A method and system for intelligent detection of machining quality of CNC machine tools

Abstract

The present invention relates to the field of image data processing technology, and specifically to a method and system for intelligent detection of the machining quality of CNC machine tool workpieces, comprising: collecting point cloud data of the CNC machine tool workpiece to be detected and the standard CNC machine tool workpiece, clustering the point cloud data of the CNC machine tool workpiece to be detected using an improved mean shift algorithm to obtain multiple clusters; obtaining the DB index of each cluster, eliminating clusters with DB indexes less than a set threshold, and obtaining final point cloud data; splicing the final point cloud data with the point cloud data of the standard CNC machine tool workpiece to generate a point cloud data model. By performing Boolean operations on the point cloud data models of the CNC machine tool workpiece to be detected and the standard CNC machine tool workpiece, abnormal areas are identified; if the area of the abnormal area is greater than the set threshold, the workpiece to be detected is determined to be abnormal. The present invention solves the problem of low accuracy in workpiece machining quality detection.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and more particularly to a method and system for intelligently detecting the machining quality of a workpiece of a numerically controlled machine tool. Background Art

[0002] As manufacturing demands ever-increasing product quality, CNC machine tools are widely used in modern industrial production. By using computers to digitally control the machining process, CNC machine tools enable high-precision and efficient machining. However, with the increasing complexity of machining processes and the diversification of workpiece shapes, traditional quality inspection methods face numerous challenges. Traditional workpiece machining quality inspection methods primarily rely on manual visual inspection and gaging, which are not only inefficient but also susceptible to human influence, making it difficult to ensure the accuracy and consistency of inspection results.

[0003] In order to improve the accuracy and efficiency of machining quality inspection, intelligent detection technology has gradually been applied to workpiece quality inspection of CNC machine tools in recent years. Traditional intelligent detection methods mostly rely on traditional feature-based machine learning models, such as support vector machines and decision trees, for defect identification. However, these methods usually require a large amount of labeled data for training, and are prone to problems such as insufficient detection accuracy and poor adaptability when faced with complex workpieces, different machining environments, and dynamic changes. Most existing technologies are unable to effectively overcome the limitations of traditional models, especially in the identification of complex workpiece shapes, subtle changes during the machining process, and new types of defects. There is still considerable room for improvement.

[0004] Patent application publication number CN113866165A discloses a workpiece inspection and defect detection system that includes monitoring workpiece images. This patent application utilizes workpiece images captured by a camera as training data to train a defect detection model to identify workpiece images containing defects. Based on the features of these training images, the system further determines the classification characteristics of an anomaly detector. Using these determined classification characteristics, the system then determines whether a workpiece image falls into an anomaly category.

[0005] However, the above technical solutions mainly rely on traditional models to achieve intelligent detection of workpieces. These models are usually trained based on existing standard features and algorithms, and do not fully consider the limitations of existing models. As a result, when faced with complex workpieces or changes in the processing environment, the accuracy of workpiece processing quality detection is not high. Summary of the Invention

[0006] In order to solve the problem of low accuracy in workpiece processing quality detection raised in the above background technology, the present invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides an intelligent detection method for the processing quality of CNC machine tool workpieces, comprising: collecting point cloud data of each position of a CNC machine tool workpiece to be detected and a standard CNC machine tool workpiece; clustering the point cloud data of each position of the CNC machine tool workpiece to be detected using an improved mean shift algorithm to obtain a plurality of clusters; obtaining the DB index of each cluster, and if the DB index is less than a first set threshold, eliminating the corresponding cluster to obtain the final point cloud data; splicing the final point cloud data and the point cloud data of each position of the standard CNC machine tool workpiece to obtain point cloud data models of the CNC machine tool workpiece to be detected and the standard CNC machine tool workpiece; performing Boolean operations on the point cloud data models of the CNC machine tool workpiece to be detected and the standard CNC machine tool workpiece to obtain the area of the abnormal region, and if the area of the abnormal region is greater than the set area threshold, determining that the CNC machine tool workpiece to be detected is abnormal; wherein, the improved mean shift algorithm includes a drift vector, which is calculated in the first The first iteration The data point belongs to The drift vector of the clusters , , where For the The first iteration The data point belongs to The initial drift vector of the clusters, For the The first iteration The data point belongs to The DB index of the clusters, For the The first iteration The data point belongs to The DB index of the clusters, is an empirical constant.

[0008] This technical solution clusters point cloud data using an improved mean-shift algorithm to remove noise and identify potential anomaly clusters. The ICP algorithm is then used to combine the point cloud data of the workpiece under inspection with that of a standard workpiece to generate an accurate 3D model. Boolean operations are used to calculate the area of the anomaly region, and combined with an area threshold to determine whether the workpiece is abnormal. This enables efficient and accurate machining quality inspection, improving both accuracy and automation.

[0009] Furthermore, the DB index of each cluster is obtained as follows:

[0010] The cluster center of each cluster is obtained by using the mean method, and the inter-cluster distance and intra-cluster distance of each cluster are calculated; the mean of the inter-cluster distance and intra-cluster distance of each cluster is used as the DB index of the cluster.

[0011] This technical solution uses the mean method to determine cluster centers and calculates the mean of inter-cluster and intra-cluster distances as the DB index, effectively improving the accuracy of clustering results. This method accurately reflects the density of each cluster and its degree of separation from other clusters, making the identification of anomalous clusters more reliable. Comprehensive evaluation of the DB index accurately identifies data clusters with anomalous characteristics, further improving the effectiveness of cluster analysis and providing more stable and accurate data support for subsequent defect detection.

[0012] Furthermore, a binocular camera or a structured light camera is used to collect point cloud data of each position of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece.

[0013] This technical solution achieves high-precision 3D data acquisition by using binocular or structured light cameras to capture point cloud data of both the CNC machine tool workpiece under inspection and the standard CNC machine tool workpiece. These cameras provide detailed depth information and rich geometric features, effectively capturing workpiece surface details, including subtle shape variations or defects. Compared to traditional 2D images, point cloud data fully reflects the workpiece's 3D structure, making subsequent inspection, comparison, and analysis more precise, significantly improving inspection accuracy and reliability.

[0014] Furthermore, it also includes performing mean filtering on the point cloud data.

[0015] The above technical solution effectively removes noise and outliers, smoothes the data, and improves the quality and accuracy of the point cloud by applying mean filtering to the point cloud data. This preprocessing method reduces instabilities caused by measurement errors or external interference, making subsequent clustering, registration, and defect detection processes more accurate and reliable. The application of mean filtering improves the consistency and usability of the point cloud data, thereby enhancing the robustness and accuracy of the entire inspection process.

[0016] Furthermore, the final point cloud data and the point cloud data of each position of the standard CNC machine tool workpiece are spliced using the ICP algorithm.

[0017] Furthermore, the set area threshold is 0.5.

[0018] Furthermore, the method further includes performing secondary clustering on the point cloud data in the corresponding cluster whose DB index is greater than the second set threshold and less than the first set threshold.

[0019] This technical solution further refines clustering results and enhances the detection of abnormal areas by performing secondary clustering on clusters whose DB indices fall between two set thresholds. Secondary clustering effectively identifies subtle differences within these clusters, preventing potential defects from being missed. This improves clustering accuracy, particularly when complex structures or irregular shapes exist within clusters. This step optimizes data segmentation, enabling detection to more accurately capture subtle anomalies in workpieces, thereby increasing overall detection sensitivity and reliability.

[0020] In a second aspect, the present invention provides an intelligent detection system for the processing quality of workpieces of CNC machine tools, comprising a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, any one of the above-described intelligent detection methods for the processing quality of workpieces of CNC machine tools is implemented.

[0021] The beneficial effects of the present invention are:

[0022] The present invention provides a high-precision and efficient intelligent detection method for the processing quality of CNC machine tool workpieces by combining point cloud data processing and intelligent algorithms. By collecting point cloud data of the workpiece to be detected and the standard CNC machine tool workpiece, and using the improved mean shift algorithm for cluster analysis, it is possible to accurately screen out abnormal areas and carefully identify potential defects. The application of the ICP algorithm ensures the high-precision splicing of the point cloud data models of the workpiece to be detected and the standard workpiece, and the area of the abnormal area is calculated through Boolean operations and judged in combination with the area threshold, making the defect detection process more accurate and reliable. In addition, preprocessing and optimization measures such as mean filtering and secondary clustering further improve the data quality and clustering accuracy, and enhance the stability and sensitivity in the quality detection of complex workpieces. This solution realizes the automated detection and intelligent evaluation of the processing quality of CNC machine tool workpieces, significantly improving the detection efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0024] Figure 1 is a flow chart schematically illustrating a method for intelligently detecting machining quality of a workpiece of a CNC machine tool according to an embodiment of the present invention;

[0025] Figure 2 The figure schematically shows a structural block diagram of an intelligent detection system for machining quality of a CNC machine tool workpiece according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0027] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] An embodiment of an intelligent detection method for machining quality of a CNC machine tool workpiece.

[0029] like Figure 1 As shown in FIG, a flow chart of an intelligent detection method for machining quality of a CNC machine tool workpiece according to an embodiment of the present invention includes the following steps:

[0030] S1: Collect point cloud data of each position of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece.

[0031] In one embodiment, a binocular camera or a structured light camera is first used to accurately capture each position of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece to obtain complete three-dimensional point cloud data. The binocular camera can effectively capture the depth information of the workpiece surface details by using the parallax difference between the two cameras, generating a high-precision three-dimensional point cloud. The structured light camera can also obtain accurate three-dimensional surface data by projecting light with a known pattern and combining it with the camera to capture the deformation of the reflected light. Both technologies have high accuracy and stability in the point cloud data acquisition process, especially in the inspection of complex shapes and precision-machined surfaces. They can provide accurate depth information, which helps to improve the reliability of subsequent analysis results.

[0032] The large amount of collected point cloud data is first processed using mean filtering to remove outliers introduced by noise or instability. By smoothing the data points, mean filtering can effectively reduce the interference of random noise, making the point cloud data smoother and more uniform, which helps improve the accuracy of subsequent point cloud processing. In practical applications, point cloud data is often affected by factors such as changes in ambient light, camera viewing angle, and workpiece surface reflections, resulting in errors or noise in some point cloud data. Therefore, mean filtering can effectively prevent the impact of this noise on the point cloud data model, thereby providing a more accurate and clear foundation for subsequent point cloud comparison and anomaly detection.

[0033] Furthermore, point cloud data processed through mean filtering possesses higher quality, providing reliable data support for subsequent point cloud comparison, matching, and anomaly detection. When compared with point cloud data from a standard workpiece, surface differences and anomalies can be effectively identified. These differences often manifest as deviations in size, shape, or position, or as minor defects introduced during the machining process. De-noising point cloud data through mean filtering provides clearer surface contours and details, more accurately reflecting the true form of the workpiece, thereby improving the sensitivity and accuracy of anomaly detection.

[0034] S2: The point cloud data of the workpiece to be inspected is clustered using the improved mean shift algorithm, and clusters that do not meet the DB index threshold are eliminated to obtain the final point cloud data.

[0035] In one embodiment, the improved mean shift algorithm includes a drift vector, which is calculated in the first The first iteration The data point belongs to The drift vector of the clusters , , where For the The first iteration The data point belongs to The initial drift vector of the clusters, For the The first iteration The data point belongs to The DB index of the clusters, For the The first iteration The data point belongs to The DB index of the clusters, is an empirical constant.

[0036] The improved mean shift algorithm introduces a dynamic adjustment mechanism for the drift vector. Specifically, by comparing the DB index of the clusters at different iterations, the drift vector is adjusted according to the changes in the affiliation of the data points. This mechanism can more accurately capture subtle changes in the data point clustering process, thereby improving the accuracy and stability of the clustering results. When the DB index of a data point changes between the old and new iterations, the drift vector will be adjusted accordingly based on the index difference, allowing the clusters to more flexibly adapt to changes in the distribution of data points during the iteration process. By introducing the empirical constant G, the amplitude and speed of the drift vector adjustment are further optimized, avoiding the problems of overfitting or slow convergence in complex data sets. The improved algorithm can effectively improve the accuracy of clustering, especially when processing noisy data or complex data structures, and can achieve more accurate anomaly detection and data pattern recognition, thereby significantly improving the performance and reliability of the model in practical applications.

[0037] In this embodiment, an improved mean-shift algorithm is first applied to cluster the point cloud data collected at various locations on the CNC machine tool workpiece being inspected, generating multiple clusters. This improved mean-shift algorithm automatically identifies different data clusters based on the distribution of data points in multidimensional space. Through continuous iteration, the algorithm adjusts cluster centers based on the density distribution of data points. The resulting clusters not only accurately reflect the overall data structure but also effectively distinguish between different regional features. This ensures precise identification of abnormal areas, particularly when minor defects are present on complex workpiece surfaces.

[0038] Next, for each cluster, the clustering effect is evaluated by calculating its DB index. The DB index is an important indicator for measuring the internal consistency and inter-cluster separation of clusters. The smaller the value, the higher the compactness of the points within the cluster, and the greater the distance between clusters, the better the clustering effect. Specifically, the calculation process of the DB index includes two parts: first, the cluster center of each cluster is determined using the mean method. The cluster center is selected based on the average position of the points within the cluster. This method has strong stability and low computational complexity. Second, the inter-cluster distance and intra-cluster distance of each cluster are calculated. The inter-cluster distance refers to the distance between clusters, while the intra-cluster distance reflects the compactness between points within the cluster. By calculating the mean of the inter-cluster distance and the intra-cluster distance, the DB index of each cluster is finally obtained.

[0039] Once the DB index for each cluster is obtained, the clusters are screened based on a preset threshold. If the DB index of a cluster is less than the first set threshold, it indicates that the data points within the cluster are sparsely distributed and may be noise or invalid. Therefore, the cluster will be removed to obtain more accurate point cloud data. This method can effectively filter out irrelevant or meaningless clusters, reduce the interference of noise on subsequent processing, and thus improve the efficiency and accuracy of data processing.

[0040] For clusters whose DB index falls between the second and first thresholds, secondary clustering is performed. The purpose of secondary clustering is to further refine the distribution of data points within these clusters, preventing small anomalies from being misclassified as normal points. By performing detailed secondary clustering on these clusters, more possible minor anomalies can be identified, improving the accuracy of anomaly detection. Secondary clustering can refine the classification structure of the point cloud and effectively distinguish potential, difficult-to-detect local defects or deformations, ensuring more accurate point cloud data and thus improving detection reliability.

[0041] S3: The final point cloud data and the point cloud data of each position of the standard CNC machine tool workpiece are spliced to obtain point cloud data models of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece.

[0042] In one embodiment, the ICP algorithm optimizes the point cloud registration result by repeatedly iterating and utilizing the geometric similarity between the workpiece to be inspected and the standard workpiece, ultimately achieving accurate alignment of the two sets of point cloud data.

[0043] Specifically, the core idea of the ICP algorithm is to continuously search for the nearest corresponding points in the point cloud to be aligned and the target point cloud during the iterative process, and calculate the transformation matrix that minimizes the error based on these matching points, thereby achieving spatial alignment of the two point clouds. The algorithm calculates the distance difference between the nearest point pairs of the source point cloud and the target point cloud, and gradually adjusts the position and direction of the source point cloud, ultimately making the overlapping area of the two point clouds as close as possible. This method based on minimum error optimization can effectively eliminate the initial posture error and improve the alignment accuracy. In particular, when there is a certain deviation in the initial position of the point cloud data, the ICP algorithm can gradually converge to the optimal solution through multiple iterations, ensuring that the final spliced point cloud data model has a high degree of accuracy.

[0044] After applying the ICP algorithm, the point cloud data of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece are aligned and fused into a complete point cloud data model. This splicing method enables seamless docking of the two workpieces in the same coordinate system, providing a reliable 3D data foundation for subsequent shape comparison, defect detection, and quality assessment. This precise registration of point cloud data not only improves the accuracy of the workpiece model but also provides high-resolution 3D views for detecting minor defects, ensuring greater precision and sensitivity in determining workpiece surface quality.

[0045] The ICP algorithm's stitching process effectively eliminates deviations or displacements that may occur during the measurement process, reduces errors caused by data noise, sensor errors, or different measurement angles, and thus improves the overall quality of the point cloud data model. Furthermore, the ICP algorithm's adaptability enables it to operate stably under varying initial conditions and point cloud data quality, ensuring the robustness of the registration process. This not only makes the technical solution more flexible and reliable in practical applications, but also provides consistent performance across diverse industrial environments.

[0046] S4: Performing a Boolean operation on the point cloud data model to obtain the area of the abnormal region. If the area of the abnormal region is greater than a set area threshold, the CNC machine tool workpiece to be inspected is determined to be abnormal.

[0047] The area threshold value may be set to 0.5, and may also be determined based on actual conditions.

[0048] In one embodiment, the two point cloud data models are processed using Boolean operations to identify abnormal regions. Boolean operations, a common geometric processing method, can efficiently perform operations such as intersection, union, and difference on two 3D models, thereby identifying geometric differences and abnormal regions between the workpiece to be inspected and the standard workpiece.

[0049] During this process, Boolean operations compare the point cloud models based on their geometric structure and calculate the areas of difference between them. Specifically, Boolean operations can effectively highlight the differences in shape, size, position, and other aspects between the workpiece to be inspected and the standard workpiece. This is especially true when there is wear, defects, or deformation on the workpiece surface. Boolean operations can clearly mark these abnormal areas. These areas of difference typically manifest as subtle geometric deformations, such as localized depressions, protrusions, or cracks. Boolean operations can clearly extract the abnormal areas by removing the overlap between these areas and the standard model.

[0050] Once these abnormal areas are identified, their impact is assessed by calculating their area. If the area of the abnormal area is greater than a preset area threshold, it indicates that the abnormality in this area is sufficient to affect the quality and function of the workpiece, and the workpiece is then determined to be abnormal. The area threshold can be set based on the workpiece's design standards or industry-specified tolerance ranges, ensuring that only abnormal areas that exceed the normal tolerance range are considered significant defects. This judgment process is highly sensitive and can accurately identify subtle but significant defect areas, ensuring that the inspection can provide consistent and accurate judgment results under various workpiece conditions.

[0051] The combination of Boolean operations and area threshold determination enables a comprehensive and accurate assessment of the quality of CNC machine tool workpieces under inspection. In practical applications, the efficiency and computational precision of Boolean operations enable processing large-scale point cloud data while ensuring the reliability of inspection results. The introduction of area thresholds prevents both false positives and missed detections during anomaly detection, ensuring that only abnormal areas that truly impact the workpiece's function and performance are marked as defective, thereby improving the accuracy and practicality of the entire inspection process.

[0052] The solution of the present invention achieves accurate workpiece quality detection through efficient point cloud data acquisition, processing and analysis. By clustering the point cloud data through an improved mean shift algorithm, abnormal areas can be effectively screened out. The point cloud data models of the workpiece to be inspected and the standard workpiece are accurately assembled using the ICP algorithm. The area of the abnormal area is then calculated through Boolean operations to ensure accurate identification of defects. In addition, secondary clustering further improves the accuracy of anomaly detection, and the application of mean filtering makes the point cloud data smoother and more reliable. The overall solution can automatically detect tiny defects in workpieces, improve the accuracy, efficiency and stability of detection, and meet the needs of high-precision CNC machine tool processing quality monitoring.

[0053] An embodiment of an intelligent detection system for machining quality of a CNC machine tool workpiece:

[0054] like Figure 2 As shown in FIG, a structural block diagram of an intelligent detection system for machining quality of a CNC machine tool workpiece according to an embodiment of the present invention includes a processor and a memory.

[0055] The present invention also provides an intelligent detection system for the machining quality of CNC machine tools. Figure 2 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the above-mentioned intelligent detection method for machining quality of CNC machine tools workpieces according to the present invention is implemented.

[0056] The intelligent detection system for machining quality of workpieces of CNC machine tools also includes other components familiar to those skilled in the art, such as a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0057] In the present invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of, accessible to, or connectable to a device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0058] In the description of this specification, "multiple" and "several" mean at least two, such as two, three or more, etc., unless otherwise clearly defined.

[0059] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

Claims

1. An intelligent detection method for machining quality of CNC machine tools, characterized in that: include: Collect point cloud data of each position of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece; Clustering the point cloud data of each position of the CNC machine tool workpiece to be inspected using an improved mean shift algorithm to obtain a plurality of clusters; obtaining a DB index of each cluster, and if the DB index is less than a first set threshold, eliminating the corresponding cluster to obtain final point cloud data; and splicing the final point cloud data with the point cloud data of each position of the standard CNC machine tool workpiece to obtain point cloud data models of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece; Performing a Boolean operation on the point cloud data model of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece to obtain the area of the abnormal region. If the area of the abnormal region is greater than a set area threshold, the CNC machine tool workpiece to be inspected is determined to be abnormal. The improved mean shift algorithm includes a drift vector, which is calculated in the first The first iteration The data point belongs to The drift vector of the clusters , , where For the The first iteration The data point belongs to The initial drift vector of the clusters, For the The first iteration The data point belongs to The DB index of the clusters, For the The first iteration The data point belongs to The DB index of the clusters, is an empirical constant.

2. The intelligent detection method for machining quality of a CNC machine tool workpiece according to claim 1, characterized in that: The DB index of each cluster is obtained as follows: The mean method is used to obtain the cluster center of each cluster, and the inter-cluster distance and intra-cluster distance of each cluster are calculated; The mean of the inter-cluster distance and intra-cluster distance of each cluster is taken as the DB index of the cluster.

3. The intelligent detection method for machining quality of a CNC machine tool workpiece according to claim 1, characterized in that: Use a binocular camera or a structured light camera to collect point cloud data of each position of the CNC machine tool workpiece to be inspected and the standard CNC machine tool workpiece.

4. The intelligent detection method for machining quality of a CNC machine tool workpiece according to claim 1, characterized in that: The method further includes performing mean filtering on the point cloud data.

5. The intelligent detection method for machining quality of a CNC machine tool workpiece according to claim 1, characterized in that: The final point cloud data and the point cloud data of each position of the standard CNC machine tool workpiece are spliced using the ICP algorithm.

6. The intelligent detection method for machining quality of a CNC machine tool workpiece according to claim 1, characterized in that: The set area threshold is 0.

5.

7. The intelligent detection method for machining quality of a CNC machine tool workpiece according to claim 1, characterized in that: The method further includes performing secondary clustering on the point cloud data in the corresponding cluster whose DB index is greater than the second set threshold and less than the first set threshold.

8. An intelligent detection system for the processing quality of CNC machine tools, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent detection method for machining quality of a CNC machine tool workpiece according to any one of claims 1 to 7 is implemented.

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