Motor rotor virtual assembly and pose optimization method based on dynamic rotation axis

Abstract

The invention discloses a motor rotor virtual assembly and pose optimization method based on a dynamic rotation axis, and the method specifically comprises the steps: obtaining the point cloud data of a rotor shaft and a rotor iron core, solving the surface mass center of each cross section of the rotor shaft and the mass centers of the two sides of each silicon steel sheet of the rotor iron core, fitting a rotor model and a rotor iron core model, and optimizing the pose of the rotor. After virtual assembly, the rotor is placed on the two rotor brackets; a rotor shaft model is discretized in the circumferential direction, a rotor overall dynamic balance equation is constructed, and a multi-objective optimization algorithm is adopted to solve the optimal solution of the assembly phase of the rotor iron core and the rotor shaft. According to the method, the influence of multiple factors such as rotor axis dynamic deviation, different contact states of the rotor and a rotor bracket when the rotor is in different circumferential phases and rotor core eccentricity on rotor dynamic balance is considered, a rotor overall dynamic balance equation is established, and the optimal solution of the rotor core and rotor shaft assembly phases is solved; and virtual assembly and pose optimization of the motor rotor are realized, so that actual assembly of the motor rotor is guided, and the dynamic balance performance of the motor rotor after actual assembly is ensured.

Description

Technical Field

[0001] The invention relates to the field of motor rotor assembly, and in particular to a motor rotor virtual assembly and posture optimization method based on a dynamic rotating axis. Background Art

[0002] With the rapid improvement of domestic precision assembly technology and automation technology, my country's high-end motor manufacturing companies have built automatic assembly production lines that integrate robots, real-time data monitoring and intelligent management technologies, thereby achieving efficient and precise manufacturing of high-end motors to meet increasingly complex market demands. This progress has significantly improved the assembly consistency and accuracy of motor products, and basically achieved automation in processing and assembly.

[0003] During the assembly process of the motor rotor, dynamic balancing is a key factor in ensuring the efficient operation of the motor. Excessive rotor imbalance will cause equipment vibration, noise and structural damage, so the dynamic balance adjustment should be fully considered when the rotor leaves the factory. Rotor dynamic balancing is a common method for measuring the imbalance of the motor rotor and correcting it. There are currently two common dynamic balancing methods: one is the weight-added method, which is difficult to control due to the added material weight, and is usually suitable for occasions where the dynamic balancing level requirements are not high, such as the G2.5 standard; the other is the weight-removal method, which is achieved by drilling or milling the rotor balancing disc. However, both methods have the obvious defect of low efficiency.

[0004] In order to solve this problem, a large number of studies have been conducted to analyze the rotor shaft. However, related papers and patents all use the axis fitted by the rotor shaft as the rotation axis of the rotor for virtual assembly and simulation. When calculating the imbalance, they do not consider that the rotation axis of the rotor may be offset due to the different contact states between the rotor phases and the rotor bracket during the rotor dynamic balancing test, resulting in a large deviation between the theoretical results and the actual results.

[0005] When facing the problem of rotor dynamic balance, few studies today consider the impact of the rotor core on assembly quality. The rotor core is made of multiple silicon steel sheets pressed together. There are three main processing methods for stacking silicon steel sheets: one is the spray glue curing method, which is to spray adhesive glue on the surface of each silicon steel sheet, then stack and cure it under high-frequency heating and high pressure to form a complete core; the second is the laser welding method, which applies pressure to both ends of the stacked silicon steel sheets and uses a high-energy laser welder to weld along the axial direction to achieve connection; the third is the self-fastening method, which uses the interference fit and friction between the materials to connect the scattered silicon steel sheets into the core. There are some potential problems in the actual application of these three methods, including insufficient tooling accuracy, uneven stress distribution, improper operation during the lamination process, and uneven spraying and melt flow. These problems lead to differential eccentricity between the silicon steel sheets in the rotor core, which further affects the dynamic balance performance of the rotor shaft and the rotor core after assembly. Summary of the invention

[0006] The object of the present invention is to provide a method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] The specific steps of the present invention are as follows:

[0009] S1. Obtain point cloud data of the rotor shaft and the rotor core surface, then establish an axis coordinate system on the rotor shaft, perform differential processing on the rotor shaft through s cross sections, solve the surface centroid of each cross section, and then establish a core coordinate system on the rotor core. The X-axis, Y-axis and Z-axis of the core coordinate system are parallel to the X-axis, Y-axis and Z-axis of the axis coordinate system respectively. The rotor core is composed of n silicon steel sheets stacked together. The centroid coordinates of each silicon steel sheet are determined based on the data points on both sides of each silicon steel sheet.

[0010] S2. Use the principal component analysis method to process the x and y coordinates of each data point in the point cloud data of the rotor shaft and the rotor core, and then use the least squares method to fit the cross-sectional models of the rotor shaft and the silicon steel sheet models of the rotor core.

[0011] S3, taking the bottom end of one of the rotor support surfaces as the origin, establish a world coordinate system, the X-axis, Y-axis and Z-axis of the world coordinate system are parallel to the X-axis, Y-axis and Z-axis of the axis coordinate system respectively; convert each cross-sectional model of the rotor shaft and each silicon steel sheet model of the rotor core after step S2 into the world coordinate system. In the world coordinate system, find the cross-sections in each cross-sectional model of the rotor shaft that are in contact with the support surfaces of the two rotor supports, perform local fitting on the rotor shaft point cloud data originally obtained in the axis coordinate system corresponding to the cross-sections in contact with each rotor support surface, and realize the reconstruction of the entire contact surface between the rotor shaft and the rotor support.

[0012] S4. Realize the three-dimensional construction of the rotor shaft model and convert it into the world coordinate system. Then virtually assemble the rotor shaft and the silicon steel sheet models of the rotor core and place them on the two rotor brackets.

[0013] S5. Discretize the rotor shaft model processed in step S4 in the circumferential direction, select one of the positions as the initial position, and record the instantaneous phase of each position relative to the initial position as α. For each instantaneous phase α, select a section in the entire contact surface between the rotor shaft and each rotor bracket, and calculate the rotor shaft rotation center corresponding to the instantaneous phase α state according to each contact point between the section and the rotor bracket.

[0014] S6. Calculate the overall static unbalance of the rotor shaft, the rotor shaft couple unbalance, the static unbalance of the rotor core and the couple unbalance of the rotor core in the instantaneous phase α state in the world coordinate system, and decompose them into components in the x-axis direction and the y-axis direction, and obtain the component of the rotor static unbalance in the x-axis direction, the component of the rotor static unbalance in the y-axis direction, the component of the rotor couple unbalance in the x-axis direction and the component of the rotor couple unbalance in the y-axis direction in the instantaneous phase α state; then construct the overall dynamic balance equation of the rotor, specifically, the rotor shaft in step S5. The model accumulates the components of the rotor static unbalance in the x-axis direction at each instantaneous phase after circumferential discretization, accumulates the components of the rotor static unbalance in the y-axis direction at each instantaneous phase, accumulates the components of the rotor couple unbalance in the x-axis direction at each instantaneous phase, and accumulates the components of the rotor couple unbalance in the y-axis direction at each instantaneous phase; finally, a multi-objective optimization algorithm is used to solve the assembly phase of the rotor core and the rotor shaft when each optimization objective reaches the minimum according to the assigned weight, which is taken as the optimal solution for the assembly phase of the rotor core and the rotor shaft.

[0015] Preferably, the step S2 is specifically as follows: the x and y coordinates of each data point in the point cloud data of the rotor shaft and the rotor core are respectively subtracted from the mean value of the respective point cloud data in the x and y coordinates to obtain the rotor shaft and the rotor core point cloud data after the centralization processing, and are respectively expressed as a matrix; then the covariance matrix of the rotor shaft and the rotor core point cloud data matrix X' after the centralization processing is calculated; then the eigenvalue decomposition of each covariance matrix is ​​performed to find the eigenvalue and eigenvector; the eigenvectors corresponding to the first k largest eigenvalues ​​are selected to obtain the matrix V composed of these eigenvectors k , convert the rotor shaft or rotor core point cloud data matrix after centralization to the new space to obtain the point cloud data matrix X after dimensionality reduction reduced =X′V k , and then reconstruct the reduced-dimensional point cloud data matrix back to the original space to obtain the reconstructed matrix X reconstructed =X reduced V k T +mean(X), where mean(X) is the mean vector of the original acquired rotor shaft or rotor core point cloud data; finally, the least squares method is used to fit the cross-sectional models of the rotor shaft and the silicon steel sheet models of the rotor core according to the point cloud data of the rotor shaft and the rotor core in the reconstructed matrix of the rotor shaft and the rotor core.

[0016] More preferably, the covariance matrix:

[0017]

[0018] Wherein, p is the total number of data points in the point cloud data of the rotor shaft or rotor core after centralization.

[0019] More preferably, the covariance matrix is ​​subjected to eigenvalue decomposition as follows:

[0020] C=V∧V T

[0021] Where V is the eigenvector matrix, Λ is the diagonal matrix of eigenvalues, and the elements on the diagonal of the diagonal matrix are the corresponding eigenvalues.

[0022] Preferably, the local fitting described in step S3 specifically comprises: using the DBSCAN algorithm to denoise the originally acquired rotor shaft point cloud data, and then performing NURBS curve fitting and reconstruction on the denoised rotor shaft point cloud data with the surface centroid point as the control point, and then reconstructing the NURBS surface of the entire contact surface between the rotor shaft and the rotor support according to the fitted NURBS curves.

[0023] Preferably, the rotor shaft rotation center corresponding to the instantaneous phase α state is calculated as follows: draw common tangents at each contact point between the cross section and the rotor bracket and perpendiculars at each common tangent at the tangent point, these perpendiculars intersect each other to form a plurality of intersections, and calculate the average of the x-coordinates and y-coordinates of these intersections respectively as the x-coordinate x of the rotor shaft rotation center corresponding to the instantaneous phase α state in the world coordinate system. a with y coordinate y a , and obtain the rotor shaft rotation center corresponding to the instantaneous phase α state.

[0024] Preferably, step S6 is as follows: the origin of the axis coordinate system is converted to O1(α), the Z1 axis of the axis coordinate system is converted to the O1O2 direction, and then the coordinates of the center of mass of each cross section of the rotor shaft in the converted axis coordinate system are converted to the world coordinate system to calculate the overall static unbalance of the rotor shaft u a and the rotor shaft unbalance q a Calculate; then transform the coordinates of the center of mass of each silicon steel sheet in the rotor core coordinate system into the world coordinate system to calculate the static unbalance u of the rotor core b and rotor core unbalance q b Calculate; then, in the world coordinate system, u a ,u b ,q a ,q b Decompose them onto the x-axis and y-axis to get u a The component u in the x-axis direction ax 、u a The component u in the y-axis direction ay 、u b The component u in the x-axis direction bx 、u b The component u in the y-axis direction by ,q a The component q in the x-axis direction ax ,q a The component q in the y-axis direction ay ,q b The component q in the x-axis direction bx ,q b The component q in the y-axis direction by , thus obtaining the rotor overall dynamic balance equation corresponding to the instantaneous phase α state

[0025]

[0026] Among them, θ is the assembly phase between the rotor core and the rotor shaft;

[0027] Assume that the rotor shaft model in step S5 has m different instantaneous phases after circumferential discretization, and the i-th instantaneous phase is denoted as α i, then the overall dynamic unbalance of the rotor at each instantaneous phase is accumulated to obtain the overall dynamic balance equation of the rotor:

[0028]

[0029] The NSGA-11 multi-objective optimization algorithm is used to solve the four optimization objectives u x (θ),u y (θ),q x (θ),q y (θ) The assembly phase θ of the rotor core and the rotor shaft when the allocated weights are all minimized is taken as the optimal solution for the assembly phase of the rotor core and the rotor shaft.

[0030] More preferably, the overall static unbalance of the rotor shaft is calculated as follows:

[0031] The centroid coordinates of each cross section of the rotor shaft in the transformed shaft coordinate system are transformed to the world coordinate system through the homogeneous transformation matrix, which is recorded as i∈[1, s]. In the world coordinate system, the distance vector from the center of mass of each cross section of the rotor shaft to the projection line of the instantaneous rotation axis O1O2 passing through point O1(α) along the z-axis direction of the world coordinate system is denoted by e i , i∈[1, s], the total mass of the rotor shaft is measured and recorded as m1, then the static unbalance at the i-th cross section of the rotor shaft is

[0032]

[0033] The overall static unbalance of the rotor shaft is

[0034]

[0035] More preferably, the average coordinates of the centroids of all cross sections and all silicon steel sheets of the rotor shaft are calculated and converted into the world coordinate system to obtain the coordinates of the centroid of the entire rotor in the world coordinate system. The vector component of the centroid of the entire rotor in the world coordinate system along the z-axis direction of the world coordinate system is assumed to be The rotor shaft unbalance is

[0036]

[0037] Among them, z ti is the component of the vector of the centroid of the i-th cross section of the rotor shaft in the world coordinate system along the z-axis direction of the world coordinate system.

[0038] More preferably, the coordinate of the mass center of the i-th silicon steel sheet in the rotor core coordinate system is The coordinates converted to the transformed axis coordinate system are labeled Will Transformed to the world coordinate system through the homogeneous transformation matrix, denoted as The static unbalance of the rotor core is

[0039]

[0040] Among them, u i ′ is the static unbalance of the i-th silicon steel sheet, m2 is the mass of the rotor core, e i ′ is the distance vector of the projection line of the instantaneous rotation axis O1O2 passing through point O1(α) after the center of mass of the i-th silicon steel sheet is converted to the world coordinate system along the z-axis direction of the world coordinate system.

[0041] The unbalance of the rotor core is

[0042]

[0043] Among them, q i is the unbalanced value of the i-th silicon steel sheet itself, z pi is the component of the vector of the mass center of the i-th silicon steel sheet in the world coordinate system along the z-axis direction of the world coordinate system.

[0044] More preferably, in step S5, the rotation centers of two sections selected in the instantaneous phase α state in the entire contact surface between the rotor shaft and the two rotor brackets are respectively recorded as O1(α) and O2(α), and the coordinates of O1(α) are recorded as z a is the z coordinate of O1(α). The line O1O2 connecting O1(α) and O2(α) is taken as the instantaneous rotation axis of the instantaneous phase α state. The angle β between the line O1O2 and the z axis of the world coordinate system is a function of α, and β = f(α); the homogeneous transformation matrix of the surface centroid coordinates of each cross section of the rotor shaft in the converted shaft coordinate system to the world coordinate system is

[0045]

[0046] The present invention has the beneficial effects:

[0047] The present invention takes into account the influence of multiple factors on the dynamic balance of the rotor, such as the dynamic offset of the rotor axis, the different contact states of the rotor with the rotor support when the rotor is in different circumferential phases, and the eccentricity of the rotor core, and establishes an overall dynamic balance equation for the rotor. Based on the overall dynamic balance equation for the rotor, multi-objective optimization is performed to solve the assembly phase of the rotor core and the rotor shaft that makes each optimization objective reach the minimum according to the assigned weight, and use this as the optimal solution for the assembly phase of the rotor core and the rotor shaft, thereby realizing the virtual assembly and posture optimization of the motor rotor, thereby guiding the actual assembly of the motor rotor and ensuring the dynamic balance performance of the motor rotor after actual assembly. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the structure of the rotor shaft.

[0049] Figure 2 It is a schematic diagram of the structure of the rotor core.

[0050] Figure 3 It is a schematic cross-sectional view of the rotor support.

[0051] Figure 4 It is a schematic diagram of finding the rotation center at a cross section selected from the entire contact surface between the rotor shaft and the rotor support according to the present invention.

[0052] Figure 5 It is a schematic diagram of the angle between the instantaneous rotation axis of the rotor shaft at the instantaneous phase and the z-axis of the world coordinate system in the present invention.

[0053] Figure 6 It is a schematic diagram of the assembly phases of the rotor core and the rotor shaft in the present invention.

[0054] Figure 7 It is an overall flow chart of the method of the present invention.

[0055] Figure 8 It is a flow chart of the assembly phase optimization algorithm of the rotor core and the rotor shaft in the present invention. DETAILED DESCRIPTION

[0056] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings.

[0057] like Figure 7 As shown in FIG. 1 , the motor rotor virtual assembly and posture optimization method based on the dynamic rotating axis has the following specific steps:

[0058] S1. Obtain the point cloud data of the rotor shaft and the rotor core. For the rotor shaft, focus on the data acquisition of the contact part between it and the rotor bracket. Then, refer to Figure 1 As shown, an axis coordinate system O1-X1Y1Z1 is established on the rotor shaft. The rotor shaft is differentiated through s cross sections. The surface centroid of each cross section is solved in Matlab, and the surface centroids of all cross sections are fitted to obtain the central axis of the rotor shaft. Among them, the surface centroid of the cross section is solved by selecting the data points at the position of the cylindrical surface of the rotor shaft corresponding to the cross section, accumulating the x-coordinates of these data points and averaging them to obtain the x-coordinate of the surface centroid, and accumulating the y-coordinates of these data points and averaging them to obtain the y-coordinate of the surface centroid. Next, refer to Figure 2 As shown, the rotor core is composed of n stacked silicon steel sheets, and a core coordinate system O2-X2Y2Z2 is established on the rotor core. The coordinates of the data points on both sides of each silicon steel sheet are imported into Matlab, and the two sides of each silicon steel sheet are reconstructed in two dimensions to obtain the precise geometric shape of each silicon steel sheet. The centroid calculation formula is applied in Matlab to determine the center of mass coordinates of each silicon steel sheet (here the rotor core is regarded as a rigid body, and the centroid and center of mass coincide).

[0059]

[0060] S2. Use principal component analysis (PCA) to process the x and y coordinates of each data point in the point cloud data of the rotor shaft and the rotor core (the z coordinate is not processed). Specifically, the x and y coordinates of each data point in the point cloud data of the rotor shaft and the rotor core are respectively subtracted from the mean value of the x and y coordinates of the respective point cloud data, so that the rotor shaft data points are distributed near the Z1 axis of the axis coordinate system O1-X1Y1Z1, and the rotor core data points are distributed near the Z2 axis of the axis coordinate system O2-X2Y2Z2, and the rotor shaft and rotor core point cloud data after centralization are obtained, and are respectively expressed as a matrix, where each row in the matrix represents the coordinates of a data point; then the covariance matrix of the rotor shaft and rotor core point cloud data matrix after centralization is calculated to describe the linear relationship between the dimensions in the point cloud data. The covariance matrix is:

[0061]

[0062] Wherein, X' is the point cloud data matrix of the rotor shaft or rotor core after centralization, and p is the total number of data points in the point cloud data of the rotor shaft or rotor core after centralization.

[0063] Next, perform eigenvalue decomposition on each covariance matrix to find the eigenvalues ​​and eigenvectors.

[0064] C=V∧V T

[0065] Where V is the eigenvector matrix, Λ is the diagonal matrix of eigenvalues, and the elements on the diagonal of the diagonal matrix are the corresponding eigenvalues. The eigenvalues ​​represent the variance of each principal component, while the eigenvectors define the direction of the principal component.

[0066] Select the eigenvectors corresponding to the first k largest eigenvalues ​​(i.e., select the principal components of dimensionality reduction) and obtain the matrix V composed of these eigenvectors k , convert the rotor shaft or rotor core point cloud data matrix after centralization to the new space to obtain the point cloud data matrix X after dimensionality reduction reduced =X′V k Here, since the rotor shaft and the rotor core are modeled as a whole later, it does not affect the later contact state analysis, so k can be relatively small and can represent most of the variance in the point cloud data after centralization. Then, the reduced-dimensional point cloud data matrix is ​​reconstructed back to the original space to obtain the reconstructed matrix X reconstructed =X reduced V k T+mean(X), where mean(X) is the mean vector of the original acquired rotor shaft or rotor core point cloud data.

[0067] The principal component analysis method can reduce the noise of the point cloud data of the rotor shaft and the rotor core, quickly remove some information introduced by noise, and select a suitable k value to retain the main features. Based on these data, the approximate overall models of the rotor shaft and the rotor core can be quickly fitted. Specifically: the x and y coordinates of each data point in the point cloud data of the rotor shaft and the rotor core are processed by the principal component analysis method, and then the cross-sectional models of the rotor shaft and the silicon steel sheet models of the rotor core are fitted using the least squares method based on the point cloud data of the rotor shaft and the rotor core in the reconstructed matrix of the rotor shaft and the rotor core.

[0068] S3. Reference Figure 3 As shown, the bottom end of one of the rotor support surfaces is taken as the origin O0, and the world coordinate system O0-X0Y0Z0 is established. In Matlab, the cross-sectional models of the rotor shaft and the silicon steel sheet models of the rotor core processed in step S2 are converted into the world coordinate system. In the world coordinate system, find the cross-sections in each cross-sectional model of the rotor shaft that are in contact with the support surfaces of the two rotor brackets (regarded as ideal arc surfaces), and perform local fitting on the rotor shaft point cloud data originally acquired in the shaft coordinate system O1-X1Y1Z1 corresponding to the cross-sections in contact with each rotor bracket support surface. Specifically, the local fitting is as follows: the DBSCAN algorithm is used to denoise the originally acquired rotor shaft point cloud data. The algorithm can effectively process data sets containing noise and irregular shapes, and clusters are formed by identifying high-density areas in the cross-sectional point cloud data in contact, thereby distinguishing noise points from valid points; then, the denoised rotor shaft point cloud data is reconstructed by NURBS curve fitting with the surface centroid point as the control point, and then the NURBS surface of the entire contact surface between the rotor shaft and the rotor bracket is reconstructed according to the fitted NURBS curves.

[0069] S4. Using the OpenGL library, the three-dimensional construction of the rotor shaft model is realized, wherein the rotor shaft model is converted into the world coordinate system; then the rotor shaft and the silicon steel sheet models of the rotor core are virtually assembled and placed on the two rotor brackets.

[0070] S5, discretize the rotor shaft model processed in step S4 in the circumferential direction, select one of the positions as the initial position, and record the instantaneous phase of each position relative to the initial position as α, with counterclockwise being positive. Figure 4As shown, for each instantaneous phase α, a section is selected from the entire contact surface between the rotor shaft and each rotor bracket, and the rotor shaft rotation center corresponding to the instantaneous phase α state is calculated according to each contact point between the section and the rotor bracket. The rotor shaft rotation center corresponding to the instantaneous phase α state is calculated as follows: a common tangent line is made at each contact point between the section and the rotor bracket, and a perpendicular line of each common tangent line at the tangent point. These perpendicular lines intersect each other to form a plurality of intersection points, and the average values ​​of the x coordinates and y coordinates of these intersection points are calculated respectively as the x coordinate x of the rotor shaft rotation center corresponding to the instantaneous phase α state in the world coordinate system. a with y coordinate y a , and the rotation center of the rotor shaft corresponding to the instantaneous phase α state is obtained. The rotation centers of the two sections selected in the entire contact surface between the rotor shaft and the two rotor brackets at the instantaneous phase α state are respectively recorded as O1(α) and O2(α), and the coordinates of O1(α) are z a is the z coordinate of O1(α). The line O1O2 connecting O1(α) and O2(α) is taken as the instantaneous rotation axis of the instantaneous phase α state. Figure 5 As shown, since the two cross-sections are not the same, there is a small angle β between the line O1O2 and the z-axis of the world coordinate system. The angle β is a function of α, and β=f(α).

[0071] S6. Perform dynamic balancing calculations on the rotor shaft and the rotor core: Since the surface centroid coordinates of each cross section of the rotor shaft in step S1 are solved in the shaft coordinate system, they need to be converted to the world coordinate system for dynamic balancing calculations on the rotor shaft. Therefore, the origin of the shaft coordinate system is first converted to O1(α), and the Z1 axis of the shaft coordinate system is converted to the O1O2 direction. After the conversion, the angle between the shaft coordinate system and the z-axis of the world coordinate system is β. Then, the surface centroid coordinates of each cross section of the rotor shaft in the shaft coordinate system after the conversion are converted to the world coordinate system for dynamic balancing calculations on the rotor shaft. Among them, the homogeneous transformation matrix for converting the surface centroid coordinates of each cross section of the rotor shaft in the shaft coordinate system after the conversion to the world coordinate system is:

[0072]

[0073] The coordinates of the centroid of each cross section of the rotor shaft in the converted shaft coordinate system are converted to the world coordinate system for dynamic balancing calculation of the rotor shaft. The process is as follows:

[0074] First, calculate the static unbalance of the rotor shaft: transform the centroid coordinates of each cross section of the rotor shaft in the transformed shaft coordinate system to the world coordinate system through the homogeneous transformation matrix T, which is recorded as i∈[1, s]. In the world coordinate system, the distance vector from the center of mass of each cross section of the rotor shaft to the projection line of the instantaneous rotation axis O1O2 passing through point O1(α) along the z-axis direction of the world coordinate system is denoted by e i, i∈[1, s], the total mass of the rotor shaft is measured and recorded as m1, then the static unbalance at the i-th cross section of the rotor shaft (mass is m1 / s) is

[0075]

[0076] The overall static unbalance of the rotor shaft is

[0077]

[0078] Since the number of parts s divided into by the rotor shaft is large enough, the unbalance of each cross section to the whole is much greater than the unbalance of each cross section itself, so the latter is not calculated. The average coordinates of the centroid of all cross sections and all silicon steel sheets of the rotor shaft are calculated and converted to the world coordinate system to obtain the coordinates of the centroid of the rotor as a whole (composed of the rotor shaft and the rotor core) in the world coordinate system. Let the vector of the centroid of the rotor as a whole in the world coordinate system along the z-axis direction of the world coordinate system be The rotor shaft unbalance is

[0079]

[0080] Among them, z ti is the component of the vector of the centroid of the i-th cross section of the rotor shaft in the world coordinate system along the z-axis direction of the world coordinate system.

[0081] refer to Figure 6 As shown in the figure, for the rotor core, the assembly phase between the rotor core and the rotor shaft is θ, θ is the main optimization object of the assembly, and the coordinate of the center of mass of the i-th silicon steel sheet in the rotor core coordinate system is The coordinates converted to the transformed axis coordinate system are labeled Then Transformed to the world coordinate system through the homogeneous transformation matrix T, denoted as The static unbalance of the rotor core is

[0082]

[0083] Among them, u i ′ is the static unbalance of the i-th silicon steel sheet, m2 is the mass of the rotor core, e i ′ is the distance vector of the projection line of the instantaneous rotation axis O1O2 passing through point O1(α) after the center of mass of the i-th silicon steel sheet is converted to the world coordinate system along the z-axis direction of the world coordinate system.

[0084] Since the silicon steel sheet has a large unbalanced pair, it needs to be taken into account, so the unbalanced pair of the rotor core is

[0085]

[0086] Among them, q i is the unbalanced value of the i-th silicon steel sheet itself, z pi is the component of the vector of the mass center of the i-th silicon steel sheet in the world coordinate system along the z-axis direction of the world coordinate system.

[0087] In order to facilitate subsequent calculations and optimization, u is converted to a ,u b ,q a ,q b The vectors are decomposed onto the x-axis and y-axis to obtain u a The component u in the x-axis direction ax 、u a The component u in the y-axis direction ay 、u b The component u in the x-axis direction bx 、u b The component u in the y-axis direction by ,q a The component q in the x-axis direction ax ,q a The component q in the y-axis direction ay ,q b The component q in the x-axis direction bx ,q b The component q in the y-axis direction by , thus obtaining the rotor overall dynamic balance equation corresponding to the instantaneous phase α state

[0088]

[0089] Among them, u x (θ,α) is the component of the rotor static unbalance in the x-axis direction at the instantaneous phase α, u y (θ,α) is the component of the rotor static unbalance in the y-axis direction at the instantaneous phase α, q x (θ,α) is the component of the rotor unbalance in the x-axis direction at the instantaneous phase α, q x (θ,α) is the component of the rotor unbalance in the y-axis direction at the instantaneous phase α.

[0090] Assume that the rotor shaft model in step S5 has m different instantaneous phases after circumferential discretization, and the i-th instantaneous phase is denoted as α i , then the overall dynamic unbalance of the rotor at each instantaneous phase is accumulated to obtain the overall dynamic balance equation of the rotor:

[0091]

[0092] Among them, u x(θ) is the cumulative value of the rotor static unbalance in the x-axis direction under each instantaneous phase state, u y (θ,α) is the cumulative value of the rotor static unbalance in the y-axis direction under each instantaneous phase state, q x (θ,α) is the cumulative value of the rotor unbalance in the x-axis direction under each instantaneous phase state, q x (θ, α) is the cumulative value of the rotor unbalance component in the y-axis direction under each instantaneous phase state.

[0093] like Figure 8 As shown, the NSGA-11 multi-objective optimization algorithm is used to solve the four optimization objectives u x (θ),u y (θ),q x (θ),q y (θ) The assembly phase θ of the rotor core and the rotor shaft when the allocated weights are all minimized is taken as the optimal solution for the assembly phase of the rotor core and the rotor shaft.

Claims

1. A motor rotor virtual assembly and posture optimization method based on a dynamic rotating axis, characterized in that: The specific steps are as follows: S1. Obtain point cloud data of the rotor shaft and the rotor core surface, then establish an axis coordinate system on the rotor shaft, perform differential processing on the rotor shaft through s cross sections, solve the surface centroid of each cross section, and then establish a core coordinate system on the rotor core. The X-axis, Y-axis and Z-axis of the core coordinate system are respectively parallel to the X-axis, Y-axis and Z-axis of the axis coordinate system. The rotor core is composed of n silicon steel sheets stacked together. The centroid coordinates of each silicon steel sheet are determined based on the data points on both sides of each silicon steel sheet; S2. Use the principal component analysis method to process the x and y coordinates of each data point in the point cloud data of the rotor shaft and the rotor core, and then use the least squares method to fit each cross-sectional model of the rotor shaft and each silicon steel sheet model of the rotor core; S3, taking the bottom end of one of the rotor support surfaces as the origin, establishing a world coordinate system, wherein the X-axis, Y-axis and Z-axis of the world coordinate system are parallel to the X-axis, Y-axis and Z-axis of the axis coordinate system respectively; converting each cross-sectional model of the rotor shaft and each silicon steel sheet model of the rotor core processed in step S2 into the world coordinate system; finding the cross-sectional areas in contact with the support surfaces of the two rotor supports in each cross-sectional model of the rotor shaft in the world coordinate system, performing local fitting on the rotor shaft point cloud data originally acquired in the axis coordinate system corresponding to the cross-sectional areas in contact with each rotor support surface, and realizing reconstruction of the entire contact surface between the rotor shaft and the rotor support; S4, realize the three-dimensional construction of the rotor shaft model and convert it into the world coordinate system, then virtually assemble the rotor shaft and the silicon steel sheet models of the rotor core and place them on the two rotor brackets; S5, discretizing the rotor shaft model processed in step S4 in the circumferential direction, selecting one of the positions as the initial position, and recording the instantaneous phase of each position relative to the initial position as α. For each instantaneous phase α, selecting a section in the entire contact surface between the rotor shaft and each rotor support, and calculating the rotor shaft rotation center corresponding to the instantaneous phase α state according to each contact point between the section and the rotor support; S6. Calculate the overall static unbalance of the rotor shaft, the rotor shaft couple unbalance, the static unbalance of the rotor core and the couple unbalance of the rotor core in the instantaneous phase α state in the world coordinate system, and decompose them into components in the x-axis direction and the y-axis direction, and obtain the component of the rotor static unbalance in the x-axis direction, the component of the rotor static unbalance in the y-axis direction, the component of the rotor couple unbalance in the x-axis direction and the component of the rotor couple unbalance in the y-axis direction in the instantaneous phase α state; then construct the overall dynamic balance equation of the rotor, specifically, the rotor shaft in step S5. The model accumulates the components of the rotor static unbalance in the x-axis direction at each instantaneous phase after circumferential discretization, accumulates the components of the rotor static unbalance in the y-axis direction at each instantaneous phase, accumulates the components of the rotor couple unbalance in the x-axis direction at each instantaneous phase, and accumulates the components of the rotor couple unbalance in the y-axis direction at each instantaneous phase; finally, a multi-objective optimization algorithm is used to solve the assembly phase of the rotor core and the rotor shaft when each optimization objective reaches the minimum according to the assigned weight, which is taken as the optimal solution for the assembly phase of the rotor core and the rotor shaft.

2. The method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis according to claim 1, characterized in that: The step S2 is specifically as follows: the x and y coordinates of each data point in the point cloud data of the rotor shaft and the rotor core are respectively subtracted from the mean value of the respective point cloud data in the x and y coordinates to obtain the rotor shaft and the rotor core point cloud data after the centralization processing, and are respectively expressed as a matrix; then the covariance matrix X' of the rotor shaft and the rotor core point cloud data matrix after the centralization processing is calculated; then the eigenvalue decomposition of each covariance matrix is ​​performed to find the eigenvalue and eigenvector; the eigenvectors corresponding to the first k largest eigenvalues ​​are selected to obtain the matrix V composed of these eigenvectors k , convert the rotor shaft or rotor core point cloud data matrix after centralization to the new space to obtain the point cloud data matrix X after dimensionality reduction reduced =X′V k , and then reconstruct the reduced-dimensional point cloud data matrix back to the original space to obtain the reconstructed matrix X reconstructed =X reduced V k T +mean(X), where mean(X) is the mean vector of the original acquired rotor shaft or rotor core point cloud data; finally, the least squares method is used to fit the cross-sectional models of the rotor shaft and the silicon steel sheet models of the rotor core according to the point cloud data of the rotor shaft and the rotor core in the reconstructed matrix of the rotor shaft and the rotor core.

3. The method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis according to claim 2, characterized in that: The covariance matrix is: Wherein, p is the total number of data points in the point cloud data of the rotor shaft or rotor core after centralization; The eigenvalue decomposition of the covariance matrix is ​​as follows: C=V∧V T Where V is the eigenvector matrix, Λ is the diagonal matrix of eigenvalues, and the elements on the diagonal of the diagonal matrix are the corresponding eigenvalues.

4. The method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis according to claim 1, characterized in that: The local fitting described in step S3 is specifically: using the DBSCAN algorithm to denoise the original acquired rotor shaft point cloud data, then performing NURBS curve fitting and reconstruction on the denoised rotor shaft point cloud data with the surface centroid point as the control point, and then reconstructing the NURBS surface of the entire contact surface between the rotor shaft and the rotor bracket based on the fitted NURBS curves.

5. The method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis according to claim 1, characterized in that: Instantaneous phase α The calculation of the rotor shaft rotation center corresponding to the state is as follows: draw common tangents at each contact point between the section and the rotor bracket and perpendiculars at the tangent points of each common tangent. These perpendiculars intersect each other to form multiple intersections. Calculate the average of the x-coordinates and y-coordinates of these intersections as the instantaneous phase α The x coordinate x of the rotor shaft rotation center corresponding to the state in the world coordinate system a with y coordinate y a , and the instantaneous phase α The state corresponds to the center of rotation of the rotor shaft.

6. The method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis according to claim 1, characterized in that: The specific step S6 is as follows: the origin of the axis coordinate system is converted to O1(α), the Z1 axis of the axis coordinate system is converted to the O1O2 direction, and then the coordinates of the center of mass of each cross section of the rotor shaft in the converted axis coordinate system are converted to the world coordinate system to calculate the overall static unbalance of the rotor shaft u a and the rotor shaft unbalance q a Calculate; then transform the coordinates of the center of mass of each silicon steel sheet in the rotor core coordinate system into the world coordinate system to calculate the static unbalance u of the rotor core b and rotor core unbalance q b Calculate; then, in the world coordinate system, u a ,u b ,q a ,q b Decompose them onto the x-axis and y-axis to get u a The component u in the x-axis direction ax 、u a The component u in the y-axis direction ay 、u b The component u in the x-axis direction bx 、u b The component u in the y-axis direction by ,q a The component q in the x-axis direction ax ,q a The component q in the y-axis direction ay ,q b The component q in the x-axis direction bx ,q b The component q in the y-axis direction by , thus obtaining the instantaneous phase α The overall dynamic balance equation of the rotor corresponding to the state in, θ Assembling phases for the rotor core and the rotor shaft; Assume that the rotor shaft model in step S5 has m different instantaneous phases after circumferential discretization, and the i-th instantaneous phase is denoted as α i , then the overall dynamic unbalance of the rotor at each instantaneous phase is accumulated to obtain the overall dynamic balance equation of the rotor: The NSGA-11 multi-objective optimization algorithm is used to solve the four optimization objectives u x (θ),u y (θ),q x (θ),q y (θ) The assembly phase θ of the rotor core and the rotor shaft when the allocated weights are all minimized is taken as the optimal solution for the assembly phase of the rotor core and the rotor shaft.

7. The method for virtual assembly and position optimization of a motor rotor based on a dynamic rotating axis according to claim 6, characterized in that: The overall static unbalance of the rotor shaft is calculated as follows: The centroid coordinates of each cross section of the rotor shaft in the transformed shaft coordinate system are transformed to the world coordinate system through the homogeneous transformation matrix, denoted as (x ti ,y ti , z ti ), i∈[1,s]; in the world coordinate system, the distance vector from the center of mass of each cross section of the rotor shaft to the projection line of the instantaneous rotation axis O1O2 passing through point O1(α) along the z-axis direction of the world coordinate system is denoted by e i , i∈[1, s], the total mass of the rotor shaft is measured and recorded as m1, then the static unbalance at the i-th cross section of the rotor shaft is The overall static unbalance of the rotor shaft is 8. The method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis according to claim 7 is characterized in that: Calculate the average coordinates of the centroid of all cross sections and all silicon steel sheets of the rotor shaft and convert them to the world coordinate system to obtain the coordinates of the centroid of the rotor as a whole in the world coordinate system. Let the component of the vector of the centroid of the rotor as a whole in the world coordinate system along the z-axis direction of the world coordinate system be z t , then the rotor shaft unbalance is Among them, z ti is the component of the vector of the centroid of the i-th cross section of the rotor shaft in the world coordinate system along the z-axis direction of the world coordinate system.

9. The method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis according to claim 8, characterized in that: The coordinates of the mass center of the i-th silicon steel sheet in the rotor core coordinate system (x pi ′,y pi ′,z pi ′) is converted to the coordinate system after the transformation and is labeled as (x pi ″,y pi ″,z pi ″), (x pi ″,y pi ″,z pi ″) is transformed into the world coordinate system through the homogeneous transformation matrix, denoted as (x pi ,y pi , z pi) , then the static unbalance of the rotor core is Among them, u i ′ is the static unbalance of the i-th silicon steel sheet, m2 is the mass of the rotor core, e i ′ is the distance vector of the projection line of the instantaneous rotation axis O1O2 passing through point O1(α) along the z-axis direction of the world coordinate system after the mass center of the i-th silicon steel sheet is converted to the world coordinate system; The unbalance of the rotor core is Among them, q i is the unbalanced value of the i-th silicon steel sheet itself, z pi is the component of the vector of the mass center of the i-th silicon steel sheet in the world coordinate system along the z-axis direction of the world coordinate system.

10. The method for virtual assembly and posture optimization of a motor rotor based on a dynamic rotating axis according to claim 9, characterized in that: In step S5, the rotation centers of two sections selected in the entire contact surface between the rotor shaft and the two rotor brackets at the instantaneous phase α are respectively recorded as O1(α) and O2(α), and the coordinates of O1(α) are recorded as (x α ,y α , z α ), z a is the z coordinate of O1(α); the line O1O2 connecting O1(α) and O2(α) is taken as the instantaneous rotation axis of the instantaneous phase α state, and the angle β between the line O1O2 and the z axis of the world coordinate system is a function of α, denoted by β=f(α); the homogeneous transformation matrix of the surface centroid coordinates of each cross section of the rotor shaft in the converted shaft coordinate system to the world coordinate system is

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