A method and system for multi-process adaptive collaborative control of boring machine spindle

Abstract

This invention relates to the field of boring machine spindle technology, and more particularly to a multi-process adaptive collaborative control method and system for boring machine spindles. The method includes: acquiring the multi-process sequence; sequentially collecting temperature field distribution data for heat treatment processes and machining status data for turning and grinding processes; constructing a dynamic error propagation model; analyzing the temperature field distribution data and the machining status data to identify the dominant error process; determining the position of the error process in the multi-process sequence; generating collaborative control commands to correct the machining parameters of subsequent processes or deriving and pre-compensating the machining parameters of the error process; adjusting the output of the heat treatment equipment based on the derived machining parameters to construct an anti-deformation temperature field inside the workpiece; and adjusting the machining center equipment parameters based on the corrected machining parameters of subsequent processes to dynamically compensate the machining parameters of the error process. This invention effectively controls the machining accuracy of multiple processes on the spindle and reduces spindle coaxiality machining errors.

Description

Technical Field

[0001] This invention relates to the field of boring machine spindle technology, and in particular to a multi-process adaptive collaborative control method and system for boring machine spindles. Background Technology

[0002] As the core component of a high-precision boring machine, the manufacturing quality of the boring machine spindle directly determines the machining accuracy and performance of the entire machine. Its manufacturing process usually includes multiple key processes such as heat treatment, turning, and grinding. Coaxiality is one of the core indicators for measuring the accuracy of the boring machine spindle. It refers to the consistency requirement of the rotation axis overlap between the key shaft sections of the spindle.

[0003] In the aforementioned multi-process manufacturing process, the machining errors of each process will inevitably be transmitted and accumulated in subsequent processes. For example, the residual stress and deformation generated by the heat treatment process, the contour deviation of the turning process, and the force-induced deformation of the grinding process will all affect each other through a certain error transmission path, and ultimately manifest as the total coaxiality error of the workpiece.

[0004] Existing technologies typically employ either process-independent control or terminal feedback compensation. Process-independent control focuses on improving the accuracy of a single process, which can optimize locally but cannot solve the systemic accumulation problem caused by the transmission, evolution, and superposition of errors between processes, and lacks traceability. Terminal feedback compensation, as a post-event remedial measure, can only passively correct the final accumulated error and cannot reach the source process of the error. Not only is the compensation efficiency low, but repeated grinding may also damage the surface integrity of the workpiece and increase production costs.

[0005] Therefore, the key to improving the final accuracy of the boring machine spindle lies in how to coordinate and control the errors in the multi-process manufacturing process. Summary of the Invention

[0006] In view of at least one of the above technical problems, the present invention provides a multi-process adaptive collaborative control method and system for boring machine spindles. By constructing a dynamic error propagation model to trace the source of the dominant error process, and adaptively selecting a feedforward pre-compensation or feedback dynamic compensation strategy based on the position of the process in the process chain, the present invention achieves cross-process collaborative control and active error suppression, and ultimately improves the machining coaxiality of the boring machine spindle.

[0007] This invention provides a multi-process adaptive collaborative control method for boring machine spindles, comprising:

[0008] S1: Obtain the multi-process sequence of heat treatment, turning and grinding processes, wherein the multi-process sequence includes the collected temperature field distribution data of the heat treatment process and the processing status data of the turning and grinding processes;

[0009] S2: Based on the multi-process sequence, construct a dynamic error propagation model, analyze the temperature field distribution data and the processing status data, and identify the error process that plays a dominant role in the total coaxiality error;

[0010] S3: Determine the position of the erroneous process in the multi-process sequence, generate a coordinated control instruction, correct the processing parameters of subsequent processes, or derive and pre-compensate the processing parameters of the erroneous process;

[0011] S4: Perform corresponding coordinated operations based on the aforementioned coordinated control instructions;

[0012] Based on the deduced processing parameters of the error process, the output of the heat treatment equipment is adjusted to construct an anti-deformation temperature field inside the workpiece;

[0013] Based on the correction of the processing parameters of subsequent processes, the parameters of the machining center equipment are adjusted to dynamically compensate for the processing parameters of the error process.

[0014] In some embodiments of the present invention, a dynamic error propagation model is constructed to identify error processes in real time, including:

[0015] Based on the multi-process sequence, establish the input-output relationship between processes and determine the error propagation path of each process to subsequent processes;

[0016] The temperature field distribution data is correlated with the processing status data, and the internal state parameters of the workpiece at the end of each process are calculated.

[0017] Following the error propagation path, the impact of the processing error of each process on subsequent processes is calculated sequentially based on the state parameters of each process.

[0018] By comparing the influence of each process, the error process that plays a dominant role in the total coaxiality error is identified.

[0019] In some embodiments of the present invention, the calculation of the internal state parameters of the workpiece at the end of each process includes:

[0020] Based on the temperature field distribution data, the residual stress state inside the workpiece and the microstructure state of the workpiece surface are calculated after the heat treatment process, and the state parameters of the heat treatment process are obtained.

[0021] Based on the machining status data and the heat treatment process status parameters, the contour deviation state of the workpiece and the new distribution state of the surface stress of the workpiece are calculated after the turning process, and the turning process status parameters are obtained.

[0022] Based on the machining status data and the turning process status parameters, the coaxiality deviation prediction status of the workpiece after the grinding process is calculated, and the grinding process status parameters are obtained.

[0023] In some embodiments of the present invention, calculating the impact of the processing error of each process on subsequent processes includes:

[0024] After the first process of the error propagation path is completed, the initial coaxial offset of the workpiece is detected.

[0025] After the second process along the error propagation path is completed, the real-time workpiece offset is detected.

[0026] The new offset is calculated based on the real-time workpiece offset and the initial coaxial offset, and is denoted as the influence amount of the first process;

[0027] The subsequent processes are calculated sequentially along the error propagation path, and the influence of the preceding process is obtained based on the difference between the real-time workpiece offset of the preceding process and the following process.

[0028] In some embodiments of the present invention, determining the position of the erroneous step in the multi-step process sequence and generating a coordinated control instruction includes:

[0029] Determine whether the erroneous process is the last process in the multi-process sequence;

[0030] If the error process is determined to be the final process, the coordinated control instruction is generated, and the processing parameters of the error process are derived and pre-compensated.

[0031] If it is determined that the error process is not the final process, the coordinated control instruction is generated to correct the processing parameters of subsequent processes.

[0032] In some embodiments of the present invention, the derivation and pre-compensation of the error processing parameters includes:

[0033] Based on the error process, obtain the actual output result that has been identified, and deduce the ideal output result during the processing of the error process.

[0034] By comparing the actual output result with the ideal output result, the output deviation is obtained, and the adjustment amount of the processing parameters required to compensate for the output deviation is obtained.

[0035] The adjustment amount is used as a pre-compensation value and applied to the processing parameters in the subsequent error processing steps.

[0036] In some embodiments of the present invention, the modification of processing parameters for subsequent processes includes:

[0037] Based on the impact of the error process output on subsequent processes, predict the additional errors that they will generate when processing workpieces with the machining errors of the error process.

[0038] Based on the additional error, the compensation amount for the machining parameters in subsequent processes is calculated in conjunction with the total coaxiality error;

[0039] The compensation amount is input into the processing equipment of the subsequent process to correct the processing parameters and offset the additional error transmitted from the error process.

[0040] In some embodiments of the present invention, step S5 is further included:

[0041] Collect measured coaxiality data of the workpiece processed after the aforementioned collaborative operation;

[0042] By combining the measured coaxiality data with the dynamic error propagation model, model deviation data is generated;

[0043] Based on the model bias data, the intrinsic parameters of the dynamic error propagation model are corrected.

[0044] The present invention also provides a multi-process adaptive collaborative control system for boring machine spindles, comprising:

[0045] The data acquisition module sequentially collects temperature field distribution data for the heat treatment process and machining status data for the turning and grinding processes; thus obtaining the multi-process sequence of the heat treatment, turning, and grinding processes.

[0046] The analysis and identification module constructs a dynamic error propagation model based on the multi-process sequence, analyzes the temperature field distribution data and the processing status data, and identifies the error process that plays a dominant role in the total coaxiality error.

[0047] The collaborative judgment module determines the position of the erroneous process in the multi-process sequence, generates collaborative control instructions, corrects the processing parameters of subsequent processes, or derives and pre-compensates the processing parameters of the erroneous process.

[0048] The cross-order coordination module performs corresponding coordination operations based on the coordination control instructions;

[0049] Based on the deduced processing parameters of the error process, the output of the heat treatment equipment is adjusted to construct an anti-deformation temperature field inside the workpiece;

[0050] Based on the correction of the processing parameters of subsequent processes, the parameters of the machining center equipment are adjusted to dynamically compensate for the processing parameters of the error process.

[0051] In some embodiments of the present invention, the analysis and identification module includes:

[0052] Error path unit: Based on the multi-process sequence, establish the input-output relationship between processes and determine the error propagation path of each process to subsequent processes;

[0053] State calculation unit: associates the temperature field distribution data with the processing state data, and calculates the internal state parameters of the workpiece at the end of each process;

[0054] Error Influence Unit: Based on the error propagation path, calculate the impact of the processing error of each process on subsequent processes according to the state parameters of each process.

[0055] Error identification unit: By comparing the influence of each process, identify the error process that plays a dominant role in the total coaxiality error.

[0056] The beneficial effects of this invention are as follows: By constructing a dynamic error propagation model through different process sequences, this invention achieves the effect of tracing the source of error generation, transmission and accumulation in the multi-process manufacturing process of heat treatment, turning and grinding. It can accurately identify the key processes that play a dominant role in the total coaxiality error. Furthermore, according to the different locations of the error source, it adaptively selects feedforward pre-compensation or feedback dynamic compensation strategies, forming a cross-process collaborative control mechanism, which effectively reduces rework and scrap caused by precision deviations, and improves manufacturing efficiency and economic benefits while ensuring high-quality production. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram illustrating the steps of the multi-process adaptive collaborative control method for boring machine spindles in an embodiment of the present invention;

[0059] Figure 2 This is a flowchart illustrating the process of constructing a dynamic error propagation model and identifying error processes in real time, as described in this embodiment of the invention.

[0060] Figure 3 This is a schematic diagram of the process for calculating the state parameters of the heat treatment process in an embodiment of the present invention;

[0061] Figure 4 This is a flowchart illustrating the calculation of turning process state parameters in an embodiment of the present invention.

[0062] Figure 5This is a schematic diagram of the process for calculating the state parameters of the grinding process in an embodiment of the present invention;

[0063] Figure 6 This is a schematic diagram of the process for determining the position of an error process and generating a collaborative control command to adjust the processing parameters in an embodiment of the present invention. Detailed Implementation

[0064] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0065] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0067] This invention provides a method such as Figures 1 to 6 The multi-process adaptive collaborative control method for boring machine spindles shown includes:

[0068] S1: Obtain the multi-process sequence of heat treatment, turning, and grinding operations. The multi-process sequence includes the collected temperature field distribution data of the heat treatment operation and the processing status data of the turning and grinding operations. Specifically, the processing sequence of heat treatment, turning, and grinding operations is not the same or may occur intermittently under different processing requirements and environments. Therefore, it is necessary to obtain the real-time multi-process sequence according to the specific situation, which also includes different data for the corresponding operations. Next, multiple sets of thermocouples or infrared thermal imagers arranged in the heat treatment furnace can be used to collect the temperature field distribution data of the workpiece in the heat treatment operation at a predetermined frequency. This data includes the temperature values ​​of each area in the furnace, the heating, holding, or cooling curves of the workpiece, and the temperature uniformity of the workpiece surface. In the turning and grinding operations, the processing status data is collected by force sensors, vibration sensors, and the status monitoring module built into the machine tool CNC system installed on the spindle of the machining center. This data includes, but is not limited to, cutting force, spindle current, spindle vibration acceleration, and feed rate. In this embodiment, the multi-process sequence mainly focuses on the heat treatment, turning, and grinding operations, which have the greatest impact on coaxiality.

[0069] S2: A dynamic error propagation model is constructed based on the multi-process sequence to analyze temperature field distribution data and processing status data, and identify the error process that plays a dominant role in the total coaxiality error. This model establishes the input-output relationship between processes and performs spatiotemporal alignment and fusion analysis of the collected temperature field distribution data and processing status data. Through finite element analysis, the workpiece state parameters after each process are calculated, including residual stress distribution, geometric deformation trend, etc., so as to accurately identify the error process that plays a dominant role in the total coaxiality error.

[0070] S3: Determine the position of the erroneous process in the multi-process sequence, generate a coordinated control instruction, correct the processing parameters of subsequent processes, or derive and pre-compensate the processing parameters of the erroneous process; that is, the coordinated control instruction is: when the erroneous process is the preceding process (such as heat treatment or turning), the instruction corrects the processing parameters of the subsequent process; when the erroneous process is the following process (such as grinding), the instruction derives and pre-compensates the processing parameters of the erroneous process.

[0071] S4: Perform corresponding coordinated operations based on coordinated control commands;

[0072] Based on the processing parameters of the derivation error process, the output of the heat treatment equipment is adjusted to construct an anti-deformation temperature field inside the workpiece. The output of the heat treatment equipment can be set to heating power and cooling rate, etc., without specific limitations. For example, the predicted deformation trend can be offset by adjusting the quenching cooling rate.

[0073] Based on the correction of machining parameters for subsequent processes, the machining center equipment parameters are adjusted to dynamically compensate for errors in the machining parameters of the process. For dynamic compensation operations, the tool path is corrected in real time to compensate for previous errors by adjusting the CNC program parameters of the machining center. For example, in the grinding process, the grinding wheel feed trajectory is adjusted according to the previous errors.

[0074] Through the technical solution of this invention, the source of error generation and transmission in the multi-process manufacturing process of heat treatment, turning, and grinding is traced. It can accurately identify the key process that plays a dominant role in the total coaxiality error. Then, according to the different locations of the error source, different control schemes are selected to generate collaborative control instructions, forming a cross-process collaborative control mechanism.

[0075] In some embodiments of the present invention, such as Figure 2 As shown, a dynamic error propagation model is constructed to identify error-prone processes in real time, including:

[0076] Based on the multi-process sequence, establish the input-output relationship between processes and determine the error propagation path of each process to subsequent processes;

[0077] The temperature field distribution data is correlated with the processing status data, and the internal state parameters of the workpiece at the end of each process are calculated.

[0078] Following the error propagation path, the impact of each process's processing error on subsequent processes is calculated sequentially based on the state parameters of each process.

[0079] By comparing the impact of each process, the error-causing process that plays a dominant role in the total coaxiality error is identified.

[0080] Starting with the heat treatment process, its output is the residual stress state and microstructure state inside the workpiece; the input of the turning process is the state of the workpiece after heat treatment, and the output is the contour deviation and surface stress distribution after turning; the input of the grinding process is the state of the workpiece after turning, and the output is the final coaxiality deviation. This determines the error propagation path. The deformation error of the heat treatment process will be propagated to the turning process, and the geometric error of the turning process will be further propagated to the grinding process.

[0081] The collected temperature field distribution data and processing status data are spatiotemporally correlated, and data synchronization is achieved through timestamp alignment. Using the workpiece coordinate system, the correspondence between the spatial coordinates of the temperature field and the processing position is established with the spindle axis as the reference. Based on the correlated data, the status parameters of each process are calculated, and the processing error data of each process are obtained sequentially along the error propagation path.

[0082] After the first process is completed, a coordinate measuring machine is used to detect the initial coaxial offset. After the subsequent processes are completed, the real-time coaxial offset is detected synchronously. The direct impact of each process is determined by the difference between the real-time offset and the previous offset. Finally, the normalized contribution ratio of the impact of each process is compared, and the process with the highest ratio is identified as the error process that plays a dominant role in the total coaxiality error.

[0083] The technical solution of this invention establishes the input-output relationship and error transmission path between processes, associates the temperature field distribution with the processing status data, quantifies the impact of each process on subsequent processes, and realizes real-time and accurate identification of the dominant error process.

[0084] In some embodiments of the present invention, such as Figures 3 to 5 As shown, the internal state parameters of the workpiece at the end of each process are calculated, including:

[0085] Based on temperature field distribution data, the residual stress state inside the workpiece and the microstructure state of the workpiece surface are calculated after the heat treatment process to obtain the state parameters of the heat treatment process.

[0086] Based on the machining status data and combined with the heat treatment process status parameters, the contour deviation status of the workpiece and the new distribution status of the surface stress of the workpiece are calculated after the turning process, and the turning process status parameters are obtained.

[0087] Based on the machining status data and combined with the turning process status parameters, the coaxiality deviation prediction status of the workpiece after the grinding process is calculated, and the grinding process status parameters are obtained.

[0088] Based on temperature field distribution data, the temperature difference and cooling rate curve between the workpiece surface and core are first identified. The stress balance relationship in different regions is analyzed through the thermal stress transformation law to obtain the spatial distribution of residual stress, such as the distribution characteristics of tensile stress in the surface and compressive stress in the core. At the same time, based on the duration and cooling rate of the surface temperature in the phase transformation range, combined with the material phase transformation kinetic model, the phase transformation type and ratio of the surface microstructure, such as the proportion of pearlite and martensite, are determined and finally integrated into the state parameters of the heat treatment process.

[0089] Using the residual stress state and surface microstructure of the heat treatment process as initial conditions, combined with turning machining state data, the geometric differences between the actual cutting trajectory and the theoretical trajectory are compared. The influence of tool elastic deformation (caused by cutting force) on contour accuracy is considered, and the deviation of the actual contour of the machined workpiece from the design value (such as the distribution characteristics of local protrusions and depressions) is quantified. Based on the magnitude and direction of the cutting force, the mechanical stress field generated during the cutting process is calculated, and it is vector-superimposed with the residual stress of heat treatment to obtain the new distribution characteristics of surface stress after turning (such as stress concentration areas and stress amplitude changes). Finally, these are integrated into the turning process state parameters.

[0090] Using the profile deviation state of the turning process as the initial geometric reference, and combining the grinding process state data (such as the abrasive cutting depth of the grinding wheel, the temperature of the grinding area, and the radial runout of the spindle), the geometric correction amount of the area to be ground is determined. Based on the grinding wheel speed and feed rate, the number of abrasive cutting times and the grinding force per unit time are calculated. The influence of the elastic deformation of the workpiece caused by the grinding force on the spindle positioning is analyzed, such as the slight bending of the workpiece under the action of the grinding force. By combining the transmission effect of the previous profile deviation and the new deformation in the grinding process, the coaxiality deviation state of the workpiece after grinding, such as the radial offset of the two ends of the spindle and the perpendicularity error, is obtained as the state parameters of the grinding process.

[0091] In some embodiments of the present invention, the calculation of the impact of the processing error of each process on subsequent processes includes:

[0092] After the first process in the error propagation path is completed, the initial coaxial offset of the workpiece is detected.

[0093] After the second process along the error propagation path is completed, the real-time workpiece offset is detected.

[0094] The new offset is calculated based on the real-time workpiece offset and the initial coaxial offset, and is recorded as the influence of the first process.

[0095] The subsequent processes are calculated sequentially along the error propagation path, and the influence of the preceding process is obtained based on the difference between the real-time workpiece offset of the preceding process and the following process.

[0096] After the first process of the error propagation path is completed, the workpiece is clamped on the inspection platform using precision tooling. The initial coaxial offset of the two ends of the workpiece is detected by a laser interferometer or a coordinate measuring machine, which is used as the reference value for subsequent influence calculation.

[0097] Then, proceed to the next process sequentially along the error propagation path. After processing, keep the clamping state unchanged to avoid secondary clamping errors. Detect the real-time coaxial offset of the workpiece again. The offset at this time includes the superposition effect of the error of the first process and the new error of this process. By subtracting the initial offset of the first process from the real-time offset, the direct impact of the first process on the subsequent processes can be obtained.

[0098] Repeat the above steps for each subsequent process. After the process is completed, detect the real-time coaxial offset. By subtracting the real-time offset of the previous process from the offset of the previous process, the influence of the previous process on the current process can be obtained.

[0099] By calculating the difference between the initial offset and the real-time offset, the abstract error impact is transformed into a quantifiable physical quantity. This breaks through the limitation that the error impact between processes in traditional processing can only be qualitatively described, and enables the traceability of error impact. At the same time, by calculating the impact along the error propagation path, the propagation trend of error between processes can be dynamically captured, providing a quantitative basis for subsequent collaborative control.

[0100] In some embodiments of the present invention, such as Figure 6 As shown, the position of the erroneous process step in the multi-process sequence is determined, and collaborative control instructions are generated, including:

[0101] Determine whether the erroneous process is the last process in a multi-process sequence;

[0102] If the error process is determined to be the last process, a coordinated control instruction is generated to derive and pre-compensate the processing parameters of the error process.

[0103] If the error process is determined to be not the final process, a coordinated control instruction is generated to correct the processing parameters of subsequent processes.

[0104] If the error-prone process is not the final process, it means that its error can be offset or weakened by parameter correction in subsequent processes. In this case, the first type of coordinated control instruction is generated to correct the processing parameters of subsequent processes. If the error-prone process is the final process, it means that its error can no longer be corrected by subsequent processes and needs to be directly compensated in this process. In this case, the second type of coordinated control instruction is generated to derive and pre-compensate the processing parameters of the error-prone process.

[0105] Traditional unified compensation requires adjusting parameters for all processes, regardless of whether the error is caused by that process, resulting in ineffective control. In contrast, this embodiment generates directional instructions only for the location of the erroneous process, reducing ineffective parameter adjustments and lowering equipment debugging time. Moreover, since the location of the erroneous process may dynamically change with processing conditions (e.g., due to differences in material hardness, the erroneous process changes from heat treatment to turning in a certain batch of workpieces), this embodiment can quickly adapt to the dynamic changes of the erroneous process by judging the location in real time and generating corresponding instructions. If the erroneous process changes from a non-final process to the final process, the instructions automatically switch from correcting the processing parameters of subsequent processes to deriving and pre-compensating the processing parameters of the erroneous process. The control strategy can be switched without manual intervention, significantly enhancing the adaptive capability of the processing process.

[0106] In some embodiments of the present invention, the processing parameters for the error-compensating process are derived and pre-compensated, including:

[0107] Based on the error process, obtain the actual output results that have been identified, and deduce the ideal output results during the processing of the error process.

[0108] By comparing the actual output results with the ideal output results, the output deviation is obtained, and the adjustment amount of the processing parameters required to compensate for the output deviation is obtained.

[0109] The adjustment amount is used as a pre-compensation value and applied to the processing parameters in subsequent error processing steps.

[0110] Based on real-time monitoring data of error processes, such as coaxiality deviation and surface roughness in grinding processes, the actual output results reflecting processing quality are extracted. According to product design standards and process parameter benchmarks (such as grinding wheel speed and heat treatment target temperature), combined with the historical best processing data of error processes, the ideal output result of the current process can be derived by referring to the processing parameters of qualified workpieces under the same material and batch. The difference between the actual output result and the ideal output result is obtained as the output deviation. Based on the output deviation result, the adjustment amount of processing parameters required to compensate for the output deviation can be obtained and applied to the subsequent error process. Specifically, if it is a heat treatment process, the heat power compensation parameters can be derived according to the location and temperature difference of the abnormal temperature field distribution area and the heat conduction characteristics of the material. For example, the heating power ratio of the abnormal area can be reduced. If it is a machining process, such as turning or grinding, the axial or radial pre-compensation amount of the tool or grinding wheel can be derived according to the real-time deformation displacement of the workpiece.

[0111] In some embodiments of the present invention, the processing parameters of subsequent processes are modified, including:

[0112] Based on the impact of the error process output on subsequent processes, predict the additional errors that they will generate when processing workpieces with machining errors from the error process.

[0113] Based on the additional error, the compensation amount for the machining parameters of subsequent processes is calculated in combination with the total coaxiality error.

[0114] The compensation amount is input into the processing equipment of the subsequent process to correct the processing parameters and offset the additional errors transmitted from the error process.

[0115] First, it is necessary to clarify which subsequent processes are directly affected by the error process, and then analyze how the output deviation of the error process is transformed into additional errors in subsequent processes through physical actions such as workpiece clamping deformation and cutting force transmission. For example, after heat treatment, the workpiece may experience elastic deformation triggered by the cutting force during turning due to uneven distribution of residual stress, resulting in a deviation between the turning profile and the theoretical trajectory. Or, the profile deviation after turning may be further amplified by the uneven distribution of contact stress between the grinding wheel and the workpiece during grinding, thus increasing the coaxiality error.

[0116] Based on the predicted additional error and the preset target threshold of the total coaxiality error, combined with the parameters that have the greatest impact on machining accuracy in subsequent processes, such as the tool feed trajectory in turning and the working pressure of the grinding wheel in grinding, the amount of machining parameter compensation that needs to be corrected is calculated and synchronized in real time to the machining center control system of the subsequent processes to achieve dynamic cancellation of additional errors.

[0117] In the technical solution of this invention, by combining the above two collaborative control commands, full coverage of error process positions is achieved. There are suitable control methods for error processes at different positions, and a dynamic response collaborative control mechanism is constructed as a whole, providing key technical support for the stable mass production of high-precision spindles.

[0118] In some embodiments of the present invention, step S5 is further included:

[0119] Collect measured coaxiality data of workpieces processed through collaborative operation;

[0120] By combining measured coaxiality data with a dynamic error propagation model, model deviation data is generated.

[0121] Based on model bias data, the intrinsic parameters of the dynamic error propagation model are corrected.

[0122] After completing the collaborative operation in step S4, the workpiece is subjected to high-precision inspection. The coaxiality measurement data of the workpiece is collected using a coordinate measuring machine or a laser interferometer. At the same time, key process parameters during the collaborative operation are recorded, such as the corrected tool feed rate, grinding wheel pressure, and heat treatment power distribution.

[0123] The measured data is compared with the predicted data of the dynamic error propagation model to generate model deviation data, which is the difference between the model's predicted value and the actual measured value. The causes of the deviation are analyzed, and the key parameters in the dynamic error propagation model are corrected based on the direction and magnitude of the deviation data. For example, if the coaxiality deviation predicted by the model is always less than the measured value, it indicates that the error propagation coefficient is set too low and needs to be increased to improve the model's sensitivity to the cumulative effect of error. If the deviation shows material batch correlation, the material property parameters in the model are adjusted to adapt to the characteristics of the new batch. The corrected parameters are updated to the model in real time to guide the processing of the next batch of workpieces, forming a closed-loop control.

[0124] In the technical solution of this invention, without manual intervention, the system can automatically identify model deviation data based on measured data and make adjustments accordingly, so that the dynamic error propagation model continuously adapts to the actual processing conditions, avoids batch quality fluctuations caused by model drift, ensures the long-term effectiveness of the collaborative control method, and enhances the adaptive capability of the collaborative control system to complex working conditions.

[0125] The present invention also provides a multi-process adaptive collaborative control system for boring machine spindles, comprising:

[0126] The data acquisition module sequentially collects temperature field distribution data for the heat treatment process and machining status data for the turning and grinding processes; thus obtaining the multi-process sequence of heat treatment, turning, and grinding.

[0127] The analysis and identification module constructs a dynamic error propagation model based on the multi-process sequence, analyzes temperature field distribution data and processing status data, and identifies the error process that plays a dominant role in the total coaxiality error.

[0128] The collaborative judgment module determines the position of the erroneous process in the multi-process sequence, generates collaborative control instructions, corrects the processing parameters of subsequent processes, or derives and pre-compensates the processing parameters of the erroneous process.

[0129] The cross-order coordination module performs corresponding coordination operations based on coordination control instructions.

[0130] Based on the derivation of the processing parameters of the error process, the output of the heat treatment equipment is adjusted to construct an anti-deformation temperature field inside the workpiece.

[0131] Based on the correction of the processing parameters of subsequent processes, the parameters of the machining center equipment are adjusted to dynamically compensate for the processing parameters of the error process.

[0132] The control system described above in this invention can effectively realize a multi-process adaptive collaborative control method for boring machine spindles, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0133] In some embodiments of the present invention, the analysis and identification module includes:

[0134] Error path unit: Based on the multi-process sequence, establish the input-output relationship between processes and determine the error propagation path of each process to subsequent processes;

[0135] State calculation unit: It correlates the temperature field distribution data with the processing state data and calculates the internal state parameters of the workpiece at the end of each process.

[0136] Error Influence Unit: Based on the error propagation path, calculate the impact of each process's processing error on subsequent processes according to the state parameters of each process.

[0137] Error identification unit: By comparing the influence of each process, it identifies the error process that plays a dominant role in the total coaxiality error.

[0138] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the methods provided by the present invention, which will not be elaborated here.

[0139] Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A multi-process self-adaptive cooperative control method for a boring machine spindle, characterized in that, Comprise: S1: acquire the multi-process process sequence of heat treatment, turning and grinding process, which includes the temperature field distribution data of the collected heat treatment process, the machining state data of the turning and the grinding process; S2: based on the multi-process process sequence, a dynamic error propagation model is constructed, the temperature field distribution data and the machining state data are analyzed, and the error process which plays a leading role in the total error of coaxiality is identified; S3: judge the position of the error process in the multi-process process sequence, generate collaborative control instruction, correct the machining parameter of subsequent process or deduce and pre-compensate the machining parameter of the error process; S4: based on the collaborative control instruction, corresponding collaborative operation is carried out; Based on the deduced machining parameter of the error process, the output of the heat treatment equipment is adjusted, and the anti-deformation temperature field is constructed inside the workpiece; Based on the corrected machining parameter of the subsequent process, the machining center equipment parameter is adjusted, and the machining parameter of the error process is dynamically compensated; Wherein, the construction of dynamic error propagation model, real-time identification of error process, includes: According to the multi-process process sequence, the input-output relationship between processes is established, and the error transmission path of each process to the subsequent process is determined; The temperature field distribution data and the machining state data are associated, and the state parameters inside the workpiece at the end of each process are calculated; According to the state parameters of each process, the influence of the machining error of each process on the subsequent process is calculated in sequence with the error transmission path as the sequence; By comparing the influence of each process, the error process which plays a leading role in the total error of coaxiality is identified.

2. The multi-process self-adaptive collaborative control method of the boring machine spindle according to claim 1, characterized in that, Calculate the state parameters inside the workpiece at the end of each process, including: Based on the temperature field distribution data, the residual stress state and the microstructure state of the workpiece surface after the heat treatment process are calculated, and the heat treatment process state parameters are obtained; Based on the machining state data, combined with the heat treatment process state parameters, the contour deviation state of the workpiece and the new stress distribution state of the workpiece surface after the turning process are calculated, and the turning process state parameters are obtained; Based on the machining state data, combined with the turning process state parameters, the coaxiality deviation prediction state of the workpiece after the grinding process is calculated, and the grinding process state parameters are obtained.

3. The multi-process adaptive collaborative control method of a boring machine spindle according to claim 1, characterized in that, Calculate the influence of the machining error of each process on the subsequent process, including: After the first process of the error transmission path is completed, detect the initial coaxial offset of the workpiece; After the second process of the error transmission path is completed, detect the real-time workpiece offset; Based on the real-time workpiece offset and the initial coaxial offset, the new offset is calculated, which is recorded as the influence of the first process; Along the error transmission path, the subsequent processes are calculated in sequence, and the influence of the previous process is obtained based on the difference between the real-time workpiece offset of the previous process and the next process.

4. The multi-process adaptive collaborative control method of a boring machine spindle according to claim 1, characterized in that, Judge the position of the error process in the multi-process process sequence, generate collaborative control instruction, including: Judge whether the error process is the last process in the multi-process process sequence; If the error process is determined to be the last process, a cooperative control instruction is generated, and the processing parameters of the error process are derived and pre-compensated; If the error process is determined to be not the last process, the cooperative control instruction is generated, and the processing parameters of the subsequent process are corrected.

5. The multi-process adaptive collaborative control method of a boring machine spindle according to claim 4, characterized in that, The derivation and pre-compensation of the processing parameters of the error process include: Based on the error process, the actual output result thereof is obtained, and the ideal output result in the processing of the error process is derived; The actual output result is compared with the ideal output result to obtain an output deviation, and an adjustment amount of the processing parameters required to compensate for the output deviation is obtained; The adjustment amount is applied as a pre-compensation value to the processing parameters in the subsequent processing of the error process.

6. The multi-process adaptive collaborative control method of a boring machine spindle according to claim 4, characterized in that, The correction of the processing parameters of the subsequent process includes: Based on the subsequent process affected by the output of the error process, the additional error generated when the subsequent process processes the workpiece with the processing error of the error process is predicted; According to the additional error, the compensation amount of the processing parameters of the subsequent process is calculated in combination with the total error of the coaxiality; The compensation amount is input to the processing equipment of the subsequent process, and the processing parameters are corrected to offset the additional error transmitted from the error process.

7. The multi-process adaptive collaborative control method of a boring machine spindle according to any one of claims 1 to 6, characterized in that, Further comprising a step S5: Collecting the coaxiality measurement data of the processed workpiece after the cooperative operation; Combining the coaxiality measurement data with the dynamic error propagation model to generate model deviation data; Based on the model deviation data, correcting the parameters in the dynamic error propagation model.

8. A boring machine spindle multi-process adaptive collaborative control system, characterized in that, It includes: A data collection module sequentially collects the temperature field distribution data of the heat treatment process, the processing state data of the turning and grinding processes, and obtains the multi-process process sequence of the heat treatment, turning and grinding processes; An analysis and identification module constructs a dynamic error propagation model based on the multi-process process sequence, analyzes the temperature field distribution data and the processing state data, and identifies the error process that plays a leading role in the total error of the coaxiality; A cooperative judgment module judges the position of the error process in the multi-process process sequence, generates a cooperative control instruction, and corrects the processing parameters of the subsequent process or derives and pre-compensates the processing parameters of the error process; A cross-sequence cooperative module performs corresponding cooperative operation based on the cooperative control instruction; Based on the derivation of the processing parameters of the error process, the output of the heat treatment equipment is adjusted to construct a deformation-resistant temperature field inside the workpiece; Based on the correction of the processing parameters of the subsequent process, the parameters of the machining center equipment are adjusted to dynamically compensate for the processing parameters of the error process. The analysis and identification module includes: An error path unit: establishes the input-output relationship between processes according to the multi-process process sequence, and determines the error transmission path of each process to the subsequent process; A state calculation unit: associates the temperature field distribution data with the processing state data, and calculates the state parameters inside the workpiece at the end of each process; An error influence unit: sequentially calculates the influence of the processing error of each process on the subsequent process according to the state parameters of each process in the order of the error transmission path. An error identification unit identifies the error process that has a dominant effect on the total error of the coaxiality by comparing the influence amounts of the processes.

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