A precision inner circle grinding electric main shaft high-precision retention design method

Abstract

The application relates to a precision inner circle grinding electric spindle high-precision retention design method, which comprises the following steps: establishing an axle system finite element model; selecting materials for bearings and a spindle; considering bearing channel parameters and pre-tightening to establish a spindle precision simulation model; establishing a spindle precision optimization model, taking minimum axle end radial displacement as an objective to match parameters under each group of pre-tightening forces; considering pre-tightening force, cycle number and stress field to establish a spindle wear simulation model; establishing a spindle wear optimization model, taking a group of pre-tightening combinations, taking minimum wear as an objective to match parameters; establishing a precision retention evaluation model to quantitatively evaluate the spindle precision retention; and outputting a design model after meeting the precision and retention requirements. The application provides a precision inner circle grinding electric spindle design method, parameters of the spindle are matched through a precision optimization model and a wear optimization model, a precision retention evaluation model is established, and the designed electric spindle meets the high-precision retention requirement.

Description

Technical Field

[0001] This invention relates to the field of electric spindle design, and more particularly to a high-precision retention design method for a precision internal grinding electric spindle. Background Technology

[0002] High-speed precision internal grinding electric spindles are core components for high-speed grinding, integrating multiple technological units. The rotational accuracy of the electric spindle directly affects the machining accuracy, efficiency, and stability of CNC machine tools. Its machining accuracy and retention level determine the uptime of the CNC machine tool and even the entire production line. During service, abnormal temperature rises, wear, and other factors can lead to a degradation of the spindle's accuracy over its lifespan, and its accuracy retention affects the spindle's working life. Therefore, improving the rotational accuracy and post-service accuracy retention of electric spindles is crucial.

[0003] Bearing raceway parameters and axial preload are crucial factors affecting spindle rotation accuracy. During service, spindles experience wear due to changes in operating conditions and bearing surface stress fields. Wear in the bearings of an electric spindle increases clearance, leading to decreased system stiffness, increased vibration, degraded thermal properties, and reduced rotational accuracy, ultimately resulting in loss of machining accuracy in CNC machine tools. Existing research on spindle rotation accuracy typically considers only the impact of a single factor, neglecting the coupled effects of multiple factors such as preload and bearing raceway parameters. While domestic and international scholars have conducted extensive research on various aspects of CNC machining center accuracy degradation, theoretical analysis and experimental studies on electric spindle accuracy degradation and retention are scarce. Existing electric spindle designs generally do not consider spindle retention after service, nor the impact of changes in bearing surface stress fields and bearing surface conditions on spindle wear-induced accuracy degradation. There is a lack of research on high-precision spindles and their retention capabilities, as well as evaluation standards for spindle accuracy retention.

[0004] Based on the differences in the above studies, a high-precision retention design method for precision internal grinding electric spindles needs to be proposed. In the high-precision design, the coupled effects of bearing raceway roundness error, harmonic order, and axial preload on the rotational accuracy of the high-speed electric spindle need to be considered. Multiple parameters need to be matched and optimized to achieve the high precision requirements of the electric spindle under no-load conditions. In the precision retention design, the influence of bearing surface condition, preload, and surface stress field on spindle wear after spindle service is further considered. Spindle wear simulation is performed, and parameters such as preload, bearing surface stress field, and cycle number are matched to minimize spindle wear, thereby reducing precision degradation over the electric spindle's lifespan. A precision retention evaluation model is established to evaluate the designed spindle and parameter combinations, ensuring that the designed electric spindle meets the high-precision retention requirements. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a high-precision retention design method for electric spindles used in precision internal grinding.

[0006] The technical solution of this invention is implemented as follows: A high-precision retention design method for a precision internal grinding electric spindle, comprising:

[0007] 1) Establish a finite element model of the spindle-bearing rotor system;

[0008] 2) Compare the performance of different materials during the bearing's service life to select suitable materials for the bearing and spindle;

[0009] 3) Considering the influence of bearing raceway parameters and preload on spindle rotation accuracy, establish an electric spindle accuracy simulation model and set simulation parameters and boundary conditions;

[0010] 4) Establish a spindle accuracy optimization model, group the preload, take the minimum radial displacement at the shaft end as the target, and perform parameter matching on the roundness error and harmonic order of the front and rear bearings under each group of preload to obtain the optimal combination of bearing parameters under each group of preload;

[0011] 5) Set the spindle accuracy threshold and determine whether the spindle accuracy meets the requirements under each set of parameters. If the spindle accuracy meets the requirements under each set of parameters, retain the parameter combination; if none of the parameters can make the spindle accuracy meet the requirements, readjust the parameter search range and repeat steps 3) to 5) until the accuracy meets the requirements.

[0012] 6) Select a set of preload and corresponding bearing raceway parameter combinations as the initial parameters for the spindle at the factory;

[0013] 7) Further consider the influence of changes in preload, number of cycles, and surface stress field on bearing wear, establish a spindle wear simulation model, and set simulation parameters and boundary conditions;

[0014] 8) Establish a spindle wear optimization model. With the minimum wear as the objective, perform parameter matching on the number of cycles and bearing surface stress field under the set of preload and bearing parameters to obtain the optimal parameter combination.

[0015] 9) Establish a precision retention evaluation model to quantitatively evaluate the precision retention under electric spindle parameters;

[0016] 10) Set a spindle accuracy retention threshold and determine whether the spindle accuracy retention meets the requirements under this parameter. If the spindle accuracy retention meets the requirements under this set of parameters, retain the parameter combination; if not, reselect a set of preload and bearing raceway parameter combinations and repeat steps 7) to 10) until the accuracy retention meets the requirements; if the optimal wear parameter combination under all preload and bearing raceway parameter sets cannot make the spindle accuracy retention meet the requirements, the parameter search range for spindle accuracy optimization needs to be readjusted, and steps 3) to 10) are repeated until the accuracy retention meets the requirements.

[0017] 11) Output the design model.

[0018] Furthermore, in step 2), the stress field variation and damage evolution of the inner and outer raceways of bearings made of different materials during service are compared to select suitable materials for the bearings. The specific method is as follows: by establishing a rigid-plastic finite element model of the bearing thermo-mechanical coupling, considering the surface roughness and initial grain size of the bearing inner raceway, the influence of different rolling-slip ratios and pressure loads on the grain size and recrystallization of the inner raceway surface is studied. Finite element simulation is performed on bearings made of different materials to obtain the microscopic response changes of the raceway subsurface material structure caused by friction, stress, and thermal effects during the service process, so as to select suitable materials for the bearings.

[0019] Furthermore, in step 3), the influence of bearing raceway parameters and preload on the spindle rotation accuracy is considered. An electric spindle accuracy simulation model is established, and simulation parameters and boundary conditions are set. The specific steps are as follows:

[0020] (1) Determine the basic parameters of the spindle and bearings, the bearing pairing method, and set the working conditions such as speed and load;

[0021] (2) Determine the preload, bearing surface roundness error, and harmonic order as input conditions, and determine the boundary conditions;

[0022] (3) The radial displacement of the spindle end under simulation conditions was calculated using Abaqus and Romax simulation software.

[0023] Furthermore, in step 4), a spindle accuracy optimization model is established, the preload is grouped, and the minimum radial displacement at the shaft end is taken as the target. The roundness error and harmonic order of the front and rear bearings under each group of preload are matched to obtain the optimal combination of bearing parameters under each group of preload.

[0024] Since preload has a significant impact on spindle accuracy and wear, to prevent the spindle accuracy from being affected by changes in preload during subsequent wear optimization due to changes in the preload after accuracy optimization, the following approach is adopted: Preload is grouped according to its range. Under a fixed preload in each group, the roundness error and harmonic order of the front and rear bearings are used as variables for parameter optimization to obtain the optimal combination of bearing raceway parameters for different preload values. The specific steps are as follows:

[0025] (1) Group the preload at equal intervals according to the range of preload values; predefine the axial preload as c, and determine the range and grouping of the preload by the following expression:

[0026] c min ≤c≤c max

[0027] In the formula: c min This is the lower limit of the preload force;

[0028] c max This is the upper limit of the preload force;

[0029] c i =[c1,c2,...,c n ]

[0030] Where: c1, c2, ..., c n Let c be the value of n sets of preload. i Let the value of the preload be the value of the i-th group;

[0031] (2) The decision variables of the predefined electric spindle accuracy optimization model are: front bearing roundness error a1, harmonic order b1, rear bearing roundness error a2, and harmonic order b2; the optimization objective is to improve the rotational accuracy of the electric spindle.

[0032] (3) Determine the range of values ​​for the decision variables, establish constraints, and establish the objective function; wherein, the design variables, constraints, and objective function are determined by the following expressions:

[0033] Decision variables:

[0034] X = (x1, x2, x3, x4) T =(a1,b1,a2,b2) T

[0035] X min ≤X≤X max

[0036] In the formula: X min This serves as the lower bound for the decision variable.

[0037] X max This represents the upper limit of the decision variables;

[0038] Constraints:

[0039]

[0040]

[0041]

[0042]

[0043] Objective function:

[0044] The spindle rotation accuracy is characterized by the radial displacement Y at the spindle end, and the objective function is:

[0045] Y = f(x) = f(a1, b1, a2, b2)

[0046] The smaller the radial displacement at the spindle end, the higher the spindle rotation accuracy. Therefore, under the condition of satisfying the values ​​of the decision variables and the constraints, Y should be minimized, that is:

[0047] minY = minf(a1,b1,a2,b2)

[0048] (4) For each group of preload, the genetic algorithm is used to solve the electric spindle accuracy influence parameters designed in steps 2) to 3) to obtain the optimal combination of front and rear bearing channel roundness error and harmonic order when the radial displacement of the spindle end is minimized under each group of preload.

[0049] Furthermore, in step 7), the effects of changes in preload, number of cycles, and surface stress field on bearing wear are further considered. A spindle wear simulation model is established, and simulation parameters and boundary conditions are set. The specific steps are as follows:

[0050] (1) Use the combination of preload and bearing raceway parameters in step 6) as the initial bearing parameters in the spindle wear simulation and set the initial working condition.

[0051] (2) Use the preload, number of cycles, surface stress, and subsurface stress as input conditions for the simulation model, and add boundary conditions;

[0052] (3) Using the Archard wear model, the wear amount is calculated by the change in spindle hardness caused by different parameter variations in the simulation; the wear formula is:

[0053]

[0054] In the formula, V is the wear volume; K is the wear coefficient; N is the normal pressure on the contact surface between the workpiece and the material; L is the tangential relative sliding distance between the mold and the workpiece; and H is the mold hardness.

[0055] The angular contact ball bearings in the electric spindle have a matching lubrication system, so lubrication is sufficient. Therefore, the wear coefficient is selected as:

[0056] K = 2.1 × 10 -7

[0057] The sliding distance between the rolling elements and raceway in a single contact of an angular contact ball bearing is:

[0058]

[0059] In the formula: V x Let be the sliding velocity at the contact point (,y) relative to the pure rolling point; be the time of relative sliding during a single contact, as shown in the formula:

[0060]

[0061] Where: V0 is the relative velocity between the rolling element and the raceway in the short semi-axis direction within the contact area; b x Let (x, y) be the half length perpendicular to the x-direction at the point (x, y);

[0062] Furthermore, in step 8), a spindle wear optimization model is established. With the goal of minimizing wear, parameter matching is performed on the number of cycles and the bearing surface stress field under the set of preload and bearing parameters to obtain the optimal parameter combination. The specific steps are as follows:

[0063] (1) The decision variables of the predefined electric spindle wear optimization model are: number of cycles r, surface stress n1, and subsurface stress n2; the optimization objective is to minimize the spindle wear.

[0064] (2) Determine the decision variables and their ranges, establish constraints, and establish the objective function; the design variables, constraints, and objective function are determined by the following expressions:

[0065] Decision variables:

[0066] X = (x1, x2, x3) T =(r,n1,n2) T

[0067] X min ≤X≤X max

[0068] In the formula: X min This serves as the lower bound for the decision variable.

[0069] X max This represents the upper limit of the decision variables;

[0070] Constraints:

[0071] rmin ≤r≤r max

[0072]

[0073]

[0074] Objective function:

[0075] With bearing wear M as the objective function, the objective function is:

[0076] M = M(x) = M(r,n1,n2)

[0077] Given the desired values ​​for the decision variables and the constraints, we require M to be minimized, i.e.:

[0078] minM = minM(r,n1,n2)

[0079] (3) The genetic algorithm is used to optimize the parameters affecting the wear of the electric spindle designed in steps 1) to 2) to obtain the optimal combination of cycle number and surface stress under the preload and bearing groove parameters when the wear of the electric spindle is minimized.

[0080] Furthermore, in step 9), a precision retention evaluation model is established to quantitatively evaluate the precision retention under the electric spindle parameters; the specific steps of the precision retention evaluation method are as follows;

[0081] (1) Set error terms and evaluation elements:

[0082] The error term is: radial displacement Y of the spindle end; the evaluation elements are: initial spindle accuracy p0, transient monitoring accuracy p1, and accuracy requirement threshold p. w The parameters are: accuracy degradation rate (ADR) and accuracy life (AL); where the spindle end radial displacement Y represents the spindle accuracy, the accuracy degradation rate (ADR) represents the spindle accuracy degradation rate, and the accuracy life (AL) represents the accuracy retention time.

[0083] (2) The values ​​of error terms and evaluation elements are determined and calculated in the simulation model. The initial accuracy p0 of the spindle and the transient accuracy p1 are obtained by measuring and calculating the radial displacement of the shaft end in the simulation model; the accuracy requirement threshold p w The spindle rotation accuracy requirements are set manually; the accuracy degradation rate (ADR) and accuracy life (AL) are calculated as follows:

[0084] When the accuracy degradation process is nonlinear, i.e., when the accuracy degradation curve f(t) is nonlinear, the accuracy degradation rate is:

[0085] ADR=f′(t)

[0086] When the accuracy degradation process is linear, its accuracy degradation rate is:

[0087]

[0088] In the formula: p0 is the initial accuracy, p1 is the transient accuracy of monitoring, and t1 is the monitoring time;

[0089] When the monitoring accuracy p1 just exceeds the working accuracy requirement p w ,Right now

[0090] p1 = p w

[0091] At this point: AL = t1-0

[0092] (3) Evaluate the accuracy retention; using the accuracy evaluation model, calculate the accuracy degradation rate (ADR) and accuracy life (AL) of the electric spindle under the optimal parameter combination in the previous step. When both indicators meet the requirements, it is considered that the spindle accuracy retention requirements are met.

[0093] In summary, the advantages and beneficial effects of the present invention are as follows:

[0094] This invention proposes a high-precision retention design method for precision internal grinding electric spindles. It considers the coupled effects of bearing raceway roundness error, harmonic order, and axial preload on the rotational accuracy of high-speed electric spindles. Under various preload forces, the bearing raceway parameters are matched and optimized to ensure the matched parameters meet the high-precision requirements of the electric spindle. Furthermore, this invention considers the influence of bearing surface condition and surface stress field on spindle wear after spindle service. A spindle wear simulation model is established to simulate spindle wear, and parameters such as preload, bearing surface stress field, and cycle number are matched to minimize spindle wear. A precision retention evaluation model is also established to ensure the electric spindle meets precision retention requirements. Compared to other spindle design methods, this invention considers the coupled effects of multiple parameters on spindle rotational accuracy, automatically matches parameters through an optimization model, and considers the precision retention after spindle service. Wear optimization is performed on the spindle, and the precision retention of the optimized spindle is evaluated, resulting in a spindle that achieves higher rotational accuracy and excellent precision retention. Attached Figure Description

[0095] Figure 1 This is a flowchart of the high-precision retention design method for a precision internal grinding electric spindle according to the present invention;

[0096] Figure 2 This is a structural diagram of the spindle-bearing rotor system of the present invention;

[0097] Figure 3 This is the finite element model of the spindle-bearing rotor system of the present invention;

[0098] In the diagram: 1-Front bearing; 2-Front bearing flange; 3-Water cooling jacket; 4-Outer shell; 5-Stator; 6-Main shaft; 7-Rotor; 8-Rear bearing; 9-Rear bearing flange; 10-Ball bearing. Detailed Implementation

[0099] The present invention will now be described in further detail with reference to the accompanying drawings.

[0100] Reference Figure 1 This invention discloses a high-precision retention design method for a precision internal grinding electric spindle, used in a spindle-bearing rotor system. Figure 2 The optimization design of ) involves the following steps:

[0101] 1. Establish a finite element model of the spindle-bearing rotor system, such as... Figure 3 As shown;

[0102] 2. By comparing the stress field changes and damage evolution patterns of the inner and outer raceways of bearings made of different materials during service, suitable materials can be selected for the bearings and spindles. The specific method is as follows:

[0103] By establishing a rigid-plastic finite element model of a high-speed angular contact thrust ball bearing with thermo-mechanical coupling, and considering the surface roughness and initial grain size of the bearing inner ring raceway, the influence of different rolling-slip ratios and pressure loads on the surface grain size and recrystallization of the bearing raceway inner ring was studied. Finite element simulation was performed on bearings of different materials to obtain the microscopic response changes of the raceway subsurface material structure caused by friction, stress, and thermal effects during the bearing's service life, which can help select suitable materials for the bearing.

[0104] The material parameters of the selected bearings and spindle can be represented in Table 1 as follows:

[0105] Table 1. Bearing and Spindle Material Parameters

[0106]

[0107] 3. Considering the influence of bearing raceway parameters and preload on the spindle rotation accuracy, establish an electric spindle accuracy simulation model and set simulation parameters and boundary conditions:

[0108] (1) Both the front and rear bearings of the electric spindle are angular contact ball bearings. The two sets of B7008 C / HQ1 bearings at the front end are configured with DT, and the two sets of B7006 C / HQ1 bearings at the rear end are configured with DT. The rated speed of the electric spindle is 24,000 rpm, the rated power is 15 kW, and the torque is 6 N·m. At the rated speed and the radial cutting force at the shaft end is 500 N, the temperature rise of the electric spindle after water cooling is no higher than 25℃. The main structural parameters of the bearings are shown in Table 2 below:

[0109] Table 2 Main structural parameters of bearings

[0110]

[0111] (2) Refine the mesh at the contact area between the bearing ball and the raceway, couple the end face of the spindle shaft to the center of the end face, and apply a radial force of 500N at the coupling point; couple the outer ring of the bearing to its own center of gravity, and release only the axial movement degree of freedom at the coupling point to achieve the adjustment of the preload in the range of 0 to 12μm; take the roundness error of 0.50 to 2.25μm, the harmonic order of the front bearing raceway of 3 to 44, and the harmonic order of the rear bearing raceway of 3 to 38.

[0112] (3) The radial displacement of the spindle end under simulation conditions was calculated using Abaqus and Romax dynamics simulation software;

[0113] 4. Establish a spindle accuracy optimization model, group the preload, and use the minimum radial displacement at the shaft end as the target. Perform parameter matching on the roundness error and harmonic order of the front and rear bearings under each group of preload to obtain the optimal bearing parameter combination under each group of preload. The specific steps are as follows:

[0114] (1) The preload is grouped into equal intervals according to the range of preload values; the preload groups are as follows:

[0115] c i =[3,6,9,12]

[0116] This indicates that axial preload of 3μm, 6μm, 9μm, and 12μm was used for analysis;

[0117] (2) The decision variables for predefined electric spindle parameter optimization are: front bearing roundness error a1, harmonic order b1, rear bearing roundness error a2, and harmonic order b2; the optimization objective is to improve the rotational accuracy of the electric spindle.

[0118] (3) Determine the range of values ​​for the decision variables, establish constraints, and establish the objective function; wherein, the design variables, constraints, and objective function are determined by the following expressions:

[0119] Decision variables:

[0120] X = (x1, x2, x3, x4) T =(a1,b1,a2,b2) T

[0121] X min ≤X≤X max

[0122] X min =(0.50,3,0.50,3) T

[0123] X max=(2.25,40,2.25,38) T

[0124] The initial values ​​and ranges of the decision variables can be represented in Table 3 as follows:

[0125] Table 3

[0126]

[0127] Constraints:

[0128] 0.50≤a1≤2.25

[0129] 3≤b1≤40, b1∈R

[0130] 0.50≤a²≤0.25

[0131] 3≤b²≤34, b²∈R

[0132] Objective function:

[0133] Under the condition that the values ​​of the decision variables and the constraints are satisfied, the radial displacement Y of the spindle end is required to be minimized, that is:

[0134] minY = minf(a1,b1,a2,b2)

[0135] (4) For each set of preload, a genetic algorithm was used for optimization. The initial population size was set to 100, and the maximum number of iterations was set to 100. The fitness value of each individual was calculated according to the objective function, and iterative calculations were performed. Under the condition that the spindle speed reached 24000 rpm and the shaft end was loaded with 500 N, the optimal solutions for bearing raceway roundness error and harmonic order with the minimum radial displacement of the spindle end under each set of preload were obtained as follows: c1=3, X=(0.95,3,0.90,5). T c2 = 6, X = (0.75, 6, 0.50, 3) T ,c3=9,X=(0.60,9,1.00,19) T ,c4=12,X=(0.50,3,0.90,19) T With the radial displacement of the spindle end within 2, the designed electric spindle meets the requirements for high rotational accuracy.

[0136] 5. Further consider the influence of changes in preload, number of cycles, and surface stress field on bearing wear, establish a spindle wear simulation model, and set simulation parameters and boundary conditions. The steps are as follows:

[0137] (1) Select a set of preload and bearing raceway parameter combinations from the optimal solution of the accuracy optimization: c2 = 6, X = (0.75, 6, 0.50, 3). TAs the initial bearing parameters in the spindle wear simulation, the initial operating conditions are set as follows: the rated speed of the electric spindle is 24,000 rpm; a 500-point load is applied to the spindle end to simulate the radial force on the spindle during grinding wheel cutting; the preload method is constant pressure preload.

[0138] (2) The maximum compressive stress on the inner raceway of the ball under the preload condition is calculated to be 1.1 GPa. Considering the simulation calculation cost, the number of cycles is taken as 10. 4 10 5 10 6 (Low-cycle cycles apply large loads with few cycles, resulting in plastic deformation upon failure; high-cycle cycles lead to fatigue failure in most parts; infinite-cycle loads are small and cause almost no damage); surface residual stress is taken as -1.2 to 0 GPa.

[0139] (3) Using the Archard wear model, the wear amount is calculated by the change in spindle hardness caused by different parameter variations in the simulation; the wear formula is:

[0140]

[0141] In the formula, V is the wear volume; K is the wear coefficient; N is the normal pressure on the contact surface between the workpiece and the material; L is the tangential relative sliding distance between the mold and the workpiece; and H is the mold hardness.

[0142] 6. Establish a spindle wear optimization model. With the goal of minimizing wear, perform parameter matching on the number of cycles and the bearing surface stress field under this set of preload and bearing parameters to obtain the optimal parameter combination. Specific steps are as follows:

[0143] (1) The decision variables of the predefined electric spindle wear optimization model are: number of cycles r, surface stress n1, and subsurface stress n2; the optimization objective is to minimize the spindle wear.

[0144] (2) Determine the decision variables and their ranges, establish constraints, and establish the objective function; the design variables, constraints, and objective function are determined by the following expressions:

[0145] Decision variables:

[0146] X = (x1, x2, x3) T =(r,n1,n2) T

[0147] X min ≤X≤X max

[0148] X min =(10 4 (-0.8, -1.2) T

[0149] X max =(10 6 ,0,-0.4) T

[0150] The initial values ​​and ranges of the decision variables can be represented in Table 4 as follows:

[0151] Table 4

[0152]

[0153]

[0154] Constraints:

[0155] 10 4 ≤r≤10 6

[0156] -0.8≤n1≤0

[0157] -1.2≤n²≤-0.4

[0158] Objective function:

[0159] With bearing wear M as the objective function, the objective function is:

[0160] M = M(x) = M(r,n1,n2)

[0161] Given the desired values ​​for the decision variables and the constraints, we require M to be minimized, i.e.:

[0162] minM = minM(r,n1,n2)

[0163] (3) The genetic algorithm was used to optimize the parameters affecting the wear of the electric spindle designed in steps 1) to 2), with an initial population size of 100 and a maximum number of iterations of 100. The fitness value of each individual was calculated according to the objective function, and iterative calculations were performed. When the spindle speed reached 24000 rpm, the shaft end was loaded with 500 N, and the maximum compressive stress of the ball bearing on the inner raceway was 1.1 Gpa, the optimal solution X = (10) for the minimum number of cycles and surface stress was obtained. 5 (-0.6, -0.75) T .

[0164] 7. Establish a precision retention evaluation model to quantitatively evaluate the precision retention under electric spindle parameters; the precision retention evaluation steps are as follows;

[0165] (1) Set evaluation elements: initial spindle accuracy p0, transient monitoring accuracy p1, and accuracy requirement threshold p0. w Precision degradation rate (ADR), precision life (AL), and error item: spindle end radial displacement (Y).

[0166] (2) The values ​​of error terms and evaluation elements are measured and calculated in the simulation model. The initial accuracy of the spindle p0 and the transient accuracy of monitoring p1 can be directly calculated from the simulation model; the accuracy requirement threshold p w As required, calculate the accuracy degradation rate (ADR) and accuracy lifetime (AL). The results are shown in Table 5 below.

[0167] Table 5

[0168]

[0169]

[0170] (3) From the results in Table 5, the maximum compressive stress of the ball on the inner raceway is 1.1 GPa, X = (10 5 (-0.6, -0.75) T Under the specified parameter combination, the spindle accuracy degradation rate (ADR) is 0.112 μm / 1000 h, and the accuracy life (AL) is 10.625 × 10⁻⁶. 3 h all meet the requirements, therefore the designed spindle meets the accuracy retention requirements.

Claims

1. A high-precision retention design method for a precision internal grinding electric spindle, characterized in that, include: 1) Establish a finite element model of the spindle-bearing rotor system; 2) Compare the performance of different materials during bearing service to select suitable materials for the bearing and spindle; 3) Considering the influence of bearing raceway parameters and preload on spindle rotation accuracy, establish an electric spindle accuracy simulation model and set simulation parameters and boundary conditions; 4) Establish a spindle accuracy optimization model, group the preload, and use the minimum radial displacement at the shaft end as the target. Match the parameters of the roundness error and harmonic order of the front and rear bearings under each group of preload to obtain the optimal bearing parameter combination under each group of preload. Since the preload has a significant impact on both spindle accuracy and wear, to prevent the spindle accuracy from being affected by changes in preload during subsequent wear optimization due to changes in the preload, the following scheme is adopted: Group the preload according to the range of preload values. Under a fixed preload in each group, optimize the parameters using the roundness error and harmonic order of the front and rear bearings as variables to obtain the optimal combination of bearing raceway parameters under different preload values. The specific steps are as follows: (1) Group the preload into equal intervals according to the range of preload values; predefine the axial preload as follows: The range and grouping of preload force are determined by the following expression: In the formula: This is the lower limit of the preload force; This is the upper limit of the preload force; In the formula: for The value of the preload force, For the first The value of the preload; (2) The decision variable of the predefined electric spindle accuracy optimization model is: front bearing roundness error. Harmonic number Rear bearing roundness error Harmonic number The optimization objective is to improve the rotational accuracy of the electric spindle. (3) Determine the range of values ​​for the decision variables, establish constraints, and establish the objective function; wherein, the design variables, constraints, and objective function are determined by the following expressions: Decision variables: In the formula: This serves as the lower bound for the decision variable. This represents the upper limit of the decision variables; Constraints: Objective function: radial displacement of the spindle end Characterizing the spindle rotation accuracy, the objective function is: The smaller the radial displacement at the spindle end, the higher the spindle rotation accuracy. Therefore, under the condition of satisfying the values ​​of the decision variables and the constraints, it is required that... Take the minimum value, that is: (4) For each group of preload, the genetic algorithm is used to solve the electric spindle accuracy influence parameters designed in steps 2) to 3) to obtain the optimal combination of front and rear bearing groove roundness error and harmonic order when the radial displacement of the spindle shaft end is minimized under each group of preload; 5) Set the spindle accuracy threshold and determine whether the spindle accuracy meets the requirements under each set of parameters; if the spindle accuracy meets the requirements under each set of parameters, retain the parameter combination; if none of the parameters can make the spindle accuracy meet the requirements, readjust the parameter search range and repeat steps 3) to 5) until the accuracy meets the requirements. 6) Select a set of preload and corresponding bearing raceway parameter combinations as the initial parameters for the spindle at the factory; 7) Further consider the influence of changes in preload, number of cycles, and surface stress field on bearing wear, establish a spindle wear simulation model, and set simulation parameters and boundary conditions; 8) Establish a spindle wear optimization model. With the minimum wear as the objective, perform parameter matching on the number of cycles and bearing surface stress field under the set of preload and bearing parameters to obtain the optimal parameter combination. 9) Establish a precision retention evaluation model to quantitatively evaluate the precision retention under electric spindle parameters; 10) Set the spindle accuracy retention threshold and determine whether the spindle accuracy retention meets the requirements under this parameter. If the spindle accuracy retention meets the requirements under this set of parameters, retain the parameter combination. If not, reselect a set of preload and bearing raceway parameter combinations and repeat steps 7) to 10) until the accuracy retention meets the requirements. If the optimal wear parameter combination under all preload and bearing raceway parameter groups cannot make the spindle accuracy retention meet the requirements, the parameter search range for spindle accuracy optimization needs to be readjusted and steps 3) to 10) repeated until the accuracy retention meets the requirements. 11) Output the design model.

2. The high-precision retention design method for precision internal grinding electric spindles according to claim 1, characterized in that: Step 2) compares the stress field changes and damage evolution of the inner and outer raceways of bearings made of different materials during service to select suitable materials for the bearings. The specific method is as follows: by establishing a rigid-plastic finite element model of the bearing thermo-mechanical coupling, considering the surface roughness and initial grain size of the bearing inner raceway, the influence of different rolling-slip ratios and pressure loads on the surface grain size and recrystallization of the bearing raceway is studied. Finite element simulation is performed on bearings made of different materials to obtain the microscopic response changes of the raceway subsurface material structure caused by friction, stress, and thermal effects during the service process, so as to select suitable materials for the bearings.

3. The high-precision retention design method for precision internal grinding electric spindles according to claim 1, characterized in that: Step 3) considers the influence of bearing raceway parameters and preload on the spindle rotation accuracy, establishes an electric spindle accuracy simulation model, and sets simulation parameters and boundary conditions. The specific steps are as follows: (1) Determine the basic parameters of the spindle and bearings, the bearing pairing method, and set the speed and load conditions; (2) Determine the preload, bearing surface roundness error, and harmonic order as input conditions, and determine the boundary conditions; (3) The radial displacement of the spindle end under simulation conditions was calculated using Abaqus and Romax dynamic simulation software.

4. The high-precision retention design method for precision internal grinding electric spindles according to claim 1, characterized in that: Step 7) further considers the influence of changes in preload, number of cycles, and surface stress field on bearing wear, establishes a spindle wear simulation model, and sets simulation parameters and boundary conditions. The specific steps are as follows: (1) Use the combination of preload and bearing raceway parameters in step 6) as the initial bearing parameters in the spindle wear simulation to set the initial working condition; (2) Using the preload, number of cycles, surface stress, and subsurface stress as input conditions for the simulation model, and adding boundary conditions; (3) Using the Archard wear model, the wear amount is calculated by the change in spindle hardness caused by different parameter variations in the simulation; the wear formula is: In the formula, This represents the wear volume; The wear coefficient; This refers to the normal pressure at the contact surface between the workpiece and the material. This refers to the tangential relative sliding distance between the grinding wheel and the workpiece. The hardness of the mold; The angular contact ball bearings in the electric spindle have a matching lubrication system, so lubrication is sufficient. Therefore, the wear coefficient is selected as: The sliding distance between the rolling elements and raceway in a single contact of an angular contact ball bearing is: In the formula: Contact area point The sliding speed relative to the pure rolling point; The time for relative sliding to occur during a single contact is given by the formula: In the formula: The relative velocity between the rolling element and the raceway in the contact area along the short semi-axis direction; For point perpendicular to Half-length in the direction.

5. The high-precision retention design method for precision internal grinding electric spindles according to claim 1, characterized in that: In step 8), a spindle wear optimization model is established. With the goal of minimizing wear, parameter matching is performed on the number of cycles and the bearing surface stress field under the set of preload and bearing parameters to obtain the optimal parameter combination. The specific steps are as follows: (1) The decision variable of the predefined electric spindle wear optimization model is: number of cycles. Surface stress Secondary surface stress The optimization objective is to minimize spindle wear. (2) Determine the decision variables and their ranges, establish constraints, and establish the objective function; The design variables, constraints, and objective function are determined by the following expressions: Decision variables: In the formula: This serves as the lower bound for the decision variable. This represents the upper limit of the decision variables; Constraints: Objective function: bearing wear Let be the objective function, and the objective function is: Given that the values ​​of the decision variables and the constraints are satisfied, it is required that... M Take the minimum value, that is: (3) The genetic algorithm is used to optimize the parameters affecting the wear of the electric spindle designed in steps 1) to 2) to obtain the optimal combination of cycle number and surface stress under the preload and bearing channel parameters when the wear of the electric spindle is minimized.

6. The high-precision retention design method for precision internal grinding electric spindles according to claim 1, characterized in that: In step 9), an accuracy retention evaluation model is established to quantitatively evaluate the accuracy retention under the electric spindle parameters; the specific steps of the accuracy retention evaluation method are as follows; (1) Setting error terms and evaluation elements: The error term is: radial displacement of the spindle end. The evaluation factor is: initial accuracy of the spindle. Monitoring transient accuracy Precision requirement threshold Precision degradation rate Precision life Among them, the radial displacement of the spindle end Characterizing spindle accuracy, accuracy degradation rate Characterizes the rate of spindle accuracy degradation and accuracy life. Duration of characterization accuracy retention; (2) Determine and calculate the values ​​of error terms and each evaluation element in the simulation model, including the initial accuracy of the spindle. Monitoring transient accuracy The radial displacement at the shaft end is calculated by measuring using a simulation model; the required accuracy threshold is [not specified]. The spindle rotation accuracy requirement set manually; accuracy degradation rate. and accuracy lifespan The calculation method is as follows: When the accuracy degradation process is nonlinear, i.e., the accuracy degradation curve... When nonlinear, the accuracy degradation rate is: When the accuracy degradation process is linear, its accuracy degradation rate is: In the formula: For initial accuracy, To monitor transient accuracy, For monitoring time; When monitoring accuracy Just exceeds the working accuracy requirements ,Right now at this time: (3) Evaluate accuracy retention; using the accuracy evaluation model, calculate the accuracy degradation rate of the electric spindle under the optimal parameter combination in the previous step. and accuracy lifespan When both indicators meet the requirements, the spindle accuracy retention requirement is considered to be satisfied.

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