Engine part burr detecting and cleaning method capable of preventing cleaning leakage

Abstract

The invention relates to the technical field of engine part machining, in particular to an engine part burr detecting and cleaning method capable of preventing cleaning leakage. The objective of the invention is to solve the technical problems of high cleaning missing rate, insufficient detection precision, poor cleaning pertinence and low production efficiency in the existing engine part burr detection and cleaning separated process. The method sequentially comprises the steps of workpiece reference positioning, replacement of a detection tool integrated with a vision and laser contour scanning module, data acquisition through full-area scanning, generation of a burr feature table through data fusion, replacement and cleaning of the tool according to the feature table, parameter adjustment and fixed-point cleaning execution, final reinspection, and parameter adjustment and re-cleaning if the tool is unqualified. The method can significantly reduce the burr cleaning missing rate, guarantees the cleaning reliability, improves the detection precision and the cleaning pertinence, and prevents the workpiece from being damaged.

Description

Technical Field

[0001] This invention relates to the field of engine component processing technology, and in particular to a method for detecting and cleaning burrs on engine components to prevent missed cleaning. Background Technology

[0002] The machining accuracy of engine components (such as cylinder blocks, cylinder heads, and seat ring bottom holes) directly affects the engine's assembly accuracy and operational stability. After drilling and tapping at the drilling and tapping center, tiny burrs are easily left on their surfaces and holes (hole chamfers, hole inner walls, intersecting channels, etc.). These burrs are generated by material plastic deformation or cutting separation. If not thoroughly removed, they will affect the assembly and performance of the components. Therefore, burr detection and cleaning are crucial steps in engine component machining. Currently, the industry generally adopts a separate processing solution: "machining at the drilling and tapping center, manual offline inspection, and secondary rework cleaning." However, the aforementioned existing technical solutions have inherent limitations and are difficult to meet the high-precision machining requirements of engine parts: the disconnect between the inspection and cleaning processes requires secondary clamping or transfer of the workpiece, which not only prolongs the processing cycle but may also cause hidden burrs to be missed due to clamping errors. Moreover, the hole layout of engine parts is complex and there are many deep holes, making it difficult for manual inspection to fully cover the inner walls of the holes and the joints of intersecting channels, resulting in a high risk of missing small burrs. The inspection method of manual visual inspection combined with simple tools is not accurate enough to accurately obtain key information such as the three-dimensional position, size and type of burrs, resulting in a lack of targeted cleaning in subsequent processes. The core advantages of drilling and tapping centers, such as high-precision positioning and automatic tool changing, are not coordinated with the burr inspection and cleaning processes, resulting in low equipment utilization and low efficiency of manual inspection and cleaning, which significantly restricts the improvement of production efficiency. At the same time, the existing cleaning process uses fixed parameters and is not adapted to the characteristics of burrs, which can easily lead to incomplete cleaning or over-cleaning that damages the workpiece. Summary of the Invention

[0003] This application discloses a method for detecting and cleaning burrs on engine parts to prevent missed cleaning, thereby solving the technical problems of low efficiency and insufficient detection and cleaning accuracy in related engine part detection and cleaning schemes.

[0004] To solve the above problems, the present invention adopts the following technical solution: This invention provides a method for detecting and cleaning burrs on engine parts to prevent missed cleaning. Applied to a drilling and tapping center, the method includes the following steps: S1, the engine parts to be processed are clamped on the worktable of the drilling and tapping center, and the workpiece is positioned using the center's positioning system to obtain its three-dimensional coordinate reference; S2, the tool magazine of the drilling and tapping center automatically changes to a detection tool, which integrates a vision inspection module and a laser contour scanning module; S3, the drilling and tapping center spindle drives the detection tool along a preset path, simultaneously activating the vision inspection module and the laser contour scanning module to inspect the surface, chamfers of holes, and other surfaces of the engine parts to be processed. S4. The inner wall of the hole is scanned and inspected in its entirety, and surface image data and inner wall contour data are collected respectively. S5. The surface image data and inner wall contour data are fused using a data fusion algorithm to generate a burr feature table containing the three-dimensional coordinates of the burr location, burr size, and burr type. S6. The drilling and tapping center automatically changes the corresponding cleaning tool according to the burr feature table, adjusts the spindle speed and feed rate to adapt to the burr characteristics, and performs fixed-point burr cleaning. S7. After cleaning, the drilling and tapping center drives the inspection tool again to re-inspect the cleaned area. If the re-inspection is qualified, the processing is completed. If it is not qualified, the process returns to step S5 to adjust the parameters and clean again.

[0005] Preferably, the visual inspection module in step S2 includes an industrial camera and a ring-shaped supplementary light source, wherein the image resolution of the industrial camera is greater than or equal to 12 million pixels and the frame rate is greater than or equal to 30fps.

[0006] Preferably, the laser contour scanning module in step S2 has a scanning resolution of 0.005-0.01 mm, a scanning step size of 0.02-0.03 mm, and a laser emission wavelength of 635-650 nm.

[0007] Preferably, step S4 specifically includes the following steps: S40, firstly, perform grayscale conversion and filtering denoising preprocessing on the surface image data, and extract the two-dimensional contour of the burr through the edge detection algorithm; S41, perform point cloud denoising and fitting processing on the contour data of the inner wall of the hole to obtain the three-dimensional contour of the burr; S42, fuse the two-dimensional contour and the three-dimensional contour through coordinate registration to generate complete burr feature data.

[0008] Preferably, the preset path in step S3 is generated by the drilling and tapping center control system based on the three-dimensional model of the engine parts, covering all holes, corners and joints of the workpiece, and the path repeatability positioning accuracy does not exceed ±0.002mm.

[0009] Preferably, the burr types mentioned in step S4 include cutting burrs, extrusion burrs, and folding burrs, and the types are distinguished by the grayscale gradient of the surface image data and the height difference of the laser contour scanning data.

[0010] Preferably, the testing tool in step S2 is detachably connected to the drilling and tapping center spindle via an ER chuck, and the chuck clamping accuracy is not less than 0.005mm.

[0011] Preferably, the burr feature table in step S4 further includes burr hardness evaluation data, which is calculated by the burr springback amount detected by the laser contour scanning detection module.

[0012] Preferably, during the re-inspection process in step S6, if the burr size is still greater than 0.05mm, it is determined to be unqualified, and the drilling and tapping center control system automatically increases the spindle speed by 10%-20% and reduces the feed rate by 5%-10% before re-performing the cleaning.

[0013] Preferably, step S6 is followed by a data storage step S7: the drilling and tapping center control system stores the three-dimensional coordinate reference, burr feature table, cleaning parameters and re-inspection results of each workpiece in real time.

[0014] The technical solution adopted in this invention can achieve the following beneficial effects: 1. By employing an inspection tool with an integrated vision inspection module and a laser contour scanning module, surface image data and inner wall contour data of parts are simultaneously acquired. Combined with a data fusion algorithm, comprehensive burr information is obtained, effectively covering hidden areas such as inner walls of holes and joints of intersecting channels that are difficult to reach with existing manual inspection. This solves the problem of missed burrs caused by secondary clamping errors and blind spots in traditional separate processes. With the immediate re-inspection after cleaning and the rework of non-conforming parts, a complete quality closed loop is formed, which can control the rate of missed burrs to an extremely low level, ensuring the assembly accuracy and operational stability of engine parts.

[0015] 2. The data fusion algorithm for the collaborative processing of dual-modal detection data can accurately extract key information such as the three-dimensional coordinates, size, and type of burrs, overcoming the shortcomings of manual inspection which is greatly affected by subjective factors and lacks accuracy. The drilling and tapping center adapts the cleaning tool and adjusts the spindle speed and feed rate based on the burr feature table to achieve vertex cleaning, effectively avoiding the problem of workpiece surface scratches caused by incomplete or excessive cleaning under the existing fixed parameter cleaning method, and ensuring the workpiece machining accuracy.

[0016] 3. This method integrates the high-precision positioning and automatic tool changing functions of the drilling and tapping center with the burr detection and cleaning processes, realizing an integrated process of "positioning-detection-cleaning-re-inspection". It eliminates the need for secondary clamping or workpiece transfer, which helps to solve the problem of existing drilling and tapping centers only performing cutting functions and having low equipment utilization. At the same time, it replaces inefficient manual detection and cleaning operations, significantly shortening the single-piece processing cycle. Combined with the planning of preset paths, it can significantly improve the efficiency of burr treatment and effectively increase the overall capacity of the production line.

[0017] 4. This method achieves full-process associated storage of workpiece three-dimensional coordinate reference, burr feature table, cleaning parameters and re-inspection results through the drilling and tapping center control system. It solves the deficiency of existing technology in lacking traceability basis. The relevant data can provide direct support for tracing the source of quality problems, facilitate the rapid location of the root cause of problems such as missed cleaning, and accumulate data foundation for subsequent process optimization, helping the production line to achieve continuous quality improvement and process iteration.

[0018] 5. This method relies on the program adaptability of the drilling and tapping center. By adjusting the preset path and detection parameters, it can adapt to different hole layouts and the processing requirements of engine parts of different specifications. There is no need to configure additional special detection or cleaning equipment, which reduces the equipment investment cost of the production line and enhances the ability to adapt to the diversified production needs of engine parts. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This application discloses a flowchart of a method for detecting and cleaning burrs on engine parts to prevent missed cleaning, based on some embodiments of the present application. Figure 1 ; Figure 2 This application discloses a flowchart of a method for detecting and cleaning burrs on engine parts to prevent missed cleaning, based on some embodiments of the present application. Figure 2 ; Detailed Implementation To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0022] Existing processes for burr detection and cleaning of engine parts employ a separate workflow, which suffers from problems such as high omission rates, insufficient detection accuracy, poor cleaning targeting, low production efficiency, and lack of traceability mechanisms, failing to meet the demands of high-precision machining. This embodiment addresses these technical issues through an integrated workflow design.

[0023] The following is in conjunction with the appendix Figures 1 to 2 This paper provides a detailed description of a method for detecting and cleaning burrs on engine parts to prevent missed cleaning, through specific embodiments and application scenarios.

[0024] The present invention provides a method for detecting and cleaning burrs on engine parts to prevent missed cleaning, comprising the following steps: S1, clamp the engine parts to be processed onto the worktable of the drilling and tapping center, and complete the workpiece reference positioning through the positioning system of the drilling and tapping center to obtain the three-dimensional coordinate reference of the workpiece. S2, the tool magazine of the drilling and tapping center automatically changes to the inspection tool, and the inspection tool integrates a vision inspection module and a laser contour scanning module; S3, the drilling and tapping center spindle drives the inspection tool to move along the preset path, and simultaneously starts the vision inspection module and the laser contour scanning module to perform full-area scanning inspection of the surface, orifice chamfer and inner wall of the engine parts to be processed, and collects surface image data and inner wall contour data respectively. S4, the surface image data and the inner wall contour data of the hole are fused by the data fusion algorithm to generate a burr feature table containing the three-dimensional coordinates of the burr location, the burr size and the burr type; S5, the drilling and tapping center automatically changes the corresponding cleaning tool according to the burr characteristic table, adjusts the spindle speed and feed rate to adapt to the burr characteristics, and performs fixed-point burr cleaning; S6. After cleaning is completed, the drilling and tapping center drives the inspection tool again to re-inspect the cleaned area. If the re-inspection is qualified, the processing is completed. If it is not qualified, return to step S5 to adjust the parameters and clean again.

[0025] Specifically, the drilling and tapping center uses the ND512D six-sided CNC drilling center. This equipment integrates multiple processing functions, adopts high-precision gear rack and pinion and servo drive, and achieves a repeatability of 0.010mm for the X, Y1, Y2, Z1, and Z2 axes. It has a maximum rapid traverse speed of 48m / min and a power-off position memory function, enabling rapid workpiece positioning. The advantages of choosing this model are precise positioning, stable operation, and suitability for multi-hole machining scenarios of engine parts. In practical applications, other models can also be selected for this component; this application embodiment does not limit this choice. The engine part to be processed can be an engine cylinder block, made of aluminum alloy, and clamped to the drilling and tapping center using a hydraulic clamp. When clamping the workpiece, ensure the workpiece reference surface is in contact with the worktable, and control the clamping pressure at 0.6-0.8MPa to avoid workpiece deformation. The tool holder of the inspection tool is made of 40Cr material and is connected to the drilling and tapping center spindle through an ER chuck. The vision inspection module is integrated on the front side of the inspection tool, and the laser contour scanning module is integrated on the front end face of the inspection tool. The data fusion algorithm adopts a feature layer fusion algorithm, specifically a fusion strategy based on wavelet transform. The cleaning tool is selected according to the burr type; specifically, a carbide scraper is used for soft burrs, and an ultrasonic cleaning head is used for hard burrs. The initial spindle speed is set to 8000r / min, and the initial feed rate is set to 0.1mm / r.

[0026] Specifically, firstly, a three-dimensional coordinate reference for the workpiece is established using a drilling and tapping center positioning system to ensure accurate positioning for inspection and cleaning; then, the inspection tool is switched, and dual inspection modules are simultaneously activated to scan along a preset path, comprehensively collecting burr-related data; the data is processed through a data fusion algorithm to obtain an accurate burr feature table, providing a basis for targeted cleaning; based on the feature table, the cleaning tool is switched and the processing parameters are adjusted to achieve fixed-point cleaning; finally, a quality closed loop is formed through re-inspection, and if it fails to meet the requirements, the parameters are readjusted for cleaning.

[0027] Understandably, this method enables an integrated process of detection, cleaning, and re-inspection, avoiding secondary clamping errors and reducing the rate of missed cleaning of minor burrs; the dual detection modules work together to improve detection accuracy, and the targeted cleaning parameters can also prevent workpiece damage; relying on the high efficiency of the drilling and tapping center, this method can significantly improve production efficiency.

[0028] Furthermore, in step S2, the visual inspection module includes an industrial camera and a ring-shaped fill light source. The image resolution of the industrial camera is greater than or equal to 12 million pixels, and the frame rate is greater than or equal to 30fps.

[0029] Specifically, in this embodiment, the industrial camera selected is the Hikvision Robotics MV-CH120-60GM model. This model uses a stacked BSI series sensor, which has a significantly better signal-to-noise ratio and dynamic range than cameras of the same level. It exhibits excellent image contrast and detail in both bright and dark areas, and enhances its response to the near-infrared band, enabling it to clearly capture images of minute burrs. The advantage of selecting this model is its high image quality and adaptability to the complex lighting conditions of industrial processing environments. In practical applications, other suitable models can also be selected for this component, and this embodiment does not limit this choice. The ring-shaped supplementary light source is a ring-shaped LED light source with a wavelength of 550-650nm, a power of 10W, and an adjustable light intensity range of 0-1000lux. It is fixed to the outside of the vision inspection module via a threaded connection and is coaxially set with the optical axis of the industrial camera to ensure uniform illumination covering the inspection area.

[0030] Understandably, the ring-shaped supplementary light source provides uniform illumination to the detection area, eliminating the influence of shadows and reflections on imaging. The 12-megapixel high-resolution industrial camera quickly captures images of the workpiece surface, and the frame rate of over 30fps ensures that no image information is missed during the movement of the inspection tool, clearly presenting the burr features of the surface and the chamfer of the hole. This design can improve the clarity and integrity of the burr images of the workpiece surface and the chamfer of the hole, making the burr edge contour clearer, reducing the false and false detection rates of burr identification, and providing high-quality image data for subsequent data fusion processing.

[0031] Furthermore, in step S2, the scanning resolution of the laser contour scanning module is 0.005-0.01 mm, the scanning step size is 0.02-0.03 mm, and the laser emission wavelength is 635-650 nm.

[0032] Specifically, in this embodiment, the laser contour scanning module uses a small 3D contour scanner. This type of 3D contour scanner has high scanning accuracy and fast response speed, making it suitable for scanning and detecting hidden areas such as the inner walls of deep holes. The laser emission wavelength is specifically selected as 650nm. This wavelength of laser has moderate laser penetration, which can avoid excessive reflection from the surface of the aluminum alloy workpiece and ensure stable scanning signal. The scanning resolution is set to 0.008mm, and the scanning step size is set to 0.025mm, balancing scanning accuracy and scanning efficiency.

[0033] Specifically, in actual operation, the laser contour scanning module scans at a constant speed along the axial and circumferential direction of the inner wall of the hole, emitting a laser with a wavelength of 635-650nm. After the laser shines on the burr surface, it is reflected to form a reflected signal. The module calculates the three-dimensional contour data of the burr by receiving the reflected signal. The high scanning resolution of 0.005-0.01mm ensures that no tiny burrs are missed, and the reasonable scanning step size ensures accuracy while avoiding excessively low scanning efficiency.

[0034] Understandably, using a laser contour scanning module can accurately identify tiny burrs in hidden areas such as the inner wall of a hole, improving the accuracy and completeness of the scanning data, providing accurate three-dimensional contour data for data fusion processing, and further reducing the missed detection rate.

[0035] Further, step S4 specifically includes the following steps: S40, first perform grayscale conversion and filtering denoising preprocessing on the surface image data, and extract the two-dimensional contour of the burr through the edge detection algorithm; S41, perform point cloud denoising and fitting processing on the contour data of the inner wall of the hole to obtain the three-dimensional contour of the burr; S42, fuse the two-dimensional contour and the three-dimensional contour through coordinate registration to generate complete burr feature data.

[0036] Specifically, in this embodiment, grayscale processing uses a weighted average method, with weight coefficients set to R=0.299, G=0.587, and B=0.114; filtering and denoising preprocessing uses a median filtering algorithm, with a filtering window size set to 3×3; edge detection uses the Canny edge detection algorithm, with a threshold range set to 50-150. Point cloud denoising uses a statistical filtering algorithm, with the number of neighboring points set to 10 and the standard deviation factor set to 1.5; fitting processing uses the least squares method to perform surface fitting on the denoised point cloud data; coordinate registration uses the ICP (Iterative Closest Point) algorithm, with the number of iterations set to 50 and the convergence threshold set to 0.001mm, using the three-dimensional coordinate reference established through the drilling center as the registration reference.

[0037] Specifically, the surface image data and the inner wall contour data of the hole are preprocessed separately to eliminate noise interference and extract their respective burr contour features. Then, using the three-dimensional coordinate reference established by the drilling center as a reference, the two-dimensional contour data and the three-dimensional contour data are fused into the same coordinate system through the coordinate registration algorithm to obtain feature data containing complete burr information.

[0038] Understandably, this solution can effectively eliminate data noise and improve the accuracy of burr contour extraction; coordinate registration ensures the accurate fusion of dual-modal data, making the generated burr feature data more complete and accurate, providing a reliable basis for subsequent targeted cleaning.

[0039] Furthermore, in step S3, the preset path is generated by the drilling and tapping center control system based on the three-dimensional model of the engine parts, covering all holes, corners and joints of the workpiece, and the path repeatability positioning accuracy does not exceed ±0.002mm.

[0040] Specifically, the 3D model of the engine components is created using UG software, exported as STEP format, and imported into the drilling and tapping center control system. In this embodiment, the drilling and tapping center control system adopts the Siemens 828D CNC system, which has a precise path planning function and can automatically generate the optimal scanning path based on the 3D model. The path planning accuracy is high and the operation is convenient. In practical applications, other models of this component can also be selected, and this application embodiment does not limit this. When generating the path, a partitioned scanning strategy is adopted, dividing the workpiece into three parts: surface area, hole area, and edge and joint area. Each area independently plans the path and then splices them together. The path repeatability positioning accuracy is guaranteed by the servo drive system of the drilling and tapping center, specifically through feedback adjustment by a high-precision encoder to ensure that the positioning error is controlled within ±0.002mm.

[0041] Understandably, this embodiment ensures that the scanning path fully covers all areas of the workpiece prone to burrs, avoiding blind spots in detection; the path repeatability positioning accuracy is high, ensuring the accuracy of the detection position and further reducing the missed detection rate.

[0042] Furthermore, in step S4, the burr types include cutting burrs, extrusion burrs, and folding burrs, and the types are distinguished by the grayscale gradient of the surface image data and the height difference of the laser contour scanning data.

[0043] Specifically, the grayscale gradient is calculated using the Sobel operator with a calculation window size of 3×3. Grayscale gradient thresholds and height difference thresholds are set: the grayscale gradient range for cutting burrs is 80-120, and the height difference range is 0.05-0.1mm; the grayscale gradient range for extrusion burrs is 120-160, and the height difference range is 0.1-0.15mm; the grayscale gradient range for folding burrs is 160-200, and the height difference range is 0.15-0.2mm. Burr types are distinguished by comparing the calculated grayscale gradients and height differences with the set thresholds.

[0044] Understandably, different types of burrs have different formation mechanisms, resulting in differences in surface roughness and three-dimensional morphology, leading to variations in the grayscale gradient of the surface image and the height difference of the laser scan. By calculating the grayscale gradient of the surface image and the height difference of the laser contour data, and comparing them with preset threshold ranges corresponding to different types of burrs, the burr type can be accurately distinguished. This method can achieve precise differentiation of burr types, providing an accurate basis for subsequent selection of suitable cleaning tools and adjustment of cleaning parameters, improving the targeting and effectiveness of cleaning, and further avoiding workpiece damage.

[0045] Furthermore, in step S2, the detection tool is detachably connected to the drilling and tapping center spindle via an ER chuck, and the chuck clamping accuracy is not less than 0.005mm.

[0046] Specifically, in this embodiment, the ER chuck is selected as the ER32 high-precision chuck. This model of chuck has a wide clamping range and high clamping accuracy, and can stably clamp the tool holder of the test tool, ensuring small coaxiality error after connection. The advantages of selecting this model are reliable clamping, high precision and convenient disassembly, which is suitable for the automatic tool change requirements of the drilling and tapping center. In practical applications, other models can also be selected for this component, and this embodiment does not limit this. The ER chuck is fixed to the spindle of the drilling and tapping center by a threaded connection with a thread specification of M16×1.5. The tightening torque is controlled at 50-60 N·m during connection. The diameter of the test tool holder is set to 16 mm, which is compatible with the clamping range of the ER32 chuck.

[0047] Understandably, the test tool holder is inserted into the clamping hole of the ER chuck, and the chuck is tightened by the automatic tool changer of the drilling and tapping center to fix the test tool; the elastic deformation of the ER chuck generates clamping force to ensure that the test tool is coaxial with the spindle; during disassembly, the chuck is released by the automatic tool changer to remove the test tool.

[0048] Furthermore, the burr feature table in step S4 also includes burr hardness assessment data, which is calculated by the burr springback amount detected by the laser contour scanning module.

[0049] Specifically, the laser contour scanning module uses pulsed laser to irradiate the burr surface, records the time difference between laser emission and reception of the reflected signal, and calculates the springback amount of the burr after irradiation; a preset table of correspondence between springback amount and hardness is used, and the smaller the springback amount, the higher the hardness of the corresponding burr; by matching the detected springback amount with the corresponding table, the hardness evaluation data of the burr is calculated, and the hardness evaluation accuracy is ±5HV.

[0050] Understandably, when a laser irradiates a burr surface, the burr will undergo slight rebound deformation. Burrs with different hardnesses will have different rebound amounts; the higher the hardness, the smaller the rebound amount. By detecting the rebound amount through a laser contour scanning module and combining it with a preset rebound amount-hardness correspondence, the burr hardness assessment data can be calculated and added to the burr feature table. This method enriches the information dimensions of the burr feature table, providing a more comprehensive basis for the precise adjustment of cleaning parameters. It allows the cleaning parameters to simultaneously adapt to the type, size, and hardness of the burr, further improving the cleaning effect and avoiding incomplete or over-cleaning due to hardness mismatch.

[0051] Furthermore, during the re-inspection process in step S6, if the burr size is still greater than 0.05mm, it is determined to be unqualified. The drilling and tapping center control system automatically increases the spindle speed by 10%-20% and reduces the feed rate by 5%-10% before re-performing the cleaning.

[0052] Specifically, the acceptable threshold for burr size is set at 0.05mm. The burr size in the re-inspection area is detected by the laser contour scanning module and compared with the acceptable threshold. If it is unacceptable, the drilling and tapping center control system automatically calculates and adjusts the parameters based on the current spindle speed and feed rate. For example, if the current spindle speed is 8000r / min, it is increased by 15% and adjusted to 9200r / min; if the current feed rate is 0.1mm / r, it is decreased by 8% and adjusted to 0.092mm / r. The scanning path during the re-cleaning is consistent with the initial cleaning path to ensure accurate cleaning position.

[0053] Understandably, clarifying the parameter adjustment standards for non-conforming workpieces makes the parameter adjustments for re-cleaning more scientific and precise. The combination of increasing the rotation speed and decreasing the feed rate can effectively improve the cleaning capability, ensure that the burr size meets the standard after re-cleaning, and at the same time avoid workpiece damage caused by over-adjustment, thereby improving the reliability of the quality closed loop.

[0054] Furthermore, the method also includes a data storage step S7, in which the drilling and tapping center control system stores the three-dimensional coordinate reference, burr feature table, cleaning parameters and re-inspection results of each workpiece in real time.

[0055] Specifically, during the machining process, the drilling and tapping center control system collects relevant data from each stage in real time. The stored data includes the workpiece number, the X / Y / Z axis coordinate values ​​of the three-dimensional coordinate reference, complete data of the burr feature table (location, size, type, hardness), the spindle speed and feed rate for each cleaning, and the re-inspection results (pass / fail, burr size when unqualified). After being organized according to a preset format, the data is stored in a solid-state drive. All data for each workpiece is stored together to form a complete machining data archive. When it is necessary to trace quality issues later, the corresponding machining data can be retrieved by the workpiece number.

[0056] Understandably, adding a data storage step to this method enables traceability of data throughout the entire processing flow, facilitating the rapid identification of the root causes of quality issues such as omissions and errors (e.g., detection deviations, improper parameter adaptation). The large amount of stored processing data provides data support for subsequent process optimization, allowing for the continuous improvement of processing quality and efficiency through data analysis to optimize detection parameters, cleanup parameters, and so on.

[0057] Specifically, for ease of understanding, taking the inspection of three key holes (intake port, exhaust port, and bolt holes) in an engine block (aluminum alloy material) as an example, the process of generating a burr feature table is as follows: 1. Scanning and acquisition: Surface image data of three holes are acquired through a vision inspection module, and contour data of the inner wall of the holes are acquired through a laser contour scanning module to determine the presence of suspected burrs at the air inlet, exhaust hole wall, and bolt hole bottom. 2. Data preprocessing: The surface image of the air inlet orifice was converted to grayscale (weighted average method) and filtered by median (3×3 window). The two-dimensional contour of the burr was extracted by the Canny algorithm (threshold 50-150), and its grayscale gradient was determined to be 95. The contour data of the exhaust hole wall was subjected to statistical filtering (10 neighborhood points, standard deviation multiple 1.5) and least squares fitting to obtain the three-dimensional contour of the burr. Its height difference was calculated to be 0.12mm. 3. Type and hardness determination: The gray scale gradient of the burr at the air inlet is 95 (80-120 range) and the height difference is 0.08mm (0.05-0.1mm range), which is determined to be a cutting burr; the springback of the burr after laser irradiation is 0.02mm. According to the preset correspondence table, the hardness evaluation value is 120HV. 4. Coordinate registration: Using the three-dimensional coordinate reference (X0=100mm, Y0=50mm, Z0=30mm) established at the drilling and tapping center as a reference, the two-dimensional / three-dimensional contour data of each burr are registered to the same coordinate system through the ICP algorithm to obtain accurate three-dimensional coordinates; 5. Generate a table: Integrate all the above information to form a complete burr feature table as follows: Table 1, Burr Characteristics Table; It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0058] Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0059] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting and cleaning burrs on engine parts to prevent missed cleaning, applied to drilling and tapping centers, characterized in that, Includes the following steps: S1, clamp the engine parts to be processed onto the worktable of the drilling and tapping center, and complete the workpiece reference positioning through the positioning system of the drilling and tapping center to obtain the three-dimensional coordinate reference of the workpiece. S2, the tool magazine in the drilling and tapping center automatically replaces the inspection tool, which integrates a vision inspection module and a laser contour scanning module; S3, the drilling and tapping center spindle drives the inspection tool to move along the preset path, and simultaneously starts the vision inspection module and the laser contour scanning module to perform full-area scanning inspection of the surface, orifice chamfer and inner wall of the engine parts to be processed, and collects surface image data and inner wall contour data respectively. S4, the surface image data and the inner wall contour data of the hole are fused by the data fusion algorithm to generate a burr feature table containing the three-dimensional coordinates of the burr location, the burr size and the burr type; S5, the drilling and tapping center automatically changes the corresponding cleaning tool according to the burr characteristic table, adjusts the spindle speed and feed rate to adapt to the burr characteristics, and performs fixed-point burr cleaning; S6. After cleaning is completed, the drilling and tapping center drives the inspection tool again to re-inspect the cleaned area. If the re-inspection is qualified, the processing is completed. If it is not qualified, return to step S5 to adjust the parameters and clean again.

2. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning as described in claim 1, characterized in that, The visual inspection module in step S2 includes an industrial camera and a ring light source. The image resolution of the industrial camera is greater than or equal to 12 million pixels, and the frame rate is greater than or equal to 30fps.

3. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning as described in claim 1, characterized in that, The laser contour scanning module described in step S2 has a scanning resolution of 0.005-0.01 mm, a scanning step size of 0.02-0.03 mm, and a laser emission wavelength of 635-650 nm.

4. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning as described in claim 1, characterized in that, Step S4 specifically includes the following steps: S40: First, perform grayscale conversion and filtering denoising preprocessing on the surface image data, and then extract the two-dimensional contour of the burr through the edge detection algorithm; S41, perform point cloud noise reduction and fitting on the inner wall contour data of the hole to obtain the three-dimensional contour of the burr. S42 uses coordinate registration to fuse the two-dimensional and three-dimensional contours, generating complete burr feature data.

5. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning as described in claim 1, characterized in that, The preset path mentioned in step S3 is generated by the drilling and tapping center control system based on the three-dimensional model of the engine parts, covering all holes, corners and joints of the workpiece, and the path repeatability positioning accuracy does not exceed ±0.002mm.

6. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning as described in claim 4, characterized in that, The burr types mentioned in step S4 include cutting burrs, extrusion burrs, and folding burrs, and the types are distinguished by the grayscale gradient of the surface image data and the height difference of the laser contour scanning data.

7. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning as described in claim 1, characterized in that, The testing tool mentioned in step S2 is detachably connected to the drilling and tapping center spindle via an ER chuck, and the chuck clamping accuracy is not less than 0.005mm.

8. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning according to claim 1, characterized in that, The burr feature table in step S4 also includes burr hardness evaluation data, which is calculated by the burr springback amount detected by the laser contour scanning detection module.

9. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning according to claim 1, characterized in that, If, during the re-inspection process described in step S6, the burr size is still greater than 0.05 mm, it is deemed unqualified. The drilling and tapping center control system will automatically increase the spindle speed by 10%-20% and decrease the feed rate by 5%-10% before re-performing the cleaning.

10. The method for detecting and cleaning burrs on engine parts to prevent missed cleaning according to claim 1, characterized in that, Step S6 is followed by data storage step S7: The drilling and tapping center control system stores the three-dimensional coordinate reference, burr feature table, cleaning parameters and re-inspection results of each workpiece in real time.

Images