Machine tool spindle temperature measuring method and system

Abstract

The invention relates to the technical field of precision machine tool control, in particular to a machine tool spindle temperature measuring method and system, and the method comprises the steps: collecting a current signal and switching frequency data of a spindle drive motor in real time, and analyzing the heating power distribution of each part of a spindle; decomposing the main shaft into a hot node network based on the physical structure layout of each part of the main shaft, and obtaining a temperature rise prediction value of each node in combination with heating power distribution; the actual temperature value of the key node is obtained, and when the deviation between the temperature rise predicted value of any node and the actual temperature value exceeds a set threshold value, temperature compensation correction is conducted on the corresponding node; and according to the corrected hot node network, predicting a periodic temperature rise gradient in a future processing path, and reversely correcting a thermal resistance parameter in the hot node network based on a workpiece geometric error obtained by in-situ measurement. Through the method, the problems of temperature rise prediction distortion, closed area temperature sensing deficiency, dynamic working condition response lag and the like caused by mechanical parameter disjunction in a traditional temperature measurement technology are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of precision machine tool control, and in particular to a method and system for measuring the temperature of a machine tool spindle. Background Art

[0002] The temperature rise of the machine tool spindle caused by bearing friction, motor loss, etc. during high-speed machining can lead to thermal deformation, which directly causes machining errors and becomes a core problem restricting precision manufacturing; traditional temperature measurement technology generally has three major defects: disconnection of mechanical system thermal characteristics, blind spots in internal thermal state perception, and dynamic response lag, resulting in insufficient accuracy of thermal error compensation.

[0003] In traditional temperature measurement technology, the installation of contact sensors will destroy the dynamic balance of the spindle and cannot monitor the temperature of closed areas such as the inside of the bearing; non-contact infrared temperature measurement is easily interfered by coolant atomization and has difficulty capturing the temporal and spatial gradient changes of the temperature field; although fiber Bragg grating sensing can achieve distributed measurement, it requires customized modification of the spindle structure and its long-term stability is affected by vibration; and the indirect thermal estimation model based on experimental data lacks mechanical parameter coupling, and has significant prediction errors under dynamic conditions such as variable speed and variable load, and cannot locate local hot spots in real time.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method and system for measuring the temperature of a machine tool spindle, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: A method for measuring the temperature of a machine tool spindle, the method comprising: Real-time acquisition of the current signal and switching frequency data of the spindle drive motor to analyze the heat power distribution of each spindle component; Decomposing the spindle into a network of thermal nodes based on the physical structure layout of the spindle components, and obtaining a predicted temperature rise value for each node in combination with the heat generation power distribution; Obtaining the actual temperature values ​​of key nodes, and performing temperature compensation correction on the corresponding node when the deviation between the temperature rise prediction value and the actual temperature value of any node exceeds a set threshold; According to the corrected thermal node network, the periodic temperature rise gradient in the future processing path is predicted, and based on the workpiece geometric error obtained by in-situ measurement, the thermal resistance parameters in the thermal node network are reversely corrected.

[0007] Furthermore, the spindle is decomposed into a hot node network based on the physical structure layout of its components, including: According to the actual physical contact areas between the inner and outer raceways of the spindle bearings and the shaft core and tool clamping surface, the spindle is divided into three core thermal nodes: the bearing thermal node, the shaft core conduction thermal node, and the tool thermal node. Based on the core thermal nodes, generating thermally conductive topological links between adjacent nodes, wherein the link density between the bearing thermal nodes and the shaft core conductive thermal nodes is determined by the ratio of the bearing roller contact pressure distribution; Converting the real-time spindle speed and the coolant flow state into the thermal resistance parameters of the bearing thermal node and the tool thermal node, and updating them into the thermal conductivity topology link; According to the deviation between the actual temperature value and the current temperature rise prediction value, the link density and the thermal resistance parameter are reversely adjusted until the prediction accuracy of the thermal node network meets the preset convergence condition.

[0008] Furthermore, measuring the spindle temperature based on the thermal node network includes: Extracting the actual temperature value of the core thermal node from the thermal conductivity topology link and aligning it with the current harmonic characteristics of the spindle drive motor in the time domain; Inputting the actual temperature value into a pre-trained time series prediction model to generate a temperature rise trend curve for each core thermal node in a future processing cycle, and dynamically adjusting the amplitude and phase of the temperature rise trend curve according to the task currently executed by the spindle; Based on the workpiece geometric error and the temperature rise trend curve, locating the maximum deviation area of ​​the thermal resistance parameter in the thermal node network, and triggering the directional enhancement of the link density; The link strengthening result is synchronized to the thermal conductivity topology link, and the above steps are repeated at preset time intervals until the residual of the temperature rise prediction value is lower than the convergence threshold for three consecutive times.

[0009] Furthermore, triggering the directional enhancement of the link density includes: When it is detected that any axial deviation in the workpiece geometric error continues to exceed the tolerance range, the temperature gradient change feature of the corresponding axial direction in the temperature rise trend curve is extracted, and mapped and matched with the thermal resistance parameter of the thermal node network; Locating a weak area of ​​the thermal conductivity topological link in the shaft core thermal node or the tool thermal node according to the matching result, and calculating a target link density increment in the weak area in combination with the current contact pressure distribution of the bearing rollers; Dynamically adjusting the distribution pattern of the thermal conductivity topological links in the weak area based on the target link density increment, and verifying the convergence of the workpiece geometric error after the adjustment in a next machining cycle; If the convergence does not meet expectations, the target link density increment is iteratively increased until the residual of the temperature rise prediction value of the hot node network and the workpiece geometric error synchronously fall within a preset range.

[0010] Furthermore, the temperature rise prediction value of each node is obtained, including: Determining the thermal resistance parameters of each thermal conductivity topological link based on the thermal conductivity of the main shaft material and the ratio of the contact pressure distribution of the bearing rollers; According to the real-time spindle speed and the coolant flow state, the convection heat transfer parameters of each core heat node along the heat conduction topology link are obtained; Injecting the heat generation power distribution into the corresponding core thermal nodes, and calculating the temperature rise prediction value of each core thermal node based on the thermal resistance parameter and the convection heat transfer parameter; When it is detected that the deviation between the temperature rise prediction value and the actual temperature value exceeds a set threshold, a temperature compensation correction is performed on the core thermal node.

[0011] Furthermore, determining the thermal resistance parameter of each thermal conductivity topology link includes: The contact area of ​​the inner and outer ring raceways of the main shaft bearing is decomposed into several contact sub-areas, each of which corresponds to the Hertzian contact pressure distribution range of a single bearing roller; Calculating a contact thermal resistance reference value of each contact sub-area according to the thermal conductivity of the spindle material and the assembly preload force detection value; Dynamically allocating thermal conduction resistance parameters between adjacent contact sub-regions based on the contact pressure distribution of the bearing rollers along the circumferential direction; If the deviation between the temperature rise prediction value corresponding to the conductive thermal resistance parameter and the actual temperature value exceeds a set threshold, the boundary division of the contact sub-region is adjusted in reverse and the conductive thermal resistance parameter is recalculated.

[0012] Furthermore, performing temperature compensation correction on the core thermal node includes: When it is detected that the temperature rise prediction value deviation of the bearing thermal node or the tool thermal node exceeds a threshold, the geometric characteristics of the current machining path and the spindle load state are extracted to generate a compensation gain coefficient positively correlated with the curvature of the machining trajectory; Dynamically increase the conduction thermal resistance weight of the corresponding node according to the compensation gain coefficient, and simultaneously reduce the convection heat transfer parameter of the adjacent node; recalculating the temperature rise prediction value of the core thermal node based on the conduction thermal resistance weight and the convection heat transfer parameter, and verifying the compensation effect through on-site temperature measurement; If the residual of the temperature rise prediction value after compensation is still higher than the threshold value, the proportional relationship between the compensation gain coefficient and the conduction thermal resistance weight is iteratively adjusted until the prediction accuracy of the thermal node network returns to the set range.

[0013] Further, the heat power distribution of each component of the main shaft is analyzed, including: The stator copper loss power is calculated based on the stator motor resistance parameter, and the rotor eddy current loss power caused by harmonic current is calculated; According to the product relationship of the real-time speed of the main shaft and the load torque, combined with the preset bearing friction coefficient, the bearing friction loss power is calculated; Based on the switching frequency data of the inverter and the DC bus voltage, the total switching loss is calculated; Based on the physical structure thermal capacity characteristics of the main shaft assembly, the stator copper loss power, the rotor eddy current loss power, the bearing friction loss power and the total switching loss are distributed to the bearing thermal node, the shaft core conduction thermal node and the tool thermal node.

[0014] The machine tool spindle temperature measurement system, the system comprises: The information acquisition module acquires the current signal and switching frequency data of the spindle driving motor in real time, and analyzes the heat power distribution of each component of the main shaft; The temperature rise prediction module decomposes the main shaft into a thermal node network based on the physical structure layout of each component of the main shaft, and obtains the temperature rise prediction value of each node combined with the heat power distribution; The node compensation module obtains the actual temperature value of the key node, and when the deviation between the temperature rise prediction value and the actual temperature value of any node exceeds the set threshold value, the temperature compensation correction is performed on the corresponding node; The thermal resistance correction module predicts the periodic temperature rise gradient in the future machining path according to the corrected thermal node network, and reversely corrects the thermal resistance parameters in the thermal node network based on the geometric error of the workpiece obtained by in-situ measurement.

[0015] Further, the temperature rise prediction module comprises: The thermal resistance determination unit determines the thermal resistance parameters of each thermal conduction topology link based on the thermal conductivity coefficient of the main shaft material and the proportion of the bearing roller contact pressure distribution; The state acquisition unit obtains the convective heat transfer parameters of each core thermal node along the thermal conduction topology link according to the real-time speed of the main shaft and the cooling liquid flow state; The temperature rise calculation unit injects the heat power distribution into the corresponding core thermal node, and calculates the temperature rise prediction value of each core thermal node based on the thermal resistance parameters and the convective heat transfer parameters; The compensation detection unit performs temperature compensation correction on the core thermal node when it is detected that the deviation between the temperature rise prediction value and the actual temperature value exceeds the set threshold value.

[0016] The technical solution of the present invention can achieve the following technical effects: High-precision reconstruction of the spindle's full-range temperature field and thermal error feedforward compensation are achieved, reducing workpiece geometric errors during high-speed and heavy-load machining. At the same time, dynamic optimization of thermal resistance parameters reduces sensor deployment requirements, thereby improving the stability and service life of the machine tool spindle under complex working conditions.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 Schematic diagram of the process of measuring the temperature of a machine tool spindle; Figure 2 Schematic diagram of decomposing the main axis into a network of hot nodes; Figure 3 Schematic diagram for obtaining the predicted temperature rise value of each node. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Embodiment 1; like Figure 1 As shown, the present application provides a method for measuring the temperature of a machine tool spindle, the method comprising: S10: Real-time acquisition of the current signal and switching frequency data of the spindle drive motor to analyze the heat power distribution of each spindle component; S20: Decompose the spindle into a network of thermal nodes based on the physical structure layout of each spindle component, and obtain the temperature rise prediction value of each node in combination with the heat power distribution; S30: Acquire the actual temperature values ​​of key nodes. When the deviation between the temperature rise prediction value and the actual temperature value of any node exceeds a set threshold, perform temperature compensation correction on the corresponding node. S40: According to the corrected thermal node network, the periodic temperature rise gradient in the future processing path is predicted, and based on the workpiece geometric error obtained by the in-situ measurement, the thermal resistance parameters in the thermal node network are reversely corrected.

[0023] Specifically, first, the operating parameters of the spindle drive motor are collected in real time, including three-phase current signals and switching frequency data. These data reflect the load condition and operating status of the motor and can be used to estimate the heat power distribution of each spindle component. The current signal can be used to calculate the copper loss heat power of the motor winding, and the switching frequency data is helpful to evaluate the switching loss heat power of the inverter or controller. In addition, according to the structure and operating parameters of the spindle, the friction heat power of components such as bearings and couplings can be estimated to obtain the heat power distribution; based on the physical structure layout of each spindle component, the spindle is divided into multiple thermal nodes, and a thermal equivalent network model, namely a thermal node network, is established. Each thermal node represents a key part in the spindle, such as the front bearing, motor rotor, motor stator, middle shaft section, rear end bearing and spindle housing. These thermal nodes are connected by thermal resistance and heat capacity to form a thermal network model. Combined with the aforementioned heat power distribution, the thermal equivalent network model is used to calculate the heat power distribution of each spindle component. The node network can predict the temperature rise of each node, thereby obtaining the temperature distribution of the spindle; in order to improve the accuracy of temperature prediction, several key nodes are selected in the spindle structure, and the actual temperature data of these positions are collected in real time through embedded or mounted temperature sensors. The collected actual temperature values ​​are compared with the predicted values. If the deviation exceeds the preset threshold, it is judged that there is an error in the current thermal network model, and the heat source or thermal resistance parameters of the corresponding node are compensated and corrected. The correction method can use algorithms such as extended Kalman filter or least squares method to optimize the model parameters; during the processing process, combined with the processing path control system, the current load cycle caused by the processing task is obtained in advance, and based on the current corrected thermal node network, the temperature rise gradient changes in future cycles are predicted. In addition, combined with the geometric error of the workpiece after processing obtained by in-situ measurement, the thermal resistance error in the thermal network is reversely identified, and the spindle thermal model is optimized again to make it more in line with the actual thermal behavior.

[0024] Through the technical solution of the present invention, high-precision reconstruction of the full-range temperature field of the spindle and thermal error feedforward compensation are achieved, and the geometric error of the workpiece is reduced during high-speed and heavy-load processing. At the same time, the demand for sensor deployment is reduced through dynamic optimization of thermal resistance parameters, thereby improving the stability and service life of the machine tool spindle under complex working conditions.

[0025] Further, if Figure 2 The spindle is decomposed into a thermal node network based on the physical structure layout of each component of the spindle, including: According to the actual physical contact area of the inner and outer ring raceway contact surface of the spindle bearing, the shaft core and the tool clamping surface, the spindle is divided into three core thermal nodes: bearing thermal node, shaft core conduction thermal node and tool thermal node; Based on the core thermal nodes, thermal conduction topology links are generated between adjacent nodes, wherein the link density between the bearing thermal node and the shaft core conduction thermal node is determined by the proportion of the bearing roller contact pressure distribution; The real-time rotational speed of the spindle and the flow state of the cooling liquid are converted into thermal resistance parameters of the bearing thermal node and the tool thermal node, and are updated to the thermal conduction topology links; According to the deviation of the actual temperature value and the current temperature rise prediction value, the link density and the thermal resistance parameter are adjusted in reverse until the prediction accuracy of the thermal node network meets the preset convergence condition.

[0026] As a preferred embodiment of the above, on the basis of the traditional thermal node model, the structural contact physical parameters are further introduced to divide the interior of the spindle into three core thermal nodes, namely the bearing thermal node, the shaft core conduction thermal node and the tool thermal node. Among them, the bearing thermal node refers to the heat concentration area formed by the bearing parts at both ends of the spindle, which usually has a stable raceway contact surface; the shaft core conduction thermal node is located in the middle section of the spindle, which mainly bears the heat path for the heat generated by the motor to be conducted outward through the spindle core; the tool thermal node is concentrated between the spindle end and the tool clamping surface, which is an important interface for direct heat transfer between the spindle and the workpiece. The thermal node division method is based on the main contact interface in the real physical structure, which is more meaningful for engineering practice and more representative of the thermal path. On the basis of this structural division, the heat transfer relationship between each thermal node is analyzed to construct a thermal conduction topological link. The topological connection strength between the bearing thermal node and the shaft core conduction heat node, that is, the link density, is set according to the ratio of the contact pressure distribution between the roller and the inner and outer rings of the bearing. The contact area and unit heat flux density can be weighted according to the pressure distribution curve simulated or measured under different load conditions of the main shaft, thereby generating a link density value with physical significance. This method is effective The model's ability to respond to changes in bearing thermal conductivity under different operating conditions has been improved, especially in conditions prone to abnormal heating, such as high loads and high-speed machining. To improve the real-time and dynamic adaptability of the thermal network, the current real-time spindle speed information and the coolant flow state are converted into dynamic thermal resistance parameters of the bearing thermal node and the tool thermal node. For example, when the spindle rotates at high speed, the contact area of ​​the bearing expands due to the increased centrifugal force, and its thermal resistance will decrease accordingly. For example, when the coolant flow rate increases, the heat taken away increases, and the thermal resistance of the node will also decrease. These dynamic thermal resistances in the thermal conductivity topology link are updated regularly. Parameters ensure that the thermal model always reflects the current real heat conduction state; during operation, the actual temperature data of key nodes is continuously obtained through the aforementioned temperature measurement mechanism and compared with the predicted value of the thermal node network model. When a significant deviation is found between the actual temperature and the predicted temperature, and exceeds the set deviation threshold, the link adaptive adjustment mechanism will be triggered. This mechanism will adjust the link density and thermal resistance parameters between the corresponding thermal nodes in the opposite direction according to the direction and magnitude of the error. This adjustment process continues until the error between the predicted value and the actual value converges to the set allowable range, thereby ensuring the prediction accuracy of the thermal node network.

[0027] Furthermore, the spindle temperature measurement based on the thermal node network includes: The actual temperature value of the core thermal node is extracted from the thermal conductivity topology link and aligned with the current harmonic characteristics of the spindle drive motor in the time domain; The actual temperature value is input into the pre-trained time series prediction model to generate the temperature rise trend curve of each core thermal node in the future processing cycle, and the amplitude and phase of the temperature rise trend curve are dynamically adjusted according to the task currently executed by the spindle; Based on the workpiece geometric error and the temperature rise trend curve, the maximum deviation area of the thermal resistance parameter in the thermal node network is located, and the directional reinforcement of the link density is triggered; The link reinforcement result is synchronized to the thermal conduction topology link, and the above steps are repeatedly executed at a preset time interval until the residual errors of the temperature rise prediction values for three consecutive times are all less than the convergence threshold.

[0028] As a preferred embodiment of the above embodiment, first, the thermal conduction topology link is established based on the spindle structure and the heat conduction path, and a plurality of key parts are taken as core thermal nodes. The nodes are connected through thermal resistance to simulate the actual heat flow distribution relationship. In the running process, the real-time temperature data of each core thermal node is obtained through the temperature sensor arranged on each core thermal node. At the same time, in order to more accurately reflect the dynamic change of the spindle thermal load, the three-phase current signals of the spindle driving motor are synchronously collected, and the current harmonic components are extracted by using the digital signal processing technology, especially the characteristic harmonic components reflecting the change of the thermal load. The above temperature data and current harmonic characteristics are aligned based on a unified time reference in the main controller to ensure the time domain consistency of the input data. The thermal node temperature sequence after time domain alignment is taken as input and sent into a time series prediction model constructed and pre-trained based on long short-term memory neural network (LSTM) to output the temperature rise trend curve of each thermal node in the future machining period. In order to improve the task adaptability of the trend curve, the amplitude and phase of the model prediction result are dynamically adjusted based on the information of the current task executed by the spindle, so that the prediction result is closer to the thermal behavior under the current actual working condition. In order to optimize the accuracy of temperature rise prediction, after collecting the workpiece machining result, the geometric error is analyzed and taken as a model reverse correction signal, which is combined with the temperature rise trend curve for joint analysis to identify the area with the largest deviation of the thermal resistance parameter in the current thermal node network. After locating the deviation area, the directional reinforcement of the thermal conduction network is triggered by increasing the link density in the area. The reinforcement operation is based on the network connection weight updating mechanism to improve the response ability to abnormal changes of thermal resistance. The link density reinforcement result is synchronized back to the original thermal conduction topology link in real time for updating the subsequent thermal conduction modeling. In the machining process, the above process is repeatedly executed at a fixed time interval, and the residual error between the trend curve prediction value and the actual sampling temperature is evaluated. When the residual errors for three consecutive times are all less than the set convergence threshold, it is considered that the model has been stabilized, and the temperature rise monitoring maintenance state will be entered.

[0029] Further, the directional reinforcement of the link density includes: When it is detected that any axial deviation in the workpiece geometric error continuously exceeds the tolerance range, the temperature gradient change characteristics of the corresponding axial direction in the temperature rise trend curve are extracted and mapped and matched with the thermal resistance parameter of the thermal node network; According to the matching result, a weak area of a heat conduction topological link in the shaft core conduction heat node or the tool heat node is located, and a target link density increment of the weak area is calculated in combination with a current bearing roller contact pressure distribution; Based on the target link density increment, a distribution mode of the heat conduction topological link of the weak area is dynamically adjusted, and convergence of a workpiece geometric error after adjustment is verified in a next machining cycle; If the convergence does not reach an expectation, the target link density increment is iteratively improved until a residual error of a temperature rise prediction value of the heat node network and the workpiece geometric error synchronously fall in a preset interval.

[0030] As a preferred embodiment of the above, three-dimensional geometric data of the workpiece is continuously collected during the spindle machining process, and error analysis is performed on the machined workpiece using a high-precision probe or online measuring device, with particular attention paid to dimensional deviations in the axial direction (such as the Z axis, X axis, or Y axis). When the geometric error value in any axial direction is detected to exceed the set tolerance range for multiple consecutive machining cycles, the link density enhancement mechanism is triggered. At this time, the temperature rise trend curve of the thermal node related to the axial direction is extracted, and its temperature gradient change characteristics are analyzed in the time domain and spatial domain, including the position, direction, and change rate of the temperature gradient steep change point; a mapping relationship is established between the temperature gradient change characteristics obtained from the temperature rise trend curve and the thermal resistance parameters in the thermal node network, and the main path or thermal node causing the temperature gradient anomaly is identified. If the gradient anomaly is concentrated in the conduction path along the axis core inside the spindle, the axis core conduction heat node is given priority; if the gradient change is concentrated in the cutting contact area near the front end of the spindle, the influence of the tool heat node is considered. On this basis, the current contact pressure distribution of the bearing roller is further introduced to correct and compensate the physical connection strength in the thermal path, thereby more accurately locating the thermal conductivity topology chain. The weak area where the change in thermal resistance in the path has the most significant impact on machining accuracy is identified. After the weak area is identified, the target link density increment for that area is calculated based on the thermal modeling simulation results and the geometric error response curve. In this embodiment, the link density is the weight in the thermal conductivity link or a parameter representing the equivalent thermal conductivity capacity. This target increment is used to simulate a more intensive heat flow in the weak area to compensate for the original model's underestimation of the high thermal resistance in that area. The distribution pattern of the thermal conductivity topological links in that area is then dynamically adjusted based on this target increment. For example, the structural strengthening of the links is achieved by increasing the connection weights between nodes, adding redundant paths, and optimizing the node distribution density. After the strengthening measures are implemented, the verification phase begins. New workpiece geometric errors are continuously collected in the next machining cycle and their changing trends are compared with those in the previous cycle to evaluate the effect of the thermal conductivity link adjustment on improving machining accuracy. If it is determined that the convergence does not meet the preset standard (for example, the error fluctuation is still greater than the set threshold or the error directionality has not changed), the target link density increment is iteratively increased and the link distribution adjustment is performed again until both the workpiece geometric error and the temperature rise prediction residual fall within the set tolerance range.

[0031] Further, if Figure 3 As shown in Figure 2, the temperature rise prediction value of each node is obtained, including: Determine the thermal resistance parameters of each thermal conductivity topology link based on the thermal conductivity of the spindle material and the proportion of the bearing roller contact pressure distribution; According to the real-time spindle speed and coolant flow state, the convection heat transfer parameters of each core thermal node along the thermal conductivity topology link are obtained; Inject the heat generation power distribution into the corresponding core thermal nodes, and calculate the temperature rise prediction value of each core thermal node based on the thermal resistance parameters and convection heat transfer parameters; When it is detected that the deviation between the temperature rise prediction value and the actual temperature value exceeds a set threshold, a temperature compensation correction is performed on the core thermal node.

[0032] As a preferred embodiment of the above embodiment, a thermal conductivity topological link is first established based on the thermophysical properties of the spindle structural material and the real-time load state. During the modeling process, the thermal conductivity of the spindle material is extracted from the material database. Combined with the spindle bearing arrangement structure, the spindle interior is divided into several thermal nodes through finite element modeling or equivalent thermal resistance network method. Every two nodes are connected by thermal conductivity links to form a topological network. Subsequently, the current contact pressure distribution between the bearing roller and the raceway is obtained through a pressure sensor or a load estimation module. The change in contact thermal resistance between each thermal node is calculated based on the proportional relationship between pressure and contact area, and the real-time distribution model of each thermal resistance parameter in the thermal conductivity topological link is further improved. According to the real-time spindle speed information and the flow state of the coolant, the built-in convection heat transfer model library is compared to extract the heat transfer coefficient of each core thermal node under the current processing condition. This step fully considers the influence of the relative movement between the surface layer and the coolant on the heat exchange efficiency when the spindle rotates, thereby determining the local convection heat transfer parameters of each node along the thermal conductivity topological link. According to the real-time power output and cutting force distribution of the spindle motor, A heat power injection model is constructed to proportionally inject heat generated by factors such as electromagnetic loss, frictional heat, and cutting heat into each corresponding core thermal node. For example, heat from the spindle drive end is mainly injected into the bearing drive node area, and cutting heat is injected into the contact area of ​​the spindle front end. Then, a heat conduction solution is performed. Based on the determined thermal resistance parameters and convective heat transfer parameters, the temperature rise prediction value of each core thermal node is calculated using a steady-state or transient thermal network iterative solution algorithm. After the above calculation process is completed, the current predicted temperature rise value is compared with the actual temperature value collected in real time. The actual temperature value is obtained by embedded thermocouples or infrared temperature sensors and is filtered and error-corrected to ensure accuracy. If the deviation between the predicted temperature rise value and the corresponding actual temperature value of any core thermal node is found to exceed a set threshold, a temperature compensation correction mechanism is triggered. The compensation mechanism mainly includes adjusting the dynamic coefficient of the thermal resistance or heat transfer parameter, refitting the heat power distribution, or using a fusion neural network to correct the model error. The compensation mechanism ensures that the predicted value of the next cycle is closer to the actual measured value, thereby improving the overall temperature prediction accuracy and robustness.

[0033] Furthermore, the thermal resistance parameters of each thermal conductivity topology link are determined, including: The contact area of ​​the inner and outer ring raceways of the main shaft bearing is decomposed into several contact sub-areas, each of which corresponds to the Hertzian contact pressure distribution range of a single bearing roller; According to the thermal conductivity of the spindle material and the assembly preload test value, the contact thermal resistance reference value of each contact sub-area is calculated; Based on the contact pressure distribution of the bearing rollers along the circumference, the conduction thermal resistance parameters between adjacent contact sub-areas are dynamically allocated; If the deviation between the temperature rise prediction value corresponding to the conductive thermal resistance parameter and the actual temperature value exceeds the set threshold, the boundary division of the contact sub-region is adjusted in reverse and the conductive thermal resistance parameter is recalculated.

[0034] As a preferred embodiment of the above, first, micro-thermal modeling is performed on the bearing structure in the main shaft that is closely related to heat conduction. Specifically, the contact area between the inner ring and the outer ring raceway of the main shaft bearing is refined and divided into several contact sub-areas along the axial and radial directions. Each contact sub-area corresponds to the Hertzian contact area between a single bearing roller and the raceway during operation. The division is based on the real-time position information of the roller on the raceway, and is dynamically refined in combination with the instantaneous load contact trajectory of the roller under the operation state of the main shaft, so as to be more in line with the actual heat conduction path; then, based on the thermal conductivity of the main shaft and bearing materials, and considering the preload detection value during the assembly process, the contact thermal resistance baseline value of each contact sub-area is calculated. The preload detection value can be obtained through the sensor or the press-fit displacement and force feedback curve during the assembly process, and is converted into one of the parameters affecting the thermal conductivity of the contact surface through the force-heat coupling model. This step effectively combines the influence of the actual assembly state of the main shaft assembly on the thermal resistance change, thereby improving the authenticity and dynamic adaptability of the modeling; then, a contact pressure distribution model of the bearing roller along the circumferential direction is introduced. The model can be embedded The system is constructed using an embedded force sensor, finite element analysis, or load inversion algorithm to achieve real-time assessment of the contact force of the roller at different locations on the raceway. Based on the contact pressure distribution, the conductive thermal resistance parameters between adjacent contact sub-regions are dynamically allocated. For example, when the contact pressure of the roller in a certain region is significantly higher than that in the adjacent region, it indicates that the contact area is larger and the heat flux density is higher, and the corresponding thermal resistance should be reduced. In this case, a lower thermal resistance value is assigned to the region in the topological link to reflect the enhanced heat conduction capability. Based on this thermal conductivity model, temperature rise prediction calculations are continued, and the predicted temperature values ​​are compared with the actual temperature measurements. When the deviation between the predicted temperature rise and the actual temperature value corresponding to a node or a group of conductive thermal resistance parameters exceeds a set threshold, a reverse adjustment mechanism is automatically triggered. This mechanism re-divides the boundaries of the relevant contact sub-regions based on the error gradient and contact heat flux distribution. Possible adjustments include expanding or reducing the coverage area of ​​the high-pressure area and merging inefficient heat channel areas. After the boundary is re-divided, the conductive thermal resistance parameters of the region are recalculated, and the thermal conductivity topological link is updated to improve the accuracy and consistency of subsequent temperature rise predictions.

[0035] Furthermore, temperature compensation corrections are performed on core hot nodes, including: When the deviation of the predicted temperature rise value of the bearing thermal node or the tool thermal node exceeds the threshold, the geometric characteristics of the current machining path and the spindle load state are extracted to generate a compensation gain coefficient that is positively correlated with the curvature of the machining path; Dynamically increase the conduction thermal resistance weight of the corresponding node according to the compensation gain coefficient, and simultaneously reduce the convection heat transfer parameters of the adjacent nodes; Based on the conduction thermal resistance weight and convection heat transfer parameters, the temperature rise prediction value of the core thermal node is recalculated, and the compensation effect is verified through on-site temperature measurement; If the residual of the temperature rise prediction value after compensation is still higher than the threshold, the proportional relationship between the compensation gain coefficient and the conduction thermal resistance weight is iteratively adjusted until the prediction accuracy of the thermal node network is restored to the set range.

[0036] As a preferred embodiment of the above, first, the path geometric features in the current processing task are extracted, including the curvature change of the tool motion trajectory, feed speed, feed depth and other information. These parameters are obtained through CNC system code analysis and real-time modeling of the tool trajectory. At the same time, the real-time load state data of the spindle corresponding to the path section is collected, including torque, power fluctuation and drive current amplitude. After fusing and analyzing these multi-source information, a compensation gain coefficient is generated. The coefficient is positively correlated with the trajectory curvature, that is, in the processing section where the angle changes drastically or the load changes suddenly, the corresponding compensation gain coefficient will increase significantly, reflecting the response ability to the sudden change in temperature rise in the local heat-sensitive area; then, based on the compensation gain coefficient, the thermal conductivity topological properties of the corresponding thermal node are dynamically adjusted, specifically by increasing the conduction thermal resistance weight in the link connected to the node, which means enhancing the heat accumulation characteristics of the path in the prediction model. At the same time, in order to maintain the thermal balance logic, the convective heat transfer parameters of other nodes directly connected to the node are synchronously reduced to simulate the situation where the local heat flow is more concentrated and the external heat dissipation capacity is reduced. This parameter collaborative adjustment mechanism enables a more realistic reflection of the transient heat accumulation phenomenon caused by local geometric loads. Then, based on the updated conductive thermal resistance weight and convective heat transfer parameters, the temperature rise prediction calculation is re-executed to obtain the compensated core thermal node temperature value. To verify the compensation effect, the measured temperature value collected by the in-situ thermocouple or infrared temperature measurement is called for comparison. When the residual of the compensated temperature rise prediction value drops to within the set error tolerance, the compensation is considered effective. If there is still a significant deviation, the residual is used as a feedback signal to further iteratively adjust the compensation gain coefficient and its proportional relationship with the conductive thermal resistance weight, such as increasing the nonlinear coupling term of the gain coefficient and thermal resistance adjustment, until the prediction accuracy returns to the set range.

[0037] Furthermore, the heat generation power distribution of each spindle component is analyzed, including: Calculate the stator copper loss power based on the spindle motor stator resistance parameters, and calculate the rotor eddy current loss power caused by harmonic current; The bearing friction loss power is calculated based on the product relationship between the real-time spindle speed and the load torque, combined with the preset bearing friction coefficient; Calculate the total switching loss based on the inverter's switching frequency data and DC bus voltage; Based on the physical structure heat capacity characteristics of the spindle assembly, the stator copper loss power, rotor eddy current loss power, bearing friction loss power and the total switching loss are allocated to the bearing heat node, the shaft core conduction heat node and the tool heat node.

[0038] As a preferred embodiment of the above, first, for the calculation of the stator copper loss power of the spindle motor part, the phase current and stator resistance parameters of the spindle motor are collected, and the Joule heat loss generated by the three-phase stator current under load, that is, the stator copper loss power, is calculated in real time. In order to further improve the accuracy of the heat source modeling, the high-order harmonic components of the motor current signal are analyzed by Fourier transform, and the rotor eddy current loss power generated by high-frequency harmonic excitation is derived in combination with the rotor structure and material parameters. This part of the loss mainly affects the rotor and its surrounding thermal nodes, especially the high-frequency thermal excitation area in the shaft core; secondly, in terms of the bearing heat source, its mechanical output power is calculated based on the product of the current real-time speed of the spindle and the load torque, and then the bearing friction coefficient preset under the specific structure of the spindle is introduced, and the friction loss power generated by the contact between the bearing roller and the inner and outer rings is calculated by the proportional distribution method. This part of the power is mainly concentrated in the bearing thermal nodes, especially the contact area between the inner ring raceway and the roller, which is one of the main reasons for the formation of local hot spots in the bearing; in order to fully cover the heat sources of the electronic components in the spindle system, the inverse The total switching loss power is derived from the inverter's heat input by collecting switching frequency data and DC bus voltage from the inverter controller and combining it with the switching loss model of the inverter device. This heat is primarily concentrated at the electronic control interface heat node near the rear of the motor, significantly impacting the heat dissipation structure at the rear end of the spindle. After quantifying the power of each heat source, the power of each heat source is spatially allocated based on the physical structural characteristics of the spindle assembly, particularly the heat capacity, heat conduction path, and structural coupling relationship of each structural unit. Specifically, the stator copper loss is primarily allocated to the shaft core conduction heat node; the rotor eddy current loss power is distributed between the shaft core and the tool heat node based on the moment of inertia and axial position distribution weights; the bearing friction loss is directly assigned to the contact heat node of the upper and lower bearings; and the switching loss is distributed to the rear bearing heat node and the motor housing proximal heat node based on the distance and heat capacity coefficient weights. This allocation process is supported by a preset structural thermal coupling coefficient matrix to ensure the rational transfer and accumulation of each heat source energy in the heat node network.

[0039] Embodiment 2; Based on the same inventive concept as the machine tool spindle temperature measurement method in the aforementioned embodiment, the present invention further provides a machine tool spindle temperature measurement system, the system comprising: The information acquisition module collects the current signal and switching frequency data of the spindle drive motor in real time and analyzes the heat power distribution of each spindle component; The temperature rise prediction module decomposes the spindle into a network of thermal nodes based on the physical structure layout of each spindle component and obtains the temperature rise prediction value of each node in combination with the heat generation power distribution; The node compensation module obtains the actual temperature values ​​of key nodes. When the deviation between the temperature rise prediction value and the actual temperature value of any node exceeds the set threshold, the corresponding node is subjected to temperature compensation correction. The thermal resistance correction module predicts the periodic temperature rise gradient in the future processing path based on the corrected thermal node network, and reversely corrects the thermal resistance parameters in the thermal node network based on the workpiece geometric error obtained by in-situ measurement.

[0040] The above-mentioned adjustment system in the present invention can effectively implement the machine tool spindle temperature measurement method, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0041] Furthermore, the temperature rise prediction module includes: A thermal resistance determination unit determines the thermal resistance parameters of each thermal conductivity topology link based on the thermal conductivity of the spindle material and the ratio of the contact pressure distribution of the bearing rollers; The state acquisition unit obtains the convection heat transfer parameters of each core thermal node along the thermal conductivity topology link based on the real-time spindle speed and coolant flow state; The temperature rise calculation unit injects the heat generation power distribution into the corresponding core thermal nodes and calculates the temperature rise prediction value of each core thermal node based on the thermal resistance parameter and the convection heat transfer parameter; The compensation detection unit performs temperature compensation correction on the core thermal node when it detects that the deviation between the temperature rise prediction value and the actual temperature value exceeds a set threshold.

[0042] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the corresponding optimization effects of the method in Example 1, which will not be repeated here.

[0043] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A method for measuring the temperature of a machine tool spindle, characterized in that: The method comprises: Real-time acquisition of the current signal and switching frequency data of the spindle drive motor to analyze the heat power distribution of each spindle component; Decomposing the spindle into a network of thermal nodes based on the physical structure layout of the spindle components, and obtaining a predicted temperature rise value for each node in combination with the heat generation power distribution; Obtaining the actual temperature values ​​of key nodes, and performing temperature compensation correction on the corresponding node when the deviation between the temperature rise prediction value and the actual temperature value of any node exceeds a set threshold; According to the corrected thermal node network, the periodic temperature rise gradient in the future processing path is predicted, and based on the workpiece geometric error obtained by in-situ measurement, the thermal resistance parameters in the thermal node network are reversely corrected.

2. The method for measuring the temperature of a machine tool spindle according to claim 1, wherein: Based on the physical structure layout of the spindle components, the spindle is decomposed into a hot node network, including: According to the actual physical contact areas between the inner and outer raceways of the spindle bearings and the shaft core and tool clamping surface, the spindle is divided into three core thermal nodes: the bearing thermal node, the shaft core conduction thermal node, and the tool thermal node. Based on the core thermal nodes, generating thermally conductive topological links between adjacent nodes, wherein the link density between the bearing thermal nodes and the shaft core conductive thermal nodes is determined by the ratio of the bearing roller contact pressure distribution; Converting the real-time spindle speed and the coolant flow state into the thermal resistance parameters of the bearing thermal node and the tool thermal node, and updating them into the thermal conductivity topology link; According to the deviation between the actual temperature value and the current temperature rise prediction value, the link density and the thermal resistance parameter are reversely adjusted until the prediction accuracy of the thermal node network meets the preset convergence condition.

3. The method for measuring the temperature of a machine tool spindle according to claim 2, wherein: The spindle temperature is measured based on the thermal node network, including: Extracting the actual temperature value of the core thermal node from the thermal conductivity topology link and aligning it with the current harmonic characteristics of the spindle drive motor in the time domain; Inputting the actual temperature value into a pre-trained time series prediction model to generate a temperature rise trend curve for each core thermal node in a future processing cycle, and dynamically adjusting the amplitude and phase of the temperature rise trend curve according to the task currently executed by the spindle; Based on the workpiece geometric error and the temperature rise trend curve, locating the maximum deviation area of ​​the thermal resistance parameter in the thermal node network, and triggering the directional enhancement of the link density; The link strengthening result is synchronized to the thermal conductivity topology link, and the above steps are repeated at preset time intervals until the residual of the temperature rise prediction value is lower than the convergence threshold for three consecutive times.

4. The method for measuring the temperature of a machine tool spindle according to claim 3, wherein: Triggering the directed enhancement of the link density, including: When it is detected that any axial deviation in the workpiece geometric error continues to exceed the tolerance range, the temperature gradient change feature of the corresponding axial direction in the temperature rise trend curve is extracted, and mapped and matched with the thermal resistance parameter of the thermal node network; Locating a weak area of ​​the thermal conductivity topological link in the shaft core thermal node or the tool thermal node according to the matching result, and calculating a target link density increment in the weak area in combination with the current contact pressure distribution of the bearing rollers; Dynamically adjusting the distribution pattern of the thermal conductivity topological links in the weak area based on the target link density increment, and verifying the convergence of the workpiece geometric error after the adjustment in a next machining cycle; If the convergence does not meet expectations, the target link density increment is iteratively increased until the residual of the temperature rise prediction value of the hot node network and the workpiece geometric error synchronously fall within a preset range.

5. The method for measuring the temperature of a machine tool spindle according to claim 1, wherein: Obtain the temperature rise prediction value of each node, including: Determining the thermal resistance parameters of each thermal conductivity topological link based on the thermal conductivity of the main shaft material and the ratio of the contact pressure distribution of the bearing rollers; According to the real-time spindle speed and the coolant flow state, the convection heat transfer parameters of each core heat node along the heat conduction topology link are obtained; Injecting the heat generation power distribution into the corresponding core thermal nodes, and calculating the temperature rise prediction value of each core thermal node based on the thermal resistance parameter and the convection heat transfer parameter; When it is detected that the deviation between the temperature rise prediction value and the actual temperature value exceeds a set threshold, a temperature compensation correction is performed on the core thermal node.

6. The method for measuring the temperature of a machine tool spindle according to claim 5, wherein: Determining the thermal resistance parameters of each thermal conductivity topology link includes: The contact area of ​​the inner and outer ring raceways of the main shaft bearing is decomposed into several contact sub-areas, each of which corresponds to the Hertzian contact pressure distribution range of a single bearing roller; Calculating a contact thermal resistance reference value of each contact sub-area according to the thermal conductivity of the spindle material and the assembly preload force detection value; Dynamically allocating thermal conduction resistance parameters between adjacent contact sub-regions based on the contact pressure distribution of the bearing rollers along the circumferential direction; If the deviation between the temperature rise prediction value corresponding to the conductive thermal resistance parameter and the actual temperature value exceeds a set threshold, the boundary division of the contact sub-region is adjusted in reverse and the conductive thermal resistance parameter is recalculated.

7. The method for measuring the temperature of a machine tool spindle according to claim 5, wherein: Performing temperature compensation correction on the core thermal node includes: When it is detected that the temperature rise prediction value deviation of the bearing thermal node or the tool thermal node exceeds a threshold, the geometric characteristics of the current machining path and the spindle load state are extracted to generate a compensation gain coefficient positively correlated with the curvature of the machining trajectory; Dynamically increase the conduction thermal resistance weight of the corresponding node according to the compensation gain coefficient, and simultaneously reduce the convection heat transfer parameter of the adjacent node; recalculating the temperature rise prediction value of the core thermal node based on the conduction thermal resistance weight and the convection heat transfer parameter, and verifying the compensation effect through on-site temperature measurement; If the residual of the temperature rise prediction value after compensation is still higher than the threshold, the proportional relationship between the compensation gain coefficient and the conduction thermal resistance weight is iteratively adjusted until the prediction accuracy of the thermal node network is restored to within the set range.

8. The method for measuring the temperature of a machine tool spindle according to claim 1, wherein: Analyze the heat generation power distribution of each spindle component, including: Calculate the stator copper loss power based on the spindle motor stator resistance parameters, and calculate the rotor eddy current loss power caused by harmonic current; The bearing friction loss power is calculated based on the product relationship between the real-time spindle speed and the load torque, combined with the preset bearing friction coefficient; Calculate the total switching loss based on the inverter's switching frequency data and DC bus voltage; Based on the physical structural heat capacity characteristics of the spindle assembly, the stator copper loss power, the rotor eddy current loss power, the bearing friction loss power and the total switching loss are allocated to the bearing heat node, the shaft core conduction heat node and the tool heat node.

9. The machine tool spindle temperature measurement system is characterized by: The system comprises: The information acquisition module collects the current signal and switching frequency data of the spindle drive motor in real time and analyzes the heat power distribution of each spindle component; The temperature rise prediction module decomposes the spindle into a network of thermal nodes based on the physical structure layout of each spindle component and obtains the temperature rise prediction value of each node in combination with the heat generation power distribution; The node compensation module obtains the actual temperature values ​​of key nodes. When the deviation between the temperature rise prediction value and the actual temperature value of any node exceeds the set threshold, the corresponding node is subjected to temperature compensation correction. The thermal resistance correction module predicts the periodic temperature rise gradient in the future processing path based on the corrected thermal node network, and reversely corrects the thermal resistance parameters in the thermal node network based on the workpiece geometric error obtained by in-situ measurement.

10. The machine tool spindle temperature measurement system according to claim 9, characterized in that: The temperature rise prediction module includes: A thermal resistance determination unit determines the thermal resistance parameters of each thermal conductivity topology link based on the thermal conductivity of the spindle material and the ratio of the contact pressure distribution of the bearing rollers; The state acquisition unit obtains the convection heat transfer parameters of each core thermal node along the thermal conductivity topology link based on the real-time spindle speed and coolant flow state; The temperature rise calculation unit injects the heat generation power distribution into the corresponding core thermal nodes and calculates the temperature rise prediction value of each core thermal node based on the thermal resistance parameter and the convection heat transfer parameter; The compensation detection unit performs temperature compensation correction on the core thermal node when it detects that the deviation between the temperature rise prediction value and the actual temperature value exceeds a set threshold.

Images