Warm-shape collaborative digital precision machining method for integral forging shaft of large rotor

Abstract

The invention discloses a temperature-shape collaborative digital precision machining method for an integral forging shaft of a large rotor. According to the method, the geometric error transfer rule model is trained and verified through the training data set and the verification data set which are obtained by performing the cutting simulation experiment through the cutting simulation model, and the geometric error of the workpiece is predicted during machining through the machining parameters, the machining area temperature field detected in real time and the geometric error transfer rule model. And when the prediction result does not meet the preset requirement, optimizing the machining parameters, calculating and analyzing the training data set and the verification data set to obtain the key geometric structure of the workpiece, and performing a dynamic balance performance simulation experiment on the workpiece geometric model introducing the geometric error of the key geometric structure after the workpiece is machined. And dynamic balance performance prediction after workpiece processing is realized. According to the method, on-machine prediction and control of the machining geometric error of the large rotor integral forging shaft can be realized, and prediction of the dynamic balance performance of the large rotor integral forging shaft can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of precision machining of rotating parts of large rotors, and in particular relates to a digital precision machining method for temperature and shape coordination of a large rotor integral forging shaft. Background Art

[0002] High-end turbine machinery equipment such as industrial steam turbines, gas turbines and aircraft engines all achieve energy conversion through large rotating parts (such as full-forged shafts with a maximum length of 6-8m, turbine discs with a maximum diameter of 800mm, etc.). The industry generally believes that the machining accuracy of such parts will directly affect the energy conversion efficiency and the safety of equipment operation, so the design side often places extremely high dimensional accuracy requirements on such parts. Among them, during the cutting process of large rotor full-forged shafts, especially in the processing of the outer cylindrical surface of large rotor full-forged shafts, the workpiece machining surface causes thermal expansion problems due to uneven temperature, resulting in nonlinear changes in geometric errors on the machining surface, which in turn causes the problem of unstable dynamic balancing performance of the workpiece. Therefore, it is particularly important to effectively control the influence of temperature on the machining deformation and achieve accurate guarantee of the dynamic balancing performance of large rotor full-forged shafts.

[0003] There are some relatively mature methods in the field of controlling thermal deformation during processing, but there is currently no method for integrating temperature, shape and property control to achieve digital precision processing in the processing of large rotating parts of turbine machinery. In order to effectively realize the accurate prediction and detection of the thermal deformation of the outer cylindrical surface of large rotating parts during processing, and effectively correlate the outer cylindrical surface dimensional error with the dynamic balancing performance, there is an urgent need for a processing method that can meet the high-performance processing requirements of the outer cylindrical surface of large rotating parts. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and to propose a digital precision machining method for temperature and shape coordination of a large rotor integral forging shaft.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The digital precision machining method for temperature and shape coordination of large rotor integral forging shaft of the present invention is specifically as follows:

[0007] Step 1: Obtain crystallographic data of workpiece samples with inhomogeneous and discontinuous characteristics.

[0008] Step 2: Establish a crystal shaping model of the workpiece with inhomogeneous and discontinuous characteristics.

[0009] Step 3: Establish the finite element model of turning processing.

[0010] Step 4: Establish a force-heat-fluid coupled cutting simulation model.

[0011] Step 5. Use the cutting simulation model to conduct cutting simulation experiments to obtain a training data set and a verification data set consisting of workpiece surface temperature field data, workpiece geometric error data, and various processing parameters; among which, the various processing parameters include tool wear, tool geometric parameters, feed rate, and cutting fluid flow rate, and the geometric error includes shape error, position error, and size error.

[0012] Step 6: Using the processing parameters and the workpiece surface temperature field data that changes with cutting time as input data and the workpiece geometric error as output data, a geometric error transmission law model is established.

[0013] Step 7: Normalize the input data of the training data set and the verification data set, and use the normalized training data set and verification data set to train and verify the geometric error transfer law model.

[0014] Step 8. Identify critical geometric structures on the workpiece.

[0015] Step nine, the CNC machine tool processes the workpiece according to the set processing parameters, and the processing control system combines the processing parameters, the real-time detected temperature field of the processing area and the geometric error transmission law model to optimize the feed rate and cutting fluid flow rate, so that the surface temperature field of the workpiece meets the preset temperature requirements, and the geometric accuracy of each position on the workpiece meets the accuracy requirements. If the optimized feed rate and cutting fluid flow rate cannot meet the temperature and accuracy requirements, replace the tool to change the tool wear or tool geometric parameters.

[0016] Step 10: After the workpiece is machined, measure the geometric errors at key geometric structure positions on the workpiece.

[0017] Step 11: Based on the geometric errors at the key geometric structure positions measured in step 10, the dynamic balancing performance of the workpiece after machining is predicted.

[0018] Preferably, the process of step one is: cutting the workpiece sample, preparing the workpiece sample into a workpiece sample, detecting the workpiece sample through a scanning electron microscope, collecting crystallographic data of the workpiece sample, and obtaining a crystallographic image of the workpiece sample, wherein when collecting grain orientation data, the grain orientation of each crystal on the workpiece sample is obtained through EBSD data analysis, when collecting grain size data, the grain size of each crystal within a preset range on the workpiece sample is selected, and when collecting dislocation density data, the microstructure distribution of the workpiece sample is measured to obtain the dislocation density of the crystal on the workpiece sample; then, the MTEX ​​plug-in in Matlab is used to generate a script, process the obtained crystallographic data of the workpiece sample, analyze and obtain the grain orientation and position coordinates of each crystal on the workpiece sample, draw a crystal orientation map, and export the crystallographic data of the workpiece sample as an Abaqus input file in text format.

[0019] More preferably, the process of step 2 is: establishing a workpiece simulation model through finite element simulation software, dividing the workpiece simulation model into a hexahedral grid, importing the Abaqus input file into the finite element simulation software, and mapping the dislocation density of the crystals on the workpiece sample, the grain orientation and grain size of each crystal to each grid node of the workpiece simulation model through the subroutine of the finite element simulation software, then defining the plastic properties of the crystals on the workpiece, and then conducting a quasi-static tensile test, combining molecular dynamics simulation to obtain the mechanical parameters of the grains and grain boundaries, and finally obtaining the workpiece crystal plasticity model, wherein the mechanical parameters of the grains include the elastic modulus and yield strength of the grains, and the mechanical parameters of the grain boundaries are the cohesive force model parameters of the grain boundaries.

[0020] Preferably, the process of step three is: establishing a tool simulation model, defining the tool material properties after dividing the grid, and then assembling the workpiece simulation model and the tool simulation model to obtain a turning processing finite element model, and setting boundary conditions and load conditions during the assembly process, setting the friction coefficient between the tool and the workpiece, and setting the thermal conductivity of the tool and the workpiece; wherein the tool is defined as a rigid body, the thermal conductivity of the tool is a constant, and for different grain orientations of each crystal on the workpiece, a thermal conductivity tensor K corresponds to each crystal, and a different thermal conductivity is given to each grain through the thermal conductivity tensor K, and the thermal conductivity tensor K is a 3×3 matrix:

[0021]

[0022] In the formula, k xx , k yy and k zz They represent the thermal conductivity along the x-axis, y-axis and z-axis respectively, k xy , k xz , k yx , k yz , k zx and k zy They represent the coupled thermal conductivity between the x-axis and the y-axis, between the x-axis and the z-axis, between the y-axis and the x-axis, between the y-axis and the z-axis, between the z-axis and the x-axis, and between the z-axis and the y-axis, respectively.

[0023] More preferably, the process of step four is: first construct an Euler domain model of the cooling process, divide the Euler domain model into a cutting fluid area and an empty area, define the cutting fluid material properties of the cutting fluid area, and mesh the Euler domain model, then assemble the Euler domain model with the turning processing finite element model to obtain a cutting simulation model, move the cutting fluid area to the contact area between the tool and the workpiece during the assembly process, constrain the load conditions of the cutting fluid area to be consistent with the tool load conditions, set the analysis step parameters and output parameters, wherein the output parameters are the temperature field data of the workpiece processing surface changing with the cutting time during the cutting simulation process and the geometric error of the workpiece after the cutting simulation.

[0024] Preferably, the process of step five is: setting the processing parameter range of the cutting and cooling strategies, the cutting processing parameters include tool wear, tool geometric parameters and feed rate, the cooling strategy processing parameter is cutting fluid flow rate, selecting multiple parameter nodes within each processing parameter range, performing orthogonal design on the parameter nodes of each processing parameter, obtaining a processing parameter combination set composed of a combination of different parameter nodes selected for each processing parameter, and dividing the processing parameter combination set into a training set and a verification set; using the cutting simulation model to perform a cutting simulation experiment using each processing parameter combination of the training set and the verification set, obtaining the temperature field of the workpiece processing surface and the geometric error of the workpiece during the cutting simulation process under each processing parameter combination of the training set and the verification set, and then obtaining a training data set and a verification data set composed of tool wear, tool geometric parameters, feed rate, cutting fluid flow rate, temperature field data and geometric error of the workpiece.

[0025] More preferably, the process of step eight is: according to the training data set and the verification data set, the first-order sensitivity index S of the shape error, position error and size error at each position on the workpiece is calculated. i (Y j ) and the overall sensitivity index S Ti (Y j ), where the first-order sensitivity index S of shape error, position error or size error is i (Y j ) is calculated as

[0026]

[0027]

[0028] Where, X i represents all the input data of the geometric error transmission law model corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set, i=1, 2, 3...n, n is the total number of data points of the workpiece surface temperature field, Y jrepresents the shape error, position error or size error data corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set, j = 1, 2 and 3 correspond to the shape error, position error and size error respectively, Var(Y j ∣X i ) is the variance of all shape errors, position errors or size errors corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set; Var(Y j ) is the total variance of all shape errors, position errors or size errors in the training data set and the validation data set, E[Y j ] is the expected value of all shape errors, position errors or size errors in the training dataset and the validation dataset;

[0029] The total sensitivity index S of shape error, position error or size error Ti (Y j ) is calculated as

[0030]

[0031] In the formula, Divide X in the training and validation datasets i The variance of all other shape errors, position errors or size errors other than the corresponding ones;

[0032] Then, the first-order sensitivity index of shape error, position error and size error at each position on the workpiece and the weighted comprehensive index of the total sensitivity index I are calculated. i :

[0033]

[0034] Where α and β are sensitivity weight coefficients, and α+β=1, α<β; ω 1 ,ω 2 and ω 3 is the error weight coefficient, and ω 1 +ω 2 +ω 3 =1;

[0035] Then, I i The values ​​are sorted from high to low, and the three positions on the workpiece corresponding to the top three values ​​are selected as the key geometric structures.

[0036] More preferably, the process of step nine is as follows: the CNC machine tool processes the workpiece according to the set processing parameters, and during the processing, the temperature field of the tool and the workpiece surface processing area is detected in real time by an infrared temperature sensor placed above the tool holder, and the detected temperature field data is transmitted to the control system, and the control system predicts the geometric error at the processing position of the workpiece under the current processing parameter combination according to the processing parameters and the real-time detected temperature field data through the geometric error transfer law model; if the temperature values ​​in the real-time detected temperature field data do not exceed the preset temperature and the predicted geometric error at the processing position meets the accuracy requirement, the CNC machine tool continues to process the workpiece according to the current processing parameter combination, otherwise the feed rate and cutting fluid flow rate are optimized (usually reducing the feed rate and increasing the cutting fluid flow rate), so that the temperature values ​​in the real-time detected temperature field data do not exceed the preset temperature and the geometric error at the processing position predicted by the geometric error transfer law model according to the optimized processing parameter combination and the real-time detected temperature field data meets the accuracy requirement, if the optimized feed rate and cutting fluid flow rate cannot meet the temperature and accuracy requirements, the tool is replaced to change the tool wear or tool geometric parameters to avoid excessive tool wear or tool geometric parameters being unsuitable for current cutting.

[0037] Preferably, in step ten, the geometric errors of key geometric structures of the workpiece are measured by a ruby ​​probe.

[0038] Preferably, the process of step eleven is: constructing a geometric model of the workpiece after processing, and importing the measured geometric error data of the key geometric structure into the finite element software, applying the unbalanced mass to the key geometric structure position of the workpiece geometric model to form an unbalanced torque, wherein the geometric model of the workpiece after processing is constructed by the process of establishing the crystal shaping model of the workpiece in step two; then applying boundary conditions to the geometric model of the workpiece, defining the support conditions at the bearing support of the workpiece, setting the analysis step to transient dynamic analysis, defining the rotation speed curve of the workpiece geometric model, and setting the time step; then performing a dynamic balancing performance simulation experiment, and outputting key parameters, and evaluating the dynamic balancing performance of the workpiece according to the key parameters; if the key parameters obtained by the simulation are all within the corresponding preset acceptable range, the processing is completed, otherwise, the key geometric structures are further refined, and the unbalanced mass of the workpiece geometric model at the key geometric structure position after refined processing is updated, and the dynamic balancing performance simulation experiment is re-performed until the dynamic balancing performance of the workpiece meets the requirements; wherein the key parameters include the displacement response of each key geometric structure, the reaction force and torque at the bearing support, the vibration spectrum of the workpiece, and the stress and strain distribution of each key geometric structure.

[0039] The present invention has the following beneficial effects:

[0040] 1. The present invention can realize on-machine prediction and control of geometric errors in machining of large rotor forged shafts, and can realize accurate prediction of dynamic balancing performance of large rotor forged shafts; specifically, the present invention establishes a workpiece simulation model, a tool simulation model and an Euler domain model of the cooling process, assembles the workpiece simulation model, the tool simulation model and the Euler domain model, and combines the plastic properties of the crystal on the workpiece to obtain a force-heat-liquid coupled cutting simulation model, and performs cutting simulation experiments through the cutting simulation model to obtain a training data set and a verification data set consisting of workpiece surface temperature field data, workpiece geometric error data and various processing parameters, and trains and verifies the established geometric error transmission law model by using the training data set and the verification data set. During the workpiece processing, the control system combines the various processing parameters , the temperature field and geometric error transmission law model of the tool and workpiece surface processing area detected in real time by the infrared temperature sensor, realizes the on-machine prediction of the workpiece processing geometric error, and optimizes the corresponding processing parameters through the control system when the prediction result does not meet the requirements, so that the prediction result meets the requirements, thereby realizing the on-machine prediction and control of the workpiece geometric error; further, the present invention uses the training data set and the verification data set to perform calculation and analysis to obtain the key geometric structure on the workpiece, detects the geometric errors of each key geometric structure on the workpiece after processing, and introduces the detection results into the geometric model of the workpiece after processing, so that the influence of unbalanced mass is introduced into the geometric model of the workpiece after processing, and then performs a dynamic balancing performance simulation experiment, thereby realizing the accurate prediction of the dynamic balancing performance of the workpiece after processing.

[0041] 2. The present invention takes into account the heterogeneity and discontinuity of the workpiece material in the process of establishing the workpiece model, collects the crystal properties of the workpiece material, and introduces the crystal properties of the workpiece material into the workpiece simulation model, thereby improving the accuracy of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the present invention.

[0043] Figure 2 The figure is a flow chart of establishing a force-heat-liquid coupled cutting simulation model in the present invention.

[0044] Figure 3 The present invention is a flow chart for establishing a geometric error transmission law model.

[0045] Figure 4 The present invention is a flowchart for realizing the prediction of the dynamic balancing performance of a workpiece. DETAILED DESCRIPTION

[0046] The present invention is further described below in conjunction with the accompanying drawings.

[0047] like Figure 1 , Figure 2, Figure 3 and Figure 4 As shown, the digital precision machining method for temperature and shape coordination of large rotor integral forging shaft of the present invention is specifically as follows:

[0048] Step 1: Obtain crystallographic data of workpiece samples with inhomogeneous and discontinuous characteristics: Cut the workpiece sample with a size of 10mm×10mm×5mm, grind and polish the surface of the workpiece sample, and then perform ultrasonic cleaning, etching, cleaning and drying in sequence to prepare the workpiece sample; then, the workpiece sample is inspected by scanning electron microscopy, and the crystallographic data on the workpiece sample is collected to obtain a crystallographic image of the workpiece sample, wherein the crystallographic data includes grain orientation (Euler angle), dislocation density and grain size. When collecting grain orientation data, EBSD data analysis is performed to obtain the workpiece sample. The grain orientation of each crystal on the workpiece sample is collected. When collecting grain size data, the grain size of each crystal in the range of 10μm~30μm on the workpiece sample is selected. When collecting dislocation density data, the microstructure distribution of the workpiece sample is measured to obtain the dislocation density of the crystal on the workpiece sample; then the MTEX ​​plug-in in Matlab is used to generate a script to process the acquired crystallographic data of the workpiece sample, analyze the grain orientation and position coordinates of each crystal on the workpiece sample, draw a crystal orientation map, and export the crystallographic data of the workpiece sample as an Abaqus input file in text format.

[0049] Step 2, establish a workpiece crystal shaping model with heterogeneous and discontinuous characteristics: establish a workpiece simulation model through finite element simulation software, divide the workpiece simulation model into hexahedral grids, and set the minimum grid size to 0.5 μm in this embodiment to ensure that the grid of the cutting area on the workpiece is fine enough to accurately capture the influence of the microstructure of the workpiece on the cutting force and deformation behavior; import the Abaqus input file into the finite element simulation software, and map the dislocation density, grain orientation and grain size of the crystal on the workpiece sample to each grid node of the workpiece simulation model through the subroutine of the finite element simulation software to ensure The simulation can accurately reflect the influence of the microstructure of the workpiece on the mechanical behavior; then define the plastic properties of the crystals on the workpiece, including integrating the dislocation density of the crystals on the workpiece sample, the grain orientation and the grain size of each crystal into the Johnson-Cook constitutive model of the workpiece and setting the fracture threshold. In this embodiment, the fracture threshold is set to 0.15; perform a quasi-static tensile test, and obtain the mechanical parameters of the grains and grain boundaries in combination with molecular dynamics simulation, and finally obtain the crystal plasticity model of the workpiece, wherein the mechanical parameters of the grains include the elastic modulus and yield strength of the grains, and the mechanical parameters of the grain boundaries are the cohesive force model parameters of the grain boundaries.

[0050] Step 3. Create a finite element model for turning: Establish a tool simulation model, define the tool material properties after dividing the grid, and define the tool as a rigid body in the simulation to reduce the calculation complexity; then assemble the workpiece simulation model and the tool simulation model to obtain a finite element model for turning, and set boundary conditions and load conditions during the assembly process, set the friction coefficient between the tool and the workpiece, and set the thermal conductivity of the tool and the workpiece; define the thermal conductivity of the tool as a constant, and for different grain orientations of each crystal on the workpiece, there is a corresponding thermal conductivity tensor K. Different thermal conductivity tensors K can be assigned to each grain by writing a subroutine of the finite element simulation software. The thermal conductivity tensor K can be expressed as a 3×3 matrix:

[0051]

[0052] In the formula, k xx , k yy and k zz They represent the thermal conductivity along the x-axis, y-axis and z-axis respectively, k xy , k xz , k yx , k yz , k zx and k zy Respectively represent the distance between the x-axis and the y-axis, between the x-axis and the z-axis, and between the y-axis and the x-axis (with k xy same), between the y-axis and the z-axis, between the z-axis and the x-axis (same as k xz same) and between the z-axis and the y-axis (same as k yz The coupled thermal conductivity is the same.

[0053] Step 4. Establish a force-heat-fluid coupled cutting simulation model; construct an Euler domain model of the cooling process, divide the Euler domain model into a cutting fluid area and an empty area, define the cutting fluid material properties of the cutting fluid area, and mesh the Euler domain model; then assemble the Euler domain model with the turning machining finite element model to obtain a cutting simulation model. During the assembly process, move the cutting fluid area to the contact area between the tool and the workpiece, constrain the load conditions of the cutting fluid area to be consistent with the tool load conditions, set the analysis step parameters and output parameters, where the output parameters are the temperature field data of the workpiece machining surface changing with cutting time during the cutting simulation and the geometric error of the workpiece after the cutting simulation, and the geometric error includes shape error, position error and size error.

[0054] Step 5, geometric error simulation experiment: set the machining parameter range of cutting and cooling strategies, the machining parameters of cutting include tool wear, tool geometric parameters and feed rate, the machining parameter of cooling strategy is cutting fluid flow rate, select multiple parameter nodes within each machining parameter range, perform orthogonal design on the parameter nodes of each machining parameter, obtain a machining parameter combination set composed of a combination of different parameter nodes selected for each machining parameter, and divide the machining parameter combination set into a training set and a verification set. In this embodiment, the parameter nodes selected within each machining parameter range are shown in Table 1; set the workpiece speed unchanged, and use the cutting simulation model to perform cutting simulation experiments under force-heat-liquid coupling using the machining parameter combinations of the training set and the verification set to obtain the temperature field of the workpiece machining surface and the geometric error of the workpiece during the cutting simulation process under the machining parameter combinations of the training set and the verification set, and then obtain a training data set and a verification data set composed of tool wear, tool geometric parameters, feed rate, cutting fluid flow rate, temperature field data and geometric error of the workpiece.

[0055] Table 1 Orthogonal simulation experiment parameters

[0056]

[0057] Step 6: Establish a geometric error transmission law model; use tool wear, tool geometry parameters, feed rate, cutting fluid flow rate, and workpiece surface temperature field data that changes with cutting time as input data, and the shape error, position error, and size error of the workpiece as output data to establish a geometric error transmission law model. Among them, the geometric error transmission law model is built using a feedforward neural network model based on the Pytorch framework. The structure of the feedforward neural network model is as follows: the number of neurons in the input layer is 5, the hidden layer is set to two layers, which is used to extract complex nonlinear features in the data, the activation function uses the ReLU function, and the number of neurons in the output layer is 3.

[0058] Step 7: Normalize the input data of the training data set and the verification data set, and use the normalized training data set and verification data set to train and verify the geometric error transfer law model established in step 6, which can accurately predict the law of workpiece geometric error and realize on-machine prediction of workpiece geometric error during machining.

[0059] Step 8: Use the variance decomposition method to identify the key geometric structures on the workpiece based on the training data set and the validation data set: calculate the first-order sensitivity index S of the shape error, position error and size error at each position on the workpiece i (Y j ) and the overall sensitivity index S Ti (Y j ), where the first-order sensitivity index S of shape error, position error or size error is i (Yj ) is calculated as

[0060]

[0061]

[0062] Where, X i represents all the input data of the geometric error transmission law model corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set (i.e., tool wear, tool geometric parameters, feed rate, cutting fluid flow rate, and workpiece surface temperature field data), and i=1, 2, 3...n, n is the total number of data points of the workpiece surface temperature field, Y j represents the shape error, position error or size error data corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set, and j = 1, 2 and 3 correspond to the shape error, position error and size error respectively. Var(E[Y j ∣X i ) is the variance of all shape errors, position errors or size errors corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set; Var(Y j ) is the total variance of all shape errors, position errors or size errors in the training data set and the validation data set, E[Y j ] is the expected value of all shape errors, position errors or size errors in the training data set and the validation data set;

[0063] The total sensitivity index S of shape error, position error or size error Ti (Y j ) is calculated as

[0064]

[0065] In the formula, Divide X in the training and validation datasets i The variance of all other shape errors, position errors or size errors except the corresponding ones.

[0066] Then, the first-order sensitivity index of shape error, position error and size error at each position on the workpiece and the weighted comprehensive index of the total sensitivity index I are calculated. i :

[0067]

[0068] Where α and β are sensitivity weight coefficients used to adjust S i and S Ti The relative importance of , and α+β=1, α<β, in this embodiment, α=0.3, β=0.7; ω1 ,ω 2 and ω 3 is the error weight coefficient, and ω 1 +ω 2 +ω 3 =1, in this embodiment ω 1 =ω 2 =ω 3 =1 / 3.

[0069] Then, I i The values ​​are sorted from high to low, and the three positions on the workpiece corresponding to the top three values ​​are selected as the key geometric structures.

[0070] Step nine, the CNC machine tool processes the workpiece according to the set processing parameters, and during the processing, the temperature field of the tool and the workpiece surface processing area is detected in real time by an infrared temperature sensor placed above the tool holder, and the detected temperature field data is transmitted to the control system. At the same time, the control system predicts the geometric error of the workpiece processing position under the current processing parameter combination according to the processing parameters and the real-time detected temperature field data through the geometric error transmission law model, wherein before processing, the wear amount of the currently used tool is measured by a laser tool setting instrument, and the wear amount of the tool is transmitted to the control system; during the workpiece processing, if the temperature values ​​in the temperature field data detected in real time by the infrared temperature sensor on the workpiece do not exceed the preset temperature and the preset If the measured geometric error at the processing position meets the accuracy requirement, the CNC machine tool continues to process the workpiece according to the current processing parameter combination. Otherwise, the adjustment mechanism of the control system is triggered, and the control system optimizes the feed rate and cutting fluid flow rate so that each temperature value in the real-time detected temperature field data does not exceed the preset temperature and the geometric error at the processing position predicted by the geometric error transmission law model based on the optimized processing parameter combination and the real-time detected temperature field data meets the accuracy requirement, avoiding problems such as thermal expansion and deformation caused by uneven temperature, and ensuring that the geometric accuracy of the workpiece meets the accuracy requirements. If the optimized feed rate and cutting fluid flow rate cannot meet the temperature and accuracy requirements, the tool is replaced to change the tool wear or tool geometric parameters.

[0071] Step 10. After the workpiece is processed, the tool is replaced with a ruby ​​probe. The CNC machine tool controls the ruby ​​probe to move accurately to the key geometric structure positions of the workpiece. The ruby ​​probe is used to precisely measure the geometric errors of each key geometric structure. After the measurement is completed, the measurement data is sent to the control system.

[0072] Step 11, accurate prediction of dynamic balancing performance: construct a geometric model of the workpiece after processing, import the measured geometric error data of the key geometric structure into the finite element software, apply the unbalanced mass to the key geometric structure position of the workpiece geometric model, form an unbalanced torque, and introduce the influence of the unbalanced mass into the workpiece geometric model. Among them, the process of establishing the crystal shaping model of the workpiece in step 2 is used to construct the geometric model of the workpiece after processing; then, boundary conditions are applied to the geometric model of the workpiece, support conditions are defined at the bearing support of the workpiece, the rolling support during actual operation is simulated, the analysis step is set to transient dynamic analysis, and the rotation of the workpiece geometric model is defined. The speed curve is used to simulate the process of the workpiece from static to the working speed of 2500rmp, and the time step is set to capture the transient response caused by the unbalanced torque during the rotation of the workpiece; then the dynamic balancing performance simulation experiment is carried out, and the key parameters are output to evaluate the dynamic balancing performance of the workpiece. The key parameters include the displacement response of each key geometric structure, the reaction force and torque at the bearing support, the vibration spectrum of the workpiece, and the stress and strain distribution of each key geometric structure. Among them, the radial displacement and its change of the key geometric structure during the rotation process can be analyzed through the displacement response of each key geometric structure, the reaction force and torque at the bearing support can be used to evaluate the additional load change caused by the unbalanced torque, and the vibration spectrum can be used to determine whether the working frequency of the workpiece is close to the natural frequency of the mechanical system during operation. The vibration frequency and resonance risk of the workpiece at different speeds can be analyzed. The stress and strain distribution of each key geometric structure can be used to analyze whether the key geometric structure has the risk of fatigue failure; if the key parameters obtained by simulation are all within the corresponding preset acceptable range, it means that the dynamic balancing performance of the workpiece obtained after processing meets the requirements and the processing is completed. Otherwise, it means that the dynamic balancing performance of the workpiece obtained after processing does not meet the requirements, and each key geometric structure needs to be further refined and improved. The unbalanced mass of the new workpiece geometric model at the key geometric structure position after fine machining is re-performed with a dynamic balancing performance simulation experiment until the dynamic balancing performance of the workpiece meets the requirements; wherein, in this embodiment, the acceptable range of displacement of the key geometric structure is 0-3mm, the acceptable range of reaction force at the bearing support is 0-10kN, the acceptable range of torque is 0-500N·m, the acceptable range of the vibration spectrum of the workpiece is that the workpiece frequency is outside the range of plus or minus 5Hz of the natural frequency of the mechanical system and its multiples, the acceptable range of stress of the key geometric structure does not exceed 0.8 of the material yield strength, and the acceptable range of total strain does not exceed 0.001.

Claims

1. A digital precision machining method for temperature and shape coordination of large rotor integral forging shafts, characterized by: The details are as follows: Step 1: Obtain crystallographic data of workpiece samples with inhomogeneous and discontinuous characteristics; Step 2: Establish a workpiece crystal shaping model with heterogeneous and discontinuous characteristics; Step 3: Establish a finite element model for turning processing; Step 4: Establish a force-heat-liquid coupled cutting simulation model; Step 5: Use the cutting simulation model to conduct a cutting simulation experiment to obtain a training data set and a verification data set consisting of workpiece surface temperature field data, workpiece geometric error data, and various processing parameters; wherein the various processing parameters include tool wear, tool geometric parameters, feed rate, and cutting fluid flow rate, and the geometric error includes shape error, position error, and size error; Step 6: Taking the processing parameters and the workpiece surface temperature field data changing with cutting time as input data and the workpiece geometric error as output data, a geometric error transmission law model is established; Step 7: normalize the input data of the training data set and the verification data set, and use the normalized training data set and the verification data set to train and verify the geometric error transfer law model; Step 8: Identify key geometric structures on the workpiece; Step 9, the CNC machine tool processes the workpiece according to the set processing parameters, and the processing control system combines the processing parameters, the real-time detected processing area temperature field and the geometric error transmission law model to optimize the feed rate and cutting fluid flow rate, so that the surface temperature field of the workpiece meets the preset temperature requirements, and the geometric accuracy of each position on the workpiece meets the accuracy requirements. If the optimized feed rate and cutting fluid flow rate cannot meet the temperature and accuracy requirements, the tool is replaced to change the tool wear or tool geometric parameters; Step 10: After the workpiece is processed, measure the geometric errors at key geometric structure positions on the workpiece; Step 11: Based on the geometric errors at the key geometric structure positions measured in step 10, the dynamic balancing performance of the workpiece after machining is predicted.

2. The method for digital precision machining of large rotor integral forging shaft by temperature and shape coordination according to claim 1 is characterized in that: The process of step one is: cutting the workpiece sample, preparing the workpiece sample into a workpiece sample, detecting the workpiece sample through a scanning electron microscope, collecting crystallographic data of the workpiece sample, and obtaining a crystallographic image of the workpiece sample, wherein when collecting grain orientation data, the grain orientation of each crystal on the workpiece sample is obtained through EBSD data analysis, when collecting grain size data, the grain size of each crystal within a preset range on the workpiece sample is selected, and when collecting dislocation density data, the microstructure distribution of the workpiece sample is measured to obtain the dislocation density of the crystal on the workpiece sample; then, the MTEX ​​plug-in in Matlab is used to generate a script, process the obtained crystallographic data of the workpiece sample, analyze and obtain the grain orientation and position coordinates of each crystal on the workpiece sample, draw a crystal orientation map, and export the crystallographic data of the workpiece sample as an Abaqus input file in text format.

3. The method for temperature-shape coordinated digital precision machining of large rotor integral forging shaft according to claim 2 is characterized in that: The process of step 2 is as follows: establish a workpiece simulation model through finite element simulation software, divide the workpiece simulation model into hexahedral grids, import the Abaqus input file into the finite element simulation software, and map the dislocation density of the crystals on the workpiece sample, the grain orientation and the grain size of each crystal to each grid node of the workpiece simulation model through the subroutine of the finite element simulation software, then define the plastic properties of the crystals on the workpiece, and then perform a quasi-static tensile test, and obtain the mechanical parameters of the grains and grain boundaries in combination with molecular dynamics simulation, and finally obtain the workpiece crystal plastic model, wherein the mechanical parameters of the grains include the elastic modulus and yield strength of the grains, and the mechanical parameters of the grain boundaries are the cohesive force model parameters of the grain boundaries.

4. The method for temperature-shape coordinated digital precision machining of large rotor integral forging shaft according to claim 1 is characterized in that: The process of step three is: establish a tool simulation model, define the tool material properties after dividing the grid, then assemble the workpiece simulation model and the tool simulation model to obtain a turning processing finite element model, and set boundary conditions and load conditions during the assembly process, set the friction coefficient between the tool and the workpiece, and set the thermal conductivity of the tool and the workpiece; wherein the thermal conductivity of the tool is defined as a constant value, and different grain orientations of each crystal on the workpiece correspond to a thermal conductivity tensor K, and different thermal conductivity is given to each grain through the thermal conductivity tensor K, and the thermal conductivity tensor K is a 3×3 matrix: In the formula, k xx , k yy and k zz They represent the thermal conductivity along the x-axis, y-axis and z-axis respectively, k xy , k xz , k yx , k yz , k zx and k zy They represent the coupled thermal conductivity between the x-axis and the y-axis, between the x-axis and the z-axis, between the y-axis and the x-axis, between the y-axis and the z-axis, between the z-axis and the x-axis, and between the z-axis and the y-axis, respectively.

5. The method for temperature-shape coordinated digital precision machining of large rotor integral forging shaft according to claim 4 is characterized in that: The process of step four is as follows: first, construct an Euler domain model of the cooling process, divide the Euler domain model into a cutting fluid area and an empty area, define the cutting fluid material properties of the cutting fluid area, and mesh the Euler domain model, then assemble the Euler domain model with the turning processing finite element model to obtain a cutting simulation model, move the cutting fluid area to the contact area between the tool and the workpiece during the assembly process, constrain the load condition of the cutting fluid area to be consistent with the tool load condition, set the analysis step parameters and output parameters, wherein the output parameters are the temperature field data of the workpiece processing surface changing with the cutting time during the cutting simulation process and the geometric error of the workpiece after the cutting simulation.

6. The method for temperature-shape coordinated digital precision machining of large rotor integral forging shaft according to claim 1 is characterized in that: The process of step five is as follows: setting the range of machining parameters for cutting and cooling strategies, wherein the machining parameters for cutting include tool wear, tool geometric parameters and feed rate, and the machining parameter for cooling strategy is cutting fluid flow rate, selecting multiple parameter nodes within each machining parameter range, orthogonally designing the parameter nodes of each machining parameter, obtaining a machining parameter combination set consisting of a combination of different parameter nodes selected for each machining parameter, and dividing the machining parameter combination set into a training set and a verification set; performing a cutting simulation experiment using each machining parameter combination of the training set and the verification set through a cutting simulation model, obtaining the temperature field of the workpiece machining surface and the geometric error of the workpiece during the cutting simulation process under each machining parameter combination of the training set and the verification set, and then obtaining a training data set and a verification data set consisting of tool wear, tool geometric parameters, feed rate, cutting fluid flow rate, temperature field data and geometric error of the workpiece.

7. The method for digital precision machining of large rotor integral forging shaft by temperature and shape coordination according to claim 6 is characterized in that: The process of step eight is: according to the training data set and the verification data set, the first-order sensitivity index S of the shape error, position error and size error at each position on the workpiece is calculated. i (Y j ) and the overall sensitivity index S Ti (Y j ), where the first-order sensitivity index S of shape error, position error or size error is i (Y j ) is calculated as Where, X i represents all the input data of the geometric error transmission law model corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set, i=1, 2, 3...n, n is the total number of data points of the workpiece surface temperature field, Y j represents the shape error, position error or size error data corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set, j = 1, 2 and 3 correspond to the shape error, position error and size error respectively, Var(Y j ∣X i ) is the variance of all shape errors, position errors or size errors corresponding to the i-th data point of the workpiece surface temperature field in the training data set and the validation data set; Var(Y j ) is the total variance of all shape errors, position errors or size errors in the training data set and the validation data set, E[Y j ] is the expected value of all shape errors, position errors or size errors in the training dataset and the validation dataset; The total sensitivity index S of shape error, position error or size error Ti (Y j ) is calculated as In the formula, Divide X in the training and validation datasets i The variance of all other shape errors, position errors or size errors other than the corresponding ones; Then, the first-order sensitivity index of shape error, position error and size error at each position on the workpiece and the weighted comprehensive index of the total sensitivity index I are calculated. i : Where α and β are sensitivity weight coefficients, and α+β=1, α<β; ω1, ω2 and ω3 are error weight coefficients, and ω1+ω2+ω3=1; Then, I i The values ​​are sorted from high to low, and the three positions on the workpiece corresponding to the top three values ​​are selected as the key geometric structures.

8. The method for digital precision machining of large rotor integral forging shaft by temperature and shape coordination according to claim 7 is characterized in that: The process of step nine is as follows: the CNC machine tool processes the workpiece according to the set processing parameters, and during the processing, the temperature field of the tool and the workpiece surface processing area is detected in real time by an infrared temperature sensor placed above the tool holder, and the detected temperature field data is transmitted to the control system, and the control system predicts the geometric error at the processing position of the workpiece under the current processing parameter combination according to the processing parameters and the real-time detected temperature field data through the geometric error transfer law model; if the temperature values ​​in the real-time detected temperature field data do not exceed the preset temperature and the predicted geometric error at the processing position meets the accuracy requirement, the CNC machine tool continues to process the workpiece according to the current processing parameter combination, otherwise the feed rate and cutting fluid flow rate are optimized so that the temperature values ​​in the real-time detected temperature field data do not exceed the preset temperature and the geometric error at the processing position predicted by the geometric error transfer law model according to the optimized processing parameter combination and the real-time detected temperature field data meets the accuracy requirement; if the optimized feed rate and cutting fluid flow rate cannot meet the temperature and accuracy requirements, the tool is replaced to change the tool wear or tool geometric parameters.

9. The method for temperature-shape coordinated digital precision machining of large rotor integral forging shaft according to claim 1 is characterized in that: In the step 10, the geometric errors of the key geometric structures of the workpiece are measured by a ruby ​​probe.

10. The method for digital precision machining of large rotor integral forging shaft by temperature and shape coordination according to claim 1 is characterized in that: The process of step eleven is as follows: constructing a geometric model of the workpiece after processing, and importing the measured geometric error data of the key geometric structure into the finite element software, applying the unbalanced mass to the key geometric structure position of the workpiece geometric model to form an unbalanced torque, wherein the geometric model of the workpiece after processing is constructed by the process of establishing the crystal shaping model of the workpiece in step two; then applying boundary conditions to the geometric model of the workpiece, defining the support conditions at the bearing support of the workpiece, setting the analysis step to transient dynamic analysis, defining the rotation speed curve of the workpiece geometric model, and setting the time step; then performing a dynamic balancing performance simulation experiment, and outputting key parameters, and evaluating the dynamic balancing performance of the workpiece according to the key parameters; if all the key parameters obtained by simulation are within the corresponding preset acceptable range, the processing is completed, otherwise, the key geometric structures are further refined, and the unbalanced mass of the workpiece geometric model at the key geometric structure position after refined processing is updated, and the dynamic balancing performance simulation experiment is re-performed until the dynamic balancing performance of the workpiece meets the requirements; wherein the key parameters include the displacement response of each key geometric structure, the reaction force and torque at the bearing support, the vibration spectrum of the workpiece, and the stress and strain distribution of each key geometric structure.

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