A precise gear hobbing forming process based on online error compensation

Abstract

The application discloses a precision gear hobbing forming process based on online error compensation and relates to the technical field of precision gear machining.The process comprises the following steps: step S1, constructing a hobbing machining full-process error source model and an error transmission matrix; step S2, building a hobbing machining online multidimensional error detection system, and collecting dynamic error data in the hobbing machining process in real time; step S3, based on the error transmission matrix constructed in step S1, decoupling and traceability analyzing the real-time dynamic error data collected in step S2, and calculating single error contribution of each error source and a gear comprehensive machining error prediction value; step S4, generating a hobbing machine tool multi-axis linkage online error compensation strategy according to the error traceability result and the comprehensive machining error prediction value; and step S5, based on the generated online error compensation strategy, controlling the hobbing machine tool to execute real-time compensation machining, completing precision gear hobbing forming, and performing closed-loop iterative optimization of machining errors.The precision gear hobbing forming process based on online error compensation has the advantages that gear machining precision is improved, machining batch consistency is optimized, and machining waste rate is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of precision gear machining technology, and in particular to a precision gear hobbing process based on online error compensation. Background Technology

[0002] Gear hobbing is one of the most commonly used machining processes in gear forming. Through the generating motion between the hob and the workpiece, gear tooth profiles that meet design requirements are cut out. It is widely used in new energy vehicles, industrial robots, aerospace, precision transmission and other fields. With the rapid development of high-end equipment manufacturing, extremely high requirements have been placed on the machining accuracy, transmission smoothness and batch consistency of gears. Traditional gear hobbing processes can no longer meet the machining needs of high-end precision gears.

[0003] Traditional gear hobbing processes primarily employ offline compensation for errors. This involves acquiring the gear machining errors using offline testing equipment such as coordinate measuring machines (CMMs) or gear measuring centers after the workpiece is machined. Based on the test results, machine tool parameters are manually adjusted or cutting tools are re-sharpened before proceeding with the next batch of workpieces. This offline compensation method suffers from numerous insurmountable drawbacks. Summary of the Invention

[0004] The purpose of this invention is to provide a precision gear hobbing forming process based on online error compensation, which can realize accurate source tracing and real-time compensation of multi-source dynamic errors in gear hobbing, and solve the problems of offline compensation lag, incomplete error decoupling, and insufficient accuracy of multi-axis collaborative compensation in traditional gear hobbing processes.

[0005] This invention provides a precision gear hobbing forming process based on online error compensation, including the following steps: Step S1, constructing an error source model and error transmission matrix for the entire gear hobbing process; Step S2: Build an online multi-dimensional error detection system for gear hobbing to collect dynamic error data in real time during the gear hobbing process; Step S3: Based on the error propagation matrix constructed in step S1, decouple and trace the source of the real-time dynamic error data collected in step S2, and calculate the individual error contribution of each error source and the predicted value of gear comprehensive machining error. Step S4: Based on the error source tracing results and the comprehensive machining error prediction value, generate an online error compensation strategy for multi-axis linkage of the gear hobbing machine. Step S5: Based on the generated online error compensation strategy, control the gear hobbing machine to perform real-time compensation machining, complete the precision gear hobbing, and perform closed-loop iterative optimization of machining errors.

[0006] Preferably, step S1 includes the following steps: Step S11: Historical data collection and preprocessing. Collect historical data of the entire process of gear hobbing under the target machining scenario, including machine tool basic parameter data, tool parameter data, workpiece blank data, historical machining error data, and working condition parameter data. Preprocess the collected historical data, including data cleaning, removal of missing and outlier values, and data standardization, and map data of different dimensions to a preset numerical range. Step S12: Error source classification and core item screening. Classify and hierarchically divide the error sources of the entire gear hobbing process, identify the core error sources that cause gear machining errors, calculate the frequency of occurrence of each type of error source in the statistical period and the weight of its influence on machining errors, and screen out high-frequency and high-impact error sources. Step S13: Error propagation law analysis. Using the multibody system dynamics theory and homogeneous coordinate transformation method, the propagation law of each error source in the gear hobbing forming kinematic chain is analyzed, and the mapping relationship between each error source and the final gear machining error is established. Step S14: Construction of error source model and transfer matrix. Based on the error source hierarchy division results and mapping relationship, construct the error source model of the entire gear hobbing process and generate the corresponding error transfer matrix.

[0007] Preferably, in step S11, the machine tool basic parameter data includes the geometric positioning accuracy, backlash, and transmission chain error of each motion axis of the machine tool; the tool parameter data includes the number of teeth, module, pressure angle, helix angle, edge wear, and radial runout of the hob; the workpiece blank data includes the material, hardness, outer diameter, inner hole accuracy, and end face runout of the blank; the historical machining error data includes the gear tooth profile error, tooth direction error, cumulative tooth pitch error, radial composite error, and common normal length variation; and the working condition parameter data includes the spindle speed, cutting feed rate, cutting depth, machine tool spindle temperature rise, ambient temperature, and cutting force during the machining process.

[0008] Preferably, step S13 includes the following steps: Step S131: Using the bed of the gear hobbing machine as the reference body, the gear hobbing forming motion chain is decomposed into five topological branches: workpiece spindle motion chain, hob spindle motion chain, radial feed motion chain, axial feed motion chain, and differential motion chain. Based on the multibody system dynamics theory, a low-order body array of each motion branch is established. Step S132: For each motion branch, the homogeneous coordinate transformation method is used to construct the homogeneous transformation matrix under the ideal motion state and the homogeneous transformation matrix under the actual motion state, which includes geometric error, thermal error and force-induced deformation error. Step S133: By calculating the difference between the ideal transformation matrix and the actual transformation matrix, the error transformation matrix of each motion branch is obtained, the transmission increment of a single error source in the motion chain is quantified, and the mapping relationship between each error source and the relative pose error between the tool and the workpiece is established.

[0009] Preferably, step S14 includes the following steps: Step S141: Set the gear comprehensive machining accuracy deviation as the top event of the error source model, set the pose error of each motion branch as the first-level intermediate event, and set the single error source as the bottom event to complete the hierarchical construction of the error source model of the entire gear hobbing process. Step S142: Based on the error transformation matrix of each error source, integrate and generate the error transmission matrix of the entire gear hobbing process. The row vectors of the matrix correspond to the various accuracy indicators of gear machining, the column vectors correspond to each individual error source, and the matrix elements are the influence coefficients of the corresponding error source on the corresponding accuracy indicator.

[0010] Preferably, step S2 includes the following steps: Step S21: Sensor deployment and hardware setup. Deploy multiple types of sensors at key locations on the gear hobbing machine and build a multi-dimensional error detection hardware system to collect dynamic error data and working condition data in real time during the machining process. Step S22: Real-time data transmission network construction. The industrial Ethernet EtherCAT protocol is used to build a real-time data transmission network to synchronously transmit the data collected by the sensor to the machine tool CNC system and the host computer data processing unit. The transmission cycle is consistent with the machine tool interpolation cycle. Step S23: Real-time data preprocessing. The data processing unit performs real-time preprocessing on the collected raw data according to the preset sampling frequency, including digital filtering, signal denoising, and data synchronization alignment. A timestamp is added to the preprocessed data to ensure accurate correspondence between the data and the machine tool's movement position. Step S24: Synchronously store the preprocessed real-time data to the local cache and industrial database, and simultaneously input the data to the error decoupling and traceability module in real time to provide data support for subsequent analysis.

[0011] Preferably, in step S21, the sensor deployment and data acquisition specifically include: installing high-precision grating encoders in the radial, axial, and circumferential directions of the hob spindle and the workpiece spindle to collect the spindle's rotation angle error, radial runout error, and axial movement error; installing linear grating rulers and circular gratings at the ends of the feed transmission chains of the machine tool's X, Y, Z, A, and C axes to collect the positioning error, following error, and backlash error of each motion axis; installing platinum resistance temperature sensors at key positions of the machine tool's spindle box, bed, column, and workpiece fixture to collect the temperature rise data and thermal deformation error of key machine tool components; installing three-dimensional force sensors at the bearing seats of the hob spindle and the workpiece spindle to collect cutting force data during the machining process and calculate the system deformation error caused by the cutting force; installing a laser displacement sensor at the hob spindle to collect the hob's edge wear and radial runout in real time; and collecting machining process parameters such as spindle speed, feed rate, and depth of cut in real time through the machine tool's CNC system.

[0012] Preferably, step S3 includes the following steps: Step S31: Establish an error decoupling equation set. Substitute the real-time dynamic error data collected in step S2 into the error propagation matrix constructed in step S1 to establish an error decoupling equation set. Step S32: Multi-source error decoupling calculation. An algorithm combining improved least squares method and wavelet threshold denoising is used to solve the error decoupling equation set, and various individual error sources such as geometric error, thermal error, force-induced deformation error, tool wear error and clamping error are separated, and the real-time values ​​of each individual error source are calculated. Step S33, Error Contribution Calculation and Error Prediction: Based on the influence coefficients in the error transmission matrix, calculate the individual contribution of each error source to the gear tooth profile, tooth direction, and tooth pitch machining accuracy indicators, as well as the comprehensive machining error prediction value after coupling all error sources. Step S34: Dynamic model optimization. Using the sliding time window method, historical processing data and error samples are continuously updated to dynamically optimize the influence coefficients in the error propagation matrix and correct the error source tracing results and error prediction values.

[0013] Preferably, step S4 includes the following steps: Step S41: Compensation trigger judgment. Based on the accuracy requirements of the gear design drawings, set the allowable threshold for each machining error. Compare the comprehensive machining error prediction value calculated in step S3 with the allowable threshold. When the measured value exceeds the threshold, online error compensation is triggered. Step S42: Establish a compensation optimization model. Based on the error source tracing results, with the goal of minimizing the comprehensive gear machining error, and with the travel, maximum feed rate, and interpolation cycle of each motion axis of the machine tool as constraints, establish a multi-axis linkage error compensation optimization model. Step S43: Compensation amount solution and interpolation command generation. The particle swarm optimization algorithm is used to solve the compensation optimization model, calculate the real-time compensation amount of each motion axis of the computer tool (X-axis, Y-axis, Z-axis, A-axis, C-axis), and generate the motion trajectory correction interpolation command for the corresponding axis. Step S44: Synchronous matching of generating motion. Combining the generating motion characteristics of gear hobbing, the compensation interpolation commands of each axis are synchronously matched to ensure that the relative motion trajectory between the tool and the workpiece conforms to the gear involute forming principle, and the final multi-axis linkage online error compensation strategy is generated.

[0014] Preferably, step S5 includes the following steps: Step S51: Real-time compensation execution. The interpolation command corresponding to the generated online error compensation strategy is sent to the CNC system of the gear hobbing machine in real time via the EtherCAT bus. The CNC system superimposes the real-time compensation amount of each axis on the original machining command and controls each motion axis of the machine tool to execute the compensated motion trajectory. Step S52, Iterative compensation of the processing process: During the compensation processing, the online detection system of step S2 continuously collects the error data of the processing process and verifies the compensation effect in real time. If the error still exceeds the preset threshold after compensation, steps S3 to S5 are repeated to perform dynamic iterative compensation. Step S53: In-machine accuracy inspection. After the single workpiece is processed, the gear's various accuracy indicators are inspected in-machine using an in-machine measurement system to obtain the actual processing error value. Step S54: Closed-loop iterative optimization. Compare the actual processing error value with the predicted error value, calculate the prediction deviation, and optimize the error propagation matrix and compensation optimization model in reverse based on the deviation value to complete the closed-loop iterative optimization of the processing error. At the same time, store the entire processing data in the historical database for continuous optimization of the subsequent model.

[0015] Therefore, the present invention adopts the above-mentioned precision gear hobbing forming process based on online error compensation to achieve accurate source tracing and real-time compensation of multi-source dynamic errors in gear hobbing, and solves the problems of offline compensation lag, incomplete error decoupling, and insufficient accuracy of multi-axis collaborative compensation in traditional gear hobbing processes.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall structure and flow of a precision gear hobbing process based on online error compensation according to the present invention. Figure 2 This is a flowchart illustrating the construction of a full-process error source model and error propagation matrix in a precision gear hobbing process based on online error compensation, according to the present invention. Figure 3 This is a schematic diagram of the process for building an online multi-dimensional error detection system for gear hobbing in a precision gear hobbing forming process based on online error compensation, according to the present invention. Figure 4 This is a schematic diagram of the process of decoupling and tracing the real-time dynamic error data based on the error transfer matrix in a precision gear hobbing process based on online error compensation, and calculating the individual error contribution of each error source and the predicted value of the gear comprehensive machining error. Figure 5 This is a schematic diagram illustrating the error decoupling and source analysis process in a precision gear hobbing process based on online error compensation according to the present invention. Figure 6 This is a schematic diagram illustrating the process of generating and executing an online error compensation strategy in a precision gear hobbing process based on online error compensation according to the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] The terms "first," "second," and similar words used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example 1 like Figures 1-6 As shown, the present invention provides a precision gear hobbing process based on online error compensation, comprising the following steps: Step S1: Construct the error source model and error propagation matrix for the entire gear hobbing process.

[0022] Step S1 includes the following steps: Step S11, Historical data collection and preprocessing, collecting 12 months of batch processing history data of gear hobbing under the target processing scenario, including machine tool basic parameter data, tool parameter data, workpiece blank data, historical processing error data, and working condition parameter data.

[0023] In step S11, the machine tool basic parameter data includes the geometric positioning accuracy, backlash, and transmission chain error of each motion axis of the machine tool. The tool parameter data includes the number of teeth, module, pressure angle, helix angle, edge wear, and radial runout of the hob. The workpiece blank data includes the blank's material, hardness, outer diameter, internal hole accuracy, and end face runout. Historical machining error data includes gear tooth profile error, tooth direction error, cumulative tooth pitch error, radial composite error, and variation in common normal length. Operating condition parameter data includes the spindle speed, feed rate, depth of cut, machine tool spindle temperature rise, ambient temperature, and cutting force during the machining process.

[0024] The collected historical data is preprocessed, including data cleaning, removal of missing and outlier values, and data standardization, mapping data of different dimensions to a preset numerical range.

[0025] Outliers were removed using the 3σ criterion, and missing values ​​were supplemented using linear interpolation. Data of different dimensions were uniformly mapped to the [0,1] interval using a minimum-maximum standardization formula, as shown in the following equation: ; in, This is the original data; and These are the maximum and minimum values ​​of the data feature, respectively; This is the standardized data.

[0026] Step S12: Error source classification and core item screening. Classify and hierarchically divide the error sources of the entire gear hobbing process, identify the core error sources that cause gear machining errors, calculate the frequency of occurrence of each type of error source in the statistical period and the weight of its influence on machining errors, and screen out high-frequency and high-impact error sources.

[0027] Step S13: Error propagation law analysis and establishment of low-order body arrays for each branch. Using multibody system dynamics theory and homogeneous coordinate transformation method, the propagation law of each error source in the gear hobbing forming kinematic chain is analyzed, and the mapping relationship between each error source and the final gear machining error is established.

[0028] Step S13 includes the following steps: Step S131: Using the bed of the gear hobbing machine as the reference body, the gear hobbing forming motion chain is decomposed into five topological branches: workpiece spindle motion chain, hob spindle motion chain, radial feed motion chain, axial feed motion chain, and differential motion chain. Based on the multibody system dynamics theory, a low-order body array of each motion branch is established.

[0029] Step S132: For each motion branch, the homogeneous coordinate transformation method is used to construct the homogeneous transformation matrix under the ideal motion state and the homogeneous transformation matrix under the actual motion state, which includes geometric error, thermal error and force-induced deformation error.

[0030] Step S133: By calculating the difference between the ideal transformation matrix and the actual transformation matrix, the error transformation matrix of each motion branch is obtained, the transmission increment of a single error source in the motion chain is quantified, and the mapping relationship between each error source and the relative pose error between the tool and the workpiece is established.

[0031] Step S14: Construction of error source model and transfer matrix. Based on the error source hierarchy division results and mapping relationship, construct the error source model of the entire gear hobbing process and generate the corresponding error transfer matrix.

[0032] Step S14 includes the following steps: Step S141: Set the gear comprehensive machining accuracy deviation as the top event of the error source model, set the pose error of each motion branch as the first-level intermediate event, and set the single error source as the bottom event to complete the hierarchical construction of the error source model of the entire gear hobbing process.

[0033] Step S142: Based on the error transformation matrix of each error source, integrate and generate the error transmission matrix of the entire gear hobbing process. The row vectors of the matrix correspond to the various accuracy indicators of gear machining, the column vectors correspond to each individual error source, and the matrix elements are the influence coefficients of the corresponding error source on the corresponding accuracy indicator.

[0034] The "gear machining accuracy exceeds grade 4 tolerance" is set as the top event of the error source model, the pose errors of the 5 motion branches are set as first-level intermediate events, and the 12 core error sources are set as bottom events, thus completing the construction of the hierarchical error source model. Based on the error transformation matrix of each error source, a 12×5-dimensional error transmission matrix is ​​generated, and the matrix elements are the influence coefficients of the corresponding error source on the corresponding accuracy index.

[0035] Step S2: Build an online multi-dimensional error detection system for gear hobbing to collect dynamic error data in real time during the gear hobbing process.

[0036] Step S2 includes the following steps: Step S21, sensor deployment and hardware construction, deploying multiple types of sensors at key positions of the gear hobbing machine, building a multi-dimensional error detection hardware system, and collecting dynamic error data and working condition data in real time during the machining process.

[0037] In step S21, the sensor deployment and data acquisition specifically include: installing 23-bit high-precision grating encoders in the radial, axial, and circumferential directions on the hob spindle and workpiece spindle to collect the spindle's rotation angle error, radial runout error, and axial movement error. Installing linear and circular gratings at the ends of the feed transmission chains of the machine tool's X, Y, Z, A, and C axes to collect the positioning error, following error, and backlash error of each motion axis. Installing platinum resistance temperature sensors at key locations on the machine tool's spindle box, bed, column, and workpiece fixture to collect temperature rise data and thermal deformation errors of key machine tool components; installing three-dimensional force sensors at the bearing seats of the hob spindle and workpiece spindle to collect cutting force data during machining and calculate the system deformation error caused by the cutting force; installing a laser displacement sensor at the hob spindle to collect the hob's edge wear and radial runout in real time; and collecting machining process parameters such as spindle speed, feed rate, and depth of cut in real time through the machine tool's CNC system.

[0038] Step S22: Real-time data transmission network. An industrial Ethernet EtherCAT protocol is used to build a real-time data transmission network with a transmission period of 1ms, fully synchronized with the interpolation period of the machine tool CNC system. A unique device ID is configured for each sensor terminal, and a network encryption key is set. A CRC cyclic redundancy check algorithm is used to ensure the accuracy of data transmission, synchronously transmitting the data collected by the sensors to the machine tool CNC system and the host computer data processing unit. The transmission period remains consistent with the machine tool interpolation period.

[0039] Step S23: Real-time data preprocessing. A data processing unit is set up to perform real-time preprocessing on the collected raw data according to the preset sampling frequency, including digital filtering, signal denoising, and data synchronization alignment. A timestamp is added to the preprocessed data to ensure accurate correspondence between the data and the machine tool's movement position.

[0040] The data processing unit processes the raw data at a sampling frequency of 1kHz, and uses a moving average filtering algorithm to denoise the data. The filter window size is set to 5, as shown in the following formula: ; in, for t - k Raw data collected in real time; Set the size of the filtering window; perform multi-sensor time synchronization alignment on the filtered data, and add a timestamp with an accuracy of 0.1ms to ensure that the data accurately corresponds to the machine tool's movement position.

[0041] Step S24, Data Storage and Distribution: The preprocessed real-time data is synchronously stored in the local cache and industrial database, and the data is simultaneously input to the error decoupling and traceability module in real time to provide data support for subsequent analysis.

[0042] Step S3, Error Decoupling and Source Analysis: Based on the error propagation matrix constructed in Step S1, decoupling and source analysis are performed on the real-time dynamic error data collected in Step S2 to calculate the individual error contribution of each error source and the predicted value of the gear comprehensive machining error.

[0043] Step S3 includes the following steps: Step S31: Establish an error decoupling equation set. Substitute the real-time dynamic error data collected in step S2 into the error propagation matrix constructed in step S1 to establish an error decoupling equation set.

[0044] Step S32: Multi-source error decoupling calculation. An algorithm combining improved least squares method and wavelet threshold denoising is used to solve the error decoupling equation set, and various individual error sources such as geometric error, thermal error, force-induced deformation error, tool wear error and clamping error are separated, and the real-time values ​​of each individual error source are calculated. Step S33: Based on the influence coefficients in the error propagation matrix, calculate the individual contribution of each error source to the gear tooth profile, tooth direction, and tooth pitch machining accuracy indicators, as well as the comprehensive machining error prediction value after coupling all error sources. Step S34: Using the sliding time window method, continuously update historical processing data and error samples, dynamically optimize the influence coefficients in the error propagation matrix, and correct the error source tracing results and error prediction values.

[0045] Step S4: Based on the error source tracing results and the comprehensive machining error prediction value, generate an online error compensation strategy for multi-axis linkage of the gear hobbing machine.

[0046] Step S4 includes the following steps: Step S41: Compensation trigger judgment. Based on the accuracy requirements of the gear design drawings, set the allowable thresholds for various machining errors. Set the allowable threshold for tooth profile error to 4μm, the allowable threshold for tooth direction error to 5μm, and the allowable threshold for cumulative tooth pitch error to 8μm. Compare the comprehensive machining error prediction value calculated in step S3 with the allowable thresholds. When the measured value exceeds the threshold, online error compensation is triggered.

[0047] Step S42: Establish a compensation optimization model. Based on the error source tracing results, with the goal of minimizing the comprehensive gear machining error, and with the travel, maximum feed rate, and 1ms interpolation cycle of each motion axis of the machine tool as constraints, establish a multi-axis linkage error compensation optimization model.

[0048] Step S43: Solving for compensation amount and generating interpolation instructions. The particle swarm optimization algorithm is used to solve the compensation optimization model, calculate the real-time compensation amount of each motion axis of the computer tool (X-axis, Y-axis, Z-axis, A-axis, and C-axis), and generate the motion trajectory correction interpolation instructions for the corresponding axes.

[0049] Step S44: Synchronous matching of generating motion. Combining the generating motion characteristics of gear hobbing, the compensation interpolation commands of each axis are synchronously matched to ensure that the relative motion trajectory between the tool and the workpiece conforms to the gear involute forming principle, and the final multi-axis linkage online error compensation strategy is generated.

[0050] Step S5: Based on the generated online error compensation strategy, control the gear hobbing machine to perform real-time compensation machining, complete the precision gear hobbing, and perform closed-loop iterative optimization of machining errors.

[0051] Step S5 includes the following steps: Step S51: Real-time compensation execution. The interpolation command corresponding to the generated online error compensation strategy is sent to the CNC system of the gear hobbing machine in real time via the EtherCAT bus. The CNC system superimposes the real-time compensation amount of each axis on the original machining command and controls each motion axis of the machine tool to execute the compensated motion trajectory.

[0052] Step S52: Iterative compensation during the processing. During the compensation process, the online detection system of step S2 continuously collects error data during the processing and verifies the compensation effect in real time. If the error still exceeds the preset threshold after compensation, steps S3 to S5 are repeated for dynamic iterative compensation.

[0053] Step S53: In-machine accuracy inspection. After the single workpiece is processed, the gear's various accuracy indicators are inspected in-machine using an in-machine measurement system to obtain the actual processing error value.

[0054] Step S54: Closed-loop iterative optimization. Compare the actual processing error value with the predicted error value, calculate the prediction deviation, and optimize the error propagation matrix and compensation optimization model in reverse based on the deviation value to complete the closed-loop iterative optimization of the processing error. At the same time, store the entire process data of this processing into the historical database for continuous optimization of the subsequent model.

[0055] Therefore, the precision gear hobbing process based on online error compensation described above can significantly improve gear machining accuracy, greatly optimize batch consistency, effectively reduce scrap rate, and simultaneously improve machining efficiency and reduce overall cost; the process has strong adaptability.

[0056] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; 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 still be made to the technical solutions of the present invention; and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A precision gear hobbing process based on online error compensation, characterized in that, Includes the following steps: Step S1: Construct the error source model and error propagation matrix for the entire gear hobbing process; Step S2: Build an online multi-dimensional error detection system for gear hobbing to collect dynamic error data in real time during the gear hobbing process; Step S3: Based on the error propagation matrix constructed in step S1, decouple and trace the source of the real-time dynamic error data collected in step S2, and calculate the individual error contribution of each error source and the predicted value of gear comprehensive machining error. Step S4: Based on the error source tracing results and the comprehensive machining error prediction value, generate an online error compensation strategy for multi-axis linkage of the gear hobbing machine. Step S5: Based on the generated online error compensation strategy, control the gear hobbing machine to perform real-time compensation machining, complete the precision gear hobbing, and perform closed-loop iterative optimization of machining errors.

2. The precision gear hobbing process based on online error compensation according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Historical data collection and preprocessing. Collect historical data of the entire process of gear hobbing under the target machining scenario, including machine tool basic parameter data, tool parameter data, workpiece blank data, historical machining error data, and working condition parameter data. Preprocess the collected historical data, including data cleaning, removal of missing and outlier values, and data standardization, and map data of different dimensions to a preset numerical range. Step S12: Error source classification and core item screening. Classify and hierarchically divide the error sources of the entire gear hobbing process, identify the core error sources that cause gear machining errors, calculate the frequency of occurrence of each type of error source in the statistical period and the weight of its influence on machining errors, and screen out high-frequency and high-impact error sources. Step S13: Error propagation law analysis. Using the multibody system dynamics theory and homogeneous coordinate transformation method, the propagation law of each error source in the gear hobbing forming kinematic chain is analyzed, and the mapping relationship between each error source and the final gear machining error is established. Step S14: Construction of error source model and transfer matrix. Based on the error source hierarchy division results and mapping relationship, construct the error source model of the entire gear hobbing process and generate the corresponding error transfer matrix.

3. The precision gear hobbing process based on online error compensation according to claim 2, characterized in that, In step S11, the machine tool basic parameter data includes the geometric positioning accuracy, backlash, and transmission chain error of each motion axis of the machine tool; the tool parameter data includes the number of teeth, module, pressure angle, helix angle, edge wear, and radial runout of the hob; the workpiece blank data includes the material, hardness, outer diameter, inner hole accuracy, and end face runout of the blank; the historical machining error data includes the gear tooth profile error, tooth direction error, cumulative tooth pitch error, radial composite error, and common normal length variation; and the working condition parameter data includes the spindle speed, cutting feed rate, cutting depth, machine tool spindle temperature rise, ambient temperature, and cutting force during the machining process.

4. The precision gear hobbing process based on online error compensation according to claim 2, characterized in that, Step S13 includes the following steps: Step S131: Using the bed of the gear hobbing machine as the reference body, the gear hobbing forming motion chain is decomposed into five topological branches: workpiece spindle motion chain, hob spindle motion chain, radial feed motion chain, axial feed motion chain, and differential motion chain. Based on the multibody system dynamics theory, a low-order body array of each motion branch is established. Step S132: For each motion branch, the homogeneous coordinate transformation method is used to construct the homogeneous transformation matrix under the ideal motion state and the homogeneous transformation matrix under the actual motion state, which includes geometric error, thermal error and force-induced deformation error. Step S133: By calculating the difference between the ideal transformation matrix and the actual transformation matrix, the error transformation matrix of each motion branch is obtained, the transmission increment of a single error source in the motion chain is quantified, and the mapping relationship between each error source and the relative pose error between the tool and the workpiece is established.

5. The precision gear hobbing process based on online error compensation according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: Set the gear comprehensive machining accuracy deviation as the top event of the error source model, set the pose error of each motion branch as the first-level intermediate event, and set the single error source as the bottom event to complete the hierarchical construction of the error source model of the entire gear hobbing process. Step S142: Based on the error transformation matrix of each error source, integrate and generate the error transmission matrix of the entire gear hobbing process. The row vectors of the matrix correspond to the various accuracy indicators of gear machining, the column vectors correspond to each individual error source, and the matrix elements are the influence coefficients of the corresponding error source on the corresponding accuracy indicator.

6. The precision gear hobbing process based on online error compensation according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Sensor deployment and hardware setup. Deploy multiple types of sensors at key locations on the gear hobbing machine and build a multi-dimensional error detection hardware system to collect dynamic error data and working condition data in real time during the machining process. Step S22: Real-time data transmission network construction. The industrial Ethernet EtherCAT protocol is used to build a real-time data transmission network to synchronously transmit the data collected by the sensor to the machine tool CNC system and the host computer data processing unit. The transmission cycle is consistent with the machine tool interpolation cycle. Step S23: Real-time data preprocessing. The data processing unit performs real-time preprocessing on the collected raw data according to the preset sampling frequency, including digital filtering, signal denoising, and data synchronization alignment. A timestamp is added to the preprocessed data to ensure accurate correspondence between the data and the machine tool's movement position. Step S24, Data Storage and Distribution: The preprocessed real-time data is synchronously stored in the local cache and industrial database, and the data is simultaneously input to the error decoupling and traceability module in real time to provide data support for subsequent analysis.

7. The precision gear hobbing process based on online error compensation according to claim 6, characterized in that, In step S21, the sensor deployment and data acquisition specifically include: installing high-precision grating encoders in the radial, axial, and circumferential directions on the hob spindle and workpiece spindle to collect the spindle's rotation angle error, radial runout error, and axial movement error; installing linear and circular gratings at the ends of the feed transmission chains of the machine tool's X, Y, Z, A, and C axes to collect the positioning error, following error, and backlash error of each motion axis; installing platinum resistance temperature sensors at key locations on the machine tool's spindle box, bed, column, and workpiece fixture to collect the temperature rise data and thermal deformation error of key machine tool components; installing three-dimensional force sensors at the bearing seats of the hob spindle and workpiece spindle to collect cutting force data during the machining process and calculate the system deformation error caused by the cutting force; installing a laser displacement sensor at the hob spindle to collect the hob's edge wear and radial runout in real time; and collecting machining process parameters such as spindle speed, feed rate, and depth of cut in real time through the machine tool's CNC system.

8. The precision gear hobbing process based on online error compensation according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Establish an error decoupling equation set. Substitute the real-time dynamic error data collected in step S2 into the error propagation matrix constructed in step S1 to establish an error decoupling equation set. Step S32: Multi-source error decoupling calculation. An algorithm combining improved least squares method and wavelet threshold denoising is used to solve the error decoupling equation set, and various individual error sources such as geometric error, thermal error, force-induced deformation error, tool wear error and clamping error are separated, and the real-time values ​​of each individual error source are calculated. Step S33, Error Contribution Calculation and Error Prediction: Based on the influence coefficients in the error transmission matrix, calculate the individual contribution of each error source to the gear tooth profile, tooth direction, and tooth pitch machining accuracy indicators, as well as the comprehensive machining error prediction value after coupling all error sources. Step S34: Dynamic model optimization. Using the sliding time window method, historical processing data and error samples are continuously updated to dynamically optimize the influence coefficients in the error propagation matrix and correct the error source tracing results and error prediction values.

9. The precision gear hobbing process based on online error compensation according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Compensation trigger judgment. Based on the accuracy requirements of the gear design drawings, set the allowable threshold for each machining error. Compare the comprehensive machining error prediction value calculated in step S3 with the allowable threshold. When the measured value exceeds the threshold, online error compensation is triggered. Step S42: Establish a compensation optimization model. Based on the error source tracing results, with the goal of minimizing the comprehensive gear machining error, and with the travel, maximum feed rate, and interpolation cycle of each motion axis of the machine tool as constraints, establish a multi-axis linkage error compensation optimization model. Step S43: Compensation amount solution and interpolation command generation. The particle swarm optimization algorithm is used to solve the compensation optimization model, calculate the real-time compensation amount of each motion axis of the computer tool (X-axis, Y-axis, Z-axis, A-axis, C-axis), and generate the motion trajectory correction interpolation command for the corresponding axis. Step S44: Synchronous matching of generating motion. Combining the generating motion characteristics of gear hobbing, the compensation interpolation commands of each axis are synchronously matched to ensure that the relative motion trajectory between the tool and the workpiece conforms to the gear involute forming principle, and the final multi-axis linkage online error compensation strategy is generated.

10. The precision gear hobbing process based on online error compensation according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Real-time compensation execution. The interpolation command corresponding to the generated online error compensation strategy is sent to the CNC system of the gear hobbing machine in real time via the EtherCAT bus. The CNC system superimposes the real-time compensation amount of each axis on the original machining command and controls each motion axis of the machine tool to execute the compensated motion trajectory. Step S52, Iterative compensation of the processing process: During the compensation processing, the online detection system of step S2 continuously collects the error data of the processing process and verifies the compensation effect in real time. If the error still exceeds the preset threshold after compensation, steps S3 to S5 are repeated to perform dynamic iterative compensation. Step S53: In-machine accuracy inspection. After the single workpiece is processed, the gear's various accuracy indicators are inspected in-machine using an in-machine measurement system to obtain the actual processing error value. Step S54: Closed-loop iterative optimization. Compare the actual processing error value with the predicted error value, calculate the prediction deviation, and optimize the error propagation matrix and compensation optimization model in reverse based on the deviation value to complete the closed-loop iterative optimization of the processing error. At the same time, store the entire processing data in the historical database for continuous optimization of the subsequent model.

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