Crystal bar precise machining system and machining method applied to grinding and polishing all-in-one machine

Abstract

The invention provides a crystal bar precise machining method and system applied to a grinding and polishing all-in-one machine, which are used for solving the problem of inaccurate machining in business scenes such as complex initial geometric state, non-uniform distribution of size deviation and insufficient dynamic adjustment in the machining process in the crystal bar machining process. Determining an initial geometric state and deviation distribution according to the initial size of the crystal bar and the characteristics of the material of the crystal bar; according to an initial geometric state, dimensional deviation distribution and the like, a corresponding dynamic adjustment scheme is determined, so that efficient and integrated precise machining of the crystal bar is realized, the machining of the crystal bar has the characteristics of high efficiency, high precision, high machining uniformity and the like, and the product quality of the crystal bar and the production efficiency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of crystal rod processing technology, and in particular to a precision crystal rod processing system and method applied to an integrated grinding and polishing machine. Background Technology

[0002] Ingot processing is a crucial step in semiconductor manufacturing, and its precision directly impacts chip performance and yield. As semiconductor device dimensions continue to shrink, the requirements for ingot processing precision are becoming increasingly stringent; even the slightest deviation can lead to product scrap or performance degradation. Currently, ingot processing technology faces significant challenges in terms of dimensional control and processing adaptability.

[0003] Existing methods largely rely on static parameter settings and manual intervention, making it difficult to address dynamic deviations caused by material properties and dimensional changes during crystal ingot processing. This statically-based approach often fails to adapt to the actual state of the crystal ingot in real time, leading to unstable processing results, especially in high-precision scenarios where the mismatch between dimensional deviations and processing modes is particularly prominent. Real-time monitoring and adjustment of dimensional deviations is one of the core technical challenges in crystal ingot processing. Due to the influence of grinding and polishing forces, material properties, and other factors during processing, the dimensions of the crystal ingot may change non-uniformly. For example, if initial dimensional deviations are not detected in time during high-speed rotation processing, subsequent grinding and polishing parameters may not match actual requirements, resulting in uneven processed surfaces or dimensional errors. Furthermore, precise control of the rotation angle directly affects processing uniformity. If the rotation angle is not dynamically compensated for based on dimensional deviations, the processing equipment cannot adapt to the actual geometry of the crystal ingot, leading to decreased processing accuracy. For instance, when processing a crystal ingot with a slightly off-diameter shape, if the rotation angle is not adjusted in time, the grinding and polishing head may over-process some areas while under-processing others, ultimately resulting in distorted crystal ingot shape.

[0004] Therefore, how to monitor the crystal rod size deviation in real time and dynamically adjust the rotation angle and polishing parameters to ensure processing accuracy and adaptability has become a key issue in the precise processing of crystal rods. Summary of the Invention

[0005] This invention provides a precision crystal rod processing system and method for use in an integrated grinding and polishing machine, which solves the problems of complex initial geometry, uneven dimensional deviation distribution, and insufficient dynamic adjustment during crystal rod processing that affect the accuracy of crystal rod prices.

[0006] This invention provides a method for precise processing of crystal rods applied to an integrated grinding and polishing machine, comprising:

[0007] The initial size and material properties of the crystal rod are acquired, and multi-point measurements are performed on the crystal rod. Combined with a pre-established material properties database, the initial geometric state and size deviation distribution of the crystal rod are determined. If the size deviation distribution exceeds a preset threshold, the size deviation distribution is classified to obtain the deviation region distribution and deviation degree classification of the crystal rod.

[0008] Based on the distribution of the deviation area and the classification of the deviation degree, the rotation angle compensation value is calculated, and the dynamic rotation angle adjustment scheme required during the processing is determined through the preset geometric state compensation model.

[0009] The dynamic rotation angle adjustment scheme is obtained, and the rotation angle of the processing equipment is adjusted. The grinding and polishing parameter configuration is updated in stages based on the degree of deviation, and the optimized grinding and polishing force and speed settings are obtained.

[0010] The dimensional changes and surface flatness data of the crystal rod are monitored in real time during the processing, and the monitoring data during the processing is obtained. If the monitoring data deviates from the preset processing uniformity threshold, the deviation data in the monitoring data is analyzed, and the grinding and polishing parameters and the rotation angle are dynamically adjusted to obtain a stable processing accuracy output.

[0011] Preferably, the crystal rod is measured at multiple points using a laser scanning device to obtain the three-dimensional coordinate data of each point on the surface of the crystal rod; first point cloud data is generated based on a point cloud processing algorithm to determine the preliminary geometric contour of the crystal rod; if the point density of the first point cloud data is lower than a preset threshold, the internal structure data of the crystal rod is obtained using an ultrasonic testing device, ultrasonic wave reflection features are extracted, and the internal size data of the crystal rod is obtained.

[0012] The first point cloud data and the internal size data are registered and integrated based on the data fusion algorithm to generate the second point cloud data, and the complete geometric state of the crystal rod is determined accordingly.

[0013] Based on a preset material property database, standard size parameters corresponding to the crystal rod material are matched, and error analysis methods are used to compare the second point cloud data with the standard parameters to obtain the size deviation distribution of the crystal rod.

[0014] Preferably, the three-dimensional coordinate data of the crystal rod surface is obtained, and the first point cloud data is generated by the point cloud processing algorithm to determine the preliminary geometric contour of the crystal rod surface;

[0015] If the point density of the first point cloud data is lower than a preset threshold, the internal structure data of the crystal rod is obtained by ultrasonic testing equipment, ultrasonic reflection features are extracted, and the internal size data of the crystal rod is obtained. Based on the first point cloud data and the internal size data, a data fusion algorithm is used for registration and integration to generate the second point cloud data and determine the complete geometric state of the crystal rod.

[0016] If the deviation between the second point cloud data and the standard size parameters in the material property database exceeds a preset threshold, the support vector machine algorithm is used to classify the deviation data to obtain the distribution of the deviation region and the degree of deviation of the crystal rod.

[0017] Preferably, deviation region distribution data is obtained from the sensors of the processing equipment, and the deviation region distribution data is segmented using an image processing algorithm to obtain the boundary coordinate set of the deviation region; if the number of points in the boundary coordinate set exceeds a preset threshold, the geometry of the deviation region is fitted by the least squares method to determine the coordinates of the center point of the deviation region.

[0018] Based on the coordinates of the center point of the deviation region, the k-means clustering algorithm is used to classify the degree of deviation and obtain the deviation level value; if the maximum value of the deviation level value exceeds the preset deviation threshold, the deviation level value is quantified to determine the quantification result of the degree of deviation classification.

[0019] The rotation angle compensation value is calculated based on the quantification results of the aforementioned deviation degree classification and the coordinate transformation matrix.

[0020] Based on the rotation angle compensation value and the preset geometric state compensation model, the machining path planning is adjusted through spatial geometric relationships to generate a dynamic rotation angle adjustment scheme; if the deviation value of the machining path planning is lower than the preset threshold, the machining equipment control command is updated by adjusting parameters in real time to determine the dynamic rotation angle adjustment scheme.

[0021] Preferably, real-time data of the processing equipment status is acquired, and the real-time data is filtered and feature extracted by the data processing module to obtain the processing deviation value and equipment operating parameters;

[0022] If the machining deviation value exceeds the pre-established deviation threshold setting, the dynamic rotation angle is adjusted according to the degree of deviation, and the updated rotation angle value is obtained after generating control commands based on the real-time control system.

[0023] Based on the deviation level classification and the updated rotation angle value, the data processing module calculates the grinding and polishing force adjustment and speed optimization settings to obtain the optimized grinding and polishing parameter configuration;

[0024] The optimized grinding and polishing parameters are combined with the control commands using a real-time control system to adjust the state of the processing equipment, thereby obtaining an equipment operating state that meets the processing accuracy assessment.

[0025] Preferably, high-precision sensors are used to collect data on dimensional changes and surface flatness during the crystal rod processing, and real-time monitoring data is obtained from the processing and stored in a preset database to obtain the original monitoring dataset;

[0026] The moving average filtering method is used to process the original monitoring dataset and calculate the average value of the data within the time window, where the length of the time window is T, and T represents the sampling time interval, to obtain the smoothed dataset;

[0027] If the size change or surface flatness data in the smoothed dataset exceeds a preset threshold, the K-nearest neighbor algorithm is used to determine the processing anomaly; the deviation of the size change or surface flatness data from the preset standard value is compared to obtain the anomaly detection result;

[0028] Based on the feedback control algorithm, the parameter adjustment amount of the processing equipment is calculated using the anomaly detection results to obtain the updated processing parameters;

[0029] The adjustment amount is determined by the deviation ratio D, where D represents the difference between the abnormal detection result and the standard value.

[0030] Preferably, real-time monitoring data is obtained from the processing equipment, and by comparing it with a preset processing uniformity threshold, it is determined whether the monitoring data deviates from the threshold and a deviation data set is obtained.

[0031] The random forest algorithm is used to analyze the feature distribution of the deviation data set, calculate the feature weight W, where W represents the contribution of each feature to the deviation, determine the adjustment direction of the grinding and polishing parameters and the rotation angle, and obtain the parameter adjustment scheme.

[0032] Dynamically adjust the grinding and polishing parameters and rotation angle of the processing equipment according to the parameter adjustment scheme, obtain the adjusted processing data, and determine whether the processing data meets the stable output accuracy requirements.

[0033] If the processing data does not meet the requirements for stable output accuracy, the adjusted processing data is subjected to feature extraction and secondary analysis through an iterative optimization algorithm to determine new grinding and polishing parameters and rotation angles and obtain stable processing accuracy output.

[0034] This invention also provides a precision crystal rod processing system for use in a grinding and polishing integrated machine, wherein the crystal rod processing is performed by the method described above, including:

[0035] The data acquisition module is used to acquire the initial size and material property data of the crystal rod, and to generate the size deviation distribution of the crystal rod through multi-point measurement;

[0036] The deviation analysis module is used to classify and grade the deviation areas when the size deviation distribution exceeds a preset threshold.

[0037] The compensation calculation module is used to calculate the rotation angle compensation value based on the distribution and grading results of the deviation area, and generate a dynamic rotation angle adjustment scheme.

[0038] The parameter optimization module is used to adjust the rotation angle and grinding / polishing parameter configuration of the processing equipment;

[0039] The real-time monitoring module is used to monitor dimensional changes and surface flatness during the processing.

[0040] The dynamic adjustment module is used to dynamically adjust the grinding and polishing parameters and rotation angle when the monitored data deviates from the processing uniformity threshold, so as to ensure stable output of processing accuracy.

[0041] Preferably, the laser scanning unit is used to acquire three-dimensional coordinate data of the crystal rod surface;

[0042] An ultrasonic testing unit is used to supplement internal structural data when point cloud density is insufficient;

[0043] The data fusion unit is used to integrate surface and internal data to generate a complete geometric state.

[0044] Preferably, the deviation analysis module uses a support vector machine algorithm to classify the deviation data and output the deviation region distribution and deviation degree classification;

[0045] The compensation calculation module classifies the degree of deviation using the k-means clustering algorithm and generates a dynamic rotation angle adjustment scheme in conjunction with the coordinate transformation matrix.

[0046] The real-time monitoring module uses a high-precision sensor to collect data and processes the data using a moving average filtering method.

[0047] The dynamic adjustment module uses the random forest algorithm to analyze deviation characteristics and adjusts parameters through iterative optimization algorithms to achieve stable processing accuracy output.

[0048] The working principle and beneficial effects of this invention are as follows:

[0049] This invention provides a method for precise processing of crystal rods applied to an integrated grinding and polishing machine, comprising: acquiring initial size and material property data of the crystal rod, performing multi-point measurements on the crystal rod, and determining the initial geometric state and size deviation distribution of the crystal rod by combining a pre-established material property database; if the size deviation distribution exceeds a preset threshold, classifying the size deviation distribution to obtain the deviation area distribution and deviation degree classification of the crystal rod; calculating a rotation angle compensation value based on the deviation area distribution and the deviation degree classification, and determining the dynamic rotation angle adjustment scheme required during processing through a preset geometric state compensation model; acquiring the dynamic rotation angle adjustment scheme, adjusting the rotation angle of the processing equipment, updating the grinding and polishing parameter configuration based on the deviation degree classification, and obtaining optimized grinding and polishing force and speed settings; monitoring the size changes and surface flatness data of the crystal rod during processing in real time and obtaining monitoring data during processing; if the monitoring data deviates from a preset processing uniformity threshold, analyzing the deviation data in the monitoring data, dynamically adjusting the grinding and polishing parameters and the rotation angle to obtain stable processing accuracy output.

[0050] This invention addresses the issue of inaccurate processing in crystal ingot manufacturing, stemming from complex initial geometry, uneven dimensional deviations, and insufficient dynamic adjustments during the process. Specifically, it determines the initial geometric state and deviation distribution based on the crystal ingot's initial dimensions and material properties. Based on this initial geometry and dimensional deviation distribution, a corresponding dynamic adjustment scheme is determined, enabling efficient and integrated precision processing of crystal ingots. This results in high efficiency, high precision, and high processing uniformity, significantly improving product quality and production efficiency.

[0051] Furthermore, this invention achieves a high degree of optimization in the processing method of crystal rods for the integrated grinding and polishing machine through the above-mentioned scheme, realizing the precise reconstruction of the initial geometric state and the complex internal structure of the crystal rod. Simultaneously, it employs machine learning algorithms (such as support vector machines) to intelligently identify and quantify the complex deviations, thereby generating a dynamic rotation angle adjustment strategy based on the crystal rod's own geometric state. Furthermore, during the processing, an adaptive control mechanism is constructed, using real-time monitored data as feedback and random forest algorithm as the core for decision-making, thus achieving online real-time optimization of grinding and polishing parameters and angles. This fundamentally and significantly improves the adaptability to individual differences in crystal rods and their dynamic process changes, thereby greatly ensuring the stable output of processing accuracy and significantly improving the surface uniformity of the crystal rod, bringing outstanding technical advantages to crystal rod manufacturing.

[0052] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0053] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0054] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0055] In the attached diagram:

[0056] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0057] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0058] according to Figure 1 As shown, this embodiment of the invention provides a method for precise processing of crystal rods applied to a grinding and polishing integrated machine, including: acquiring initial size and material property data of the crystal rod, performing multi-point measurements on the crystal rod, and determining the initial geometric state and size deviation distribution of the crystal rod by combining a pre-established material property database;

[0059] If the size deviation distribution exceeds a preset threshold, the size deviation distribution is classified to obtain the deviation region distribution and deviation degree classification of the crystal rod;

[0060] Based on the distribution of the deviation area and the classification of the deviation degree, the rotation angle compensation value is calculated, and the dynamic rotation angle adjustment scheme required during the processing is determined through the preset geometric state compensation model.

[0061] The dynamic rotation angle adjustment scheme is obtained, and the rotation angle of the processing equipment is adjusted. The grinding and polishing parameter configuration is updated in stages based on the degree of deviation, and the optimized grinding and polishing force and speed settings are obtained.

[0062] The dimensional changes and surface flatness data of the crystal rod are monitored in real time during the processing, and the monitoring data during the processing is obtained. If the monitoring data deviates from the preset processing uniformity threshold, the deviation data in the monitoring data is analyzed, and the grinding and polishing parameters and the rotation angle are dynamically adjusted to obtain a stable processing accuracy output.

[0063] This invention addresses the issue of inaccurate processing in crystal ingot manufacturing, stemming from complex initial geometry, uneven dimensional deviations, and insufficient dynamic adjustments during the process. By determining the initial geometric state and deviation distribution of the crystal ingot based on its initial dimensions and material properties, and then determining corresponding dynamic adjustment schemes based on these factors, this invention achieves efficient, integrated, and precise crystal ingot processing. This results in high efficiency, high precision, and high processing uniformity, significantly improving product quality and production efficiency.

[0064] More specifically, this invention acquires surface and internal dimensional data of crystal rods through multi-point laser scanning and ultrasonic testing, generates high-precision point cloud data, and combines it with a material property database to accurately determine the initial geometric state and dimensional deviation distribution of the crystal rods. When the deviation exceeds the standard, a support vector machine and k-means clustering algorithm are used to classify the deviation area and degree, calculate the rotation angle compensation value, and generate a dynamic adjustment scheme. This invention ensures processing uniformity and accuracy stability by real-time monitoring of dimensional changes and surface flatness, combined with random forest algorithm to analyze deviation characteristics, dynamically optimize grinding and polishing parameters and rotation angle, achieving high-precision and high-consistency output of crystal rod processing, significantly improving processing efficiency and product quality.

[0065] Furthermore, this invention achieves a high degree of optimization in the processing method of crystal rods for the integrated grinding and polishing machine through the above-mentioned scheme, realizing the precise reconstruction of the initial geometric state and the complex internal structure of the crystal rod. Simultaneously, it employs machine learning algorithms (such as support vector machines) to intelligently identify and quantify the complex deviations, thereby generating a dynamic rotation angle adjustment strategy based on the crystal rod's own geometric state. Furthermore, during the processing, an adaptive control mechanism is constructed, using real-time monitored data as feedback and random forest algorithm as the core for decision-making, thus achieving online real-time optimization of grinding and polishing parameters and angles. This fundamentally and significantly improves the adaptability to individual differences in crystal rods and their dynamic process changes, thereby greatly ensuring the stable output of processing accuracy and significantly improving the surface uniformity of the crystal rod, bringing outstanding technical advantages to crystal rod manufacturing.

[0066] In one embodiment, a laser scanning device is used to perform multi-point measurements on the crystal rod and obtain the three-dimensional coordinate data of each point on the surface of the crystal rod; a first point cloud data is generated based on a point cloud processing algorithm to determine the preliminary geometric contour of the crystal rod; if the point density of the first point cloud data is lower than a preset threshold, an ultrasonic testing device is used to obtain the internal structure data of the crystal rod, extract ultrasonic reflection features, and obtain the internal size data of the crystal rod; a second point cloud data is generated by registering and integrating the first point cloud data and the internal size data based on a data fusion algorithm, and the complete geometric state of the crystal rod is determined accordingly; based on a preset material property database, standard size parameters corresponding to the crystal rod material are matched, and an error analysis method is used to compare the second point cloud data with the standard parameters to obtain the size deviation distribution of the crystal rod.

[0067] In this embodiment, a laser scanning device is used to perform multi-point measurements on the surface of the crystal rod to obtain three-dimensional coordinate data. Further, the three-dimensional coordinate data is processed based on a point cloud registration algorithm to generate first point cloud data and determine the preliminary geometric contour of the crystal rod. If the point density of the first point cloud data is lower than a preset threshold, the crystal rod is scanned by an ultrasonic testing device to obtain its internal structure data and extract ultrasonic wave reflection features to obtain internal size data.

[0068] Next, based on the data fusion algorithm, the first point cloud data and the internal dimension data are registered and integrated to generate the second point cloud data, which is used to determine the complete geometric state of the crystal rod. The standard dimension parameters corresponding to the crystal rod material are matched from the preset material property database, and the dimension deviation distribution is calculated based on the second point cloud data and the standard dimension parameters. The deviation regions of the dimension deviation distribution are classified by the K-means clustering algorithm to obtain the deviation region classification results. The second point cloud data in the larger deviation regions of the deviation region classification results is locally optimized by the interpolation algorithm, and the final geometric state of the crystal rod is determined by generating the optimized point cloud data.

[0069] More specifically, in this embodiment, the laser scanning device is a three-dimensional laser scanner. The three-dimensional laser scanner is used to perform multi-point measurements on the crystal rod to collect three-dimensional coordinate data of each point on the surface of the crystal rod. For example, multiple measurement points are evenly distributed on the cylindrical surface of the crystal rod, and the coordinate format of each point is (x,y,z). This forms a high-precision surface point set data, which serves as the input basis for subsequent processing. The collected data is downsampled, and then the preliminary geometric contour of the crystal rod, the number of iterations, and the threshold are fitted to fit a cylindrical model. Its parameters include the axial direction vector and radius data, thereby determining the preliminary geometric contour of the crystal rod as an initial estimate of the surface morphology and providing an external reference for internal verification.

[0070] Next, the point density of the first point cloud data is evaluated. If the point density of the first point cloud data is lower than the preset threshold, the crystal rod is measured at multiple points using a laser scanning device to obtain the three-dimensional coordinate data of each point on the surface. A point cloud is generated based on the triangular mesh algorithm. The average point spacing is calculated. If it exceeds the preset threshold, ultrasonic testing is triggered. The scanning path is scanned at intervals along the axial direction of the crystal rod.

[0071] Furthermore, data on the internal structure of the crystal rod, such as the reflection echo time series, are acquired. For example, the distance corresponding to the peak reflection time at depth S is detected at the interface. The ultrasonic reflection features are extracted and the signal is transformed and decomposed to obtain the instantaneous amplitude and phase. Then, the internal dimensions of the crystal rod (such as the average wall thickness and the diameter of the internal cavity) are calculated. The internal dimensions of the crystal rod are used to indicate the distribution of potential internal defects and to provide internal geometric constraints for supplementing the surface point cloud. Furthermore, the first point cloud data and the internal dimensions data are registered. The initial alignment is performed by extracting axis feature matching through principal component analysis (PCA). After determining the convergence threshold and the number of iterations, the data is fused to generate the second point cloud data, thus expanding the total number of points. The expanded second point cloud data is used to determine the complete geometric state of the crystal rod (for example, by calculating the standard deviation of surface curvature and the deviation rate of internal cavity volume to evaluate the overall integrity). If the deviation is less than the threshold, it is judged to be in a qualified state, thereby realizing the multimodal consistency verification of the crystal rod from the surface to the interior.

[0072] The pre-set material property database contains standard parameters for silicon crystal rods, such as standard diameter, length, and tolerance. For example, after matching the current crystal rod material to monocrystalline silicon, the corresponding standard size parameters are extracted. Error analysis methods are used to fit the parameters to the second point cloud data, calculate the radial deviation of each discrete point (e.g., average deviation, maximum deviation), and generate a size deviation distribution heatmap. The spatial distribution of deviation is predicted by a Gaussian process regression model, thereby quantifying the processing accuracy of the crystal rod and providing a quantitative basis for the subsequent correction and optimization of the crystal rod. The entire process forms a closed-loop logical chain from surface scanning to internal fusion and then to deviation analysis, achieving the goal of fully automated evaluation.

[0073] In one embodiment, three-dimensional coordinate data of the crystal rod surface is acquired, and a point cloud processing algorithm is used to generate the first point cloud data to determine the preliminary geometric contour of the crystal rod surface. If the point density of the first point cloud data is lower than a preset threshold, the internal structure data of the crystal rod is acquired through an ultrasonic testing device, and ultrasonic wave reflection features are extracted to obtain the internal size data of the crystal rod. Based on the first point cloud data and the internal size data, a data fusion algorithm is used for registration and integration to generate the second point cloud data to determine the complete geometric state of the crystal rod. If the deviation between the second point cloud data and the standard size parameters in the material property database exceeds a preset threshold, a support vector machine algorithm is used to classify the deviation data to obtain the distribution of the deviation region and the degree of deviation of the crystal rod.

[0074] In this embodiment, the three-dimensional coordinates of the crystal rod surface are acquired. First point cloud data is generated based on a point cloud processing algorithm to determine the preliminary geometric contour of the crystal rod surface. If the point density of the first point cloud data is lower than a preset threshold, internal structural data of the crystal rod is collected using an ultrasonic testing device, and ultrasonic wave reflection features are extracted to obtain the internal dimension data of the crystal rod. Based on the first point cloud data and the internal dimension data, a data fusion algorithm is used for registration and integration to generate second point cloud data, and the complete geometric state of the crystal rod is determined. If the deviation between the second point cloud data and the standard dimension parameters in the material property database exceeds a preset threshold, a support vector machine algorithm is used to classify the deviation data, obtaining the distribution of deviation regions and the degree of deviation. For the distribution of deviation regions, a point cloud segmentation algorithm is used to extract the boundary point cloud data of the deviation regions to obtain the geometric features of the deviation regions. Based on the geometric features of the deviation regions, a three-dimensional model of the deviation regions is generated using a three-dimensional reconstruction algorithm to obtain the spatial morphology of the deviation regions. If the spatial morphology of the deviation regions matches a preset defect template library with a degree higher than a preset threshold, a template matching algorithm is used to determine the crystal rod defect type, obtaining the crystal rod defect classification result.

[0075] More specifically, in the stage of acquiring three-dimensional coordinate data of the crystal rod surface, the crystal rod surface is first scanned with high precision using a laser scanner to collect surface point cloud coordinates. For example, 10,000 point data points are acquired at a resolution of 0.1 mm to form an initial point set. Then, point cloud processing algorithms (such as voxel grid filtering and statistical outlier removal methods) are used to reduce noise and simplify the point cloud. The voxel size and standard deviation of the neighborhood statistical threshold are set for the filtering parameters to generate the first point cloud data. This data contains the preliminary geometric contour of the crystal rod surface, such as the estimated shape of an elliptical cylinder with average outer diameter and length information.

[0076] The density of the first point cloud is calculated using the density calculation formula ρ=N / A, where N is the number of points and A is the projected area. It is then determined whether the density of the first point cloud is lower than a preset threshold. If it is lower than the preset threshold, the ultrasonic testing equipment is triggered to intervene and perform an internal scan of the crystal rod, collecting the reflection signal data of the crystal rod and extracting ultrasonic reflection characteristics such as time delay and amplitude peak value. The internal size data of the crystal rod, such as the internal cavity diameter and wall thickness uniformity deviation, is obtained by analyzing the waveform through inverse Fourier transform.

[0077] Next, based on the first point cloud data and internal dimension data, the iterative nearest point (ICP) data fusion algorithm is used for registration and integration. Initial registration uses edge point alignment based on feature point matching (such as curvature threshold). After the iterative convergence error is less than a preset value, the second point cloud data is generated by fusion, and the total number of points is expanded to comprehensively judge the overall integrity of the crystal rod's complete geometric state. This embodiment also introduces local wall thickness deviation values. If the deviation between the second point cloud data and the standard dimension parameters (such as outer diameter and inner diameter) in the material property database exceeds a preset threshold, the deviation data is classified based on the support vector machine (SVM) algorithm. The input feature vector includes deviation amplitude, position coordinates, and curvature change. The kernel function is the radial basis function (RBF) with a parameter of γ = 0.5. The training set is trained based on a binary classification model with 1000 historical samples. The classification result outputs that the crystal rod deviation area distribution is concentrated in the top 10% area and the deviation degree is classified as moderate level II, thereby obtaining the final mean deviation value and maximum deviation value, which guides subsequent process optimization. The entire process is implemented through an automated software link to achieve closed-loop analysis, ensuring logical consistency in the geometric integrity assessment from the surface to the interior.

[0078] In one embodiment, deviation region distribution data is acquired from sensors of the processing equipment. An image processing algorithm is used to segment the deviation region distribution data to obtain a set of boundary coordinates for the deviation regions. If the number of points in the boundary coordinate set exceeds a preset threshold, the geometry of the deviation region is fitted using the least squares method to determine the coordinates of the center point of the deviation region. Based on the center point coordinates of the deviation region, a k-means clustering algorithm is used to classify the degree of deviation and obtain deviation level values. If the maximum value among the deviation level values ​​exceeds a preset deviation threshold, the deviation level values ​​are quantized to determine the quantification result of the deviation degree classification. Based on the quantification result of the deviation degree classification and a coordinate transformation matrix, a rotation angle compensation value is calculated. Based on the rotation angle compensation value and a preset geometric state compensation model, the processing path planning is adjusted through spatial geometric relationships to generate a dynamic rotation angle adjustment scheme. If the deviation value of the processing path planning is lower than a preset threshold, the processing equipment control commands are updated in real time by adjusting parameters to determine the dynamic rotation angle adjustment scheme.

[0079] In this embodiment, sensors are used to acquire deviation area distribution data, and an image segmentation algorithm is used to process the deviation area distribution data to obtain a set of boundary coordinates of the deviation area. If the number of points in the boundary coordinate set exceeds a preset threshold, the geometry of the deviation area is fitted using the least squares method to determine the coordinates of the center point of the deviation area. Then, based on the coordinates of the center point of the deviation area, a k-means clustering algorithm is used to classify the degree of deviation to obtain a deviation level value. If the maximum value of the deviation level value exceeds a preset threshold, the deviation level classification quantization result is determined through quantization processing. Further, based on the deviation level classification quantization result, a rotation angle compensation value is calculated in conjunction with the coordinate transformation matrix. The processing path planning is adjusted using the rotation angle compensation value and a preset geometric state compensation model to generate a dynamic rotation angle adjustment scheme. If the deviation value of the processing path planning is lower than a preset threshold, the equipment control command is updated through real-time parameter adjustment to determine the dynamic adjustment scheme.

[0080] Furthermore, deviation region distribution data is acquired from sensors, and a three-dimensional coordinate dataset (x, y, z) containing multiple points is generated using the point cloud data of the workpiece surface collected by the sensors. The deviation value is defined as the distance between the actual point and the ideal model, ranging from -0.5 mm to 0.5 mm. The deviation region is segmented using the region growing method in image processing algorithms. Seed points are set: deviation value and neighborhood threshold, to obtain a boundary coordinate set containing multiple points. If the number of points in the boundary coordinate set exceeds the preset threshold, the least squares method is used to fit the deviation region into an elliptical shape, and the ellipse parameters and center point coordinates are calculated. Based on the center point coordinates, the k-means clustering algorithm (k=3) is used to classify the degree of deviation and input the deviation value dataset. After multiple iterations, three levels are obtained: slight, moderate, and severe, with the maximum deviation level value being 0.45 mm. If the maximum deviation level exceeds the preset threshold of 0.4mm, the deviation level is mapped to the 0-1 range using a linear quantization function (deviation value / 0.5mm), resulting in a quantization result of 0.9. Based on the quantization result, and combined with the coordinate transformation matrix (rotation matrix R = [cosθ, -sinθ; sinθ, cosθ]), the rotation angle compensation value is calculated. Based on the rotation angle compensation value and the preset geometric state compensation model, and by adjusting the machining path according to the spatial geometric relationship, a dynamic rotation angle adjustment scheme is generated to reduce the path deviation. If the path deviation is lower than the threshold, the control instructions of the machining equipment are updated by adjusting parameters in real time (such as reducing the feed rate) to generate the final dynamic rotation angle adjustment scheme and ensure machining accuracy.

[0081] In one embodiment, real-time data of the processing equipment status is acquired, and the real-time data is filtered and feature extracted by a data processing module to obtain processing deviation values ​​and equipment operating parameters. If the processing deviation value exceeds a pre-established deviation threshold, the dynamic rotation angle is adjusted according to the degree of deviation, and an updated rotation angle value is obtained after generating control commands based on the real-time control system. Based on the degree of deviation and the updated rotation angle value, the data processing module calculates the grinding and polishing force adjustment and speed optimization settings to obtain an optimized grinding and polishing parameter configuration. The optimized grinding and polishing parameter configuration is combined with the control commands using the real-time control system to adjust the processing equipment status to obtain an equipment operating status that meets the processing accuracy assessment.

[0082] In this embodiment, real-time data on the status of the processing equipment is acquired. Temperature, vibration, and rotational speed data are extracted from the processing equipment via a sensor acquisition module to obtain a real-time data set. A data processing module performs high-pass filtering and principal component analysis on the real-time data set to extract processing deviation values ​​and equipment operating parameters, resulting in a processing deviation feature set. If the processing deviation value in the feature set exceeds a preset deviation threshold, a deviation grading algorithm determines the adjustment level based on the degree of deviation, resulting in a deviation adjustment level. Based on the deviation adjustment level, a dynamic angle calculation module adjusts the rotation angle, generating control commands and obtaining an updated rotation angle value. The data processing module performs a weighted calculation on the updated rotation angle value and the deviation adjustment level to generate a grinding and polishing force adjustment value and speed optimization configuration, resulting in an optimized grinding and polishing parameter set. A real-time control system combines the optimized grinding and polishing parameter set with the control commands, adjusting the operating status of the processing equipment through actuators to obtain equipment status data that meets processing accuracy requirements. A status monitoring module continuously collects and analyzes the equipment status data, updating the real-time data set to obtain dynamic feedback data on the equipment's operating status.

[0083] Furthermore, real-time data on the processing equipment status is collected via sensors, including spindle vibration frequency, temperature, and rotational speed. The data processing module uses a Kalman filter algorithm to reduce noise in the vibration signal, sets filtering parameters, and extracts and calculates processing deviation values. Characteristic values ​​include vibration amplitude and peak frequency. If the deviation exceeds a preset threshold, it is classified as slight or moderate deviation. Then, a PID control algorithm dynamically adjusts the rotation angle to obtain an updated rotation angle value. Based on the deviation level and the updated angle, a linear regression model is used to calculate grinding and polishing force adjustment and speed optimization, thereby generating optimized grinding and polishing parameter configurations. The real-time control system combines the parameter configurations with control commands via a PLC. Parameters include grinding and polishing force and rotational speed, and control commands include rotational angle values. A PWM signal with a 60% duty cycle is generated to drive the servo motor, thereby adjusting the equipment status. The surface roughness of the workpiece is evaluated based on processing accuracy to confirm that the equipment's operating status meets requirements. If the deviation continues to exceed the standard, the system automatically records logs and triggers maintenance reminders to ensure process stability.

[0084] In one embodiment, high-precision sensors are used to collect data on dimensional changes and surface flatness during the crystal rod processing. Real-time monitoring data is obtained from the processing and stored in a preset database to obtain the original monitoring dataset. A moving average filtering method is used to process the original monitoring dataset and calculate the average value of the data within a time window, where the time window length is T, and T represents the sampling time interval, to obtain a smoothed dataset. If the dimensional change or surface flatness data in the smoothed dataset exceeds a preset threshold, a processing anomaly is determined using the K-nearest neighbor algorithm. The deviation of the dimensional change or surface flatness data from a preset standard value is compared to obtain an anomaly detection result. Based on a feedback control algorithm, the parameter adjustment amount of the processing equipment is calculated using the anomaly detection result to obtain updated processing parameters. The adjustment amount is determined by the deviation ratio D, where D represents the difference between the anomaly detection result and the standard value.

[0085] In this embodiment, dimensional change data and surface flatness data during the crystal rod processing are collected by sensors and stored in a preset database to obtain the original monitoring dataset. The original monitoring dataset is processed using a moving average filtering method, and the average value over a time window of length T (where T represents the sampling time interval) is calculated to obtain a smoothed dataset. If the dimensional change data or surface flatness data in the smoothed dataset exceeds a preset threshold, the K-nearest neighbor algorithm is used to classify the data points to determine processing anomalies, thereby obtaining anomaly detection results. The deviation between the anomaly detection results and preset standard values ​​is compared, and the deviation ratio D (where D represents the difference between the anomaly detection results and the standard value) is calculated to obtain deviation data. The deviation data is processed using a feedback control algorithm, and the parameter adjustment amount of the processing equipment is calculated to obtain updated parameters. The updated parameters are used to adjust the processing equipment and generate adjusted processing control commands to obtain optimized processing data. The optimized processing data is then used to update the standard values ​​in the preset database to obtain updated standard values.

[0086] Furthermore, in this embodiment, dimensional changes and surface flatness data during the crystal rod processing are collected by sensors. For example, a laser rangefinder is used to continuously collect dimensional data at intervals, and the surface flatness of the crystal rod is measured by an optical interferometer. The dimensional change data and flatness data are stored in a database in the form of timestamps, generating a raw monitoring dataset containing time, size (millimeters), and flatness (micrometers). Data processing is performed on the dimensional and flatness data collected in a particular instance, with a time window T set to 3 seconds (i.e., 3 sampling points). The calculation formula is: average value = (x_t + x_{t-1} + x_{t-2}) / 3. A smoothed result of the dimensional data is obtained based on the average value, and a similar flatness value is obtained based on the smoothed result. Based on a preset threshold dimensional change value and... The smoothness value is used to check the smoothness data, and a smoothness threshold is judged. If the smoothness threshold is reached, anomaly detection is triggered. The K-nearest neighbor algorithm (K=3) is used with the historical normal dataset as the training set to calculate the Euclidean distance between the current point and historical points. If the average distance of the three nearest points is greater than a preset value, it is judged as an anomaly. If the calculated distance is greater than the preset value, the anomaly is confirmed. The anomaly point is compared with the standard value, the preset deviation D value and the smoothness value. The adjustment amount is calculated using a feedback control algorithm: the adjustment amount is positively correlated with the deviation ratio D, and the adjustment amount = deviation D value × 0.5. Based on the adjustment amount, the size adjustment amount and the smoothness adjustment amount are obtained, and the processing equipment parameters such as tool feed speed and polishing pressure are updated to ensure processing quality. The whole process is realized automatically by the program, and data processing and parameter adjustment are completed in real time.

[0087] Furthermore, in this embodiment, the deviation characteristics of the monitoring data are analyzed using a random forest algorithm to calculate the feature weight W. When W > 0.5, the polishing parameters and rotation angle are dynamically adjusted, where the polishing force adjustment amount = F(W) × reference force, and F(W) is a linear function. Specifically, real-time monitoring data is obtained from the processing equipment. By comparing it with a preset processing uniformity threshold, it is determined whether the monitoring data deviates from the threshold and a deviation data set is obtained. The feature distribution of the deviation data set is analyzed using a random forest algorithm to calculate the feature weight W, where W represents the contribution of each feature to the deviation. The adjustment direction of the polishing parameters and rotation angle is determined, and a parameter adjustment scheme is obtained. The polishing parameters and rotation angle of the processing equipment are dynamically adjusted according to the parameter adjustment scheme. The adjusted processing data is obtained, and it is determined whether the processing data meets the stable output accuracy requirements. If the processing data does not meet the stable output accuracy requirements, the adjusted processing data is subjected to feature extraction and secondary analysis using an iterative optimization algorithm to determine new polishing parameters and rotation angle and obtain a stable processing accuracy output.

[0088] In this embodiment, real-time monitoring data is acquired from the processing equipment. The monitoring data is compared with a preset processing uniformity threshold to determine whether the monitoring data deviates from the threshold, thus obtaining a deviation data set. Next, a random forest algorithm is used to analyze the deviation dataset and calculate the feature weight W to determine the adjustment direction of the polishing parameters and rotation angle, thereby obtaining a parameter adjustment scheme. Then, the polishing parameters and rotation angle of the processing equipment are dynamically adjusted according to the parameter adjustment scheme to obtain the adjusted processing data. At the same time, it is determined whether the adjusted processing data meets the stable output accuracy requirements. If the adjusted processing data meets the stable output accuracy requirements, a stable processing accuracy output is obtained. If the adjusted processing data does not meet the stable output accuracy requirements, an iterative optimization algorithm is used to extract features from the adjusted processing data. A second analysis is performed based on the feature extraction results to determine new polishing parameters and rotation angle to obtain an updated parameter adjustment scheme. Based on the updated parameter adjustment scheme, the polishing parameters and rotation angle of the processing equipment are adjusted again, that is, new processing data is acquired. Finally, it is determined again whether the stable output accuracy requirements are met, and a stable processing accuracy output result is obtained.

[0089] More specifically, in precision optical lens processing equipment, real-time monitoring data is acquired from the polishing machine. This data includes surface roughness, polarization deviation, and thickness uniformity coefficient of variation. This data is collected by sensors and transmitted to the edge computing unit, where it is compared against preset processing uniformity thresholds, polarization deviation thresholds, and thickness coefficient of variation thresholds. If the preset processing uniformity thresholds and polarization deviations exceed these thresholds, a deviation data set is calculated. After preprocessing, this deviation data set is input into a random forest algorithm model. The accuracy is verified using a training set, and the feature distribution of the deviation data set, such as roughness correlation and polarization correlation, is analyzed. Furthermore, the feature weights W are calculated.

[0090] Wherein, the feature weight W is any one of the following: grinding and polishing pressure, polishing fluid flow rate, rotation angle and / or rotation speed, and W represents the contribution of each feature to the deviation;

[0091] The maximum contribution of the rotation angle to the polarization deviation is quantified by the SHAP value interpreter. The grinding and polishing parameters are determined by increasing the pressure and decreasing the flow rate, and adjusting the rotation angle by 15 degrees counterclockwise. The parameter adjustment scheme is generated, which includes increasing the pressure from a1 (kPa) to a2 (kPa), decreasing the flow rate from b1 (L / min) to b2 (L / min), and adjusting the angle from c1 degrees to c2 degrees. According to this scheme, the grinding and polishing parameters and rotation angle of the processing equipment are dynamically adjusted. The adjustment is executed in real time by the PLC controller, and the adjusted processing data (e.g., roughness reduced to 0.7nm, polarization deviation 1.8%, thickness variation coefficient 0.08) is obtained and input into the statistical process control model to determine whether the stable output accuracy requirement is met. This requirement is defined as data fluctuation less than the threshold 1.5σ and mean deviation less than 0.5%. The σ value and mean deviation of the adjusted data are calculated and confirmed to meet the requirements without further iteration. If not, the adjusted processing data is feature extracted by iterative optimization algorithms such as genetic algorithms. Two features of principal component variance contribution rate are extracted using PCA dimensionality reduction for secondary analysis. For example, the population size is initialized to 50, crossover rate is 0.8, mutation rate is 0.01, iteration is 20 generations, and the fitness function is to minimize the total sum of squared deviations. After convergence, new grinding and polishing parameters, pressure, flow rate, and rotation angle are determined and adjusted clockwise / counterclockwise to achieve stable processing accuracy output, such as an overall accuracy improvement of 18.2%. This forms a closed-loop optimization to ensure that the uniformity of the optical lens continues to meet the standard.

[0092] This invention also provides a precision crystal rod processing system applied to an integrated grinding and polishing machine. The crystal rod processing system comprises: a data acquisition module for acquiring initial crystal rod dimensions and material property data, and generating a dimensional deviation distribution of the crystal rod through multi-point measurements; a deviation analysis module for classifying and grading deviation areas when the dimensional deviation distribution exceeds a preset threshold; a compensation calculation module for calculating rotation angle compensation values ​​based on the deviation area distribution and grading results, and generating a dynamic rotation angle adjustment scheme; a parameter optimization module for adjusting the rotation angle and grinding / polishing parameter configuration of the processing equipment; a real-time monitoring module for monitoring dimensional changes and surface flatness during processing; and a dynamic adjustment module for dynamically adjusting the grinding / polishing parameters and rotation angle when the monitored data deviates from the processing uniformity threshold, ensuring stable output of processing accuracy.

[0093] The laser scanning unit is used to acquire three-dimensional coordinate data of the crystal rod surface; the ultrasonic detection unit is used to supplement internal structure data when the point cloud density is insufficient; and the data fusion unit is used to integrate surface and internal data to generate a complete geometric state.

[0094] The deviation analysis module classifies deviation data using a support vector machine algorithm and outputs the deviation region distribution and deviation severity level. The compensation calculation module classifies the deviation severity using a k-means clustering algorithm and generates a dynamic rotation angle adjustment scheme by combining a coordinate transformation matrix. The real-time monitoring module collects data using a high-precision sensor and processes the data using a moving average filtering method. The dynamic adjustment module analyzes deviation characteristics using a random forest algorithm and adjusts parameters through an iterative optimization algorithm to achieve stable machining accuracy output.

[0095] During crystal ingot processing, this system first performs multi-point measurements on the initial crystal ingot using a laser scanner, extracts material property data of the silicon crystal ingot from a material property database, fits the initial geometric state model of the crystal ingot using the least squares algorithm, and calculates the dimensional deviation distribution, standard deviation distribution, and the location of the maximum deviation point on the crystal ingot. Based on the deviation data, a preset threshold is set; if the threshold is exceeded, the deviation areas are classified as high-deviation areas in the middle section and low-deviation areas at the ends, and the degree of deviation is graded as Level 1 or Level 2, thus forming a deviation map for subsequent compensation. Based on the deviation area distribution and grading, a geometric compensation model is used to calculate the rotation angle compensation value and generate a dynamic rotation angle adjustment scheme. The angles of the middle section and both ends of the crystal ingot are calculated based on the geometric compensation model formula. This scheme outputs the angle sequence in real time through a PID controller algorithm to ensure a uniform processing path.

[0096] After obtaining the dynamic rotation angle adjustment scheme, the servo motor rotation angle of the processing equipment is automatically adjusted to the compensation value. At the same time, the grinding and polishing parameters are updated according to the deviation level, and the configuration is optimized by combining the genetic algorithm to obtain the setting parameters of grinding and polishing force and processing speed under the first-level deviation and the second-level deviation.

[0097] The processing is monitored in real time by sensors, collecting dimensional change data and surface flatness parameters N times per second. If the monitored data deviates from the preset uniformity threshold, the high-frequency vibration components in the deviation spectrum are analyzed by Fourier transform, the grinding and polishing parameters are dynamically adjusted, and the rotation angle is fed back for adaptive adjustment. Through closed-loop control, the processing accuracy is stabilized within 0.05mm. The whole process forms a tight logical chain from measurement to feedback, ensuring the final geometric consistency and surface quality of the crystal rod.

[0098] This invention addresses the issue of inaccurate processing in crystal ingot manufacturing, stemming from complex initial geometry, uneven dimensional deviations, and insufficient dynamic adjustments during the process. Specifically, it determines the initial geometric state and deviation distribution based on the crystal ingot's initial dimensions and material properties. Based on this initial geometry and dimensional deviation distribution, a corresponding dynamic adjustment scheme is determined, enabling efficient and integrated precision processing of crystal ingots. This results in high efficiency, high precision, and high processing uniformity, significantly improving product quality and production efficiency.

[0099] Furthermore, this invention achieves high optimization in the processing of crystal rods in an integrated grinding and polishing machine through the above-mentioned scheme, realizing precise reconstruction of the initial geometric state and complex internal structure of the crystal rod. Simultaneously, it employs machine learning algorithms (such as support vector machines) to intelligently identify and quantify complex deviations, thereby generating a dynamic rotation angle adjustment strategy based on the crystal rod's own geometric state. Furthermore, during processing, an adaptive control mechanism is constructed, using real-time monitored data as feedback and random forest algorithm as the core for decision-making, thus achieving online real-time optimization of grinding and polishing parameters and angles. This fundamentally and significantly improves the adaptability to individual differences in crystal rods and their dynamic process changes, thereby greatly ensuring stable output of processing accuracy and significantly improving the surface uniformity of the crystal rod, bringing outstanding technical advantages to crystal rod manufacturing.

[0100] This system combines processing methods, utilizing a data acquisition module to obtain initial size and material property data of the crystal rod. A multi-point measurement system scans the crystal rod surface, and combined with a pre-set material property database, calculates the initial geometric state and dimensional deviation distribution of the crystal rod. If the dimensional deviation distribution exceeds a preset threshold, a clustering algorithm is used to classify the deviation distribution, obtaining the deviation region distribution and deviation severity level. Based on the deviation region distribution and deviation severity level, combined with a preset geometric state compensation model, a rotation angle compensation value is calculated, and a dynamic rotation angle adjustment scheme is determined during processing. The rotation angle is then adjusted by the processing equipment control system, and the polishing parameters are updated based on the deviation severity level to obtain an optimized polishing force and speed configuration. A real-time monitoring module monitors the dimensional changes and surface flatness data of the crystal rod during processing. Processing monitoring data is acquired through an optical measurement system. If the processing monitoring data deviates from a preset processing uniformity threshold, a support vector machine algorithm is used to analyze the deviation data and dynamically adjust the polishing parameters and rotation angle to obtain the adjusted processing parameter configuration. Finally, based on the adjusted processing parameter configuration, the processing equipment control system updates the polishing force and speed settings to obtain a stable processing accuracy output.

[0101] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for precise machining of crystal rods applied to an integrated grinding and polishing machine, characterized in that, include: The initial size and material properties of the crystal rod are acquired, and multi-point measurements are performed on the crystal rod. Combined with a pre-established material properties database, the initial geometric state and size deviation distribution of the crystal rod are determined. If the size deviation distribution exceeds a preset threshold, the size deviation distribution is classified to obtain the deviation region distribution and deviation degree classification of the crystal rod. Based on the distribution of the deviation area and the classification of the deviation degree, the rotation angle compensation value is calculated, and the dynamic rotation angle adjustment scheme required during the processing is determined through the preset geometric state compensation model. The dynamic rotation angle adjustment scheme is obtained, and the rotation angle of the processing equipment is adjusted. The grinding and polishing parameter configuration is updated in stages based on the degree of deviation, and the optimized grinding and polishing force and speed settings are obtained. The dimensional changes and surface flatness data of the crystal rod are monitored in real time during the processing, and the monitoring data during the processing is obtained. If the monitoring data deviates from the preset processing uniformity threshold, the deviation data in the monitoring data is analyzed, and the grinding and polishing parameters and the rotation angle are dynamically adjusted to obtain a stable processing accuracy output.

2. The method for precision machining of crystal rods applied to an integrated grinding and polishing machine as described in claim 1, characterized in that, The crystal rod is multi-point measured using a laser scanning device to obtain the three-dimensional coordinate data of each point on the surface of the crystal rod; the first point cloud data is generated based on the point cloud processing algorithm to determine the preliminary geometric contour of the crystal rod; if the point density of the first point cloud data is lower than a preset threshold, the internal structure data of the crystal rod is obtained using an ultrasonic testing device, the ultrasonic wave reflection features are extracted, and the internal size data of the crystal rod is obtained. The first point cloud data and the internal size data are registered and integrated based on the data fusion algorithm to generate the second point cloud data, and the complete geometric state of the crystal rod is determined accordingly. Based on a preset material property database, standard size parameters corresponding to the crystal rod material are matched, and error analysis methods are used to compare the second point cloud data with the standard parameters to obtain the size deviation distribution of the crystal rod.

3. The method for precision machining of crystal rods applied to an integrated grinding and polishing machine as described in claim 1, characterized in that, The three-dimensional coordinate data of the crystal rod surface is obtained, and the first point cloud data is generated by the point cloud processing algorithm to determine the preliminary geometric contour of the crystal rod surface. If the point density of the first point cloud data is lower than a preset threshold, the internal structure data of the crystal rod is obtained by ultrasonic testing equipment, ultrasonic reflection features are extracted, and the internal size data of the crystal rod is obtained. Based on the first point cloud data and the internal size data, a data fusion algorithm is used for registration and integration to generate the second point cloud data and determine the complete geometric state of the crystal rod. If the deviation between the second point cloud data and the standard size parameters in the material property database exceeds a preset threshold, the support vector machine algorithm is used to classify the deviation data to obtain the distribution of the deviation region and the degree of deviation of the crystal rod.

4. The method for precision machining of crystal rods applied to an integrated grinding and polishing machine as described in claim 1, characterized in that, The deviation region distribution data is obtained from the sensors of the processing equipment. The deviation region distribution data is segmented using an image processing algorithm to obtain the boundary coordinate set of the deviation region. If the number of points in the boundary coordinate set exceeds a preset threshold, the geometry of the deviation region is fitted by the least squares method to determine the coordinates of the center point of the deviation region. Based on the coordinates of the center point of the deviation region, the k-means clustering algorithm is used to classify the degree of deviation and obtain the deviation level value; if the maximum value of the deviation level value exceeds the preset deviation threshold, the deviation level value is quantified to determine the quantification result of the degree of deviation classification. The rotation angle compensation value is calculated based on the quantification results of the aforementioned deviation degree classification and the coordinate transformation matrix. Based on the rotation angle compensation value and the preset geometric state compensation model, the machining path planning is adjusted through spatial geometric relationships to generate a dynamic rotation angle adjustment scheme; if the deviation value of the machining path planning is lower than the preset threshold, the machining equipment control command is updated by adjusting parameters in real time to determine the dynamic rotation angle adjustment scheme.

5. The method for precision machining of crystal rods applied to an integrated grinding and polishing machine as described in claim 1, characterized in that, The real-time data of the processing equipment status is obtained, and the real-time data is filtered and feature extracted by the data processing module to obtain the processing deviation value and equipment operating parameters; If the machining deviation value exceeds the pre-established deviation threshold setting, the dynamic rotation angle is adjusted according to the degree of deviation, and the updated rotation angle value is obtained after generating control commands based on the real-time control system. Based on the deviation level classification and the updated rotation angle value, the data processing module calculates the grinding and polishing force adjustment and speed optimization settings to obtain the optimized grinding and polishing parameter configuration; The optimized grinding and polishing parameters are combined with the control commands using a real-time control system to adjust the state of the processing equipment, thereby obtaining an equipment operating state that meets the processing accuracy assessment.

6. The method for precision machining of crystal rods applied to an integrated grinding and polishing machine as described in claim 1, characterized in that, The size change and surface flatness data of the crystal rod are collected by high-precision sensors during the crystal rod processing. Real-time monitoring data is obtained from the processing and stored in a preset database to obtain the original monitoring dataset. The moving average filtering method is used to process the original monitoring dataset and calculate the average value of the data within the time window, where the length of the time window is T, and T represents the sampling time interval, to obtain the smoothed dataset; If the size change or surface flatness data in the smoothed dataset exceeds a preset threshold, the K-nearest neighbor algorithm is used to determine the processing anomaly; the deviation of the size change or surface flatness data from the preset standard value is compared to obtain the anomaly detection result; Based on the feedback control algorithm, the parameter adjustment amount of the processing equipment is calculated using the anomaly detection results to obtain the updated processing parameters; The adjustment amount is determined by the deviation ratio D, where D represents the difference between the abnormal detection result and the standard value.

7. The method for precision machining of crystal rods applied to an integrated grinding and polishing machine as described in claim 1, characterized in that, Real-time monitoring data is obtained from the processing equipment. By comparing the data with a preset processing uniformity threshold, it is determined whether the monitoring data deviates from the threshold and a set of deviation data is obtained. The random forest algorithm is used to analyze the feature distribution of the deviation data set, calculate the feature weight W, where W represents the contribution of each feature to the deviation, determine the adjustment direction of the grinding and polishing parameters and the rotation angle, and obtain the parameter adjustment scheme. Dynamically adjust the grinding and polishing parameters and rotation angle of the processing equipment according to the parameter adjustment scheme, obtain the adjusted processing data, and determine whether the processing data meets the stable output accuracy requirements. If the processing data does not meet the requirements for stable output accuracy, the adjusted processing data is subjected to feature extraction and secondary analysis through an iterative optimization algorithm to determine new grinding and polishing parameters and rotation angles and obtain stable processing accuracy output.

8. A precision crystal rod processing system for use in a grinding and polishing integrated machine, wherein crystal rod processing is performed by the method described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire the initial size and material property data of the crystal rod, and to generate the size deviation distribution of the crystal rod through multi-point measurement; The deviation analysis module is used to classify and grade the deviation areas when the size deviation distribution exceeds a preset threshold. The compensation calculation module is used to calculate the rotation angle compensation value based on the distribution and grading results of the deviation area, and generate a dynamic rotation angle adjustment scheme. The parameter optimization module is used to adjust the rotation angle and grinding / polishing parameter configuration of the processing equipment; The real-time monitoring module is used to monitor dimensional changes and surface flatness during the processing. The dynamic adjustment module is used to dynamically adjust the grinding and polishing parameters and rotation angle when the monitored data deviates from the processing uniformity threshold, so as to ensure stable output of processing accuracy.

9. A crystal rod precision processing system applied to a grinding and polishing integrated machine as described in claim 8, characterized in that, A laser scanning unit is used to acquire three-dimensional coordinate data of the crystal rod surface; An ultrasonic testing unit is used to supplement internal structural data when point cloud density is insufficient; The data fusion unit is used to integrate surface and internal data to generate a complete geometric state.

10. A precision crystal rod processing system applied to a grinding and polishing integrated machine as described in claim 1, characterized in that, The deviation analysis module uses the support vector machine algorithm to classify deviation data and output the distribution of deviation regions and the classification of deviation severity. The compensation calculation module classifies the degree of deviation using the k-means clustering algorithm and generates a dynamic rotation angle adjustment scheme in conjunction with the coordinate transformation matrix. The real-time monitoring module uses a high-precision sensor to collect data and processes the data using a moving average filtering method. The dynamic adjustment module uses the random forest algorithm to analyze deviation characteristics and adjusts parameters through iterative optimization algorithms to achieve stable processing accuracy output.

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