486 results about "Product defect" patented technology

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

The invention provides a production line product quality traceability method based on industrial vision, and the method comprises the steps: extracting key feature parameters, including a texture orientation angle, a texture period and a surface roughness parameter, capable of reflecting the wear degree of a tool from the preprocessed texture image data, and constructing a multi-dimensional feature vector of the surface micro texture of a product; according to the multi-dimensional feature vector of the product surface micro-texture, in combination with the corresponding relation between the tool wear degree and the surface texture feature, obtaining a wear degree estimated value of the current tool, and comparing the wear degree estimated value with a wear early warning threshold value; and for the identified suspected defective products, identifying defect types by analyzing surface textures, comprehensively analyzing material attributes and designing dimensional tolerance factors, and judging whether surface texture abnormity is caused by tool wear or product defects.

The invention provides an intelligent detection system for forging defects of a forge piece product, and relates to the technical field of industrial intelligent detection.The intelligent detection system comprises the steps that multi-angle images of a to-be-detected workpiece are collected and then spliced, and a complete surface expansion view of the product is generated; obtaining defect types of the defect candidate regions through region coordinates and region sizes of the defect candidates; obtaining a confidence value of a corresponding defect type judgment result; a lightweight deep network recognition model is called for secondary judgment, new sample data are generated in a manner of supporting manual annotation, and an equipment end is connected with a programmable controller for intelligent defect detection of the workpiece to be detected. According to the invention, the problems of incomplete defect coverage, failure to realize high-precision automatic identification of multiple types of defects and influence on the detection accuracy and the production efficiency caused by diversified and complex forging surface defects and limited image acquisition angles in the prior art can be solved, comprehensive acquisition and splicing of multi-angle images are realized, and the detection accuracy and the production efficiency are improved. The technical effect of improving the defect detection accuracy of the forge piece product is achieved.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention provides an industrial product defect automatic classification method and system, and the method comprises the steps: collecting original industrial product defect image data, and constructing a labeled image sample set and an unlabeled image sample set; constructing a training image sample set based on the labeled image sample set and the unlabeled image sample set in combination with a plurality of image synthesis strategies; based on the training image sample set, introducing a transfer learning strategy and fusing an attention mechanism, and constructing and optimizing an industrial product defect classification model; performing semi-supervised joint training and online learning based on the training image sample set and the real-time small-batch image sample set; and constructing an industrial product defect identification log based on the real-time image flow sample set, the classification model parameters and the corresponding classification prediction function. On the basis of multi-strategy image enhancement and semi-supervised training, transfer learning and a channel attention mechanism are fused, expansion of industrial product defect image samples and fine defect identification are achieved, and the method is suitable for an intelligent defect detection system in various industrial manufacturing fields.

The invention discloses an industrial zero sample anomaly detection method and system based on cross-modal prompt learning, and relates to the technical field of computer vision and deep learning. The method comprises the following steps: acquiring RGB images and point cloud data containing product defects and normal states in an industrial scene, and preprocessing the point cloud data; constructing an anomaly detection model, preliminarily extracting information representation of the RGB image and the point cloud data by using the anomaly detection model, and performing further feature extraction and semantic alignment operation through prompt information; training the anomaly detection model based on a cross-modal collaborative mechanism, and testing the trained anomaly detection model by adopting a collaborative modulation strategy; and performing anomaly detection on a to-be-detected product by using the trained anomaly detection model. According to the method, the real-time defect detection precision and robustness can be remarkably improved in an industrial environment, especially under the conditions of data scarcity and complex modality.

A flexible highly-smart defect detection apparatus and a control method. The apparatus comprises a plurality of rotating fixtures (1), a detection module (2), a plurality of flipping modules and an image processing system (5). Each rotating fixture (1) is provided with a gripping assembly (11) for gripping a product, and drives the gripping assembly to rotate to adjust the angle of the product. The detection module (2) comprises a six-axis robotic arm (21), a detection camera (22) and a lighting lamp (23), wherein the six-axis robotic arm (21) adjusts the detection angle of the detection camera (22). Each rotating fixture (1) cooperates with the six-axis robotic arm (21) to enable the detection camera (22) to capture an image of a product. After the detection module (2) completes detection of one side of the product, a flipping module (3) flips the product thus enabling detection of the other side of the product. The image processing system (5) connected to the detection camera (22) acquires and segments a detection image from the detection camera (22), and uses a model to perform model inference and area limitation on the segmented detection image, so as to identify product defects.

The invention discloses a rendering-based artificial industrial product defect synthesis method and device. Firstly, modeling and generalization are carried out on the reasonable shape of a 3D model of a normal sample; the modeling defect result of the target sample is simulated by modifying the shielding, the metallization and the roughness, the three variables are designed into parameters or variables which can be updated, and defect simulation is carried out on the corresponding area according to a subsequent loss function. And projecting the sample 3D model into which the defect is introduced into a standard space to obtain an industrial product image of the multi-view defect product with the standard resolution. Calculating image similarities of different artificial samples and a small number of real defect samples; label-based discrimination is carried out on a real sample and a defect sample through an adversarial network of an image, and the similarity between a generated defect and an actual defect is improved through loss functions of the two parts. According to the method, a multi-view defect generation result is optimized instead of evaluating and simulating defects in pixel.

The invention provides a product defect tracing method based on knowledge graph reasoning. The method comprises the following steps: constructing a knowledge graph based on a product hierarchical structure of a bill of materials and a manufacturing process hierarchical structure of a process route; when it is detected that data of a certain node in the knowledge graph is changed, calculating an update priority weight of a change node based on the occurrence frequency of the change node in a historical defect path and the hierarchy depth in the double-hierarchy structure; determining an updating range according to the updating priority weight, and updating the knowledge graph; and positioning defect nodes in the updated knowledge graph according to system response, executing path search from the defect nodes, identifying potential root cause nodes, and realizing product defect tracing. According to the method, accurate and real-time defect traceability of the product is realized based on multi-level feature correlation analysis of causal reasoning and dynamic knowledge graph construction and enhanced reasoning.

The utility model discloses full-automatic FPC appearance detection equipment, which belongs to the technical field of FPC and PCB automatic detection and comprises an equipment main body, a bulk material camera module, a bulk material light source module, a tray pulling module, an empty tray receiving module, a feeding lifting module, a main camera positioning module, a discharging empty tray lifting module, a discharging blister tray suction frame, a defective product chuck structure and a non-defective product chuck structure. A feeding X shaft, a feeding Y shaft, a feeding R shaft, a feeding Z shaft and a suction nozzle at the tail end of the feeding Z shaft are arranged in the equipment main body; the automatic feeding and discharging structure can meet feeding and discharging of double-station detection, efficiency is improved, the automatic empty disc receiving structure runs in a layered mode, the discharging speed is increased, a mechanical arm does not need to wait, efficiency is higher, the double-station structure is adopted, the material rate is two times that of other single stations at the same time, and the production efficiency is improved. The equipment can accurately detect product defects, the detection process is full-automatic and free of external interference, and the efficiency is high by adopting double-station alternate detection.

The invention relates to the technical field of industrial mechanical arm control, in particular to a mechanical arm execution control method for industrial production, which comprises the following steps: collecting parameters in real time; generating a geometric risk index; generating a mechanical risk index; extracting image features; abnormal mechanical arm judgment; determining an adjusting mechanical arm; and generating an adjustment instruction. According to the method, the inclination angle, the plane offset distance, the contact image features, the tightening torque and the force value of the tail end of the mechanical arm are monitored in real time in a multi-dimensional mode, a dual-risk judgment model based on geometric offset and mechanical loading is constructed, image texture fluctuation and posture change are further combined, the continuously abnormal mechanical arm is accurately positioned, and the safety of the mechanical arm is improved. And geometric and mechanical risk indexes are evaluated again after adjustment, so that the aluminum shell positioning and bolt assembling precision is guaranteed, and the problems that the production efficiency is reduced and the product defect rate is increased due to assembling quality fluctuation caused by mechanical arm positioning errors and inaccurate force control are effectively solved.

The invention discloses a product defect unsupervised semantic segmentation method and device based on a pre-training model, and is used for solving the technical problem that the defect classification precision is not ideal due to an existing industrial defect unsupervised semantic segmentation method. The method comprises the following steps: acquiring a to-be-detected industrial product image, an industrial product reference image corresponding to the to-be-detected industrial product image and a plurality of to-be-matched defect categories; a preset segmentation network of a self-learning point generation prompter based on defect potential space features is adopted to generate a target defect segmentation mask according to a to-be-detected industrial product image and an industrial product reference image, and a target cutting image is generated based on the target defect segmentation mask, so that the target cutting image with visual prompt can be generated; and processing the target cutting image and a plurality of defect categories to be matched through a preset defect classification network based on a self-learning defect category prompt module and a defect image-text cross-modal feature interaction module, and combining segmentation and classification, thereby improving the defect classification precision.

The invention discloses a mold injection molding full-process automation system based on layered cooperative control, which comprises a central control layer, a module scheduling layer and an equipment execution layer, and clock synchronization among the layers is realized through an industrial Ethernet. The central control layer comprises a dynamic scheduling server and a digital twinborn verification module, the module scheduling layer comprises a mold intelligent scheduling module, an injection molding process control module and a product quality closed loop module, and the equipment execution layer comprises a manipulator cooperation unit and a sprue material processing unit. By constructing a three-layer cooperative control architecture of a central control layer, a module scheduling layer and an equipment execution layer and combining a modular hardware carrier and a cross-layer data closed loop, common industrial problems such as cooperative level splitting, mold control marginalization and abnormal response delay are systematically solved; and finally, the comprehensive benefits that the die changing efficiency is remarkably improved, the product reject ratio is greatly reduced, and the energy consumption is obviously reduced are achieved.

The invention relates to the technical field of plastic production, in particular to an intelligent processing method and system for plastic mold process parameters. The method comprises the following steps: carrying out a mold cavity pressure test on plastic mold equipment to obtain mold cavity pressure distribution data; constructing a local flow field model of the exhaust groove according to the pressure distribution data of the mold cavity; performing pressure transfer characteristic analysis by using the exhaust duct local flow field model to generate pressure transfer increase characteristic data; predicting the actual exhaust capacity of the exhaust groove according to the pressure transmission increase characteristic data, and generating actual exhaust capacity prediction data; intelligent inflation and exhaust control is carried out through the actual exhaust capacity prediction data, and optimized exhaust control parameters are obtained; and performing exhaust groove cooperative work control according to the optimized exhaust control parameters, and generating intelligent exhaust control sequence data. By dynamically adapting to analysis of the pressure change of the mold cavity, accurate control over intelligent inflation and exhaust is achieved, the exhaust efficiency is optimized, product defects are reduced, and the quality of plastic products is effectively improved.

The invention discloses a quality defect root cause tracing method based on linkage of standard data and supply chain data, and relates to the technical field of quality defect tracing, and the method comprises the steps: constructing a standard parameter model according to a product design technology, and constructing a supply chain multi-dimensional process data model according to product production data; performing identification code mapping and time sequence alignment, and establishing a standard-supply chain dynamic linkage model; detecting to obtain product quality defect data, and determining a product quality deviation index and a standard index deviation value; constructing a product defect directed traceability graph; and carrying out root cause defect quantification on the product defect directed traceability graph, generating a defect root cause probability graph, and carrying out root cause visual traceability. The technical problems of inaccurate product quality defect root cause positioning and low decision-making efficiency in the prior art are solved, and the technical effects of realizing the linkage tracing of the quality defect root cause by the standard data and the supply chain data and improving the root cause positioning accuracy and the decision-making efficiency are achieved.

The invention relates to the technical field of image processing, in particular to a magnetic material product surface defect simulation method and system. The method comprises the following steps: carrying out physical field coupling modeling on a magnetic material product to obtain a physical field coupling model of the product; carrying out product defect image acquisition on the magnetic material product to obtain a product surface defect image set; based on the product surface defect image set, performing defect form pedigree database construction on the magnetic material product to obtain a product defect form pedigree database; performing product defect form environment interference simulation on the magnetic material product to obtain defect detection environment interference simulation data; and performing space-time defect response simulation on the magnetic material product based on the defect detection environment interference simulation data to obtain a product defect detection environment dynamic model. The simulation result of the method better conforms to the actual production condition, and the practical adaptability is enhanced.

The invention relates to the technical field of digital twinning, in particular to a digital twinning monitoring system for a precision servo press production line, which comprises a physical production line module, an edge calculation module, a cloud digital twinning module and a visual monitoring module, wherein the physical production line module is used for monitoring and collecting production line data in real time; the edge calculation module preprocesses and analyzes the collected original data, judges product defects, equipment faults and employee abnormal states in real time to trigger edge early warning, and outputs a multi-dimensional data set; the cloud digital twinborn module is used for constructing a digital twinborn body, establishing a real-time coupling simulation model fusing electromagnetism, heat, force and sound to simulate the internal physical process of equipment in real time, predicting the performance change and potential fault of the equipment, positioning the fault root cause and predicting the residual life; and the visual monitoring module is used for carrying out multi-dimensional visual presentation and providing decision support. Therefore, the problems of insufficient production line staff monitoring, single physical field monitoring, lack of fault predictability and the like are solved.

The invention belongs to the technical field of industrial detection, and discloses a structured knowledge-driven industrial defect detection method and system, and the method comprises the steps: obtaining manufacturing data related to a product, and constructing a knowledge graph which reflects the relation between the defect characteristics of the industrial product and the machining process and environment through the manufacturing data; regarding the established industrial defect knowledge graph as a heterogeneous information network, and performing deep feature aggregation on various entities and relationships in the industrial defect knowledge graph by using a heterogeneous graph neural network technology to form a high-dimensional feature vector for guiding coupling of multiple environmental factors; establishing an industrial image illumination model by using a deep transfer learning technology, and simulating image features of industrial products under different acquisition conditions; an industrial product defect data enhancement framework based on a conditional diffusion model is constructed by training a conditional noise prediction network, a real and diversified industrial product image is generated according to the industrial product defect data enhancement framework, and industrial product defect detection is completed.

The invention relates to the field of false tooth machining equipment, in particular to an intelligent standby cutter switching method and device for false tooth machining equipment, and the method comprises the steps: obtaining state parameters and use parameters of a current cutter in real time; according to the state parameters and the use parameters of the current cutter, whether the condition of replacing the standby cutter is met or not is judged; when the current cutter meets the condition of replacing the standby cutter, analyzing the replacement reason of the current cutter, and obtaining specification parameters of the current cutter; matching a target standby tool according to the specification parameter of the current tool; generating a replacement strategy of the target standby cutter according to the replacement reason of the current cutter; and on the basis of the replacement strategy and the target standby tool, replacing the tool of the false tooth processing equipment, and updating the state parameter and the use parameter of the replaced tool. By accurately analyzing the use state of the tool, it is ensured that the tool is replaced in time when going wrong, machining errors or product defects caused by tool abrasion or other problems are avoided, and therefore the machining precision and the product quality are guaranteed.

The invention relates to the technical field of defect detection, and provides a super-precision spring defect detection method and system, and the method comprises the steps: obtaining the surface deformation information of a to-be-detected super-precision diaphragm spring; extracting current defect feature data from the surface deformation information; storing the current defect feature data to a defect feature cache region; comparing the current defect feature data with historical defect feature data stored in a defect feature cache region to judge whether the current defect feature data has space-time constancy or not, and obtaining a space-time constancy judgment result; according to the time-space constancy judgment result, defect type marks corresponding to the current defects are judged as system artifacts or real product defects; and generating a detection report, and displaying the defect type mark based on a preset display rule for distinguishing and displaying the system artifacts and the real product defects. The method has the advantages that system artifacts and real product defects are distinguished, the detection accuracy and efficiency are improved, and unnecessary production interruption and verification cost are reduced.

The invention discloses a semi-solid magnesium alloy forming management control system and method, and relates to the technical field of intelligent management, and the method comprises the following steps: sending a first regulation and control signal, calculating the similarity between an actual finished product picture and a first picture, judging the defect of an actual finished product, screening a second control parameter, and setting a first change amount; sending a second regulation and control signal to obtain a third control parameter, calculating the temperature change rate of the subspace, screening the fault subspace, sending a maintenance signal, and establishing a first association relationship. The quality of the finished product is improved by matching the first control parameters corresponding to the finished product model and the mold model, the generation of the defects is effectively detected and prevented by acquiring the actual finished product picture and the historical defect information, setting the similarity threshold and judging the actual finished product defects, intelligent control is realized through the association relationship, and the production efficiency is improved. The production efficiency and the product quality are improved, and defects are reduced, so that the waste of materials is reduced, and the production cost is reduced.

The invention discloses film product defect detection equipment and a method thereof. The film product defect detection equipment comprises a transmission assembly, a marking assembly and a pressure assembly, the transmission assembly is used for carrying out detection transmission on a to-be-detected film, the marking assembly is used for carrying out real-time detection and marking positioning on physical defects on the film, and the pressure assembly is used for carrying out tensile detection on the film; the film product defect detection method comprises the following steps: S1, a preparation stage; s2, a transmission stage; s3, a defect detection marking stage; and S4, a tensile detection stage. A second electric air cylinder pushes a pressure roller to carry out tensile detection on a film, a reciprocating lead screw, a lead screw nut, a push block, a clamping block, a pressing block and other structures in the same-pressure assembly are matched with one another, synchronous small-range tensile detection can be carried out on the film in the moving process of the pressure roller, the synchronism of detection of all parts is ensured through a synchronization unit, and the detection accuracy is improved. Reliable detection data is provided for the tensile property of a film product, and the product quality is guaranteed.

The invention belongs to the technical field of machine vision detection, and discloses a product appearance defect identification method and device, equipment and a medium. The method comprises the following steps: obtaining a to-be-recognized image of a product appearance, and processing the to-be-recognized image into a binary image; carrying out contour extraction in the OpenCV model to obtain a contour boundary of the product defect in the to-be-identified image; using a Douglas-Peucker algorithm to extract contour vertex coordinates from the contour boundary, and calculating the contour area of the product defect based on the contour vertex coordinates; determining a minimum bounding box containing a contour boundary through a contour searching method, extracting vertex coordinates of the bounding box, and calculating the area of the bounding box based on the vertex coordinates of the bounding box; and based on the contour area and the bounding box area, calculating an area error ratio of the contour area to the bounding box area, and based on the area error ratio, determining an identification result including qualified product appearance and unqualified product appearance. The detection efficiency can be effectively improved, and the product omission ratio is reduced.

The invention discloses an industrial product surface defect traceability method, system and device, and relates to the technical field of industrial product surface defect traceability, and the method comprises the steps: collecting actual defect time sequence data of a product and supplementary data generated by a digital twin system, and building a multi-view topological expression; extracting consistency characteristics of each view to obtain space-time integration characteristics of defect evolution; performing instance mining according to the multi-view topological expression and the space-time integration characteristics to establish a surface defect quality analysis knowledge graph; defining type vectors of head entities and tail entities for various relationships in the knowledge graph, and generating initial semantic representations of newly added entities; constructing a knowledge graph evolution process of dynamic graph updating to perform sub-graph level updating, and performing graph enhancement by generating soft facts and confidence thereof; a reinforcement learning recommendation framework is constructed, and product surface defect traceability is realized; according to the method, the reasoning capability of the model under the sparse atlas is improved, and the reliability and interpretability of a reasoning path are maintained.

The invention relates to the technical field of steel rolling, and discloses a Q355B steel hot rolling process optimization method based on dynamic temperature control, which comprises the following steps: blank pretreatment, dynamic heating control, high-pressure water descaling, rough rolling process, finish rolling process, laminar cooling and coiling process. According to the method, a collaborative system of multi-section variable-temperature heating and intelligent temperature control is constructed, temperature distribution in a furnace and the heating rate are monitored and dynamically adjusted in real time, it is guaranteed that the core surface temperature of a plate blank is uniform and consistent, thermal stress and the internal crack tendency in the heating process are eliminated, and a high-temperature blank uniform in structure and stable in performance is provided for follow-up rolling; meanwhile, the high-pressure water descaling system optimizes jet flow parameters and introduces chemical additives, so that a protective layer is formed while oxide scales on the surface are removed, the surface cleanliness and quality of the plate blank are improved, and product defects caused by incomplete descaling and surface damage are avoided.

The invention discloses a paper product packaging defect real-time detection system based on edge calculation, and relates to the technical field of packaging defect detection. Comprising a multi-modal data acquisition module for acquiring packaging multi-dimensional image data by using a camera and a sensor; the edge intelligent processing unit and the heterogeneous architecture are combined with dynamic scheduling to realize rapid data processing; the defect feature enhancement network is used for improving an algorithm to accurately extract defect features; the edge decision optimization engine is used for reinforcing learning to optimize a detection strategy; the industrial-grade communication module is used for guaranteeing data transmission through 5G and TSN hybrid; the closed-loop repair execution system is used for realizing automatic repair of defects; in addition, auxiliary modules such as a traceability module and an energy optimization module are further arranged, and all the modules cooperate to achieve efficient detection and repair of packaging defects. The paper product packaging detection level is greatly improved, the speed is improved by 2.5 times, energy consumption is reduced, detection and closed loop repairing are achieved, intelligent upgrading of a production line is promoted, and the labor cost and the product reject ratio are reduced.

The utility model relates to the technical field of automobile production, and discloses an automobile stamping die with a positioning mechanism, which comprises a bottom plate, a rectangular plate is fixedly mounted on the surface of the bottom plate, a rectangular groove is formed in the surface of the rectangular plate, a second spring is arranged in the rectangular groove, and a lower stamping die is fixedly mounted at the top of the second spring; and the hydraulic devices are fixedly mounted at the top of the bottom plate, and the number of the hydraulic devices is four. The device can be quickly and accurately placed at a required position, and the adjustment time is shortened, so that the production speed is increased, the overall production efficiency is improved, the stability and the accuracy of the die in the stamping process can be ensured, product defects or unqualified products caused by position deviation are avoided, the product quality is further improved, and the production cost is reduced. Meanwhile, friction and abrasion of the die in the stamping process can be reduced, the service life of the die is prolonged, and the frequency of maintaining and replacing the die is reduced.

The invention relates to the technical field of textile manufacturing safety monitoring, in particular to a textile processing risk perception intelligent identification analysis system which comprises a video analysis module, an equipment prediction module, a product tracing module, a risk fusion module and an early warning response module. Wherein the video analysis module is used for outputting personnel violation signals; the equipment prediction module is used for carrying out fault probability calculation by fusing violation signals and time sequence data and outputting an equipment fault probability; the product tracing module is used for outputting defect source information; the risk fusion module is used for constructing a comprehensive risk index; and the early warning response module is used for triggering a multi-stage early warning instruction according to the threshold interval of the comprehensive risk index. According to the invention, through fusion analysis and graded early warning of multi-source risk factors, intelligent collaborative management and control of personnel, equipment and product defects in the textile production process are realized, and the accuracy of risk identification and the timeliness of safety response are effectively improved.

The invention discloses an irregular texture product appearance defect detection method and device, and the method comprises the steps: collecting N good product sample images through an area-array camera, taking one image as a reference image, and obtaining a product contour shape positioning template through an edge positioning extraction function; preprocessing all good product sample images by using a detection preprocessing function, obtaining position information of good product samples through a product contour shape positioning template, aligning all positions of the good product samples by using an affine transformation function, and introducing an improved performance function for training to obtain a good product detection model; collecting a detection image, performing positioning preprocessing, and obtaining position information of a target by using a product contour shape positioning template; carrying out detection preprocessing, transforming the preprocessed image and a good product detection model to the same reference position, and carrying out point-by-point comparison to screen out different parts; according to the invention, rapid and accurate detection of the appearance defects of the irregular texture detection area is realized under the conditions that the number of die-cutting product defect samples is small and the variety of products is large.

The utility model discloses a quick grinding device for a stainless steel conical surface sealing pipe nozzle, which mainly comprises a grinding structure, a power system and a control system, the grinding structure comprises a threaded positioning sleeve, a grinding head, a spring, a bearing and a retainer ring, the threaded positioning sleeve is in threaded connection with the pipe nozzle, and the grinding head is connected with the power system. And the grinding head is matched with the bearing and the spring in the thread positioning sleeve to rotationally grind the conical surface. By means of the device, the problem of product defects generated in the production and manufacturing process and after use of the sealing pipe nozzle can be rapidly solved, the requirement for the sealing performance of products is met, a traditional manual grinding treatment scheme is replaced, and production efficiency is improved. The grinding device can be quickly expanded, and is suitable for grinding conical nozzles with different specifications by replacing grinding structures with different specifications.