Grinding method for arc-shaped end face teeth on a ring-shaped base plate

Abstract

This enables digital, high-precision machining of arc-shaped end face teeth on a disc-shaped base plate. [Solution] The gear teeth that need to be machined on the disc-shaped base plate are divided into multiple groups, and the disc-shaped base plate is rotated by a small angle α each time the machining of the last pair of gear teeth in each group is completed. Here, a neural network is introduced before grinding to optimize the large and small angles of rotation of the disc-shaped base plate. If the position of the disc-shaped base plate is misaligned during the grinding process, the position of the disc-shaped base plate is adjusted. In this invention, the cumulative value of angular errors is reduced by using a uniform error distribution method, the wear of the grinding wheel is accurately modeled and measured by "thermomechanical coupling", the grinding wheel is modified / replaced, and local quantitative regrinding of the disc-shaped base plate is performed by comparing the surface defects of the arc end face teeth of the disc-shaped base plate with the theoretical discretized normal deviation of the surface of the arc end face teeth, thereby realizing digitized high-precision machining of the arc end face teeth of the disc-shaped base plate.

Description

Technical Field

[0001] The present invention belongs to the technical field of manufacturing of a disk-shaped mother board used for a turbo machine rotor, and specifically relates to a digital precision grinding method for arc end faces teeth on the disk-shaped mother board.

Background Art

[0002] Arc end face teeth are precise end face splines, which are used for connection at the shaft end, play a role in positioning and torque transmission, and have become an important technology for the core of a gas turbine. The processing ability of arc end face teeth is one of the main capabilities in the gas turbine manufacturing process. In addition, the accuracy requirements for arc end face teeth are very high. It is necessary that the center lines of each convex tooth and concave tooth all face the center. Each tooth is evenly distributed along the circumference. The elements of the convex and concave teeth need to fit tightly and have equal curvature and high uniformity in order for each tooth to receive equal stress when transmitting torque. The difficulty in processing arc end face teeth mainly lies in its detection standard tool. That is, the manufacturing of the disk-shaped mother board. The disk-shaped mother board is required to be processed based on the designed related parameters, and its geometric tolerance requirements are very high, and the processing difficulty is very high. Furthermore, in the conventional method for processing arc end face teeth on a disk-shaped mother board, every time the grinding wheel completes the grinding of a pair of gear teeth, the material of the disk-shaped mother board rotates 360° / Z times (Z is the number of gear teeth of the disk-shaped mother board). Therefore, the problem of cumulative angle error due to non-uniform angular distribution is not considered, nor is the error due to vibration considered. Therefore, there is a need for a method for processing arc end face teeth on a disk-shaped mother board that can reduce vibration and cumulative angle error.

Summary of the Invention

[0003] The object of the present invention is to provide a digital precision grinding method for arc end face teeth on a disk-shaped mother board in order to overcome the drawbacks of the prior art.

[0004] To achieve the above object, the present invention adopts the following technical solutions.

[0005] The digital precision grinding method for arc-shaped end face teeth on a disc-shaped base plate according to the present invention is: Step 1 involves replacing the jig on the rotary table of a numerically controlled machine tool with a four-jaw chuck, clamping a horizontally positioned disc-shaped base plate in the four-jaw chuck, attaching a grinding wheel to the spindle of the machine tool, and attaching a 3D point cloud device to the machine tool. Next, a 3D coordinate system of the machine tool is created with the reference point of the numerically controlled machine tool as the origin, and the center coordinates of the rotary table, the center coordinates of the grinding wheel, and the center coordinates of the 3D point cloud device are determined. Step 2 involves identifying the center of the disc-shaped base plate material using a 3D point cloud device, translating the disc-shaped base plate material using the four locking jaws of a 4-jaw chuck so that the center of the disc-shaped base plate material and the center of the rotary table are aligned vertically, setting the relative positional relationship between the grinding wheel and the disc-shaped base plate material during grinding, then driving the spindle by a numerically controlled machine tool so that the grinding wheel moves in parallel, so that the relative position between the grinding wheel and the disc-shaped base plate material satisfies a predetermined relative positional relationship between the materials, and so that the grinding wheel is positioned above the disc-shaped base plate material. Step 3 involves controlling a grinding wheel with a numerically controlled machine tool to perform grinding on a disc-shaped base plate, wherein the grinding process consists of: Step 1: Controlling the downward movement of a rotating grinding wheel with the numerically controlled machine tool to grind a pair of gear teeth on the disc-shaped base plate; Step 2: Controlling the grinding wheel with the numerically controlled machine tool so that it rises back to its original position after completing the grinding of the pair of gear teeth on the disc-shaped base plate, and controlling the rotary table so that the disc-shaped base plate rotates by a large angle β in conjunction with the upward movement, and then repeating Step 1. The second step is to evaluate the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process. If the maximum wear depth or maximum stress is greater than the corresponding predetermined discard value, the grinding wheel is replaced with a new one. Otherwise, the following process is performed in the third step: if the maximum wear depth or maximum stress is less than the corresponding predetermined grinding value, the process is continued; otherwise, the grinding wheel is removed, surface shape and wear feature data of the grinding wheel are obtained, the grinding wheel is regrinded, and then the grinding wheel is reinstalled. The second step is repeated until the grinding of one group of gear teeth is completed, and the grinding wheel is raised back to its original position using a numerically controlled machine tool. Here, one group of gear teeth refers to all gear teeth that can be machined within a range not exceeding 360° when the disc-shaped base material is rotated sequentially at a large angle β. The fourth step involves controlling the rotary table with a numerically controlled machine tool to rotate the disc-shaped base material at a small angle α, and then repeating steps 1 to 3. The fourth step is repeated until all gear teeth on the disc-shaped base material are machined. This includes a fifth step which continues until the grinding process is completed, in which a neural network is introduced before grinding the disc-shaped base material to optimize the large angle β and small angle α for rotating the disc-shaped base material, the large angle β for each rotation of the disc-shaped base material is made the same and an initial value is given, and the small angle α for each rotation is designed to be variable, and in the process of grinding the disc-shaped base material, after the grinding of a pair of teeth on the disc-shaped base material is completed, if the position of the disc-shaped base material is misaligned, a step 3 is performed to adjust the position of the disc-shaped base material, The gear teeth obtained by grinding a disc-shaped base plate are arc-shaped end face teeth, and the process includes step 4 of inspecting whether there are defects on the surface of each arc-shaped end face tooth, and if defects are found, regrinding the defective area.

[0006] Preferably, in the step 2, as a process of identifying the center of the disk-shaped mother board material by the three-dimensional point group device, collect the three-dimensional point group of the disk-shaped mother board material using the three-dimensional point group device, and obtain the inner contour circle or the outer contour circle of the upper surface of the disk-shaped mother board material. Take any two points on the inner contour circle or the outer contour circle as a chord, and then take any two other points on the inner contour circle (the circle forming the hole wall of the center hole provided in the disk-shaped mother board material) or the outer contour circle (the circle forming the outer wall of the disk-shaped mother board material) as a chord. Create the perpendicular bisectors of the two chords, and set the intersection point of the two perpendicular bisectors as the center of the disk-shaped mother board material.

[0007] Preferably, in the step 2, set the relative positional relationship between the grinding wheel and the disk-shaped mother board material during the grinding process, and set the position coordinates of the two ideal machining points of the grinding wheel and the disk-shaped mother board material during the grinding process. Here, the ideal machining point is defined as the intersection point of a circle with a radius equal to the average value of the inner circle radius and the outer circle radius of the grinding wheel and a circle with a radius equal to the average value of the inner circle radius and the outer circle radius of the disk-shaped mother board material when the relative position between the grinding wheel and the disk-shaped mother board material satisfies the relative positional relationship of a predetermined material.

[0008] More preferably, the relative positional relationship between the grinding wheel and the disk-shaped mother board material during the grinding process satisfies the following relational expression.

Number

Number

[0010] Preferably, in step 3, during the grinding process of each pair of gear teeth on the disc-shaped base plate, a vibration sensor fixed to the disc-shaped base plate detects the vibration signal value of the disc-shaped base plate. If the detected vibration signal value exceeds a predetermined value, it is determined that a displacement has occurred in the position of the disc-shaped base plate after the grinding of the pair of gear teeth is completed. The disc-shaped base plate position adjustment process is as follows: After the machining of the pair of gear teeth is completed, the position coordinates of the actual machining points of the two gear teeth are calculated from the image captured by the camera. Furthermore, the position error between these two actual machining points and the corresponding ideal machining points is obtained, and the disc-shaped base plate is moved in parallel using the four locking jaws of the four-jaw chuck to align the positions of these two actual machining points with the positions of the corresponding ideal machining points.

[0011] Preferably, as a decision process for replacing or regrinding the grinding wheel in step 3, the amount of grinding on the grinding wheel surface, working time, and working conditions are input into a predictive simulation model, and the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process are evaluated. If the maximum wear depth or maximum stress is greater than the corresponding predetermined discard value, the grinding wheel is replaced with a new one. Otherwise, the following is determined: if the maximum wear depth or maximum stress is less than the corresponding predetermined grinding value, the process continues; otherwise, the grinding wheel is removed, and surface shape and wear feature data of the grinding wheel are acquired using laser triangulation, and the surface of the grinding wheel after processing is regrinded.

[0012] More preferably, the process for constructing the predictive simulation model is: The first step in constructing a grinding simulation model of a disc-shaped base plate and grinding wheel involves constructing a 3D simulation model of the disc-shaped base plate and grinding wheel using the 3D CAD software Solidworks, importing the 3D simulation model of the disc-shaped base plate and grinding wheel into the finite element analysis software ABAQUS, inputting the material properties of the disc-shaped base plate and grinding wheel (including elastic modulus, Poisson's ratio, and wear coefficient), then performing mesh division on the 3D simulation model of the disc-shaped base plate and grinding wheel, followed by setting boundary conditions for the 3D simulation model of the disc-shaped base plate and grinding wheel, thereby obtaining the grinding simulation model of the disc-shaped base plate and grinding wheel. The second step involves setting the working conditions, which include load, rotational speed, working time, and working temperature; then running ABAQUS to perform a grinding simulation to obtain the wear depth and stress distribution of the 3D simulation model of the grinding wheel after the grinding simulation; and further, using ABAQUS post-processing, to draw the wear depth distribution map and stress distribution map of the 3D simulation model of the grinding wheel after the grinding simulation. The grinding amount on the surface of the grinding wheel is changed, and the third stage is a repetition of the first and second stages, and The fourth stage involves creating training and validation datasets based on the amount of grinding wheel surface material removed, the working time, the working conditions, and corresponding grinding wheel wear depth and stress distribution data obtained from simulations, and then training and validating a constructed predictive simulation model to evaluate the maximum wear depth of the grinding wheel surface after processing and the maximum stress during the processing step.

[0013] Preferably, the process in step 4 involves using an eddy current detection method to check whether defects exist on the surface of each arc end face tooth, obtaining the coordinates of each discrete point in the defect region using a three-dimensional point cloud device, calculating the normal deviation between each discrete point and the corresponding point coordinate on the surface of the arc end face tooth of the theoretical disc-shaped base plate, and if the variance of the normal deviation between each discrete point in the defect region and the corresponding point coordinate on the surface of the theoretical arc end face tooth is greater than a predetermined value, the defect region on the surface of the arc end face tooth is reground. [Effects of the Invention]

[0014] The present invention has the following beneficial effects compared to the conventional technology. 1. The present invention can reduce the cumulative value of angular errors by employing a method for uniform error distribution. Specifically, in the present invention, gear teeth that need to be machined on a disc-shaped base plate are divided into multiple groups, each group contains K pairs of gear teeth arranged at equal intervals, the angle between two adjacent pairs of gear teeth in each group is β, and in the machining process, the grinding of the gear teeth in each group is completed in order, and each time the machining of the last pair of gear teeth in each group is completed, the disc-shaped base plate is rotated by a small angle α, and each time the machining of the remaining pairs of teeth in each group is completed, the disc-shaped base plate is rotated by an angle β, thereby making the angular distribution in the machining process more uniform, the cumulative angular errors are uniformly distributed across the K groups, the cumulative angular errors are reduced, and the machining efficiency is improved. This invention considers the influence of the rotational accuracy of the rotary table of a numerically controlled machine tool on the machining accuracy of a disc-shaped base plate. By introducing a neural network before grinding the disc-shaped base plate material and optimizing the large angle β and small angle α for rotation of the disc-shaped base plate material, a rational large angle β and each small angle α for rotation can be quickly calculated and obtained, thereby ensuring machining accuracy and improving machining efficiency. Furthermore, this invention considers the problem of the disc-shaped base plate material shifting due to large vibrations generated when the grinding wheel and the disc-shaped base plate material come into contact. By adjusting the position of the disc-shaped base plate material after it has shifted, the machining accuracy of the disc-shaped base plate is further ensured. Therefore, this invention can realize digital high-precision machining of the arc end face teeth of a disc-shaped base plate.

[0015] 2. The present invention evaluates the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process. If the maximum wear depth or maximum stress is greater than the corresponding discard value, the grinding wheel is replaced with a new one. Otherwise, the following determination is made: If the maximum wear depth or maximum stress is less than the corresponding predetermined grinding value, the process continues. Otherwise, the grinding wheel is removed, surface shape and wear feature data of the grinding wheel are obtained, the grinding wheel is reground, and then the grinding wheel is reinstalled. This ensures the processing accuracy of the next disc-shaped base plate and shortens the inspection time of the grinding wheel.

[0016] 3. In this invention, after the processing of the disc-shaped base plate is completed, the surface of the arc-shaped end face teeth on the disc-shaped base plate is inspected. The surface of the arc-shaped end face teeth on the disc-shaped base plate is inspected to see if defects exist. If defects exist on the surface of the arc-shaped end face teeth, regrinding is performed on the defective area of ​​the surface of the arc-shaped end face teeth to improve the processing accuracy of the surface of the arc-shaped end face teeth. Here, in this invention, by using an eddy current detection method to inspect the surface of the arc-shaped end face teeth, a highly sensitive, highly efficient, and high-speed inspection effect can be achieved. Furthermore, point cloud technology is used to discretize the defective region to form discrete point coordinates, and the normal deviation between the discrete points on the actual surface of the arc-shaped end face teeth and the corresponding points on the ideal surface of the arc-shaped end face teeth is calculated. If the variance of the normal deviation between each discrete point in the defective region and the corresponding point coordinates on the theoretical surface of the arc-shaped end face teeth is greater than a predetermined value, regrinding is performed on the corresponding defective region to achieve the objective of local quantitative regrinding. [Brief explanation of the drawing]

[0017] [Figure 1] This is a flowchart of the present invention. [Figure 2] This is a flowchart for adjusting the position of the disc-shaped base plate material in the present invention. [Figure 3] This is a flowchart for optimizing the angle α of the disc-shaped base plate material in the present invention. [Figure 4]This flowchart is used to construct a simulation model for predicting the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process, according to the present invention. [Figure 5] This is a flowchart for surface inspection of the arc-shaped end face teeth of a disc-shaped base plate according to the present invention. [Figure 6] This is a schematic diagram of the machining flow of the arc-shaped end face teeth of the disc-shaped base plate in the present invention. [Modes for carrying out the invention]

[0018] The present invention will be described in detail below, along with the drawings.

[0019] As shown in Figures 1, 2, 3, 4, and 5, the digital precision grinding method for the arc end face teeth of a disc-shaped base plate according to the present invention includes the following four steps.

[0020] Step 1: Replace the jig on the rotary table of the numerically controlled machine tool (NC machine tool) with a 4-jaw chuck (intelligent fixed jaw force controlled 4-jaw chuck), clamp the horizontally positioned disc-shaped base plate material into the 4-jaw chuck, attach the grinding wheel to the spindle of the machine tool, and attach a 3D point cloud device (e.g., laser radar, 3D laser scanner) to the machine tool. Here, both the disc-shaped base plate material and the grinding wheel are entirely circular in shape. Next, establish the 3D coordinate system of the machine tool with the reference point of the numerically controlled machine tool (hereinafter sometimes simply referred to as "machine tool") as the origin, and determine the center coordinate point of the rotary table, the center coordinate point of the grinding wheel (center coordinate point of the spindle of the machine tool), and the center coordinate point of the 3D point cloud device. Here, the accuracy of the machine tool's 3D coordinate system depends on a grating ruler that detects linear or angular displacement, and accurately feed back the machine tool's position information to the numerical control system, thereby achieving high-precision closed-loop control and improving machining accuracy, efficiency, and stability.

[0021] Step 2: The center of the disc-shaped matrix material is identified using a 3D point cloud device, and the disc-shaped matrix material is translated using the four locking jaws of the 4-jaw chuck so that the center of the disc-shaped matrix material and the center of the rotary table are aligned (coincided) vertically. The process of identifying the center of the disc-shaped matrix material using a 3D point cloud device is as follows: The top surface contour circle of the disc-shaped matrix material is obtained using 3D point cloud technology, any two points on the circle are taken as chords, then any other two points on the circle are taken as chords, and perpendicular bisectors of the two chords are created. The intersection of the two perpendicular bisectors is taken as the center of the disc-shaped matrix material.

[0022] Next, the relative positional relationship between the grinding wheel and the disc-shaped base plate material during processing is set, and the positional coordinates of two ideal processing points on the grinding wheel and the disc-shaped base plate material during grinding are set. Here, the ideal processing point is defined as the intersection point of a circle whose radius is the average value of the inner and outer radii of the ring-shaped grinding wheel and a circle whose radius is the average value of the inner and outer radii of the disc-shaped base plate material, when the relative position between the grinding wheel and the rotary grinding machine material satisfies a predetermined relative positional relationship between the materials.

[0023] Next, the spindle is driven by a numerically controlled machine tool so that the grinding wheel moves in parallel, and the relative position between the grinding wheel and the disc-shaped base plate satisfies a predetermined relative positional relationship between materials, and in the initial state, the grinding wheel is positioned above the disc-shaped base plate. Here, in the grinding process of the disc-shaped base plate, the processing position is changed by controlling the raising and lowering of the grinding wheel and the rotation of the disc-shaped base plate, so the displacement of the grinding wheel in the Z direction is not considered. When determining the relative positional relationship between the grinding wheel and the disc-shaped base plate during grinding, the relative position between the grinding wheel and the disc-shaped base plate is set to satisfy the following relational equation.

number

[0024] Step 3: As shown in Figure 6, the grinding wheel is controlled by a numerically controlled machine tool to perform grinding on the disc-shaped base plate. The grinding process consists of: a first step in which the numerically controlled machine tool controls the descent of the rotating grinding wheel to grind a pair of gear teeth on the disc-shaped base plate; a second step in which the numerically controlled machine tool controls the grinding wheel to rise back to its original position after completing the grinding of a pair of gear teeth on the disc-shaped base plate, and controls the rotary table so that the disc-shaped base plate rotates by a large angle β in conjunction with the rise, and then repeats the first step; a third step in which the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process are evaluated, and if the maximum wear depth or maximum stress is greater than the corresponding predetermined discard value, the grinding wheel is replaced with a new one, otherwise the following process is performed, when the maximum wear depth or maximum stress is less than the corresponding predetermined grinding value. The process continues as follows: the grinding wheel is removed if not, surface shape and wear feature data of the grinding wheel are obtained, the grinding wheel is reground, then the grinding wheel is reattached, the second step is repeated until the grinding of one group of gear teeth is completed, the grinding wheel is controlled by a numerically controlled machine tool to raise it back to its original position, where one group of gear teeth refers to all gear teeth that can be machined within a range not exceeding 360° when the disc-shaped base material is rotated sequentially at a large angle β; the fourth step is to control the rotary table by a numerically controlled machine tool to rotate the disc-shaped base material at a small angle α, then repeat the first to third steps; and the fifth step is to repeat the fourth step until the grinding of all gear teeth on the disc-shaped base material is completed.

[0025] Furthermore, considering the influence of the rotational accuracy of the rotary table of the numerically controlled machine tool on the machining accuracy of the disc-shaped base plate, the small angle α may not reach the design angle between two adjacent gear teeth on the disc-shaped base plate (i.e., the ideal small angle). Therefore, it is necessary to introduce a neural network before grinding the disc-shaped base plate material (i.e., before performing step 3) to optimize the large angle β and small angle α for rotating the disc-shaped base plate material. Here, the large angle β for each rotation of the disc-shaped base plate material is designed to be the same (however, the specific value of β needs to be optimized), the small angle α for each rotation is designed to be variable (the specific value of α may alternate between large and small values), and it is assumed that β = 360° / K in the initial state. Here, K is the logarithm of the number of gear teeth included in each group, and both β and K are integers. The optimization process is as follows.

[0026] (a) Set the feature vectors to be input to the neural network. The feature vectors of the neural network represent the operation during the processing of the disc-shaped base material. t and processing state S t Includes. Operation a t to a t We define it as (β, α). α is a matrix composed of each small angle α of rotation of the disc-shaped base plate material. Processing state S t teeth

number

[0027] For example, if we need to machine 88 teeth on a disc-shaped base plate, and the logarithm of the gear teeth in each group of teeth is K=4, then when machining the first pair of gear teeth with a grinding wheel, S t =(88,1,0,0,0), and when machining the second pair of gear teeth with a grinding wheel, S t =(88,2,β,0,0), and when machining the 5th pair of gear teeth with a grinding wheel, St=(88,5,3β+α,α,ε).

[0028] (b) Construct a neural network architecture. The particle swarm algorithm is adopted as the optimization algorithm for the neural network, and the learning factors and inertia weights of the neural network are set, and the maximum number of generated particle swarms, the number of iterations and the step size are set. Here, the optimization process in the particle swarm algorithm is as follows: (1) Input the training sample. The parameters in the training sample include the number of teeth Z of the disc-shaped base plate, the large angle β in the initial state, the ideal small angle α1, and the small angles α of the disc-shaped base plate material in the initial state. (2) Within a predetermined range of small angle α (i.e., within the allowable range of local error ε), the optimal value P of the small angle α corresponding to each individual. best and the optimal value G of the small angle α corresponding to the particle group. bestUpdate the value. Here, the optimal value P for the small angle α corresponding to the individual is updated. best G is the optimal solution experienced by a single particle during its iterative process, and represents the optimal value of the small angle α corresponding to the group of particles. best This is the most preferred of the optimal solutions for the corresponding small angle α in the current iteration process for all particles. The update process is as follows: (i) Calculate fitness for each individual S. i For this, the current value X of each small angle α i The corresponding fitness f(X i The fitness function is calculated and is inversely proportional to εmax. (ii) Compare the fitnesss. The calculated fitness f(X i The fitness f(P) obtained by calculating the optimal value of the corresponding small angle α for each individual up to the present (in the initial state, each initial small angle α is set to the optimal value of each small angle α for each individual) is calculated based on the individual's current optimal value of small angle α. best Compared with f(X i )>f(P best If so, the optimal value of the small angle α corresponding to the individual is P best =X i Update it to f(P best ) = f(X i Update to ). (3) Each fitness value f(P) after updating all individuals best ) is the optimal value G of the small angle α corresponding to the particle group. best The fitness f(G) obtained by calculating based on (in the initial state, the initial small angle α is set to the optimal value of the small angle α of the particle group) best ) compared with f(P best )>f(G best If so, the optimal value of the small angle α corresponding to the particle group is G best =P best Update it to f(G best ) = f(P best Update to ). (4) Processing state S of each individual t Update. (5) If εmax is less than a predetermined value, output the small angle α, large angle β and number of iterations for each optimized disc-shaped base plate; otherwise, update the large angle β to the next value in descending order within the selectable range (the selectable values ​​for the large angle β are integers obtained by calculating β = 360° / K and it must be ensured that the logarithm K of the gear teeth in each group after the update is an integer), reset each small angle α of the disc-shaped base plate to its initial value, and then return to (2).

[0029] In this embodiment, the step size is set to the minimum rotation angle of the numerically controlled machine tool, and the learning factors of the neural network are set to c1=c2=2. The magnitude of the inertia weights determines whether the neural network excels in global optimization or local optimization. Dynamic inertia weights make it easier to find better optimization results than fixed values, so the inertia weights w are set within the range of 0.4 to 0.9.

[0030] Furthermore, considering that significant vibrations may occur when the grinding wheel and the disc-shaped base plate come into contact, potentially causing a (slight) displacement of the disc-shaped base plate, and in order to ensure the machining accuracy of the disc-shaped base plate, if the disc-shaped base plate has shifted position after the grinding of a pair of teeth on the disc-shaped base plate is completed, its position is adjusted. Of course, it should be noted that such a displacement is small, and the machining accuracy of the pair of teeth still meets the requirements. To avoid the accumulation of subsequent machining errors, if a slight displacement is suspected, the position of the disc-shaped base plate is adjusted immediately. Specifically, during the grinding process of each pair of gear teeth on the disc-shaped base plate, a vibration sensor fixed to the disc-shaped base plate detects the vibration signal value of the disc-shaped base plate. If the detected vibration signal value exceeds a predetermined value, it is determined that a (slight) displacement has occurred in the position of the disc-shaped base plate after the grinding of the pair of gear teeth is completed. After completing the machining of the pair of gear teeth, the position coordinates of the actual machining points of these two gear teeth (the intersection points of the currently machined gear teeth on the disc-shaped base material after grinding and a circle on the disc-shaped base material whose radius is the average of the inner and outer radii) are calculated using images taken with a camera (for example, a microscope camera is used). Furthermore, the positional error between these two actual machining points and the corresponding ideal machining points is obtained, and the disc-shaped base material is moved in parallel using the four locking jaws of the four-jaw chuck to align the positions of these two actual machining points with the positions of the corresponding ideal machining points.

[0031] Furthermore, the decision process for replacing or regrinding the grinding wheel in Step 3 is as follows: The amount of grinding on the grinding wheel surface, working time, and working conditions are input into a predictive simulation model to evaluate the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process. If the maximum wear depth or maximum stress is greater than the corresponding predetermined discard value, the grinding wheel is replaced with a new one; otherwise, the decision is made as follows: If the maximum wear depth or maximum stress is less than the corresponding predetermined grinding value, the process continues; otherwise, the grinding wheel is removed, and surface shape and wear feature data of the grinding wheel are obtained using laser triangulation, and the surface of the grinding wheel after processing is regrinded. Here, the process for obtaining the surface shape of the grinding wheel using laser triangulation is as follows: A laser beam emitted from a laser is irradiated onto the surface of a grinding wheel. Due to changes in the unevenness and curvature of the grinding wheel surface, the projected beam fluctuates due to changes in the contour position, and the image formed on the PSD (Position Sensitive Detector) detection element also undergoes displacement changes accordingly. The PSD detection element collects the image formed by the reflected light, and filters and smooths the collected image. Based on the positional correspondence between the image point and the measurement point, the height value of the grinding wheel surface can be obtained. By performing interpolation reconstruction on the dispersed sampling data, 3D coordinate data of the grinding wheel surface is obtained. Using drawing software, a 3D shape diagram of the grinding wheel surface is drawn using the collected 3D coordinate data of the grinding wheel surface, and data analysis is performed to obtain wear feature data of the grinding wheel surface. Here, a standard sphere is added and the sensor head is adjusted so that the emitted light ray is always perpendicular to the intersection line of the grinding wheel surface and the triangular plane of light, thereby eliminating measurement deviations caused by the tilt angle.

[0032] Furthermore, in order to save time in detecting the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process, a predictive simulation model is constructed to predict the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process. Subsequently, in the grinding process of the disc-shaped base plate material, the predictive simulation model is used to predict the maximum wear depth of the grinding wheel surface after grinding and the maximum stress during the grinding process. The process for constructing the predictive simulation model is as follows.

[0033] Stage 1: Construct a grinding simulation model of the disc-shaped base plate and grinding wheel. A 3D simulation model of the disc-shaped base plate and grinding wheel is constructed using Solidworks, and this 3D simulation model is imported into ABAQUS. At this time, the material properties of the disc-shaped base plate and grinding wheel are input. Here, the material properties include the modulus of elasticity, Poisson's ratio, and coefficient of wear. Next, meshing is performed on the 3D simulation model of the disc-shaped base plate and grinding wheel. Here, meshing can be performed using hexahedral virtual elements, and the mesh density can be appropriately increased in the wear region of the 3D simulation model of the grinding wheel to ensure that the mesh density is high enough to capture the details of wear. The mesh density can be appropriately decreased in the non-wear region of the 3D simulation model of the grinding wheel, thereby reducing the computer's operating time. Next, boundary conditions are set for the 3D simulation models of the disc-shaped base plate and the grinding wheel to obtain grinding simulation models of the disc-shaped base plate and the grinding wheel.

[0034] Stage 2: Set the working conditions. The working conditions include load, rotational speed, working time, and working temperature, ensuring that the simulated environment matches the actual machining environment. Next, run ABAQUS to perform a grinding simulation to obtain the wear depth and stress distribution of the 3D simulation model of the grinding wheel after the grinding simulation. Furthermore, use the post-processing function of ABAQUS to draw the wear depth distribution map and stress distribution map of the 3D simulation model of the grinding wheel after the grinding simulation.

[0035] Stage 3: Change the amount of material removed from the grinding wheel surface and repeat Stages 1 and 2.

[0036] Stage 4: Based on the amount of grinding wheel surface material removed, the working time, the working conditions, and the corresponding grinding wheel wear depth distribution and stress distribution data obtained from simulations, a set of training and validation datasets are created. These datasets are then trained and validated against a constructed predictive simulation model to evaluate the maximum wear depth of the grinding wheel surface after processing and the maximum stress during the processing steps.

[0037] Step 4: The gear teeth obtained by grinding the disc-shaped base plate are arc-face teeth, and the surface of each arc-face tooth of the machined disc-shaped base plate is inspected for defects. If defects are found on the surface of the arc-face teeth of the disc-shaped base plate, the defective areas on the surface of each arc-face tooth of the disc-shaped base plate are reground to improve the machining accuracy of the disc-shaped base plate. Here, the eddy current testing method is used to inspect the surface of each arc-face tooth of the disc-shaped base plate for defects. A changing magnetic field is applied around the disc-shaped base plate, and an alternating current is passed through the detection coil to generate an alternating magnetic field perpendicular to the disc-shaped base plate. By bringing the detection coil close to the arc-face teeth of the disc-shaped base plate, eddy currents are induced on the surface of the arc-face teeth of the disc-shaped base plate, and at the same time, a magnetic field in the opposite direction to the original magnetic field is generated, canceling out a portion of the original magnetic field. This causes changes in the resistance and inductance of the detection coil. If there are defects on the surface of the arc-shaped end face teeth, the intensity and distribution of the eddy current field change, and the coil impedance changes. By analyzing these changes, it is possible to determine whether or not there are surface defects on the arc-shaped end face teeth of the disc-shaped base plate. In addition, the correction coefficient used is adjusted according to the conductivity of the disc-shaped base plate material. A high correction coefficient is selected if the conductivity of the disc-shaped base plate material is high, and a low correction coefficient is selected otherwise.

[0038] A 3D point cloud device is used to obtain the coordinates of each discrete point in the surface defect region of the arc-shaped end face teeth of the disc-shaped base plate. The normal deviation between each discrete point and the corresponding point coordinate on the surface of the theoretical arc-shaped end face teeth of the disc-shaped base plate is calculated. The normal deviation value is an index that reflects the difference between the actual surface of the arc-shaped end face teeth and the ideal surface of the arc-shaped end face teeth. If the variance of the normal deviation between each discrete point in the defect region and the corresponding point coordinate on the surface of the theoretical arc-shaped end face teeth is greater than a predetermined value, the defect region on the surface of the arc-shaped end face teeth is reground.

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