76 results about "Material defect" patented technology

The invention discloses a nondestructive testing method for composite material defects, and relates to the technical field of nondestructive testing, and the method comprises the following steps: firstly, collecting a scanning waveform of a test piece A, determining a potential defect area based on an echo amplitude and a time difference, and outputting a sequence containing space coordinates, an original waveform and focusing parameters; an effective time window is intercepted after preprocessing, sub-bands are generated through wavelet packet decomposition, and total energy is calculated and normalized to obtain an energy vector; secondly, based on a known sample, evaluating the separability of sub-bands by using a Fisher criterion, sorting the sub-bands, determining an optimal energy dimension through cross validation, combining sub-band energy features with phase and time difference features into composite vectors, inputting the composite vectors into a dual-channel lightweight deep network, outputting defect categories and confidence coefficients, and mapping the defect categories and confidence coefficients to a C scanning frame image; and finally, backtracking the three-dimensional coordinates, calculating the defect volume and the residual wall thickness, and comparing with a material performance database to output a conclusion. The problem of misjudgment caused by echo waveform similarity is solved, and detection closed-loop optimization is achieved.

The invention relates to a logistics robot system capable of sorting steel body material defects, which builds a sorting-logistics-endurance integrated whole-course autonomous system architecture, and integrates a heterogeneous dual-core processor architecture with deep hardware. The two key technologies of a YOLOv11-SG lightweight defect detection model and a lightweight LCC-LCC topology wireless charging system are organically combined, a state machine driven full-process cooperative control logic is matched, a complete autonomous sorting logistics solution is formed, the lightweight defect detection model guarantees independent perception of a terminal, the endurance problem is solved through the wireless charging technology, and the system is simple in structure and convenient to operate. And by combining the multi-module collaborative hardware and process design, the robot can autonomously operate in narrow and small scenes such as a 400mm narrow channel, the dependence on cloud computing and manual intervention is thoroughly eliminated, and a low-cost intelligent upgrading path is provided for medium and small-scale logistics scenes.

The invention discloses a labeling quality intelligent detection method and system for multiple materials, and the method comprises the steps: S1, collecting original image data of paper, plastic and metal labels through an industrial camera, and carrying out the preprocessing, and obtaining standardized image data; s2, designing an improved edge detection network, extracting an initial edge feature of the label, generating a linear edge feature through curve fitting, calculating a label rotation angle, and executing rotation transformation to obtain image data after rotation correction; s3, constructing a double-branch network comprising material and defect branches, extracting material types, generating defect features in combination with the material types, and finally generating structured defect features; and S4, according to the structured defect features, generating material tolerance, and then calculating a quality score and a quality grade. According to the method, the problems of material misjudgment and defect detection omission caused by low rotation correction precision of the special-shaped label and staged processing of multi-material defect detection in a traditional method can be solved.

The invention relates to the technical field of blank defect detection, in particular to a large-scale shaft forging blank defect identification method based on ultrasonic flaw detection data, and the method comprises the following steps: obtaining an ultrasonic A scanning signal of a large-scale shaft forging blank; extracting a back scattering signal from the ultrasonic A scanning signal; the back scattering signals are preprocessed; calculating the power spectrum density of the back scattering signal; obtaining characteristic parameters related to depth based on the power spectrum density; calculating to obtain a dynamic attenuation coefficient changing along with the depth based on the characteristic parameters; a compensation factor is obtained through exponential operation; compensating the original echo peak amplitude of the defect detected in the A scanning signal by using the compensation factor to obtain a defect signal amplitude; and comparing the amplitude of the defect signal with a threshold value of a standard DAC curve under a corresponding depth, and judging whether a defect exists or not. According to the invention, the accuracy and reliability of defect identification are improved.

The invention relates to the technical field of thermal interface material defect detection, in particular to a nondestructive detection method and system for thermal interface material application defects, and the system comprises an electric displacement table, heating laser, detection laser, a photoelectric detector, an acoustic optical modulator, a lock-in amplifier, an optical element and a data processing unit; the electric displacement table is used for fixing a sample and accurately controlling the position of the sample; the sample is of a silicon-thermal interface material-substrate sandwich structure; the upper-layer silicon wafer of the sample is plated with a metal film as a transduction layer; the heating laser is used for heating the sample; the acoustic optical modulator is used for modulating the heating laser, so that the heating laser heats the sample in a sine wave form; the detection laser is used for detecting sample temperature fluctuation; the photoelectric detector is used for collecting and detecting laser reflection signals; the lock-in amplifier is used for extracting and detecting laser phase and amplitude signals. According to the invention, non-destructive rapid detection of the defects of the thermal interface material in an application scene can be realized.

The application discloses a heat-shrinkable tube detection method based on image recognition and relates to the technical field of power material defect detection, and is used for solving the problems of defect profile distortion, positioning deviation and insufficient classification accuracy caused by optical ghost interference in heat-shrinkable tube detection; the mapping relationship of light refraction paths is established based on the brightness gradient difference and the refractive index difference, the theoretical ghost interference fringes are generated by dynamically adjusting the optical path difference of the interferometer, and the abnormal areas deviating from the theoretical distribution are screened; meanwhile, the residual ghost interference is eliminated by combining light source incidence angle optimization and defect displacement amount calculation, and finally, the precise classification and positioning of defects are realized through multi-dimensional matching of geometric parameters and historical defect data, the detection accuracy is improved, the dependence on complex algorithms is reduced, and the real-time detection demand of industrial production lines is adapted.

This invention relates to the field of materials evaluation technology, specifically proposing a material defect detection method and system based on multi-dimensional feature fusion. The method involves synchronously acquiring thermal response data through pulse and phase-locked loop dual-modal thermal excitation, extracting four-dimensional features in the time and frequency domains, and constructing a unified pixel-level feature vector through standardized preprocessing. Then, a response map is generated through weighted fusion, and candidate abnormal regions are screened. Finally, defect determination is completed by combining dual-branch classification and decision-level fusion. This approach addresses both shallow and deep defect detection in the tested flat plate material component, effectively distinguishing between various defects such as interlayer debonding, microcracks, and porosity, solving the problems of poor accuracy and easy confusion in single thermal excitation detection. Furthermore, the standardized preprocessing process improves feature stability, enabling accurate determination of defect location and type, thus enhancing the accuracy and comprehensiveness of defect detection in the tested flat plate material component.

The invention relates to the technical field of computer vision, in particular to a material defect detection method for keyboard and keycap production, and the method comprises the steps: obtaining an initial image and a grayscale image of a keyboard and a keycap to be detected; obtaining a suspected defect area in the gray level image according to the gray level difference of pixel points in the gray level image; for any suspected defect area, acquiring a defect coefficient of each pixel point in the any suspected defect area according to color features of pixel points of a corresponding area in the initial image and distribution features and texture features of the pixel points of the any suspected defect area; obtaining a self-adaptive small-scale weight and a self-adaptive large-scale weight of each pixel point by using a defect coefficient of each pixel point in the gray level image; according to the self-adaptive small-scale weight and the self-adaptive large-scale weight of each pixel point, an enhanced image is obtained by using a Retinex algorithm, and the accuracy of material defect detection of keyboard and keycap production is improved.

The application discloses a kind of microwave thermal imaging detection method and its detection device, belong to non-metal material defect detection technical field, it includes heating box, the bottom of heating box is provided with rotating table, rotating table is provided with gripper, the top of heating box is provided with microwave heating device, spray pipeline, infrared camera and CCD camera, microwave heating device is connected with signal generator, infrared camera and CCD camera are electrically connected with image processing module.This scheme uses fluorescent penetrant to penetrate non-metal workpiece to be measured, uses the high sensitivity response of penetrant to microwave pulse to generate heat signal, uses heat signal to carry out microwave thermal sensing imaging, can fully detect the internal condition of defect;At the same time, fluorescent penetrant is used for fluorescent imaging, the visualization of defect position can be realized, so as to detect the distribution of defect surface;Two kinds of imaging are fused and analyzed, realize the comprehensive detection of surface shape and internal structure for non-metal workpiece defect.

The application provides a spinning rib body folding and meat defect control method and system for a cross inner rib cylinder, comprising the following steps: S1, adopting multi-roller collaborative spinning; S2, establishing a finite element simulation model based on an actual core mold, a blank and a spinning roller; S3, adjusting the corner angle distribution and radial offset distribution of the spinning rollers in the spinning roller group in a power reduction mode to reduce the folding and meat defect in the finite element simulation; S4, if the folding and meat defect is not eliminated, further eliminating the folding and meat defect by increasing the attack angle of the spinning roller group; and S5, performing a process test based on the spinning parameters used in the simulation. The spinning rib body defect control method provided by the application can effectively inhibit the generation of the folding and meat defect of the cylinder spinning inner rib, and improve the size accuracy of the cylinder.

The invention discloses an alternating-current electromagnetic field detection method for subsurface defects of a metal material. The method comprises the following steps: performing model analysis on electromagnetic field distribution of the subsurface defects; designing and optimizing a subsurface defect detection probe; building a subsurface defect detection system; and making artificial defects to carry out experimental verification on a simulation result. Aiming at the problem that the detection defect depth of ACFM is insufficient, compared with existing equipment, the detection depth is improved, the efficiency and accuracy of metal material defect detection are further improved, and a reference is provided for guaranteeing industrial safety and prolonging the service life of the equipment.

The utility model discloses a winged part mould relates to mould technical field, including upper mould, the inside fixedly connected with forming assembly of upper mould, the bottom fixedly connected with lower mould of forming assembly, the inside of lower mould is provided with resilience subassembly, the top of upper mould is provided with injection moulding hole, and forming assembly includes male die. This winged part mould is driven male die to go down by upper mould, makes the precise clamping of male block and recess, positioning column and positioning groove, can rapidly and stably fixed cavity, effectively prevents cavity displacement in the injection moulding process, ensures the size accuracy of forming part, through the cooperation of injection moulding hole and glue hole, and the molten plastic can even, quick filling cavity, reduces bubble and material defect such as lack, promotes part quality, and this kind of structure design is reasonable, and the operation is simple, improves production efficiency, and reduces the scrap rate, is helpful to the realization of efficient, high -quality injection moulding production of enterprise, strengthens market competitiveness.

The application discloses a prestressed middle plate production line mold for a subway station, which comprises a fixed bottom plate, a prestressed middle plate mold fixedly connected to the upper surface of the fixed bottom plate and used for pouring concrete, a tensioning assembly and a control assembly. In order to solve the problem that common steel bar tensioning equipment may be broken due to stress mutation or material defects when stress is applied, and the mold or equipment is easily damaged by instant energy release, the tensioning assembly is designed, the inside of the tensioning assembly is provided with a buffer assembly, the buffer assembly can play a buffering role when stress is applied, can absorb impact energy when overload occurs, and the control assembly is further designed, sliding friction is triggered under over-limit tension through the control assembly, the maximum transmission force is limited, and equipment damage caused by sudden breakage is avoided.

Aspects of the present application relate to the automated characterization of material defects in paved surfaces. More specifically, in accordance with one or more aspects of the present application, a location processing service may be utilized to utilized machine-learned algorithms in the characterization of material defects in paved surfaces. The characterizations are illustratively associated with a hierarchical set of categories. The location processing service can further utilize machine learned algorithms to identify one or more remediation recommendations corresponding to the associated characterization of the paved surface.

The invention provides a metal material defect detection method. The method comprises the following steps: acquiring a 2D grey-scale map and a 3D depth map of a to-be-detected metal material; analyzing the 2D grey-scale map by using a preset dichotomy model, and determining whether the metal material to be detected has surface defects or not; responding to the condition that the metal material to be detected has the surface defect, analyzing the 2D grey-scale map by using a preset multi-classification model, and determining the type of the surface defect of the metal material to be detected; and under the condition that a preset condition is met, analyzing the 2D grey-scale map and the 3D depth map by using a preset fusion model, and determining whether the to-be-detected metal material has a deformation defect or not. The invention further provides metal material defect detection equipment, a computer readable medium and a computer program product.

The utility model relates to a medicine packing material defect detecting, sorting and material collecting device, and relates to the field of medical equipment, the medicine packing material defect detecting, sorting and material collecting device comprises a first conveyor belt and a second conveyor belt, the first conveyor belt and the second conveyor belt are provided with separation blades at intervals, and medicine boxes are placed between the adjacent separation blades; infrared sensors used for detecting whether box tongues stretch out of the conveying belts are arranged on the two sides of the first conveying belt in the width direction, the first conveying belt and the second conveying belt are each provided with a plane detection assembly used for detecting the large end faces of medicine boxes, a box turning assembly is arranged between the first conveying belt and the second conveying belt, and a discharging assembly is arranged at the tail end of the second conveying belt; the discharging assembly is provided with a waste removing assembly used for removing damaged box bodies. The detection device has the effects of improving the detection accuracy and the detection efficiency of the defects of the medicinal materials packed in the box, and further removing the defective box.

1. Name of the product in this design: Intelligent Detection Equipment for Roll Material Defects. 2. Purpose of this design: For use in intelligent detection equipment for defects in rolled materials. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The utility model relates to the technical field of nondestructive testing, and discloses a metal material defect nondestructive testing device which comprises an ultrasonic nondestructive testing instrument, a dustproof mechanism is arranged on the left side of the top of the ultrasonic nondestructive testing instrument, and the dustproof mechanism comprises a protective cover. The bottom of the protective cover is fixedly connected to the left side of the top of the ultrasonic nondestructive detector, a dustproof plate is arranged in the protective cover, and the bottom of the dustproof plate is in lap joint with the top of the ultrasonic nondestructive detector. Through the cooperation of the protective cover, the protective cap, the knob, the worm, the worm gear, the screw rod, the connecting block and the guide rod, the dustproof plate is conveniently driven to move at the top of the ultrasonic nondestructive detector, and the right side of the bottom of the dustproof plate is moved to the top of the connecting socket to block the connecting socket, so that dust can be prevented from being attached and accumulated in the connecting socket; and unstable signal transmission or poor contact is caused.

The invention relates to the technical field of industrial production line control, in particular to a digital visual control system and method for a synthetic mica material production line. The method comprises the following steps: acquiring industrial data of a mica material, and extracting a data feature vector to obtain a batch quality coefficient, temperature-pressure fitting data and lattice distortion data; outputting a mica material defect distribution diagram by taking the temperature-pressure fitting data unfolded along the direction of the kiln as an abscissa, taking the lattice distortion data as an axis coordinate and combining with the batch quality coefficient; therefore, the digital visual control system based on multi-dimensional data fusion is constructed, the key technical problems that in the traditional mica material production process, technological parameters are difficult to control accurately, product quality fluctuation is large, and the defect rate is high are solved, and the production efficiency, the product quality stability and the technological control intelligent level are improved.

The embodiment of the application discloses a PVC sizing material defect detection method and an intelligent sorting system based on AI visual recognition. The method comprises the following steps: collecting continuous multi-frame visual imaging data of the PVC sizing material in the production conveying process; combining production conveying state monitoring information, analyzing the position offset trajectory and the shape contour change of the PVC sizing material, and extracting inter-frame feature correlation information; generating a cross-frame defect tracking link based on the inter-frame feature correlation information, tracking the shape abnormal area, and determining a suspected surface defect area; mining surface defect features through edge contour recognition; inputting the surface defect features into a layered reasoning mining model, reversely deducing internal implicit defect types, and mining distribution trends to generate a defect detection result. The embodiment of the application realizes full-dimensional accurate identification of PVC sizing material defects from the surface to the interior.

The present application relates to the technical field of embryo material defect detection, and particularly relates to a large shaft forging embryo material defect identification method based on ultrasonic flaw detection data, which comprises the following steps: obtaining an ultrasonic A-scan signal of a large shaft forging embryo material; extracting a backscattering signal from the ultrasonic A-scan signal; preprocessing the backscattering signal; calculating the power spectral density of the backscattering signal; obtaining a characteristic parameter related to depth based on the power spectral density; calculating a dynamic attenuation coefficient changing with depth based on the characteristic parameter; obtaining a compensation factor through exponential operation; compensating the original echo peak amplitude of a detected defect in the A-scan signal by using the compensation factor to obtain a defect signal amplitude; comparing the defect signal amplitude with a threshold of a standard DAC curve at a corresponding depth to determine whether a defect exists. The present application improves the accuracy and reliability of defect identification.

The invention belongs to the field of mechanical property test and evaluation of metal materials, and discloses a method for judging the tensile fracture quality of a hot-rolled steel plate. The method comprises the following steps: acquiring a sample with the special fracture; whether yield and necking phenomena occur or not before fracture is confirmed, and mechanical property data are obtained; comparing the performance data with a qualified reference sample; if it is confirmed that yield necking occurs and no significant difference exists in performance, it is directly judged that normal ductile fracture occurs; if the performance difference is obvious, microscopic morphology observation is carried out, and if dimple characteristics are observed, normal ductile fracture is still judged; if yield necking does not occur, microscopic observation judgment is performed, and if cleavage or intergranular fracture characteristics are observed, brittle fracture is judged. According to the method for judging the tensile fracture quality of the hot-rolled steel plate, normal special fractures and material defects of high-strength steel can be quickly and accurately distinguished, unnecessary microscopic reinspection is avoided, judgment standards are unified, quality disputes can be reduced, and material application is promoted.

The embodiment of the invention discloses a PVC sizing material defect detection method based on AI visual identification and an intelligent sorting system. The method comprises the steps that continuous multi-frame visual imaging data of PVC sizing materials in the production and conveying process are collected; in combination with the production conveying state monitoring information, analyzing the position offset track and the shape contour change of the PVC sizing material, and extracting inter-frame feature association information; generating a cross-frame defect tracking link based on the inter-frame feature association information, tracking a form abnormal region and determining a suspected surface defect region; surface defect features are mined through edge contour recognition; and inputting the surface defect features into a hierarchical reasoning mining model, reversely deducing internal hidden defect types, mining a distribution trend, and generating a defect detection result. According to the embodiment of the invention, the full-dimensional accurate recognition of the defects of the PVC sizing material from the surface to the interior is realized.

The invention relates to a method for removing object parts from objects, in which material defects are locally induced (80) in an object material along object parts to be removed; the object parts are removed (88) from the objects by etching the objects in an etching medium which etches the object material containing the material defects more strongly than defect-free object material; the objects are arranged (82) in a holder (40) before the etching; the holder (40) is arranged (84) in a filter basket (10) before the etching; for the purpose of etching the objects, at least some of a carrier system (60) formed from the filter basket (10) with the holder (40) arranged therein is arranged (86) in the etching medium; at least a portion of the object parts removed from the objects are retained (90) in the filter basket. The invention further relates to a filter basket and a holder and to a carrier system for carrying out the method.

The invention discloses a composite material defect monitoring method and system based on a fiber grating sensor. The method comprises the following steps: implanting fiber bragg grating sensors in a composite material and forming an array, then acquiring a central wavelength offset measured by each fiber bragg grating sensor in real time by adopting a signal demodulation device, calculating a corresponding stress gradient, temperature change rate or vibration frequency offset according to the central wavelength offset, and constructing a defect characteristic pattern; and finally, performing defect detection by adopting a defect detection module, and performing multi-stage early warning according to a defect detection result. The technical problems that in the prior art, the implanting process of the fiber grating sensor in the composite material is complex, the interface bonding strength of the sensor and the base material is difficult to guarantee, and multi-parameter and multi-scale defect monitoring is difficult to achieve are solved, and the defects in the composite material structure are recognized and positioned more accurately.

The application discloses a modified plastic reinforced injection molding method and device, and relates to the technical field of injection molding. The modified plastic reinforced injection molding method comprises the following steps: raw material hot melting and mixing, mold preheating, mold clamping and clamping, pressure boosting injection molding, pressure maintaining and shaping, cooling and solidifying, mold opening and discharging. The modified plastic reinforced injection molding method and device, through uniform fusion of glass fibers and molten plastics, greatly improve the mechanical strength, rigidity and deformation resistance of modified plastics. The pressure boosting injection molding cooperates with an exhaust structure to eliminate defects such as bubbles and material defects, and the product is compact and smooth. The pressure maintaining and precise temperature control cooling guarantee the size stability and reduce deformation. The worm gear and worm clamping block type clamping simplifies mold disassembly and improves mold changing efficiency. The discharging assembly linkage automatically ejects the product, reduces manual operation, and improves overall production efficiency and molding quality.

The invention discloses an automatic material cutting method and system of an injection molding machine based on artificial intelligence, and relates to the technical field of material cutting management of injection molding machines, and the method comprises the following steps: carrying out data matching processing on material characteristics of a to-be-cut material, and determining the material cutting speed of the injection molding machine; and obtaining a material image of the to-be-cut material, performing feature analysis processing on the material image of the to-be-cut material, and determining the position coordinates of the defect of the material. According to the method, the material cutting speed of the injection molding machine is determined by carrying out data matching on the material characteristics of the to-be-cut material, because if the cutting speed is too high or too low, the temperature of the cutting position is too high, the cutting position of the material is deformed, and if the deformed material is used for manufacturing a product, the yield is reduced; and then feature analysis is carried out through the material image of the to-be-cut material, the position of the material defect is determined, when the material is cut, the position of the material defect is avoided, the use of the material with the defect for manufacturing a product is avoided, and the yield is improved.

The invention belongs to the technical field of material fatigue life prediction, and particularly relates to an alloy fatigue life machine learning prediction method considering manufacturing defect characteristics, which comprises the following steps: establishing a data set containing a plurality of alloys; normalizing the data set, and segmenting the data set into a training set, a test set and a verification set; fitting and checking the defect features, and sampling to obtain normalized defect feature samples conforming to distribution; physical constraints between the fatigue life-defect characteristics and the fatigue life-load are coded; designing a non-full-connection physical information neural network considering manufacturing defect features, and training the network; and performing life prediction on the data set by using a neural network. The method is simple, efficient and highly innovative, constructs the physical information neural network considering the manufacturing defect characteristics in a designable manner, forms a high-precision prediction method for the fatigue life, and forms a method for calculating the failure probability for the dispersity of material defects.

The invention relates to the field of material defect detection, in particular to a material defect detection method, device and equipment and a medium, and the method comprises the following steps: determining a plurality of irradiation angles of a detected material irradiating X rays according to target forming process information of the detected material; sequentially controlling an X-ray generator to emit X-rays based on the plurality of irradiation angles, so that the X-rays irradiate from one side of the detected material; determining respective X-ray digital imaging signals of the plurality of irradiation angles based on the energy of the X-rays of the plurality of irradiation angles received by the detector; and material defect detection is carried out based on the X-ray digital imaging signals of the multiple irradiation angles. The data acquisition efficiency is improved, so that the defect detection efficiency of the composite material is improved.

The invention discloses a preparation method of a T-shaped elevator guide rail. Through preheating treatment, the temperature is adjusted according to the thickness of steel, the quality is matched, the time is adjusted, the heat conduction efficiency is optimized, material defects and energy waste are avoided, and the defect of extensive preheating is effectively overcome; the rolling force is calculated in real time based on the steel yield strength, the deformation amount and the roller radius, the T-shaped section size is accurately controlled, springback or cracks are prevented, and the defect of insufficient cold rolling precision is overcome; the quenching temperature is accurately set according to the critical temperature, so that the structure is uniform and fine, the hardness consistency is improved, and the defect of extensive quenching control is overcome; the straightening force is combined with the guide rail bending deflection, the elastic modulus and the section inertia moment to calculate, automatic and accurate straightening is achieved, residual stress is reduced, the anti-fatigue performance is enhanced, and the manufacturing stability and reliability of the guide rail are comprehensively improved through scientific parametric control.