202 results about "Defect size" patented technology

A method of computing a manufacturing yield of an integrated circuit having device shapes includes sub-dividing the integrated circuit into failure mechanism subdivisions (each of the failure mechanism subdivisions includes one or more failure mechanism and each of the failure mechanisms includes one or more defect mechanisms), partitioning the failure mechanism subdivisions by area into partitions, pre-processing the device shapes in each partition, computing an initial average number of faults for each of the failure mechanisms and for each partition by numerical integration of an average probability of failure of each failure mechanism, (the numerical integration produces a list of defect sizes for each defect mechanism, and the computing of the initial average includes setting a maximum integration error limit, a maximum sample size for a population of each defect size, and a maximum number of allowable faults for each failure mechansim), and computing a final average number of faults for the integrated circuit by iterativelly reducing a statistical error of the initial average number of faults for each of the failure mechanisms until the statistical error is below an error limit.

A visual detection device for parts mainly comprises an imaging system, an optical lens component, a lighting system, a mechanical adjusting system, a displacement detection system and a computer, wherein the imaging system consists of a CCD camera and an image acquisition card; the optical lens component consists of a lens body, an amplifying objective lens, and the like; the lighting system is respectively used for detecting surface defects and measuring topographic sizes; the mechanical adjusting system consists of displacement platforms used for adjusting the positions of the parts; the displacement detection system is used for recording the displacement distance of the displacement platforms; and the computer is used for recording and processing images. By shooting edge images of theparts through the imaging system and recording the displacement distance of the displacement platforms through the displacement detection system, the detection device calculates the topographic sizessuch as diameter, and the like, detects the surface defects of the whole spherical surface by the combined rotation of rotary tables, measures the defect sizes accurately, records the number of the defects and meets the two detection requirements of topography measurement and surface detection. By utilizing the visual microscopic detection technology, the device has high system resolution, good integrated performance and simple measurement process.

The invention discloses an electromagnetic resonance-based nondestructive testing method for metal materials. According to the method, responding resonance signals of eddy current testing are obtained through multipled resonant capacitors in testing coils, comparative analysis of the responding resonance signals is carried out, and the relationships between a defect size and a signal amplitude and between defect depth and a signal phase are calibrated in advance, thereby enabling convenient nondestructive testing of metal materials. The electromagnetic resonance-based nondestructive testing method not only can produce same defect detection effects as a plurality of conventional electromagnetic testing methods like single-frequency eddy current and pulsed eddy current do, but also can effectively detect internal defects in a workpiece and detect workpieces with complicated surface conditions such as a casting surface, an unsmooth surface, a surface with oil stains, a rust-resisting paint, a corrosion layer or other pollutants, etc., without contact under the condition of a high liftoff value; according to the method, no cleaning is needed for test pieces, and particularly, no cumbersome processes like magnetization and demagnetization of magnetic metal materials are needed.

The object of the invention is to make available implant elements or other medical products made of biological material with optimum mechanical and biological properties for a wide group of materials by introduction of a specially designed surface layer onto a strong core, where the surface layer is densified by means of hot isostatic pressing and dimensioned according to the basic fracture mechanics equation so that the thickness of the surface layer is less than c in the equation KIC=Ysigma c½, where Klc is the fracture toughness, Y a position and shape factor, c the critical defect size, and sigma the permitted stress for the actual material and the chosen design stress.

According to the present invention, techniques including a method and apparatus for classifying and displaying images are provided. In an embodiment of the present invention a defect image classification method using inspected objects is provided. The method includes defect images obtained from at least one inspected object. Next a set of defect images is classified into a specified category, which has a feature. The defect images are arranged for display according to the feature and then displayed. The arranging of the defect images may also be based on an evaluation value for each defect image. Another embodiment provides a defect image classification method using inspected objects. Defect images are obtained from at least one inspected object. Next the defect images are classified into a plurality of categories and at least two information items for example, a defect distribution diagram showing locations of defects in the inspected object, information associated with a category of the plurality of categories, and a defect size distribution, are displayed.

A gas-insulated apparatus is equipped inside with multiple sensors for sensing a partial discharge signal caused by a foreign substance, and a frequency analyzing section for analyzing the frequency of the sensed partial discharge signal. A defect type judging section estimates the defect type of the foreign substance from the signal obtained through the frequency analysis or from the voltage phase distribution of partial discharge synchronized with the frequency of the voltage applied to the gas-insulated apparatus, and a location determining section estimates the defect location and signal intensity from the multiple partial discharge signals. A defect size calculating section estimates the defect size for each defect type from the signal intensity and field distribution at the defect location. Dielectric breakdown risk evaluation is performed based on the defect size and type, and the insulation failure risk is diagnosed from the partial discharge.

The invention discloses a safety evaluating method of pressure equipment containing crack defects, which comprises the following steps: 1, carrying out nondestructive testing and a standard stretching experiment; 2, obtaining a fully plastic solution factor h1 corresponding to the hardenability value n of different materials, and computing 1n(h1/h1(n=1)), wherein h1(n=1) is the value of h1 when nis equal to 1; 3, drawing a relationship curve between (h1/h1(n=1)) and n, fitting the relation curve into a linear relationship, recording the slope rate as B, and computing a defect size influence factor phi according to the formula phi=exp(-B), wherein exp presents an exponential function using a natural logarithm e as a base index; 4, establishing a failure evaluating curve and computing the positions of discontinuities; and 5, determining the plastic damage extent L'r and the fracture failure extent K'r of the pressure equipment, and judging the safety state of the pressure equipment containing the crack defects. In the method of the invention, the influence of the defect size factor phi to the safety is considered so that the evaluation is more accurate; phi is not limited by the material parameter n so that the invention can be simply and easily used for computing any materials.

A method and apparatus for inspecting specimens or patterned transmissive substrates, such as photomasks, for unwanted particles and features, particularly those associated with contacts, including irregularly shaped contacts. A specimen is illuminated by a laser through an optical system comprised of a laser scanning system, individual transmitted and/or reflected light collection optics and detectors collect and generate signals representative of the light transmitted by the substrate. The defect identification of the substrate is performed using those transmitted light signals. Defect identification is performed using an inspection algorithm by comparing image feature representations of a test specimen with a reference specimen, and using a boundary computer and flux comparison device to establish tight boundaries around contacts and compute flux differences between the test and reference specimen contacts. Defect sizes are reported as ratio of flux difference, and entire contacts are highlighted for review.

The invention provides an intelligent analysis system and method for magnetic flux leakage detection data in a pipeline. The process comprises the following steps: adopting time domain-based sparse sampling and KNN-softmax data complete set construction module, obtaining a complete magnetic flux leakage data set; a pipeline connection assembly discovery method based on combination of selective search and a convolutional neural network is adopted in the discovery module to obtain the accurate position of the weld joint; adopting an abnormal candidate region searching and identifying method based on a Lagrangian multiplication frame and multi-source magnetic flux leakage data fusion in the discovery model to find out defective magnetic flux leakage signals; Adopting a defect quantification method based on a random forest in a quantification module to obtain a defect size; a pipeline solution improved based on the ASME B31G standard is adopted in the solution module, and an evaluation result is output. An analysis method is provided from the overall perspective, and pretreatment, connection assembly detection and abnormity detection, defect size inversion and final maintenance decision are achieved.

The invention is a quantizing method for detecting corrosive defect by using magnetic leakage. The invention uses the distribution property of the detected leakage magnetic field to implement quantizing corrosive defect size according to a specified algorithm. The concretely implementing method: firstly making a sample with three series of standard defects, according to the leakage magnetic detected data, determining the length, width and coefficient in deep quantizing method, thus obtaining a corrosive defect quantizing method and providing a basis for evaluating using state of equipment and equipment maintenance. The invention effectively overcomes the disadvantage of being difficult to quantize corrosive defect and defect depth in existing techniques, and the method of the invention is simple and easy to apply and able to be implemented, manually and by computer, high-efficiency and having a wider application prospect.

The invention discloses a multi-frequency eddy current testing system and method for evaluating carbon fiber plate defect depth. The system comprises a composite-material to-be-tested part, a multi-frequency sine excitation signal generating module, an eddy current sensing unit, a signal processing and separating module, a signal collecting module and a depth inversion module, wherein the eddy current sensing unit comprises a transmission cable, a probe control mechanism, an eddy current excitation coil and an eddy current detecting coil; the signal processing and separating module comprises a pre-amplifier and a multi-frequency detecting signal separating module. The multi-frequency eddy current testing system and method for evaluating the carbon fiber plate defect depth has the advantage that defect size information is obtained by measuring the peak sensing voltage and peak frequency of different-depth defects, and accordingly the defect depth of composite plates can be evaluated.

The invention relates to the technical field of maintenance of bearing holes and in particular relates to a method for repairing a main bearing hole of a body. The main bearing hole refers to the design, assembly and processing reference of the body, the central position is not allowed to deflect, the pore diameter is not allowed to be enlarged, and a brushing electroplating process cannot be used due to the reasons that the coating is small and easy to strip and cannot be machined and the like. However, thermal deformation of a welding part is increased by a common surfacing method, and the generated welding stress cannot be effectively eliminated; and meanwhile, the crack influence is large, the welding period is long, and the repair welding success rate is low. Aiming at the problems, a repair method for performing main bearing hole laser cladding surfacing and restoring the original precision of a drawing by virtue of machining is adopted, the repair processes of the main bearing hole of the body include four parts, namely cladding surfacing, surfacing part cover replacement and machining, detection of the repaired body and body cleaning and rust removal, wherein laser cladding surfacing is the key point of the whole repair process, and global surfacing and local surfacing of the pore diameter can be adopted aiming at the defect size and severity. The invention discloses an improved method for repairing the main bearing hole of the body.

The invention discloses a gas turbine blade defects extraction and analysis method based on region segmenting detection, comprising the following steps: firstly segmenting a gas turbine blade into several regions by a lead wire, and carrying out defect detection on the segmented regions through a DR (Digital Radiography) device to obtain a blade whole gray level image A; then, moving the lead wire for certain distance to carry out defect detection inside each region, and thus obtaining a blade whole gray level image B; fusing the images A and B together to obtain a whole X-ray gray level original image without a lead wire; preprocessing the whole X-ray gray level original image and thus obtaining an X-ray detection image of the blade; further processing the X-ray detection image to obtain a binary image with outstanding defects, and building a two-dimensional coordinate system on the binary image, and carrying out location analysis on the existing internal defects; and finally, calculating amount of perimeter pixels and amount of area pixels of the defects in the binary image, thus obtaining actual defect size through conversion, and carrying out qualitative and quantitative analysis on the existing internal defects. The method provided by the invention realizes rapid and automatic extraction and analysis on internal defects of the blade.

The invention provides a perambulated inspection system and method, wherein the perambulated inspection system comprises an installation frame, an upper computer, a plurality of cameras and a plurality of light sources; the cameras and the light sources are arranged on the installation frame; the installation frame is arranged above a target object and can move relative to the target object; the light sources are used for irradiating the target object; the cameras are used for shooting target object images so as to obtain target images; the cameras are used for sending the target images to the upper computer; and the upper computer is used for analyzing and processing the target images. According to the perambulated inspection system and method, disclosed by the invention, the effects of high efficiency on track perambulated inspection, accurate track defect positioning and high accuracy on defect size inspection can be achieved.

The invention discloses a multi-source data fusion-based system and method for predicting pipeline corrosion defect size. The system comprises a sensor group, a lower computer and an upper computer, wherein the sensor group is arranged at the head end or the tail end of a pipeline and is in contact with a transmission medium; the sensor group is connected with the lower computer connected with the upper computer. The method comprises the steps of firstly building a gray GM (1,1) prediction model and a BP neural network model by using historical data; then using the two models to obtain a prediction value; next building an inducted orderly weighted harmonic average (IOWHA) operator combination model according to the historical data and the prediction value of the two models; for new data, using the three models in sequence to predict the corrosion defect size in three directions of axial direction, circumference and depth respectively, so that a corrosion defect size prediction value of the pipeline is finally acquired. By adopting the system and the method, the prediction accuracy is higher, and the input of data is reduced.

The invention discloses a frequency characteristic-based nondestructive detection method of stress waves of wood. The frequency characteristic-based nondestructive detection method comprises the following steps of: mounting piezoelectric type acceleration sensors around raw wood uniformly, knocking the No. 0 sensor by using a pulse hammer, and acquiring and storing output signals of the sensors by using a data acquisition card; carrying out K-point fast Fourier transform on the acquired signals and then solving an actual measurement frequency response function; establishing an observation matrix according to the actual measurement frequency response function and a frequency response function of healthy wood; carrying out blind source separation on the basis of second order statistic and establishing a frequency response function between a defect point and an observation point; and finally, judging whether the interior of the wood has defects as well as defect quantity and defect sizes according to a clustering result. The nondestructive detection method of the stress waves based on the frequency response function of the wood is not subject to interference from reflected wave and refracted wave signals and is more accurate in detection result, capable of automatically detecting information, such as whether the interior of the wood has defects as well as the defect sizes, and simple and convenient in detection process and strong in practicability.

The present invention discloses a calculation method for separating symmetric and flexural mode wave packet and extracting weak signal of the bending mode. Based on the wave structure characteristic theory of axisymmetric mode and bending mode, the method uses finite element software to establish correlative model, extracts transient displacement signal of signal acquisition nodes of a guided-wave monitoring surface, and conducts delay superimposing on the collected signals on the frequency domain in accordance with relative formula; then the superimposed frequency domain signal is subjected to the inverse Fourier transform to obtain guided-wave mode wave packet of each order after separation; and Hilbert-Huang envelope is employed to calculate reflection coefficient for each mode. The method uses secondary development function of finite element software to compile the secondary development program, quantitatively analyze the influence degree of the parameters such as characteristics of defect size, location distribution, the number of defects and the center excitation frequency on the amplitude value of the flexural mode reflection echo generated from the defect locations through modal conversion, thereby integrally utilize reflection coefficients of axisymmetric mode and bending mode to evaluate the distribution of defect locations.

An automated, non-invasive method for classifying, detecting, and counting micropipes contained within silicon wafers, and generally any assortment of transparent wafers. Classifying, detecting, and counting micropipes takes place through the use of a data processing algorithm that incorporates information regarding: defect size; pit signature; area of pit signature when comparing a topography, specular, or scatter images; and detecting a tail within the standard pit signature. The method of the present invention teaches the development of a topography defect map, specular defect map, and scatter defect map for a complete analysis of the surface of a particular transparent wafer. Conventional detection, classification, and counting of micropipes involve characterization of micropipes in a manual fashion and rely upon an extremely invasive form of sample preparation. The method disclosed in the present invention is completely automated and non-invasive with regards to the treatment of the transparent wafer to be analyzed.

The invention discloses a casting quality test method and device and an application method of the device. The casting quality test device comprises three parts including a casting test conveying mechanism, a casting surface defect test device and a defect identification and classification mechanism, wherein the casting test conveying mechanism can be used for fixing a polished casting to be testedon a vacuum suction disc; the polished casting to be tested is conveyed into the casting surface defect test device through a conveyor belt; the device mainly applies a digital image processing technology, and a mounted industrial CCD (Charge Coupled Device) camera is used for acquiring an image; and a digital image processing algorithm designed on a background is used for carrying out defect identification on a casting surface polishing image. In an identification process, surface defect positions of the casting need to be obtained, and defect size and area also need to be calculated, so that defect grades are judged and classification of defect types is realized. A parameter report is generated according to a final identification result and a tested casting body is marked; and castingswith qualified polishing and castings with unqualified polishing are classified and output through the defect identification and classification mechanism.

A surface defect data display and management system comprises a risk score calculation unit for calculating the magnitude of an influence a surface defect on a wafer detected by a wafer inspection system or review system has upon a reduction in the yield of a final product as a risk score of the surface defect based on a defect size of the surface defect on the wafer and a pattern concentration obtained from design data of a pattern figure nearby a location corresponding to the position of the surface defect, and a correlation graph and defect image display unit for preparing a correlation graph showing the correlation between the defect size and the risk score of each defect, displaying the prepared correlation graph on the display apparatus and displaying additionally a defect image list of one or more defects selected by using the correlation graph.

When size of a defect on an increasingly miniaturized pattern is obtained by defect inspection apparatus in the related art, a value is inconveniently given, which is different from a measured value of the same defect by SEM. Thus, a dimension value of a defect detected by defect inspection apparatus needs to be accurately calculated to be approximated to a value measured by SEM. To this end, size of the defect detected by the defect inspection apparatus is corrected depending on feature quantity or type of the defect, thereby defect size can be accurately calculated.

The invention discloses a transparent plate defect detection device based on machine vision and a detection method thereof, belongs to the technical field of intelligent defect detection of transparent materials, and solves the technical problems of false detection, missing detection and low efficiency in detection of internal defects and surface defects of transparent plates. According to the technical scheme, the device comprises a detection system composed of an industrial camera, an industrial lens, an industrial light source and a stepping motor, the detection system is placed in a cameraobscura, the light source is installed on the side surfaces of a conveying belt and a detected material, and the industrial camera is installed at a vertical position above the detected material forshooting. The detection system adopts a computer to realize Ethernet communication with the industrial camera to obtain an image, and processes the image by adopting a method of first binarization noise reduction, then Gaussian filtering and binarization enhancement to obtain contour information. The defect size information and the defect area are calculated in a mode of comparing by adopting a shooting graduated scale. According to the invention, the labor can be saved, the cost is reduced, the detection accuracy is improved, and the size detection error range is 0.5-5.0%.

The invention discloses a rolling bearing outer ring defect full-size quantitative diagnosis method. Firstly primary diagnosis is performed on an actual measurement signal based on an impact time difference to obtain a basic size and selectable sizes of an outer ring defect. Secondly a nonlinear dynamics model of a bearing system is built, and through numerical solving, a vertical-horizontal synchronous simulation vibration signal corresponding to each defect size is obtained. For the situation that a defect angle is 270 degrees, Fourier transform is performed on the actual measurement signaland a simulation signal of each selectable defect size to obtain corresponding amplitude spectrums, a minimum diagnosis parameter Min/delta fN/ of the actual measurement signal is solved, and an outerring defect size is diagnosed out. For the situation that the defect angle is 250-290 degrees, an outer ring defect full-size quantitative diagnosis standard database is established based on a vertical-horizontal synchronous standard deviation, and by determining an attribution interval of the vertical-horizontal synchronous standard deviation of the actual measurement signal, the outer ring defect size is diagnosed out.