Machining point position deviation compensation method and device, electronic equipment and storage medium

Abstract

The invention provides a machining point position deviation compensation method and device, electronic equipment and a storage medium, and belongs to the technical field of precision machining.The machining point position deviation compensation method comprises the steps that sampling deviation data, corresponding to a rotation angle, of a thin-wall rotary body part is obtained; based on the sampling deviation data, generating a predictive deviation model representing the surface deviation of the thin-wall rotary body part; and through the predictive deviation model, a deviation compensation value corresponding to each to-be-machined point position of the thin-wall rotary body part is determined. And the deviation compensation of the machining point position of the deformed part can be accurately and efficiently realized.

Description

Technical Field

[0001] This disclosure relates to the field of precision machining technology, and in particular to a method, apparatus, electronic device, and storage medium for compensating for machining point deviations. Background Technology

[0002] In the field of precision manufacturing, especially in aerospace and energy, high-precision machining is often required for barrel-shaped thin-walled rotating parts, such as high-density drilling or grooving on the circumference of engine casings or combustion chamber components.

[0003] However, these thin-walled rotating parts typically lack rigidity. During the clamping process before machining, the clamping force of the fixture can easily cause millimeter-level elastic deformation of the part. In addition, uneven surface roughness of the part blank or machining marks from previous processes can also cause deviations between the actual surface contour of the thin-walled rotating part and the theoretical computer-aided design model. If these deviations are ignored and machining is carried out directly according to the theoretical model, it will lead to misalignment of holes and distortion of shape, seriously affecting product quality.

[0004] In related technologies, after a thin-walled rotating part is fixed, a probe is used to contact the workpiece surface point by point according to the theoretical coordinates of preset machining points, and the actual position value is collected through touch feedback. After the actual positions of all machining points have been measured, subsequent machining operations are performed based on these actual position values. However, this point-by-point measurement method is inefficient. For parts with thousands of dense holes, point-by-point probing will consume a lot of auxiliary time. Secondly, since the position of the deformation of the thin-walled rotating part is unknown, there is a risk of the probe hitting the thin-walled rotating part during the measurement process. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, and storage medium for machining point deviation compensation, which can accurately and efficiently achieve deviation compensation for machining points of deformable parts.

[0006] The technical solution disclosed herein is implemented as follows: In a first aspect, this disclosure provides a method for compensating for machining point deviations, which includes: acquiring sampled deviation data of a thin-walled rotating part corresponding to a rotation angle; generating a predictive deviation model characterizing the surface deviation of the thin-walled rotating part based on the sampled deviation data; and determining the deviation compensation value corresponding to each machining point of the thin-walled rotating part through the predictive deviation model.

[0007] Secondly, this disclosure provides a machining point deviation compensation device, which includes: an acquisition part, a generation part, and a determination part; the acquisition part is configured to acquire sampled deviation data of a thin-walled rotating part corresponding to a rotation angle; the generation part is configured to generate a predictive deviation model characterizing the surface deviation of the thin-walled rotating part based on the sampled deviation data; and the determination part is configured to determine the deviation compensation value corresponding to each machining point of the thin-walled rotating part through the predictive deviation model.

[0008] Thirdly, this disclosure provides an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the machining point deviation compensation method as described in the first aspect.

[0009] Fourthly, this disclosure provides a computer-readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the machining point deviation compensation method as described in the first aspect.

[0010] Fifthly, this disclosure provides a computer program product, wherein the computer program product includes a computer program or instructions, which, when run on a processor, cause the processor to execute the computer program or instructions to implement the steps of the machining point deviation compensation method as described in the first aspect.

[0011] In a sixth aspect, this disclosure provides a chip including a processor and a communication interface coupled to the processor, the processor being used to run programs or instructions to implement the machining point deviation compensation method as described in the first aspect.

[0012] This disclosure provides a method for compensating for machining point deviations. By acquiring a set of sampled deviation data, a predictive deviation model representing the shape of a thin-walled rotating part in its current state is generated. Then, the deviation compensation value for any machining point is derived through the predictive deviation model. This decouples measurement and machining, allowing the set of measurement points to be different from the set of machining points, thereby fundamentally solving the efficiency problem of the point-by-point paradigm when facing dense machining point arrays. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the hardware environment of a processing system provided in this disclosure.

[0014] Figure 2 This is a flowchart illustrating a method for compensating for deviations at processing points, as provided in this disclosure.

[0015] Figure 3This is a schematic diagram of the optical path of the data acquisition device provided in this disclosure to the wall surface of the thin-walled rotating part.

[0016] Figure 4 This is a schematic diagram of the sampling deviation data and corresponding rotation angles provided in this disclosure.

[0017] Figure 5 This is a flowchart illustrating another processing point deviation compensation method provided in this disclosure.

[0018] Figure 6 This is a flowchart illustrating the processing point deviation compensation method under collaborative control provided in this disclosure.

[0019] Figure 7 This is a schematic diagram illustrating the filtering process of initial deviation data provided in this disclosure.

[0020] Figure 8 A schematic diagram of the process for obtaining a predictive bias model through curve fitting provided in this disclosure.

[0021] Figure 9 A schematic diagram of the fitted curve provided in this disclosure.

[0022] Figure 10 This is a flowchart illustrating another processing point deviation compensation method provided in this disclosure.

[0023] Figure 11 This is a structural block diagram of a processing point deviation compensation device provided in this disclosure.

[0024] Figure 12 This is a schematic diagram of the hardware structure of an electronic device provided in this disclosure. Detailed Implementation

[0025] The technical solutions in the embodiments of this disclosure will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure are within the scope of protection of this disclosure.

[0026] See Figure 1 , Figure 1 This is a schematic diagram of the hardware environment of a machining system provided in an embodiment of this disclosure. The machining point deviation compensation method provided in this disclosure can be run on various CNC machine tools that require precision machining of thin-walled rotating parts, and is particularly suitable for multi-axis linkage CNC machining centers, such as six-axis CNC machining centers.

[0027] Figure 1The six-axis CNC machine tool 100 shown can be a laser drilling or laser cutting device. The six-axis CNC machine tool 100 includes a machine tool control system (not shown in the figure). The six-axis CNC machine tool 100 has six or more controllable motion axes, for example, three linear axes (X-axis, Y-axis, Z-axis) and three rotary axes, namely rotary axes B, C, and A in the figure.

[0028] The six-axis CNC machining center 100 also includes a rotary axis 101 for clamping a workpiece, referred to as axis A in the figure, which is rotatable about its central axis. A thin-walled rotating part 102 is clamped on the rotary axis 101 by a fixture, and the actual contour deformation of the thin-walled rotating part 102 may occur due to the clamping force.

[0029] The six-axis CNC machining center 100 also includes a tool axis 104. In this embodiment, a laser (not shown) can be mounted on the tool axis 104 for performing laser drilling and other processing on the thin-walled rotating part 102.

[0030] According to an embodiment of this disclosure, a data acquisition device 103 is rigidly fixed (or mounted) to a tool shaft 104. The data acquisition device 103 moves together with the tool shaft 104. The measurement axis of the data acquisition device 103 (e.g., the laser beam it emits) points towards the surface of the thin-walled rotating part 102.

[0031] Specifically, the data acquisition device 103 can be a laser rangefinder, such as a laser rangefinder based on the principle of laser triangulation. The principle is as follows: a laser emitter projects a beam of light at a specific angle onto the thin-walled rotating part 102. The reflected light is focused by a lens group onto a CCD or CMOS sensor to form a light spot. Based on the geometric trigonometric relationship between the light spot displacement, the baseline distance, and the lens focal length, the distance or displacement of the object is accurately calculated. This type of sensor is non-contact, avoiding the collision risk associated with contact-based sensors in related technologies.

[0032] The machine tool control system is electrically connected and communicates with all components of the machine tool, including the X-axis, Y-axis, Z-axis, A-axis, B-axis, and C-axis rotary axes, as well as the data acquisition device 103 and the laser (not shown), via bus, input / output interfaces, network communication, and serial communication. The machine tool control system can precisely control the rotational speed of the rotary axis 101 and simultaneously read the measurement data from the data acquisition device 103 at high frequency.

[0033] The machine tool control system 105 integrates the machining point deviation compensation function described in this disclosure. This function is executed after the thin-walled rotary part 102 is clamped and fixed, but before the formal machining begins. First, the thin-walled rotary part 102 is controlled to move along the A rotation axis. At the same time, the data acquisition device 103 rapidly acquires the circumferential deviation data of the thin-walled rotary part 102 at a set frequency to establish a predictive deviation model of the thin-walled rotary part 102 in the current clamping state. Then, based on the predictive deviation model, the deviation values ​​of all points to be machined are calculated. The coordinates of the theoretical points to be machined are combined with the corresponding deviation values ​​and the actual machining compensation coordinates are calculated through a spatial decomposition algorithm. Finally, the laser processing head is controlled to perform high-precision machining using the actual machining coordinates.

[0034] The execution subject of the machining point deviation compensation method provided in this disclosure can be the aforementioned machine tool control system. The following, in conjunction with the accompanying drawings, provides a detailed description of the specific embodiments and the machining point deviation compensation method provided in this disclosure.

[0035] like Figure 2 As shown, this disclosure provides a method for compensating for machining point deviations, which may include the following steps S201 to S203.

[0036] In step S201, sampling deviation data corresponding to the rotation angle of the thin-walled rotating part are obtained.

[0037] To ensure accurate sampling deviation data acquisition, before data acquisition begins, the thin-walled rotary part 102 needs to be clamped onto the machine tool's rotary axis 101, ensuring that the rotation center of the thin-walled rotary part 102 is concentric with the center point of the machine tool's rotary axis 101. The machine tool control system 105 moves each joint to the theoretical starting position of the measurement point on the thin-walled rotary part 102. To ensure that the data acquisition device 103 can accurately measure the normal deviation compensation value, its measurement optical path (axis) needs to be as perpendicular as possible to the wall surface of the thin-walled rotary part 102.

[0038] Therefore, in this embodiment, the B-axis or C-axis of the machine tool is controlled to rotate, so that the optical path of the data acquisition device 103 is nearly perpendicular to the wall surface of the thin-walled rotating part 102. Specifically, this perpendicularity can be ensured by activating the Rotated Tool Center Point (RTCP) based on the theoretical starting position. This ensures that the position of the linear axis is automatically compensated when adjusting the posture of the B-axis or C-axis, so that the measurement point of the data acquisition device 103 is always accurately maintained at the starting position in the workpiece coordinate system. After activating RTCP, the B-axis or C-axis is controlled to swing at this theoretical starting position until the optical path of the data acquisition device 103 is nearly perpendicular to the wall surface of the thin-walled rotating part 102. This near-perpendicularity refers to an allowable tolerance range, for example, an allowable angle range of 5°. Under this alignment posture, the value measured by the data acquisition device 103 is the sampling deviation data of the initial position at this starting position. Figure 3 The figure shows a schematic diagram of the optical path of the data acquisition device 103 to the wall of the thin-walled rotating part 102. The solid line in the figure shows the optical path perpendicular to the wall of the thin-walled rotating part 102, and the dashed line shows the optical path, indicating that a deviation of 5° is allowed.

[0039] As the thin-walled rotary part 102 rotates along the A-axis, the data acquisition device 103 collects a series of deformation deviation values ​​at multiple rotation angles along the circumferential path of the thin-walled rotary part 102, forming a set of discrete sampling deviation data, with one sampling deviation data corresponding to one rotation angle. Each sampling deviation data refers to the difference between the theoretical surface position of the point to be processed generated by the circumferential array of the thin-walled rotary part 102 (in the current clamping and deformation state) and the actual surface position at the point to be processed due to deformation.

[0040] like Figure 4 As shown, solid dots represent sampling points, i.e., the deviation value of the sampling point is collected to obtain the corresponding sampling deviation data. The rotation angle corresponding to each sampling deviation data is the angle of rotation of the A rotation axis when the position of the data acquisition device 103 is fixed. For example, if sampling point a is the first sampling point, the data acquisition device 103 collects the first sampling deviation data, and the corresponding rotation angle is 0 degrees. The A rotation axis rotates clockwise as shown by the arrow. To collect the sampling deviation data of sampling point b, the A rotation axis needs to rotate ∠1 degrees, that is, the rotation angle corresponding to the sampling deviation data of sampling point b is ∠1 degrees.

[0041] In step S202, a predictive deviation model characterizing the surface deviation of the thin-walled rotating part is generated based on the sampled deviation data.

[0042] The collected sampling deviation data is processed to generate a continuous mathematical model representing the actual circumferential deformation profile of the thin-walled rotating part 102 in the current clamping state. This continuous mathematical model is the predictive deviation model. The sampling deviation data is finite, while the continuous mathematical model is infinite. This predictive deviation model can describe the deviation compensation value at any point on the circumference, even data points not included in the sampling deviation data.

[0043] In step S203, the deviation compensation value corresponding to each processing point of the thin-walled rotating part is determined by the predictive deviation model.

[0044] Multiple processing points refer to the positions of processing equipment (such as a laser) that performs processing on the thin-walled rotating body part 102. For example, if it is necessary to process a hole including N holes on the thin-walled rotating body part 102, the theoretical positions of the processing points corresponding to the N holes are determined. The deviation compensation value is to compensate for the theoretical positions so that the holes processed by the processing equipment at the compensated positions are at the desired positions of the thin-walled rotating body part 102.

[0045] For each point to be processed, the coordinates representing that point are used as input to the predictive bias model, and the output is the bias compensation value corresponding to that point.

[0046] This embodiment of the disclosure obtains a set of sampling deviation data to generate a predictive deviation model representing the shape of a thin-walled rotating part in its current state. Then, the deviation compensation value of any point to be processed is derived through the predictive deviation model, decoupling measurement and processing, allowing the set of measurement points to be different from the set of processing points, thereby fundamentally solving the efficiency problem of the point-by-point paradigm when facing a dense array of processing points.

[0047] In some embodiments, such as Figure 5 As shown, the machining point deviation compensation method may include the following steps S501 to S503.

[0048] In step S501, during the continuous rotation of the thin-walled rotating part, sampling deviation data at different rotation angles are acquired.

[0049] Continuous rotation refers to the rotary shaft 101 rotating continuously at a set, non-zero angular velocity, such as rotating 360 degrees in one revolution, driving the thin-walled rotary part 102 to move continuously, completing all data acquisition in one go. Compared to the point-by-point measurement mode of rotating, stopping, measuring, rotating again, stopping again, and measuring again, its efficiency is limited by the start-stop acceleration and deceleration time and the single-point measurement time. For example, if 1000 holes need to be made in a 1000-inch thin-walled rotary part 102, the corresponding required time is the product of the time for rotating, stopping, and measuring the deviation of one machining point and 1000. Compared to the point-by-point measurement method, the embodiments of this disclosure replace a large number of point-by-point measurements with a single scan modeling, which is extremely efficient.

[0050] In step S502, a predictive deviation model characterizing the surface deviation of the thin-walled rotating part is generated based on the sampled deviation data.

[0051] In step S503, the deviation compensation value corresponding to each processing point of the thin-walled rotating part is determined by the predictive deviation model.

[0052] In some embodiments, such as Figure 6 As shown, the machining point deviation compensation method may include the following steps S601 to S605.

[0053] In step S601, the sampling frequency is determined based on the rotational speed of the thin-walled rotating part and the required number of sampling points.

[0054] In step S602, during the continuous rotation of the thin-walled rotating part, multiple initial deviation data are collected according to the sampling frequency.

[0055] While the A-axis rotates continuously, the data acquisition device 103 is controlled to synchronously acquire the deformation deviation value at a acquisition period T. The acquisition period T is the time interval between two measurements by the data acquisition device 103, T=20 milliseconds, that is, the acquisition frequency f=20000Hz.

[0056] In this process, the rotational speed V of the A-axis and the acquisition period T are matched together to obtain the initial deviation data of the required number of sampling points M.

[0057] Specifically, before scanning begins, the machine tool control system estimates the minimum number of sampling points M required to ensure fitting accuracy, based on processing requirements, such as the number of holes N to be processed. The specific conversion relationship is: M = 360 / (T × V), where 360 ​​represents one full rotation. In practice, if one full rotation is not required, it can be set according to actual needs, such as 180 representing half a rotation, etc. The unit of T is milliseconds, and the unit of V is degrees per millisecond.

[0058] In this embodiment, the machine tool control system ensures that the collected initial deviation data is sufficiently dense to meet accuracy requirements, based on the estimated number of sampling points M required for fitting, the rotational speed V that the machine tool can achieve in its actual stable state, and the minimum acquisition period T of the data acquisition device 103. Through this coordinated control of continuous rotation and synchronous acquisition, a series of initial deviation data is efficiently acquired.

[0059] For example, as shown in Table 1, there are several initial deviation data points corresponding to rotation angles that were collected. The rotation angle Aj is in degrees, and the initial deviation data Δj is in millimeters.

[0060] Table 1 Initial deviation data corresponding to multiple rotation angles

[0061] In step S603, multiple initial deviation data are filtered to obtain sampled deviation data.

[0062] Since the data acquisition device 103 is usually a laser rangefinder, the uneven surface roughness, oil stains, or differences in reflective properties of the thin-walled rotating part 102 can cause the laser rangefinder readings to produce outliers at certain angles, i.e., abnormal values ​​that are far from the true contour.

[0063] like Figure 7 As shown, the horizontal axis represents the rotation angle in degrees, and the vertical axis represents the deviation value in mm, which is the value corresponding to each initial deviation data. The hollow circles in the figure are the collected initial deviation data. It can be seen from the figure that points c and d in the initial deviation data deviate significantly from the overall trend. Therefore, they are outliers. Points c and d are corrected by filtering to obtain the corresponding sampling deviation data. The solid circles in the figure represent points e and f.

[0064] In some feasible approaches, outliers are corrected by replacing the value of any point among multiple initial bias data with the median of the initial bias data within its neighborhood window, thus obtaining the sampled bias data. More specifically, for a discrete initial bias data x[n], a sliding window of length L=2k+1 is used for filtering, where k is a positive integer (e.g., k=1, 2, 3, etc.) and L is the window size (e.g., 3 points, 5 points, 7 points, etc.), and its output... Wherein, median represents the median operator. This filtering method is simple and efficient, and can effectively filter out outliers in the initial deviation data. The filtering algorithm of this disclosure is only an example and is not intended to limit this disclosure. Any other algorithm that can process outliers into normal values ​​that conform to the overall trend, such as mean filtering, Gaussian filtering, etc., are all within the protection scope of this disclosure.

[0065] Compared to point-by-point measurement, measurement errors caused by laser refraction due to workpiece surface roughness cannot be effectively corrected because each point in point-by-point measurement is a processing point (e.g., 100 processing points), with a large relative interval between points and limited numerical reference value between adjacent processing points. In contrast, this disclosure uses high-frequency continuous scanning, resulting in a large number of initial deviation data points (e.g., 1000). Adjacent initial deviation data points, corresponding to processing points with small distances, exhibit strong correlations. Therefore, outliers can be corrected using initial deviation data within the neighborhood, effectively reducing measurement errors caused by laser refraction due to workpiece surface roughness. Furthermore, compared to directly using initial deviation data containing outliers for fitting, which leads to a decrease in the accuracy of the generated predictive deviation model, this disclosure uses filtered sampled deviation data for fitting, effectively improving the prediction accuracy of the predictive deviation model.

[0066] In step S604, a predictive deviation model characterizing the surface deviation of the thin-walled rotating part is generated based on the sampling deviation data.

[0067] In step S605, the deviation compensation value corresponding to each processing point of the thin-walled rotating part is determined by the predictive deviation model.

[0068] For the obtained discrete sampling deviation data, the predictive deviation model can be an interpolation function. That is, if the point to be processed is in the discrete sampling deviation data, the deviation compensation value corresponding to that sampling deviation data is determined as the deviation compensation value for that point. If the point to be processed is not in the discrete deviation data, the deviation compensation value corresponding to that point is determined by linear interpolation based on multiple discrete deviation data adjacent to it. However, this method has relatively low accuracy. Therefore, in some embodiments, such as... Figure 8 As shown, the machining point deviation compensation method may include the following steps S801 to S803.

[0069] In step S801, sampling deviation data corresponding to the rotation angle of the thin-walled rotating part are obtained.

[0070] In step S802, the sampling deviation data is curve-fitted to obtain a predictive deviation model.

[0071] The discrete sampling deviation data is converted into a continuous mathematical model, namely a predictive deviation model, through fitting. The specific curve fitting method is not limited in this disclosure. The fitted predictive deviation model can represent the actual circumferential deformation profile of the thin-walled rotating part 102. This allows for the accurate acquisition of corresponding deviation compensation values ​​at any point to be processed.

[0072] In some feasible approaches, cubic spline curve fitting is performed on the sampling bias data to obtain a predictive bias model. Specifically, This indicates the obtained rotation angle. Corresponding deviation compensation value The mathematical expression of the predictive bias model obtained by fitting is as follows: ,in Indicates the rotation angle The deviation compensation value corresponding to the processing point. i For the first i At each processing point , , , , .

[0073] For example, combined Figure 7 ,like Figure 9 As shown, the curve obtained by fitting a cubic spline curve is the mathematical expression of the predictive bias model.

[0074] Instead of using a single high-order polynomial to fit all the data (a single high-order polynomial may cause violent oscillations between data points), cubic spline curve fitting uses a piecewise function approach to ensure that a complete and continuous predictive bias model is obtained. This predictive bias model accurately and smoothly represents the actual circumferential deformation profile of the thin-walled rotating part 102 in the current clamping state.

[0075] In step S803, the deviation compensation value corresponding to each processing point of the thin-walled rotating part is determined by the predictive deviation model.

[0076] In some embodiments, such as Figure 10 As shown, the machining point deviation compensation method may include the following steps S1001 to S1004.

[0077] In step S1001, sampling deviation data corresponding to the rotation angle of the thin-walled rotating part are obtained.

[0078] In step S1002, a predictive deviation model characterizing the surface deviation of a thin-walled rotating part is generated based on the sampled deviation data.

[0079] In step S1003, the deviation compensation value corresponding to each processing point of the thin-walled rotating part is determined by the predictive deviation model.

[0080] In step S1004, the deviation compensation value is spatially decomposed in the three-dimensional machining coordinate system to obtain the compensated coordinates of the point to be processed in the three-dimensional machining coordinate system.

[0081] After obtaining the continuous predictive deviation model, this model represents the actual deformation profile of the thin-walled rotating part 102. At this point, the compensated coordinates of all machining points can be calculated using this predictive deviation model, which are the final actual machining coordinates.

[0082] Specifically, the theoretical machining coordinates of the undeformed machining points of the thin-walled rotating part 102 are known in advance. For example, N laser holes need to be machined, and the machining angle corresponding to each machining point is Aj (j=1, 2...N). These machining angles Aj are read from memory and input into the predictive deviation model to obtain the deviation value corresponding to each machining angle Aj. This step does not require the data acquisition device 103 to perform measurements again; it is purely mathematical calculation, thus making it extremely efficient.

[0083] However, the deviation value corresponding to each processing angle Aj is obtained. It's one-dimensional, and this deviation value needs to be correctly compensated for to the three-dimensional coordinates of that point. First, obtain the coordinate information and deviation compensation amount of each point to be processed in the processing coordinate system before compensation, such as:

[0084] in, It is the deviation in the measurement direction (i.e., the direction close to the vertical wall) of the data acquisition device 103, which is determined by the coordinate system of the six-axis machine tool. and Commonly defined It measures the angles of the B and C rotary axes of the machine tool during measurement. This represents the theoretical displacement of the point to be processed in the X, Y, and Z axes. It refers to the machining angles of the B and C rotary axes during machining.

[0085] Theoretical coordinates of the processing point ( Spatial decomposition and compensation are performed to obtain the compensated coordinates of the final processing point, i.e., the actual processing coordinates. Specifically, it is obtained through the following conversion formula: ; ; ; The coordinates are the actual displacements of the point to be processed in the X, Y, and Z axes, i.e., the compensated coordinates.

[0086] For example, Table 2 shows the theoretical machining coordinate information before partial compensation, and Table 3 shows the actual machining coordinate information after compensation.

[0087] Table 2 Theoretical machining coordinate information before compensation

[0088] Table 3. Actual machining coordinate information after compensation

[0089] Wherein, the X, Y, and Z coordinates represent the displacement in the X, Y, and Z axis directions, in mm; the B and C coordinates represent the rotation degrees of the B and C rotation axes; and the A coordinate represents the rotation degree of the A rotation axis, all in °.

[0090] This disclosure provides a specific implementation of applying the one-dimensional deviation compensation value output by the predictive deviation model to the actual six-dimensional machining coordinate system. Through spatial decomposition compensation, it is ensured that the compensation amount is accurately applied in the correct direction, thereby guaranteeing the positioning accuracy of the final machining point.

[0091] This disclosure significantly improves efficiency through a single scan and modeling prediction approach. First, by synchronously and rapidly acquiring sampling deviation data during the rotation of the thin-walled rotating part, hundreds or even thousands of sampling deviation data points characterizing the entire circumferential deformation profile can be obtained in a very short time to complete one rotation. This efficiency is significantly higher than the traditional method of moving, measuring, and stopping point by point. Second, by filtering and fitting the massive amount of collected sampling deviation data, this disclosure generates a continuous predictive deviation model. This not only effectively overcomes the problem of laser ranging being susceptible to errors caused by surface roughness, thus improving accuracy, but also allows the generated predictive deviation model to predict the deviation compensation value at any angle on the circumference, even if that angle is not directly measured. Finally, based on this predictive deviation model, this disclosure can calculate the compensation coordinates of all points to be processed in one go. Therefore, this disclosure, while ensuring high-precision positioning, greatly improves the adaptive positioning efficiency of dense array processing, effectively overcoming the shortcomings of existing technologies.

[0092] Figure 11 This is a structural block diagram of a machining point deviation compensation device disclosed herein, such as... Figure 11As shown, it includes: an acquisition part 1101, a generation part 1102, and a determination part 1103; the acquisition part 1101 is configured to acquire sampling deviation data of the thin-walled rotating part corresponding to the rotation angle; the generation part 1102 is configured to generate a predictive deviation model characterizing the surface deviation of the thin-walled rotating part based on the sampling deviation data; the determination part 1103 is configured to determine the deviation compensation value corresponding to each processing point of the thin-walled rotating part through the predictive deviation model.

[0093] In some embodiments, the acquisition portion 1101 is configured to acquire sampling deviation data at different rotation angles during the continuous rotation of the thin-walled rotating part.

[0094] In some embodiments, the determining part 1103 is configured to determine the sampling frequency based on the rotational speed of the thin-walled rotating part and the required number of sampling points; the acquiring part 1101 is configured to acquire multiple initial deviation data according to the sampling frequency during the continuous rotation of the thin-walled rotating part; and to filter the multiple initial deviation data to obtain the sampling deviation data.

[0095] In some embodiments, the acquisition portion 1101 is configured to replace the value of any point among a plurality of initial deviation data with the median of the initial deviation data within its neighborhood window to obtain sampled deviation data.

[0096] In some embodiments, the generation section 1102 is configured to perform curve fitting on the sampled bias data to obtain a predictive bias model.

[0097] In some embodiments, curve fitting includes cubic spline curve fitting.

[0098] In some embodiments, the machining point deviation compensation device further includes a spatial decomposition part, which is configured to spatially decompose the deviation compensation value in a three-dimensional machining coordinate system to obtain the compensated coordinates of the point to be processed in the three-dimensional machining coordinate system.

[0099] In this embodiment, each part can implement the processing point deviation compensation method provided in the above method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0100] Please refer to Figure 12This illustration shows a schematic diagram of the hardware structure of an electronic device provided in an exemplary embodiment of this disclosure. In some examples, the electronic device may be at least one of devices such as a smartphone, smartwatch, desktop computer, laptop, virtual reality terminal, augmented reality terminal, wireless terminal, and laptop computer. The electronic device has communication capabilities and can access wired or wireless networks. The term "electronic device" can refer to one of multiple terminals; those skilled in the art will understand that the number of such terminals may be more or less.

[0101] like Figure 12 As shown, the electronic device in this disclosure may include one or more of the following components: processor 1210 and memory 1220.

[0102] Optionally, the processor 1210 connects various parts within the electronic device using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1220, and by calling data stored in the memory 1220. Optionally, the processor 1210 can be implemented using at least one hardware form of Digital Signal Processing (DSP), Field Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1210 can integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Neural-network Processing Unit (NPU), and baseband chip. Specifically, the CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display on the touch screen; the NPU is used to implement Artificial Intelligence (AI) functions; and the baseband chip is used for wireless communication. It is understandable that the aforementioned baseband chip may not be integrated into the processor 1210, but may be implemented using a separate chip.

[0103] The memory 1220 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 1220 may include a non-transitory computer-readable storage medium. The memory 1220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 1220 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data created based on the use of the electronic device, etc.

[0104] In addition, those skilled in the art will understand that the structure of the electronic device shown in the above figures does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the electronic device may also include a display screen, camera assembly, microphone, speaker, radio frequency circuit, input unit, sensors (such as accelerometer, angular velocity sensor, light sensor, etc.), audio circuit, Wi-Fi module, power supply, Bluetooth module, etc., which will not be described in detail here.

[0105] This disclosure also provides a computer-readable storage medium storing at least one instruction that is executed by a processor to implement the machining point deviation compensation method as described in the above embodiments.

[0106] This disclosure also provides a computer program product including computer instructions stored in a computer-readable storage medium; a processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the processing point deviation compensation method described in the above embodiments.

[0107] This disclosure also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described processing point deviation compensation method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0108] It should be understood that the chip mentioned in the embodiments of this disclosure may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0109] In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, apparatuses, servers, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0113] Those skilled in the art will recognize that the functions described in this disclosure in one or more of the examples above can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0114] It should be noted that the technical solutions described in this disclosure can be combined arbitrarily as long as they do not conflict.

[0115] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for compensating for machining point deviations, characterized in that, The method for compensating for machining point deviations includes: Obtain sampling deviation data corresponding to the rotation angle of the thin-walled rotating part; Based on the sampling deviation data, a predictive deviation model characterizing the surface deviation of the thin-walled rotating body part is generated; The deviation compensation value corresponding to each processing point of the thin-walled rotating part is determined by the predictive deviation model.

2. The processing point deviation compensation method according to claim 1, characterized in that, The acquisition of sampling deviation data corresponding to the rotation angle of the thin-walled rotating part includes: During the continuous rotation of the thin-walled rotating part, the sampling deviation data at different rotation angles are acquired.

3. The processing point deviation compensation method according to claim 2, characterized in that, The method for compensating for deviations at processing points further includes: The sampling frequency is determined based on the rotational speed of the thin-walled rotating part and the required number of sampling points; The acquisition of sampling deviation data corresponding to the rotation angle of the thin-walled rotating part includes: During the continuous rotation of the thin-walled rotating part, multiple initial deviation data are collected according to the sampling frequency. The sampled deviation data is obtained by filtering the multiple initial deviation data.

4. The processing point deviation compensation method according to claim 3, characterized in that, The step of filtering the plurality of initial deviation data to obtain the sampled deviation data includes: The value of any point among the plurality of initial deviation data is replaced with the median of the initial deviation data within its neighborhood window to obtain the sampling deviation data.

5. The machining point deviation compensation method according to any one of claims 1 to 4, characterized in that, The step of generating a predictive deviation model characterizing the surface deviation of the thin-walled rotating body part based on the sampled deviation data includes: The sampling deviation data is subjected to curve fitting to obtain the predictive deviation model.

6. The processing point deviation compensation method according to claim 5, characterized in that, The curve fitting includes cubic spline curve fitting.

7. The processing point deviation compensation method according to claim 1, characterized in that, The method for compensating for deviations at processing points further includes: In the three-dimensional machining coordinate system, the deviation compensation value is spatially decomposed to obtain the compensated coordinates of the point to be processed in the three-dimensional machining coordinate system.

8. A processing point deviation compensation device, characterized in that, The processing point deviation compensation device includes: an acquisition part, a generation part, and a determination part; The acquisition section is configured to acquire sampling deviation data corresponding to the rotation angle of the thin-walled rotating part; The generation section is configured to generate a predictive deviation model characterizing the surface deviation of the thin-walled rotating body part based on the sampled deviation data. The determining part is configured to determine the deviation compensation value corresponding to each processing point of the thin-walled rotating part through the predictive deviation model.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the machining point deviation compensation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the machining point deviation compensation method as described in any one of claims 1 to 7.

Images