodule for updating the global feedforward gain coefficient based on the period residual energy index, and the phase lead constraint control unit uses the coefficient to dynamically scale the amplitude of the parameter adjustment instruction sequence.

6. The system of claim 5, wherein, The gain scheduling sub-module adopts a cumulative step algorithm based on residual energy to update the global feedforward gain coefficient, and the update rule is: wherein, is the updated global feedforward gain coefficient, is the global feedforward gain coefficient of the current period, is a preset learning rate step, is a period residual energy index, which is obtained by integrating the absolute value of the control residual sequence in the cutting interval.

7. The system of claim 1, wherein, The phase lead angle in the phase lead constraint control unit is determined based on the product of the sum of the current loop filter delay time of the servo system, the interpolation calculation period and the inertia lag constant of the mechanical transmission chain and the real-time angular velocity of the spindle, and the phase lead angle defines the advance amount of the control instruction relative to the position of the physical load in the angle domain coordinate system.

8. The system of claim 1, wherein, The multi-dimensional data synchronous acquisition unit uses a field programmable gate array (FPGA) as a hardware carrier, and triggers parallel latching of data through a hardware interrupt signal.

9. The system of claim 1, wherein, The parameter adjustment instruction sequence includes a feed rate adjustment instruction, and the phase lead constraint control unit is configured with inverse mapping logic, which establishes an inverse proportional gain function between the feed rate and the cutting load, generates a low feed rate instruction in the high load characteristic region of the static angle-load topological atlas, and generates a high feed rate instruction in the low load characteristic region.

10. The system of claim 1, wherein, The angle domain static atlas construction unit comprises a period superposition submodule for obtaining original cutting data streams of a plurality of continuous rotation periods, and performing superimposed average processing on load data at the same angle index position by using a synchronous average algorithm.

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