A method, system, device, and storage medium for identifying end-face deformation of disk-type parts.

Abstract

This invention belongs to the field of industrial component inspection technology, specifically disclosing a method, system, device, and storage medium for identifying end-face deformation of disc-shaped parts. It involves collecting and filtering a dataset of the end-face morphology of the disc-shaped part to be inspected, obtaining a first sinusoidal principal component dataset and a second sinusoidal principal component dataset to determine whether the disc-shaped part is deformed. When deformation is determined, the front and rear end-face morphology datasets, the first sinusoidal principal component dataset, and the first sinusoidal principal component dataset are used to calculate a consistency factor for the circumferential deformation trend and a consistency factor for the circumferential deformation amount. Finally, based on these factors, the end-face deformation category of the disc-shaped part is determined, enabling efficient detection and identification of end-face deformation in disc-shaped parts. This invention can achieve accurate calculation of the degree of end-face deformation in disc-shaped parts, thus accurately and efficiently identifying the end-face deformation status of disc-shaped parts.

Description

Technical Field

[0001] This invention belongs to the field of industrial component testing technology, specifically relating to a method, system, device, and storage medium for identifying end-face deformation of disc-shaped parts. Background Technology

[0002] As a core component of the aero-engine, the rotor assembly must withstand periodic and time-varying coupled loading of various loads, including axial force, centrifugal force, and temperature load, during service. The rotor discs are primarily connected by interference-fit bolts. Under alternating loads, these disc components can warp and deform. This warping significantly impacts the stability of the rotor connection, and insufficient connection stability directly leads to excessive rotor vibration, thus affecting normal engine operation. Therefore, measuring the deformation of engine disc components is of paramount importance.

[0003] Currently, the topographic data of one side of the end face of the stop of engine disc-type parts can only determine which measurement sampling points are high points and which measurement sampling points are low points. It is impossible to fully calculate and identify the overall deformation of the end face. Since the overall deformation of the front and rear end faces of the stop of disc-type parts cannot be effectively identified and considered, it is impossible to formulate a targeted repair plan. This results in a low first-time assembly success rate of the rotor and a high frequency of rework and troubleshooting, which directly affects the overall maintenance cycle and maintenance cost of the engine. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and storage medium for identifying end-face deformation of disc-shaped parts, in order to solve the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Firstly, a method for identifying end-face deformation of disc-shaped parts is provided, including:

[0007] Obtain the end face morphology dataset of the disc-shaped part. The end face morphology dataset includes a front face morphology dataset and a rear face morphology dataset. The front face morphology dataset contains morphology data of several front sampling points, and the rear face morphology dataset contains morphology data of several rear sampling points.

[0008] Wavelet decomposition was used to filter the front-end surface topography dataset and the back-end surface topography dataset respectively, and the first sinusoidal principal component dataset and the second sinusoidal principal component dataset in the front-end surface topography dataset were extracted.

[0009] The first sinusoidal principal component dataset is compared with the set first threshold interval to determine whether the first sinusoidal principal component dataset falls entirely within the first threshold interval. The second sinusoidal principal component dataset is compared with the set second threshold interval to determine whether the second sinusoidal principal component dataset falls entirely within the second threshold interval. If the first sinusoidal principal component dataset falls entirely within the first threshold interval and the second sinusoidal principal component dataset falls entirely within the second threshold interval, it is determined that the end face of the disc-shaped part is not deformed; otherwise, it is determined that the end face of the disc-shaped part is deformed.

[0010] When it is determined that there is deformation on the end face of the disc-shaped part, the first sinusoidal principal component dataset and the second sinusoidal principal component dataset are divided into several intervals according to the circumferential phase, and the morphological data change trend factor of each interval is calculated using a preset change trend factor calculation model. Then, the circumferential deformation trend consistency factor is calculated based on the morphological data change trend factor of each interval.

[0011] The front face topography dataset and the back face topography dataset are respectively translated and transformed to ensure that all data in the front face topography dataset and the back face topography dataset are not less than zero after translation and transformation, thus obtaining the first transformed dataset and the second transformed dataset. The first transformed dataset, the second transformed dataset, the first sinusoidal principal component dataset and the second sinusoidal principal component dataset are substituted into the preset deformation consistency factor calculation model to calculate the deformation consistency factor of several sampling points. Then, the full circumferential deformation consistency factor is calculated based on the deformation consistency factor of each sampling point.

[0012] The end face deformation category of disc-shaped parts is determined based on the consistency factor of the circumferential deformation trend and the consistency factor of the circumferential deformation amount, and the end face deformation category of disc-shaped parts is displayed.

[0013] In one possible design, the front-end sampling point topography data in the front-end topography dataset is {s} i The topography data of the back-end sampling points in the back-end surface topography dataset is {v}. i The front-end sampling point morphology data and the back-end sampling point morphology data are obtained by circumferential synchronous sampling measurement of the front and back faces of the disc-shaped part. i represents the sampling point number of the synchronous sampling. The total number of circumferential synchronous sampling points is n, where n is a positive integer greater than 1. The first sinusoidal principal component dataset contains several first sinusoidal principal component data {W}. i The second sinusoidal principal component dataset contains several second sinusoidal principal component data {Z}. i}

[0014] In one possible design, the step of dividing the first and second sinusoidal principal component datasets into several intervals according to the circumferential phase, and calculating the morphological data change trend factor of each interval of the model using a preset change trend factor, includes:

[0015] The first and second sinusoidal principal component datasets are divided into N intervals according to their circumferential phase, with k sampling points in each interval. Then, a pre-defined trend factor calculation model is used to calculate the morphological data trend factor for each interval. The trend factor calculation model is as follows:

[0016]

[0017] Among them, Q j The factor characterizing the trend of morphological data change in the j-th interval, where j is the interval number, W jk W represents the first sinusoidal principal component data of the last sampling point in the j-th interval of the first sinusoidal principal component dataset. j1 Z represents the first sinusoidal principal component data of the first sampling point in the j-th interval of the first sinusoidal principal component dataset. jk Z represents the second sinusoidal principal component data of the last sampling point in the j-th interval of the second sinusoidal principal component dataset. j1 This is the second sinusoidal principal component data of the first sampling point in the j-th interval of the second sinusoidal principal component dataset.

[0018] In one possible design, the calculation of the circumferential deformation trend consistency factor based on the morphological data change trend factor of each interval includes:

[0019] The morphological data change trend factor of each interval is substituted into the calculation formula of the circumferential deformation trend consistency factor to obtain the circumferential deformation trend consistency factor. The calculation formula of the circumferential deformation trend consistency factor is as follows:

[0020]

[0021] Among them, QQ represents the consistency factor of the deformation trend in the whole circumference.

[0022] In one possible design, the front-side topography dataset and the back-side topography dataset are respectively subjected to translation transformation processing, so that all data in the front-side topography dataset and the back-side topography dataset are not less than zero after the translation transformation processing, to obtain a first transformed dataset and a second transformed dataset. The first transformed dataset, the second transformed dataset, the first sinusoidal principal component dataset, and the second sinusoidal principal component dataset are then substituted into a preset deformation consistency factor calculation model to calculate several sampling point deformation consistency factors, including:

[0023] A baseline value e1 is selected for the front-side and back-side topography datasets. If the topography data of each front-side sampling point in the front-side topography dataset and the topography data of each back-side sampling point in the back-side topography dataset are all not less than 0, then e1 = 0. If there is a front-side sampling point in the front-side topography dataset or a back-side sampling point in the back-side topography dataset that is less than 0, then e1 is the absolute value of the minimum value in the front-side and back-side topography datasets, i.e., e1 = |min{s i v i}|;

[0024] Using the reference value e1, translation transformations are performed on the front face topography dataset and the back face topography dataset respectively to obtain the first transformed dataset and the second transformed dataset. The first transformed dataset ss i For ss i =s i +e1, Second Transformed Dataset vv i For vv i =v i +e1;

[0025] Substituting the first transformed dataset, the second transformed dataset, the first sinusoidal principal component dataset, and the second sinusoidal principal component dataset into a preset deformation consistency factor calculation model, deformation consistency factors for several sampling points are calculated. The deformation consistency factor calculation model is as follows:

[0026]

[0027] Among them, D i F represents the deformation consistency factor corresponding to sampling point i. i The value of the absolute value of the first sinusoidal principal component data and the absolute value of the second sinusoidal principal component data corresponding to sampling point i is the maximum value.

[0028] In one possible design, calculating the full circumferential deformation consistency factor based on the deformation consistency factor at each sampling point includes:

[0029] Substituting each deformation consistency factor into the formula for calculating the full circumferential deformation consistency factor, the full circumferential deformation consistency factor is obtained. The formula for calculating the full circumferential deformation consistency factor is as follows:

[0030]

[0031] Among them, FF represents the consistency factor of deformation in the entire circumferential direction.

[0032] In one possible design, determining the end-face deformation category of the disc-shaped part based on the consistency factor of the circumferential deformation trend and the consistency factor of the circumferential deformation amount includes:

[0033] The consistency factors of the circumferential deformation trend and the consistency factor of the circumferential deformation amount are imported into a preset end face deformation classification table for comparison to determine the end face deformation category of the disc-shaped part. The end face deformation classification table contains several end face deformation categories, and each end face deformation category is associated with the corresponding circumferential deformation trend consistency factor interval and circumferential deformation amount consistency factor interval.

[0034] Secondly, a system for recognizing end-face deformation of disc-shaped parts is provided, comprising an acquisition unit, an extraction unit, a judgment unit, a first calculation unit, a second calculation unit, and a recognition unit, wherein:

[0035] The acquisition unit is used to acquire the end face morphology dataset of the disc-shaped parts. The end face morphology dataset includes a front face morphology dataset and a rear face morphology dataset. The front face morphology dataset contains morphology data of several front sampling points, and the rear face morphology dataset contains morphology data of several rear sampling points.

[0036] The extraction unit is used to filter the front surface topography dataset and the back surface topography dataset using wavelet decomposition to extract the first sinusoidal principal component dataset from the front surface topography dataset and the second sinusoidal principal component dataset from the back surface topography dataset.

[0037] The determination unit is used to compare the first sinusoidal principal component dataset with a set first threshold interval to determine whether the first sinusoidal principal component dataset falls entirely within the first threshold interval, and to compare the second sinusoidal principal component dataset with a set second threshold interval to determine whether the second sinusoidal principal component dataset falls entirely within the second threshold interval. If the first sinusoidal principal component dataset falls entirely within the first threshold interval and the second sinusoidal principal component dataset falls entirely within the second threshold interval, then it is determined that the end face of the disc-shaped part is not deformed; otherwise, it is determined that the end face of the disc-shaped part is deformed.

[0038] The first calculation unit is used to divide the first sinusoidal principal component dataset and the second sinusoidal principal component dataset into several intervals according to the circumferential phase when it is determined that there is deformation on the end face of the disc-shaped part, and to calculate the morphological data change trend factor of each interval using a preset change trend factor calculation model, and then calculate the full circumferential deformation trend consistency factor based on the morphological data change trend factor of each interval.

[0039] The second calculation unit is used to perform translation transformation on the front surface topography dataset and the back surface topography dataset respectively, so that all data in the front surface topography dataset and the back surface topography dataset are not less than zero after translation transformation, to obtain the first transformation dataset and the second transformation dataset. The first transformation dataset, the second transformation dataset, the first sinusoidal principal component dataset and the second sinusoidal principal component dataset are substituted into the preset deformation consistency factor calculation model to calculate the deformation consistency factor of several sampling points. Then, the full circumferential deformation consistency factor is calculated based on the deformation consistency factor of each sampling point.

[0040] The identification unit is used to determine the end face deformation category of the disc-shaped part based on the consistency factor of the full circumferential deformation trend and the consistency factor of the full circumferential deformation amount, and to display the end face deformation category of the disc-shaped part.

[0041] Thirdly, a device for identifying end-face deformation of disc-shaped parts is provided, comprising:

[0042] Memory, used to store instructions;

[0043] A processor is configured to read instructions stored in the memory and execute the method described in any one of the first aspects above, according to the instructions.

[0044] Fourthly, a computer-readable storage medium is provided, on which instructions are stored, which, when executed on a computer, cause the computer to perform any of the methods described in the first aspect. A computer program product containing instructions is also provided, which, when executed on a computer, cause the computer to perform any of the methods described in the first aspect.

[0045] Beneficial Effects: This invention collects and filters the end-face morphology dataset of the disc-shaped parts to be inspected, obtaining a first sinusoidal principal component dataset and a second sinusoidal principal component dataset. Then, based on these datasets, it determines whether the disc-shaped parts are deformed. If deformation is detected, it calculates a consistency factor for the circumferential deformation trend and a consistency factor for the circumferential deformation amount using the front and rear end-face morphology datasets, the first sinusoidal principal component dataset, and the first sinusoidal principal component dataset. Finally, it determines the type of end-face deformation of the disc-shaped parts based on these factors, enabling efficient detection and identification of end-face deformation in disc-shaped parts. This invention allows for accurate calculation of the degree of end-face deformation in disc-shaped parts, enabling accurate and efficient identification of end-face deformation. It can effectively assist in developing end-face repair plans for disc-shaped parts on the production floor, reducing the assembly and maintenance cycle and cost of the applied equipment. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the steps in the method of Embodiment 1 of the present invention;

[0048] Figure 2 This is a schematic diagram of the system configuration in Embodiment 2 of the present invention;

[0049] Figure 3 This is a schematic diagram of the device configuration in Embodiment 3 of the present invention. Detailed Implementation

[0050] It should be noted that the descriptions of these embodiments are intended to aid in understanding the invention and do not constitute a limitation thereof. The specific structural and functional details disclosed herein are merely for describing exemplary embodiments of the invention. However, the invention may be embodied in many alternative forms and should not be construed as being limited to the embodiments described herein.

[0051] It should be understood that, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments according to the specific circumstances.

[0052] Specific details are provided in the following description to provide a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. For example, the system may be shown in block diagrams to avoid obscuring the example with unnecessary details. In other embodiments, well-known processes, structures, and techniques may be shown without non-essential details to avoid obscuring the embodiments.

[0053] Example 1:

[0054] This embodiment provides a method for identifying end-face deformation of disk-shaped parts, which can be applied to corresponding end-face deformation identification servers for disk-shaped parts, such as... Figure 1 As shown, the method includes the following steps:

[0055] S1. Obtain the end face morphology dataset of the disc-shaped part. The end face morphology dataset includes a front face morphology dataset and a rear face morphology dataset. The front face morphology dataset contains morphology data of several front sampling points, and the rear face morphology dataset contains morphology data of several rear sampling points.

[0056] In specific implementation, an air-bearing turntable can be used to measure and collect end-face topography datasets of disc-shaped parts. These datasets include a front-face topography dataset and a rear-face topography dataset. The front-face topography dataset contains topography data from several front-end sampling points, and the rear-face topography dataset contains topography data from several rear-end sampling points. The front-end sampling point topography data in the front-face topography dataset is {s}. i The topography data of the back-end sampling points in the back-end surface topography dataset is {v}. i The morphological data of the front and rear sampling points are obtained by circumferentially synchronously sampling and measuring the front and rear faces of the disc-shaped part. 'i' represents the sampling point number in the synchronous sampling, and the total number of circumferential synchronous sampling points is n, where n is a positive integer greater than 1. The corresponding morphological data is one-dimensional data obtained by circumferential synchronous sampling and measuring along the front and rear faces of the disc-shaped part using a high-precision dial indicator.

[0057] S2. Wavelet decomposition is used to filter the front-end surface morphology dataset and the back-end surface morphology dataset respectively, and the first sinusoidal principal component dataset in the front-end surface morphology dataset and the second sinusoidal principal component dataset in the back-end surface morphology dataset are extracted.

[0058] In specific implementation, after obtaining the end-face morphology dataset, wavelet decomposition can be used to filter the front-face and rear-face morphology datasets respectively, based on the number of sampling points and the significance of the sinusoidal characteristics of the data changes. This extracts the first sinusoidal principal component dataset from the front-face morphology dataset and the second sinusoidal principal component dataset from the rear-face morphology dataset. For example, the DB4 wavelet algorithm in MATLAB can be used to perform a 5-level decomposition of the original end-face morphology data, and the sinusoidal components at the 5th level can be reconstructed to establish a data filtering model. This model is then used to filter the front-face and rear-face morphology datasets respectively, extracting the first sinusoidal principal component dataset from the front-face morphology dataset and the second sinusoidal principal component dataset from the rear-face morphology dataset. The first sinusoidal principal component dataset contains several first sinusoidal principal component data {W}. i The second sinusoidal principal component dataset contains several second sinusoidal principal component data {Z}. i}

[0059] S3. Compare the first sinusoidal principal component dataset with the set first threshold interval to determine whether the first sinusoidal principal component dataset falls entirely within the first threshold interval. Compare the second sinusoidal principal component dataset with the set second threshold interval to determine whether the second sinusoidal principal component dataset falls entirely within the second threshold interval. If the first sinusoidal principal component dataset falls entirely within the first threshold interval and the second sinusoidal principal component dataset falls entirely within the second threshold interval, then it is determined that the end face of the disc-shaped part is not deformed; otherwise, it is determined that the end face of the disc-shaped part is deformed.

[0060] In specific implementation, after obtaining the first and second sinusoidal principal component datasets, the first sinusoidal principal component dataset is compared with a set first threshold interval [X1, Y1] to determine whether all the first sinusoidal principal component data in the first sinusoidal principal component dataset falls within the first threshold interval [X1, Y1], where X1 and Y1 are the lower and upper limits of the first threshold interval, respectively. Similarly, the second sinusoidal principal component dataset is compared with a set second threshold interval [X2, Y2] to determine whether all the second sinusoidal principal component data in the second sinusoidal principal component dataset falls within the second threshold interval [X2, Y2], where X2 and Y2 are the lower and upper limits of the second threshold interval, respectively. If all the first sinusoidal principal component data in the first sinusoidal principal component dataset falls within the first threshold interval [X1, Y1] and all the second sinusoidal principal component data in the second sinusoidal principal component dataset falls within the second threshold interval [X2, Y2], the end face of the disc-shaped part is determined to be undeformed; otherwise, the end face of the disc-shaped part is determined to be deformed. X1, Y1, X2, and Y2 can be statistically selected based on the measurement results of the actual end face morphology data of the corresponding disc-shaped parts during processing and manufacturing.

[0061] S4. When it is determined that there is deformation on the end face of the disc-shaped part, the first sinusoidal principal component dataset and the second sinusoidal principal component dataset are divided into several intervals according to the circumferential phase, and the morphological data change trend factor of each interval is calculated using a preset change trend factor calculation model. Then, the circumferential deformation trend consistency factor is calculated based on the morphological data change trend factor of each interval.

[0062] In specific implementation, after determining that the end face of the disc-shaped part is deformed, the first and second sinusoidal principal component datasets are divided into N intervals according to the circumferential phase, with k sampling points in each interval. Then, a preset trend factor calculation model is used to calculate the morphological data trend factor for each interval. The trend factor calculation model is as follows:

[0063]

[0064] Among them, Q j The factor characterizing the trend of morphological data change in the j-th interval, where j is the interval number, W jkW represents the first sinusoidal principal component data of the last sampling point in the j-th interval of the first sinusoidal principal component dataset. j1 Z represents the first sinusoidal principal component data of the first sampling point in the j-th interval of the first sinusoidal principal component dataset. jk Z represents the second sinusoidal principal component data of the last sampling point in the j-th interval of the second sinusoidal principal component dataset. j1 This is the second sinusoidal principal component data of the first sampling point in the j-th interval of the second sinusoidal principal component dataset.

[0065] Then, the morphological data change trend factors of each interval are substituted into the calculation formula of the circumferential deformation trend consistency factor to obtain the circumferential deformation trend consistency factor. The calculation formula of the circumferential deformation trend consistency factor is as follows:

[0066]

[0067] Among them, QQ represents the consistency factor of the deformation trend in the whole circumference.

[0068] S5. Perform translation transformation on the front face topography dataset and the back face topography dataset respectively, so that all data in the front face topography dataset and the back face topography dataset are not less than zero after translation transformation, to obtain the first transformation dataset and the second transformation dataset. Substitute the first transformation dataset, the second transformation dataset, the first sine principal component dataset and the second sine principal component dataset into the preset deformation consistency factor calculation model to calculate the deformation consistency factor of several sampling points. Then, calculate the full circumferential deformation consistency factor based on the deformation consistency factor of each sampling point.

[0069] In practice, a baseline value e1 is selected for the front-end and back-end surface topography datasets. If the topography data of each front-end sampling point in the front-end surface topography dataset and the topography data of each back-end sampling point in the back-end surface topography dataset are all not less than 0, then e1 = 0. If there is any front-end sampling point topography data in the front-end surface topography dataset or any back-end sampling point topography data in the back-end surface topography dataset that is less than 0, then e1 is the absolute value of the minimum value in the front-end and back-end surface topography datasets, i.e., e1 = |min{s i v i}|;

[0070] Using the reference value e1, translation transformations are performed on the front face topography dataset and the back face topography dataset respectively to obtain the first transformed dataset and the second transformed dataset. The first transformed dataset ss i For ss i =s i +e1, Second Transformed Dataset vv i For vv i =v i+e1;

[0071] Substituting the first transformed dataset, the second transformed dataset, the first sinusoidal principal component dataset, and the second sinusoidal principal component dataset into a preset deformation consistency factor calculation model, deformation consistency factors for several sampling points are calculated. The deformation consistency factor calculation model is as follows:

[0072]

[0073] Among them, D i F represents the deformation consistency factor corresponding to sampling point i. i The value of the absolute value of the first sinusoidal principal component data and the absolute value of the second sinusoidal principal component data corresponding to sampling point i is the maximum value.

[0074] Then, the consistency factors of each deformation amount are substituted into the formula for calculating the consistency factor of the full circumferential deformation amount to obtain the consistency factor of the full circumferential deformation amount. The formula for calculating the consistency factor of the full circumferential deformation amount is as follows:

[0075]

[0076] Among them, FF represents the consistency factor of deformation in the entire circumferential direction.

[0077] S6. Determine the end face deformation category of the disc-shaped part based on the consistency factor of the full circumferential deformation trend and the consistency factor of the full circumferential deformation amount, and display the end face deformation category of the disc-shaped part.

[0078] In practical implementation, after calculating the consistency factor of the circumferential deformation trend and the consistency factor of the circumferential deformation amount, the end face deformation category of the disc-shaped part can be determined based on these factors, and the end face deformation category of the disc-shaped part can be displayed. For example, when determining the end face deformation category of a disc-shaped part, the consistency factor of the circumferential deformation trend and the consistency factor of the circumferential deformation amount can be imported into a preset end face deformation classification table for comparison to determine the end face deformation category of the disc-shaped part. The end face deformation classification table, as shown in Table 1 below, contains several end face deformation categories, and each end face deformation category is associated with a corresponding circumferential deformation trend consistency factor interval and a circumferential deformation amount consistency factor interval.

[0079] Table 1 Classification of End Face Deformation

[0080]

[0081] According to the end face deformation classification table, if the calculated consistency factor of the full circumferential deformation trend is 1.18 and the consistency factor of the full circumferential deformation amount is 0.77, then it can be determined that the end face deformation category of the disc-shaped part meets the full circumferential small deformation standard. Similarly, the end face deformation classification table can be set and adjusted according to the actual situation.

[0082] The method described in this embodiment can accurately calculate the degree of deformation of the end face of disc-shaped parts, so as to accurately and efficiently identify the deformation of the end face of disc-shaped parts. It can effectively assist the production site in formulating end face repair plans for disc-shaped parts, and reduce the equipment assembly and maintenance cycle and cost.

[0083] Example 2:

[0084] This embodiment provides a system for recognizing end-face deformation of disc-shaped parts, such as... Figure 2 As shown, it includes an acquisition unit, an extraction unit, a judgment unit, a first calculation unit, a second calculation unit, and an identification unit, wherein:

[0085] The acquisition unit is used to acquire the end face morphology dataset of the disc-shaped parts. The end face morphology dataset includes a front face morphology dataset and a rear face morphology dataset. The front face morphology dataset contains morphology data of several front sampling points, and the rear face morphology dataset contains morphology data of several rear sampling points.

[0086] The extraction unit is used to filter the front surface topography dataset and the back surface topography dataset using wavelet decomposition to extract the first sinusoidal principal component dataset from the front surface topography dataset and the second sinusoidal principal component dataset from the back surface topography dataset.

[0087] The determination unit is used to compare the first sinusoidal principal component dataset with a set first threshold interval to determine whether the first sinusoidal principal component dataset falls entirely within the first threshold interval, and to compare the second sinusoidal principal component dataset with a set second threshold interval to determine whether the second sinusoidal principal component dataset falls entirely within the second threshold interval. If the first sinusoidal principal component dataset falls entirely within the first threshold interval and the second sinusoidal principal component dataset falls entirely within the second threshold interval, then it is determined that the end face of the disc-shaped part is not deformed; otherwise, it is determined that the end face of the disc-shaped part is deformed.

[0088] The first calculation unit is used to divide the first sinusoidal principal component dataset and the second sinusoidal principal component dataset into several intervals according to the circumferential phase when it is determined that there is deformation on the end face of the disc-shaped part, and to calculate the morphological data change trend factor of each interval using a preset change trend factor calculation model, and then calculate the full circumferential deformation trend consistency factor based on the morphological data change trend factor of each interval.

[0089] The second calculation unit is used to perform translation transformation on the front surface topography dataset and the back surface topography dataset respectively, so that all data in the front surface topography dataset and the back surface topography dataset are not less than zero after translation transformation, to obtain the first transformation dataset and the second transformation dataset. The first transformation dataset, the second transformation dataset, the first sinusoidal principal component dataset and the second sinusoidal principal component dataset are substituted into the preset deformation consistency factor calculation model to calculate the deformation consistency factor of several sampling points. Then, the full circumferential deformation consistency factor is calculated based on the deformation consistency factor of each sampling point.

[0090] The identification unit is used to determine the end face deformation category of the disc-shaped part based on the consistency factor of the full circumferential deformation trend and the consistency factor of the full circumferential deformation amount, and to display the end face deformation category of the disc-shaped part.

[0091] Example 3:

[0092] This embodiment provides a device for identifying end-face deformation of disc-shaped parts, such as... Figure 3 As shown, at the hardware level, it includes:

[0093] The data interface is used to establish data communication between the processor and the external data acquisition terminal.

[0094] Memory, used to store instructions;

[0095] The processor is used to read the instructions stored in the memory and execute the end face deformation identification method for disk-type parts in Embodiment 1 according to the instructions.

[0096] Optionally, the device also includes an internal bus. The processor, memory, and data interface can be interconnected via the internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0097] The memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory. The processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0098] Example 4:

[0099] This embodiment provides a computer-readable storage medium storing instructions. When these instructions are executed on a computer, the computer performs the disk-type part end-face deformation identification method described in Embodiment 1. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems.

[0100] This embodiment also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the disc-type part end-face deformation identification method of Embodiment 1. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system.

[0101] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying end-face deformation of disc-shaped parts, characterized in that, include: Obtain the end face topography dataset of the disc-shaped part. The end face topography dataset includes a front face topography dataset and a rear face topography dataset. The front face topography dataset contains topography data of several front sampling points, and the rear face topography dataset contains topography data of several rear sampling points. The front sampling point topography data in the front face topography dataset is {s}. i The topography data of the back-end sampling points in the back-end surface topography dataset is {v}. i The morphological data of the front-end sampling point and the morphological data of the back-end sampling point are obtained by circumferential synchronous sampling measurement of the front-end and back-end surfaces of the disc-shaped parts. i represents the sampling point number of the synchronous sampling. The total number of circumferential synchronous sampling points is n, where n is a positive integer greater than 1. Wavelet decomposition was used to filter the front-end and back-end surface topography datasets respectively, extracting the first sinusoidal principal component dataset from the front-end surface topography dataset and the second sinusoidal principal component dataset from the back-end surface topography dataset. The first sinusoidal principal component dataset contains several first sinusoidal principal component data {W}. i The second sinusoidal principal component dataset contains several second sinusoidal principal component data {Z}. i }; The first sinusoidal principal component dataset is compared with the set first threshold interval to determine whether the first sinusoidal principal component dataset falls entirely within the first threshold interval. The second sinusoidal principal component dataset is compared with the set second threshold interval to determine whether the second sinusoidal principal component dataset falls entirely within the second threshold interval. If the first sinusoidal principal component dataset falls entirely within the first threshold interval and the second sinusoidal principal component dataset falls entirely within the second threshold interval, it is determined that the end face of the disc-shaped part is not deformed; otherwise, it is determined that the end face of the disc-shaped part is deformed. When determining that the end face of a disc-shaped part is deformed, the first and second sinusoidal principal component datasets are divided into several intervals according to the circumferential phase. A preset trend factor calculation model is used to calculate the morphological data trend factor for each interval. Then, based on the morphological data trend factors for each interval, a full circumferential deformation trend consistency factor is calculated, including: The first and second sinusoidal principal component datasets are divided into N intervals according to their circumferential phase, with k sampling points in each interval. Then, a pre-defined trend factor calculation model is used to calculate the morphological data trend factor for each interval. The trend factor calculation model is as follows: Among them, Q j The factor characterizing the trend of morphological data change in the j-th interval, where j is the interval number, W jk W represents the first sinusoidal principal component data of the last sampling point in the j-th interval of the first sinusoidal principal component dataset. j1 Z represents the first sinusoidal principal component data of the first sampling point in the j-th interval of the first sinusoidal principal component dataset. jk Z represents the second sinusoidal principal component data of the last sampling point in the j-th interval of the second sinusoidal principal component dataset. j1 This refers to the second sinusoidal principal component data of the first sampling point in the j-th interval of the second sinusoidal principal component dataset. The morphological data change trend factor of each interval is substituted into the calculation formula of the circumferential deformation trend consistency factor to obtain the circumferential deformation trend consistency factor. The calculation formula of the circumferential deformation trend consistency factor is as follows: Among them, QQ represents the consistency factor of the circumferential deformation trend; The front face topography dataset and the back face topography dataset are respectively translated and transformed to ensure that all data in the front face topography dataset and the back face topography dataset are not less than zero after translation and transformation, thus obtaining the first transformed dataset and the second transformed dataset. The first transformed dataset, the second transformed dataset, the first sinusoidal principal component dataset and the second sinusoidal principal component dataset are substituted into the preset deformation consistency factor calculation model to calculate the deformation consistency factor of several sampling points. Then, the full circumferential deformation consistency factor is calculated based on the deformation consistency factor of each sampling point. The end face deformation category of disc-shaped parts is determined based on the consistency factor of the circumferential deformation trend and the consistency factor of the circumferential deformation amount, and the end face deformation category of disc-shaped parts is displayed.

2. The method for identifying end-face deformation of disc-shaped parts according to claim 1, characterized in that, The front-end and rear-end surface topography datasets are respectively subjected to translation transformations to ensure that all data in both datasets are not less than zero after the transformations, resulting in a first transformed dataset and a second transformed dataset. These datasets, along with the first and second sinusoidal principal component datasets, are then substituted into a preset deformation consistency factor calculation model to calculate several deformation consistency factors for each sampling point, including: A baseline value e1 is selected for the front-side and back-side topography datasets. If the topography data of each front-side sampling point in the front-side topography dataset and the topography data of each back-side sampling point in the back-side topography dataset are all not less than 0, then e1 = 0. If there is a front-side sampling point in the front-side topography dataset or a back-side sampling point in the back-side topography dataset that is less than 0, then e1 is the absolute value of the minimum value in the front-side and back-side topography datasets, i.e., e1 = |min{s i v i }|; Using the reference value e1, translation transformations are performed on the front face topography dataset and the back face topography dataset respectively to obtain the first transformed dataset and the second transformed dataset. The first transformed dataset ss i For ss i =s i +e1, Second Transformed Dataset vv i For vv i =v i +e1; Substituting the first transformed dataset, the second transformed dataset, the first sinusoidal principal component dataset, and the second sinusoidal principal component dataset into a preset deformation consistency factor calculation model, deformation consistency factors for several sampling points are calculated. The deformation consistency factor calculation model is as follows: Among them, D i F represents the deformation consistency factor corresponding to sampling point i. i The value of the absolute value of the first sinusoidal principal component data and the absolute value of the second sinusoidal principal component data corresponding to sampling point i is the maximum value.

3. The method for identifying end-face deformation of disc-shaped parts according to claim 2, characterized in that, The step of calculating the circumferential deformation consistency factor based on the deformation consistency factor of each sampling point includes: Substituting each deformation consistency factor into the formula for calculating the full circumferential deformation consistency factor, the full circumferential deformation consistency factor is obtained. The formula for calculating the full circumferential deformation consistency factor is as follows: Among them, FF represents the consistency factor of deformation in the entire circumferential direction.

4. The method for identifying end-face deformation of disc-shaped parts according to claim 1, characterized in that, The determination of the end face deformation category of the disc-shaped part based on the consistency factor of the full circumferential deformation trend and the consistency factor of the full circumferential deformation amount includes: The consistency factors of the circumferential deformation trend and the consistency factor of the circumferential deformation amount are imported into a preset end face deformation classification table for comparison to determine the end face deformation category of the disc-shaped part. The end face deformation classification table contains several end face deformation categories, and each end face deformation category is associated with the corresponding circumferential deformation trend consistency factor interval and circumferential deformation amount consistency factor interval.

5. A system for identifying end-face deformation of disc-shaped parts, characterized in that, It includes an acquisition unit, an extraction unit, a judgment unit, a first calculation unit, a second calculation unit, and an identification unit, wherein: The acquisition unit is used to acquire the end face morphology dataset of the disc-shaped part. The end face morphology dataset includes a front face morphology dataset and a rear face morphology dataset. The front face morphology dataset contains morphology data of several front sampling points, and the rear face morphology dataset contains morphology data of several rear sampling points. The front sampling point morphology data in the front face morphology dataset is {s}. i The topography data of the back-end sampling points in the back-end surface topography dataset is {v}. i The morphological data of the front-end sampling point and the morphological data of the back-end sampling point are obtained by circumferential synchronous sampling measurement of the front-end and back-end surfaces of the disc-shaped parts. i represents the sampling point number of the synchronous sampling. The total number of circumferential synchronous sampling points is n, where n is a positive integer greater than 1. The extraction unit is used to filter the front-end surface topography dataset and the back-end surface topography dataset using wavelet decomposition to extract the first sinusoidal principal component dataset from the front-end surface topography dataset and the second sinusoidal principal component dataset from the back-end surface topography dataset. The first sinusoidal principal component dataset contains several first sinusoidal principal component data {W}. i The second sinusoidal principal component dataset contains several second sinusoidal principal component data {Z}. i }; The determination unit is used to compare the first sinusoidal principal component dataset with a set first threshold interval to determine whether the first sinusoidal principal component dataset falls entirely within the first threshold interval, and to compare the second sinusoidal principal component dataset with a set second threshold interval to determine whether the second sinusoidal principal component dataset falls entirely within the second threshold interval. If the first sinusoidal principal component dataset falls entirely within the first threshold interval and the second sinusoidal principal component dataset falls entirely within the second threshold interval, then it is determined that the end face of the disc-shaped part is not deformed; otherwise, it is determined that the end face of the disc-shaped part is deformed. The first calculation unit, when determining that there is deformation on the end face of a disc-shaped part, divides the first and second sinusoidal principal component datasets into several intervals according to the circumferential phase, and calculates the morphological data change trend factor for each interval using a preset change trend factor calculation model. Then, based on the morphological data change trend factor of each interval, it calculates the overall circumferential deformation trend consistency factor, including: The first and second sinusoidal principal component datasets are divided into N intervals according to their circumferential phase, with k sampling points in each interval. Then, a pre-defined trend factor calculation model is used to calculate the morphological data trend factor for each interval. The trend factor calculation model is as follows: Among them, Q j The factor characterizing the trend of morphological data change in the j-th interval, where j is the interval number, W jk W represents the first sinusoidal principal component data of the last sampling point in the j-th interval of the first sinusoidal principal component dataset. j1 Z represents the first sinusoidal principal component data of the first sampling point in the j-th interval of the first sinusoidal principal component dataset. jk Z represents the second sinusoidal principal component data of the last sampling point in the j-th interval of the second sinusoidal principal component dataset. j1 This refers to the second sinusoidal principal component data of the first sampling point in the j-th interval of the second sinusoidal principal component dataset. The morphological data change trend factor of each interval is substituted into the calculation formula of the circumferential deformation trend consistency factor to obtain the circumferential deformation trend consistency factor. The calculation formula of the circumferential deformation trend consistency factor is as follows: Among them, QQ represents the consistency factor of the circumferential deformation trend; The second calculation unit is used to perform translation transformation on the front surface topography dataset and the back surface topography dataset respectively, so that all data in the front surface topography dataset and the back surface topography dataset are not less than zero after translation transformation, to obtain the first transformation dataset and the second transformation dataset. The first transformation dataset, the second transformation dataset, the first sinusoidal principal component dataset and the second sinusoidal principal component dataset are substituted into the preset deformation consistency factor calculation model to calculate the deformation consistency factor of several sampling points. Then, the full circumferential deformation consistency factor is calculated based on the deformation consistency factor of each sampling point. The identification unit is used to determine the end face deformation category of the disc-shaped part based on the consistency factor of the full circumferential deformation trend and the consistency factor of the full circumferential deformation amount, and to display the end face deformation category of the disc-shaped part.

6. A device for identifying end-face deformation of disc-shaped parts, characterized in that, include: Memory, used to store instructions; A processor is configured to read instructions stored in the memory and execute the end face deformation identification method for disc-type parts according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the end-face deformation identification method for disc-type parts as described in any one of claims 1-4.

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