456 results about "Non destructive" patented technology

The invention discloses an irregular particle three-dimensional shape measurement device and method based on speckle imaging, and belongs to the field of irregular particle three-dimensional shape measurement, and the irregular particle three-dimensional shape measurement device comprises an irregular particle shakeout device which is used for enabling irregular particles to fall in a sparse free falling body form, and is used for forming lamellar laser vertical to the falling direction of the irregular particles, the laser sheet generating module is used for forming complete speckle distribution on the surfaces of the irregular particles; the laser sheet generating module is used for generating a laser sheet, the multi-view image collecting module is used for collecting speckle distribution on the surfaces of irregular particles in a multi-view mode and forming speckle images, the synchronous control system is used for controlling the laser sheet generating module and the multi-view image collecting module and synchronizing the time of the laser sheet generating module and the multi-view image collecting module, and the three-dimensional reconstruction processing unit is used for obtaining three-dimensional shape data of the irregular particles according to the speckle images. According to the invention, the irregular particles can be rapidly, non-destructively and in-situ measured so as to meet the requirements of aeroengine erosion research on the precision, efficiency and integrity of irregular particle data.

The invention relates to the technical field of metal material heat treatment quality detection, in particular to a metal material rapid annealing effect detection method based on acoustic response, which comprises the following steps: step 1, acoustic excitation and synchronous acquisition; step 2, multi-physics field coupling compensation: calculating the phase compensation amount of the vibration signal based on the temperature distribution data and the acoustic characteristic temperature dependence of the material; the method comprises the following steps: acquiring an environment vibration frequency spectrum through a production line mechanical vibration monitoring device, and separating an excitation response component from an original vibration signal by adopting adaptive filtering; 3, annealing sensitive feature extraction; 4, dynamic quality mapping and control: inputting the real-time feature vector into a pre-trained machine learning model, and outputting a recrystallization proportion and a grain size; and when the output parameter exceeds the process threshold value, the control parameter adjustment of the annealing furnace is triggered. Through a non-destructive and accurate acoustic monitoring technology, the annealing effect of the metal material can be efficiently and accurately monitored in a high-speed, high-noise and strong-interference industrial environment.

The invention discloses a method and system for detecting abnormal welding spots in the spot welding process. The method comprises the steps that a time sequence data sequence collected in real time in the welding process is obtained; calculating a first-order difference sequence of the time sequence data sequence; screening a target data segment representing a stable trend based on the first-order difference sequence; performing linear fitting on the first K data points of the target data segment to obtain a first trend direction; performing linear fitting on the last K data points of the target data segment to obtain a second trend direction; calculating the variable quantity of the first trend direction and the second trend direction; and when the variable quantity exceeds a preset threshold value, it is judged that welding is abnormal, and an alarm is triggered. The stable trend section is screened through the differential sequence, noise interference is effectively eliminated, and it is ensured that the quality criterion is based on reliable data. And whether the welding spot is abnormal or not is quickly and accurately judged, so that real-time and non-destructive spot welding quality on-line monitoring is realized.

The invention discloses a device and method for measuring the abrasion amount of a spray needle of an impulse turbine, and the device comprises a fixed platform which is used for placing the spray needle to be measured, and the spray needle to be measured is provided with a plurality of points to be measured; the inner and outer slide rails are arranged at the top of the fixed platform, are positioned on the outer side of the to-be-measured spray needle and axially coincide with the to-be-measured spray needle; the rotating platform is in sliding connection with the inner and outer sliding rails and can rotate around the to-be-measured spray needle; the telescopic platform is arranged at the top of the rotating platform, and the end, away from the rotating platform, of the telescopic platform is connected with an inclined platform; the distance measuring probe system is detachably connected to the inclined platform, and the measuring end of the distance measuring probe system faces the to-be-measured spray needle; the measuring device is combined with a multi-degree-of-freedom adjusting mechanism to realize accurate positioning and vertical alignment of the probe, then the probe system is used for reading the displacement and deducting the thickness of the sleeve to obtain the real abrasion depth, non-destructive high-precision abrasion amount detection of any point of a complex curved surface can be realized, and the measuring device is suitable for a hydropower station overhaul site.

The invention provides a fatigue test method, system and equipment based on texture change of carbon fibers and a medium, and belongs to the technical field of material tests.The method comprises the steps that a carbon fiber composite material is selected as a test sample; applying a fatigue load to the test sample according to a designed test condition, and collecting a surface image of the test sample; the method comprises the following steps of: preprocessing an acquired image, clustering according to the gray level and texture similarity of pixels to obtain a plurality of clustering subareas, and marking different clustering areas by using different colors to complete color mapping; extracting texture feature parameters from the image after color mapping, and performing PCA dimension reduction to obtain key texture feature parameters; and comparing the key texture feature parameters of the image under different test conditions, identifying texture feature changes, and completing fatigue performance evaluation. According to the method, image processing and texture feature analysis are combined, so that non-destructive testing of the CFRP fatigue performance is realized, and the testing accuracy and efficiency are improved.

The invention discloses a cable terminal crimping quality detection method, device and equipment and a storage medium, and relates to the technical field of cable testing, and the method comprises the steps: exciting a cable terminal through a current pin in the crimping process of the cable terminal, collecting the voltage of the cable terminal through a voltage pin, and obtaining a real-time voltage signal; and evaluating the crimping quality of the cable terminal based on the resistance change condition of the real-time resistance value in the crimping process. According to the invention, the detection behavior and the crimping process are carried out synchronously, and the real-time monitoring of the dynamic change of the resistance in the crimping process is realized, so that the method can be applied to the one-by-one full detection of a production line, and the measurement mode of separating current excitation and voltage collection is adopted. A system error caused by lead resistance introduced by a common lead of current excitation and voltage acquisition is avoided, and real-time performance, non-destructive performance and high precision are effectively considered when the crimping quality of the cable terminal is evaluated.

The invention provides a method for monitoring the internal temperature of a single cell of a power battery, and relates to the technical field of battery monitoring, a monitoring method and a cell assembly are included, the cell assembly comprises a cell column, the cell column is wrapped with a cell lamination, the inner side of the cell lamination is provided with a miniature thin film thermocouple sensor, and the miniature thin film thermocouple sensor is arranged in the cell column. Aiming at the problems of insufficient precision, limited environmental adaptability, low intelligent degree and lack of thermal runaway prediction capability in monitoring of the internal temperature of a single cell of a power battery, a micro thin film thermocouple sensor array is embedded, an intelligent data processing flow and a multi-source data fusion design are adopted, and the thermal runaway prediction capability of the single cell of the power battery is improved. And the monitoring precision, reliability and automation level are obviously improved. Firstly, high-precision non-destructive detection and environmental adaptability are realized. The miniature thin film thermocouple sensor array is embedded between the positive and negative plates and the diaphragm, is attached to the non-coating area of the positive plate, and is in direct contact with the active material to construct a three-dimensional distributed temperature monitoring network.

The invention discloses a detection method for fertilizer production, and relates to the technical field of fertilizer detection.The detection method comprises the following steps that a near-infrared multispectral visual detection system is installed at the working section before packaging of a fertilizer production line, the positions of a camera and an illumination source are adjusted, and multispectral images of fertilizer particles are collected; sequentially carrying out dark current correction and flat field correction on the acquired image, eliminating noise and uneven illumination, and then carrying out median filtering to remove random noise; selecting a waveband image with the best contrast ratio, separating particles from the background through threshold segmentation, and positioning each independent particle after optimizing the boundary through morphological processing; and based on the multi-band reflectivity difference, analyzing the surface material of the particles pixel by pixel, and counting the proportion of the coating area of each particle to obtain the coating coverage rate of the single particle. By introducing the near-infrared multispectral visual detection technology and the coating uniformity quantitative analysis model, non-destructive and high-precision online detection of the coating coverage rate of the fertilizer can be realized.

The invention belongs to the technical field of image processing, and particularly relates to an inonotus obliquus raw material granularity and uniformity detection method based on image analysis, and the method comprises the steps: obtaining an inonotus obliquus raw material gray scale image, and carrying out the preprocessing and binaryzation to obtain an adhesion body candidate object; screening particle core seed points through distance transformation in combination with an adaptive threshold strategy based on image statistical characteristics; calculating the attribution probability of a pixel to a seed point through a geodesic distance, achieving the soft segmentation of an adhesion region through a negative index model, and extracting a non-destructive segmentation line to obtain independent particles; and calculating morphological parameters of the particles, and obtaining a global uniformity index by combining the size dispersion and the shape regularity. According to the method, the accuracy of adhesion segmentation of irregular inonotus obliquus raw materials with complex textures is improved, and the robustness and accuracy of granularity detection are improved.

The invention relates to the technical field of information, in particular to a black land plough layer thickness analysis method based on machine learning. The scheme has the advantages that double-frequency radar signals (200MHz low frequency and 2GHz high frequency) are combined with power amplification and a broadband antenna, a double-layer feature comparison model is constructed by utilizing the frequency sensitivity inversion characteristic of dielectric constant difference between a plough layer and a plough pan, interface details are captured in cooperation with a 1GS / s super-Nyquist sampling rate, a strong regression model is constructed in combination with an AdaBoost algorithm, and a high-precision feature comparison model is constructed. The method achieves the high-precision, non-destructive and large-area rapid detection of the thickness of the plough layer of the black land, and solves the problems that a conventional method is time-consuming and labor-consuming, is low in precision, and cannot give consideration to the penetration depth and resolution of a single-frequency radar.

The invention discloses a real-time monitoring method for local corrosion of a copper base material based on a discrete FBG sensor, and belongs to the technical field of metal corrosion monitoring. FBG strain sensors and temperature compensation sensors are distributed on the surface of the copper base material, and a temperature compensation mechanism is matched to eliminate measurement errors caused by environment temperature changes, so that the real-time monitoring of local corrosion of the copper base material is realized. Bragg wavelength drift caused by corrosion product accumulation in the corrosion process is collected in real time, and then real-time and continuous tracking of the corrosion dynamic process is obtained. The method realizes early recognition, accurate positioning and quantitative evaluation of local corrosion of the copper base material, has the advantages of non-destructiveness, high sensitivity, long-term stability and the like, and is suitable for corrosion health monitoring of copper structures such as ocean engineering, buildings, pipelines and the like.

The invention provides a carbon fiber three-dimensional density measurement method and system, and relates to the technical field of material detection. Comprising the following steps: irradiating a to-be-detected sample with X-rays, and collecting a plurality of interference images formed after the X-rays penetrate through the to-be-detected sample at different angles of the to-be-detected sample; calculating two-dimensional density distribution of the to-be-detected sample at each angle according to the interference image; and carrying out tomographic reconstruction on the two-dimensional density distribution at the plurality of angles to obtain a three-dimensional density distribution image of the sample to be detected. According to the carbon fiber three-dimensional density measurement method, a multi-angle X-ray interference image is collected, and the density change of the carbon fiber reinforced composite material is reflected by using phase information, so that the density resolution of the carbon fiber reinforced composite material is improved; the method is non-destructive, and is suitable for efficient and reliable analysis of the internal structure of a complex material.

The invention provides a hard and brittle material subsurface damage mechanism signal classification method based on an acoustic emission technology. The method comprises the following steps: firstly, acquiring an acoustic emission original signal and surface appearance of the hard and brittle material through grinding tests at various grinding speeds; then, carrying out preprocessing and time-frequency domain feature extraction on the acoustic emission signals by using a wavelet packet signal processing algorithm and short-time Fourier transform; finally, distinguishing a micro-crack signal, a radial crack signal and a transverse crack signal from the characteristic space by combining the damage characteristics and acoustic emission characteristic parameters of the hard and brittle material, and obtaining a characteristic frequency band range corresponding to each crack damage mode; a hard and brittle material subsurface damage mechanism signal classification model is constructed, and a theoretical basis is provided for monitoring of grinding subsurface damage of hard and brittle materials under complex working conditions. According to the method, the damage mechanism of the hard and brittle material can be quickly identified, the subsequent online evaluation of the subsurface damage of the hard and brittle material is realized, and the technical problem of non-destructive accurate monitoring in the grinding process is solved.

The invention belongs to the technical field of prefabricated part mounting systems, and particularly relates to a quick mounting and accurate positioning system for prefabricated parts of an assembled house, comprising a prefabricated part transportation module for transporting the prefabricated parts from a production site to a construction site; according to the invention, through a millimeter-level high-precision positioning reference point of the positioning reference module, an accurate space coordinate reference is provided for component installation; the component posture adjusting module has a fine adjustment function, the fine adjustment precision reaches + / -0.1 mm, and the component posture can be finely adjusted; the precise positioning control module adopts a hybrid algorithm based on fuzzy control and neural network control, parameters can be automatically adjusted according to different installation environments and component types, and positioning precision and stability are improved. Meanwhile, the quality detection module is combined with a non-destructive detection technology and an image recognition technology, the installed component is comprehensively detected, internal defects can be accurately recognized and marked, and strength detection is conducted on the connecting part to ensure firm connection.

The invention provides a phase classification method for coal combustion fly ash, and relates to the technical field of coal combustion byproduct analysis. The phase classification method comprises the following steps: performing consolidation treatment on a coal combustion fly ash sample to obtain a to-be-analyzed target sample; carrying out element component collection on the plurality of particle micro-areas in the to-be-analyzed target sample based on scanning electron microscope energy spectrum analysis to obtain the contents of main control elements and target elements; and according to the contents of the main control element and the target element, performing phase classification on the plurality of particles contained in the coal combustion fly ash sample, and determining the phase category to which each particle belongs. According to the method, through consolidation sample preparation and scanning electron microscope energy spectrum analysis, non-destructive and multi-element micro-area analysis of the fly ash particles is achieved, phase classification is completed based on the element content, and the classification accuracy and the research foundation are improved.

The invention discloses a friction welding bead offset detection method, friction welding equipment and a workpiece. The friction welding bead offset detection method comprises the steps that a target workpiece is placed on an XY moving platform, and the target workpiece is provided with a first detection symbol and a second detection symbol; the XY moving platform, the Z-axis mechanism and the C-axis mechanism are used for controlling a stirring needle to conduct friction stir welding on the target workpiece, so that a friction welding bead is formed on the target workpiece; controlling the stirring needle to perform primary dotting on the first detection symbol so as to form a first detection point on the target workpiece; the stirring needle is controlled to perform secondary dotting on the second detection symbol so as to form a second detection point on the target workpiece, and the difference value of the C angle parameter of the C-axis mechanism between the primary dotting and the secondary dotting is 180 degrees; and according to the first detection symbol, the first detection point, the second detection symbol and the second detection symbol, friction welding bead offset detection is conducted, and an offset detection result is obtained. According to the invention, offset detection can be carried out on the friction welding bead in a non-destructive manner.

The invention provides a rock-soil moisture content and permeability testing system and method based on electromagnetic waves and infrared rays, and particularly relates to the technical field of geotechnical engineering testing and multi-source signal detection. According to the test system, construction of a rock-soil permeability experiment is realized through a rock-soil sample permeability test platform, the electromagnetic wave detection module realizes perception of a water-containing state in rock-soil, and the infrared thermal imaging module realizes perception of a wet boundary and seepage expansion characteristics of a rock-soil surface. The data acquisition and control module realizes integral control of a penetration experiment and acquisition of dielectric characteristics and infrared images, the signal fusion and inversion module realizes fusion of the dielectric characteristics and the infrared images and joint inversion of dynamic moisture content and penetration performance, and the result processing and visualization module realizes visual display of an inversion result; the method has the remarkable advantages of non-destructiveness, real-time performance, high spatial resolution, high adaptability, intelligent processing capacity and the like, and comprehensive characterization of the permeability behavior and the moisture content evolution process of the rock and soil sample is achieved.

The invention provides a non-destructive detection method for the content of chlorophyll and carotenoid in tobacco leaves, and belongs to the technical field of image data process.The method comprises the steps that a standardized image collection environment is constructed, and RGB images are obtained through an industrial area-array camera; performing threshold segmentation, contour extraction and rotation correction by using an HSV color space to obtain a standardized leaf image; a double-branch CNN network structure is constructed, and three attention mechanisms of channel attention, space attention and cross attention are integrated; training the network through an Adam optimizer, and evaluating the performance of the model by using a decision coefficient, a root-mean-square error and a relative analysis error; and finally, carrying out visual analysis by adopting a Grad-CAM technology, verifying the recognition effect of the model on pigment distribution, and realizing high-precision non-destructive rapid detection from image data to pigment content.

The method comprises inspecting a longitudinal end (3) of a metal bar (1), with a step of acquiring data using at least one multi-element probe (5), and a step of post-processing the data; during the data acquisition step, moving the at least one multi-element probe (5) and the metal bar (1) relative to one another such that the at least one multi-element probe (5) sweeps a side surface (9) circumferentially and / or axially, the at least one multi-element probe (5) emitting, at regularly spaced defined positions, an incident ultrasonic signal towards a region of the longitudinal end (3) and detecting the returned signal, the returned signal being recorded with the corresponding circumferential and / or axial position of the multi-element probe (5) with respect to the side surface (9); the incident ultrasonic signal comprising multiple successively emitted incident signals that are distinct from one another, the returned signal comprising a matrix of elementary time-domain signals corresponding to the echoes of each of the incident signals detected by each receiving element (7); during the data post-processing step, the returned signal is post-processed by determining, in the elementary time-domain signals, the echoes returned by each point of the region of the longitudinal end (3) and by combining the echoes into a combined signal that characterizes the point.

The invention relates to the technical field of analysis and testing, in particular to a method and system for detecting the cold resistance degree of pennisetum purpureum based on spectral imaging, and the method comprises the steps: obtaining and correcting a spectral image of a pennisetum purpureum leaf; determining biochemical values of various induction components, calculating weights by using principal component analysis, and screening out the component with the maximum weight as a target prediction index; extracting the average spectrum data of the region of interest, and constructing a regression prediction model in combination with a target index measured value after preprocessing and characteristic wave band screening; and finally, the index content is inversed by using the model to represent the cold resistance degree. According to the method, key physiological indexes are scientifically screened through principal component analysis, a high-precision non-destructive detection model is established in combination with an optimal spectrum algorithm, and nondestructive, rapid and high-throughput evaluation of the cold resistance of the object grass is realized.

The invention relates to a mobile device and measurement method developed to non-detructively measure the strength of concrete using electromagnetic (EM) waves in the microwave (MW) frequency band, which shows the resistance of the structure against earthquakes and external effects by measuring the strength of the concrete without damaging the reinforced concrete structure. The technical field of the invention is the non-destruuctive measurement of concrete strength using electromagnetic waves (EMW) in the microwave frequency band. This method is an alternative to destructive measurements by taking cores from structural elements such as columnsi beams, and slabs within the construction field, thus preventing damage to structures due to strength measurements and providing more rapid and non-destructive measurements from more regions. Results are evaluated using artificial intelligence techniques, providing a much more accurate strength measurement result instantly.

A system and a method for non-destructive characterizations of objects using Laser Acoustic Resonance Spectroscopy (LARS)-based diagnostic techniques are provided. The system includes using a laser doppler vibrometer to measure vibrational responses of objects. The method includes measuring vibrational frequency responses of a first object and a second object, performing a spectra analysis of the vibrational frequency responses, determining a frequency shift based on the spectra analysis, and indicating a difference between the first object and the second object or a presence of a defect in the second object if the determined frequency shift exceeds a predefined threshold value. The difference between the first object and the second object may indicate the presence of a void, a crack, or a plurality of pores in the second object, or can be used for validating or authenticating the second object as a defective or counterfeit item, in various embodiments.

The invention discloses a valve life test method based on multi-modal sensing and deep learning, which comprises the following steps: acquiring high-frequency original data of valve operation through a multi-modal sensing system consisting of a broadband acoustic sensor array, a vibration accelerometer and a high-precision micro-flowmeter; the method comprises the following steps: automatically extracting features sensitive to early degradation from original data by using a deep learning model fused with a one-dimensional convolutional neural network and a Transform encoder; the high-dimensional features are mapped into single health index values through neural network regression to quantify the performance degradation degree; and a historical sequence of the health indexes is learned by using a time sequence convolutional network, and a future evolution trajectory is predicted, so that the residual life of the valve is accurately calculated. Sensitive detection and non-destructive life prediction of early weak faults of the valve are achieved, and the defects that a traditional physical testing method is long in period, high in cost and incapable of early warning are overcome.

The invention provides a device for simulating the vertical migration rule of oil and gas field ground pollutants and a simulation method, and relates to the technical field, the device comprises a soil column leaching module, a spraying module and an underground water control module, the soil column leaching module and the underground water control module are both arranged in an underground foundation pit, and the soil column leaching module comprises a soil column experiment device; a sensor device is inserted into the soil column experimental device, the sensor device is electrically connected with the data acquisition device, the soil column experimental device is provided with a soil sampling window, the spraying module comprises a spraying pipe, one end of the spraying pipe is communicated with the reservoir, and the other end of the spraying pipe extends above the soil column experimental device. On the basis that only surface water is concerned to drive pollutant migration originally, the underground water control module and the water supply Markov bottle are additionally arranged, and fluctuation of the underground water level can be achieved; long-term and continuous in-situ monitoring of soil moisture in-situ data can be achieved in real time, non-destructive collection of samples can be achieved, and the research quality of the vertical migration rule of oil and gas field ground pollutants is improved.

This invention discloses a method for directional experimental verification and performance testing guided by computational simulation in materials chemistry, belonging to the field of materials performance testing technology. The method includes: acquiring the structural parameters of semiconductor devices and initial parameters of interface defects; calculating the characteristic time constant of carrier trapping by interface traps; simulating and generating a theoretical capacitance-voltage hysteresis loop; then performing bidirectional voltage scan tests within a sensitive scan rate range; finally, updating the inversion parameters and resimulating, iteratively adjusting until the characteristic deviation between the theoretical loop and the measured data meets the convergence condition, thus outputting an interface state density distribution map. This invention, through computational simulation-guided directional experiments, achieves accurate capture and non-destructive in-situ characterization of the dynamic response characteristics of interface states, effectively establishing the dynamic structure-property relationship between microscopic interface defects and macroscopic device performance, and significantly improving the accuracy and reliability of interface defect parameter inversion.

Disclosed are a method of predicting the number of thermal donors using the lifetime of a silicon wafer and a device for performing the method, the method including measuring the lifetime and the number of thermal donors of a sample wafer before and after donor elimination, comparing the same to obtain correlation data, and obtaining the lifetime of a wafer selected for inspection during wafer production processing before and after donor elimination, and predicting the number of thermal donors based on the correlation data. The correlation data can be constructed using a four-probe, and the degree of generation of thermal donors can be predicted in a non-destructive manner based on the correlation data, thereby reducing the reduction in production yield without damaging the wafer, and improving the reliability of the prediction of the number of thermal donors, while having high analysis capability for the front surface of the wafer.

The invention discloses an ultrasonic welding pseudo soldering detection system and method, and relates to the technical field of ultrasonic welding detection, the system comprises a hardware assembly and a visual software module, the hardware assembly comprises an upper camera, a lens, an annular light source, a PLC lower computer and the like, and the visual software module configures program instructions such as initialization, image acquisition, feature detection and the like; according to the detection method, images are collected through the upper camera, detection of the expanded material size, the grid edge width, the grid area and the peripheral overflow area is completed in sequence, and pseudo soldering is automatically judged in combination with a visual algorithm. Non-destructive online full detection is achieved, the detection precision reaches + / -0.01 mm, the single-station detection rhythm is smaller than or equal to 1 second, the problems that traditional detection products are large in waste and high in misjudgment risk are solved, the method is suitable for new energy power battery copper-aluminum bar metal ultrasonic welding products, and the detection efficiency and the quality control level are improved.

The invention belongs to the technical field of data processing, and particularly relates to a die forging gear mechanical property detection method based on data processing, which comprises the following steps of: standardizing process parameters of a die forging gear to construct a feature vector, forming a training sample set together with mechanical property data of the die forging gear, training a decision tree regression model, and calculating the mechanical property of the die forging gear. Calculating the similarity between the to-be-detected die forging gear and a decision path of a leaf node in the decision tree regression model according to the feature vector of the to-be-detected die forging gear, and calculating a local refinement prediction value of the to-be-detected die forging gear under the leaf node according to the feature vector of the to-be-detected die forging gear and the proximity of a training sample in the leaf node and the representativeness of the training sample in the leaf node; and performing weighted fusion on the local refinement prediction value according to the decision path similarity to obtain a mechanical property prediction value of the to-be-tested die forging gear. According to the invention, non-destructive detection of the mechanical properties of the die forging gear is realized, and the detection efficiency and the quality control level of the die forging gear are improved.

The invention discloses a method for rapidly generating non-uniform structure characteristics and mechanical properties of a weld zone, and belongs to the technical field of welding quality assessment, and the method comprises the following steps: S1, obtaining a characteristic temperature-time curve representing a weld thermal history through a dynamic temperature measurement method; s2, inputting the characteristic temperature-time curve and the chemical components of the base metal into a thermal-metallurgical coupling model, and predicting a non-uniform microstructure phase distribution diagram of the weld seam area; and S3, based on a semi-empirical mechanical model, mapping the non-uniform microstructure phase distribution diagram into a mechanical property distribution diagram of the welding seam area, and completing rapid identification of the microstructure characteristics and the mechanical properties. According to the rapid generation method for the non-uniform structure characteristics and the mechanical properties of the welding seam area, rapid and non-destructive identification of the non-uniform structure and the properties of the welding seam is achieved, the defects of lag and damage of a traditional method are overcome, and an effective means is provided for online evaluation of the welding quality and process optimization.

The invention provides a method for constructing a fusion width prediction model of a laser stitch welding interface. The method comprises the following steps: collecting surface fusion width data and fusion depth data of a plurality of welding seams of laser stitch welding of a first workpiece and a second workpiece which are overlapped with each other, and interface fusion width data at an interface position between the first workpiece and the second workpiece; and constructing a data set by taking the collected surface fusion width data and fusion depth data as input data and taking the interface fusion width data as output data, and training the interface fusion width prediction model by using the data set. According to some embodiments of the invention, a laser stitch welding interface fusion width prediction model can be constructed, so that convenient and non-destructive interface fusion width prediction can be realized by using the prediction model in production, and the evaluation of the laser stitch welding quality is facilitated. The invention further provides a corresponding laser stitch welding interface fusion width prediction method and system, electronic equipment and a computer program product.