5426 results about "Surface roughness" patented technology

The invention provides an equipment supervision data real-time analysis method and system, and the method comprises the steps: calculating an energy value of a frequency band corresponding to a sub-band signal, determining the energy characteristics of the surface roughness and dust deposition distribution uniformity of a blade in each sub-band in a square sum form, and generating energy distribution vectors which are arranged according to a frequency band sequence; performing time-frequency analysis on the vibration signal by using the acquired real-time rotating speed information of the gas turbine, identifying a frequency component corresponding to the eccentric distance value of the rotor and the vibration frequency offset, and determining the amplitude of the frequency component; according to the classification result and the real-time rotating speed information, the comprehensive influence index of the material density change rate and the ash deposition layer thickness difference is calculated, the fault early warning level is determined through a weighted average method, and an early warning signal is generated; and according to the change trend of the early warning signal and the blade edge wear degree, a random forest algorithm is adopted to predict a development curve of the vibration frequency offset in the future target time, and real-time equipment supervision analysis data are obtained.

The invention relates to the field of particle size distribution measurement, in particular to a particle size distribution measurement method and system based on machine learning and a light scattering method.The method comprises the steps that a to-be-measured particle sample is irradiated through a light source, and meanwhile light scattering intensity data of different scattering angles and different wavelengths are collected through a multi-angle scattered light detector; constructing a multi-modal deep learning model, and forming an integrated learning architecture in combination with a support vector machine model and a random forest model; inputting the preprocessed multi-wavelength light scattering intensity data into the trained integrated learning architecture to obtain a preliminary particle size distribution result of the to-be-detected particle sample; based on the improved Mie scattering theory, a high-precision theoretical model considering the particle shape, the refractive index, the surface roughness and the internal structure is constructed; an improved Bayesian optimization algorithm is adopted, and the initial particle size distribution result is optimized and adjusted by minimizing the error between the theoretical light scattering intensity and the actually collected light scattering intensity, so that a corrected particle size distribution result is obtained.

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

The invention relates to the field of abrasion morphology determination and abrasion loss calculation, and discloses an abrasion morphology and contact stress analysis and prediction method and system based on a rough contact surface, a medium and equipment, and the method comprises the steps: constructing a dynamic contact model, and obtaining an accurate movement rule and a stress analysis result of a moving mechanical component; constructing a three-dimensional rough surface digital twinborn model conforming to an actual contact surface; establishing a contact stress field multi-physical field analysis model to obtain elastic deformation and a friction heat effect; based on the contact stress distribution and the local wear rate which are obtained in real time, carrying out dynamic reconstruction on the wear morphology, and establishing a nonlinear iteration relation between the wear loss and the contact stress distribution; determining an accurate boundary condition based on an accurate movement rule and a stress analysis result of the moving mechanical component, and constructing a failure criterion based on contact stress gradient mutation and surface roughness evolution; key parameters are calibrated through experiments, and a mapping relation database of wear rate-stress state-morphological characteristics is established so as to carry out friction wear analysis.

The invention discloses a cleaning control method and system based on an electronic cleaning agent, and the method comprises the steps: obtaining the distribution characteristics of pollutants and a material sensitivity judgment result through obtaining the micron-sized particle distribution data, chemical residue concentration data and surface roughness parameters of the pollutants on the surface of an electronic workpiece; according to the pollutant distribution characteristics and the material sensitivity judgment result, matching a candidate formula from a preset cleaning agent formula database, calculating pollutant removal rate predicted values of different concentration and time combinations through a linear regression algorithm, and determining optimized cleaning agent formula parameters and cleaning strength control parameters; and in the cleaning process, the residual quantity of pollutants and the surface damage degree are collected in real time, if the residual quantity of the pollutants exceeds a preset threshold value T1 or the surface damage degree exceeds a preset threshold value T2, cleaning agent injection pressure and cleaning time parameters are dynamically adjusted, and updated cleaning execution parameters are generated. Accurate and efficient cleaning of the surface of the electronic workpiece is achieved.

The invention discloses a numerical control machining equipment state monitoring system based on big data analysis, and relates to the technical field of intelligent manufacturing. The method is used for solving the problems of correlation analysis and real-time feedback between tool wear and machining quality in the machining process. A time sequence correlation feature vector is extracted through a vibration signal, an acoustic emission signal and main shaft axial micro-displacement data, a machining quality degradation index is calculated in combination with a nonlinear regression model, and the machining quality is monitored in real time. And on the basis of adaptive feature weight adjustment of the types of the processing materials, feature fusion during processing of different materials is enhanced, and the precision of quality evaluation is improved. And establishing a nonlinear mapping relation between the tool wear and the surface roughness by utilizing a gradient lifting tree model, and realizing accurate prediction of the tool wear and the machining quality. And finally, through a closed-loop dynamic adjustment mechanism, the cutting speed and the feeding amount are automatically adjusted according to the machining quality degradation index and the fault positioning result, and the stability of the machining process and the product quality are ensured.

The invention relates to the technical field of material testing, and discloses a coating performance simulation method based on high and low temperature circulation and prestress loading. The method comprises the following steps: constructing a coating matrix composite geometric model containing a microporous structure by acquiring actually measured working condition data of a pipeline or a part, and quantifying surface roughness and interface curvature characteristics in combination with reverse engineering; a dynamic temperature-pressure coupling equation is generated based on the thermal expansion effect of a high-pressure medium, a micropore optical load is converted into equivalent heat flux density through a ray tracing algorithm, transient boundary conditions of a temperature field and a stress field are associated, a crack initiation path is simulated through an extended finite element method, and a life attenuation curve is predicted in combination with a Paris formula. And material parameters are dynamically corrected based on experimental data, and a multi-parameter collaborative optimization model is generated. According to the method, the problems of interface stress evaluation distortion and life prediction deviation under the combined load are solved, and multi-physics field co-simulation support is provided for coating compression resistance optimization.

The invention relates to the technical field of ship rust removal, in particular to a large ship surface self-adaptive rust removal method based on laser rust removal, and provides the following scheme that a ship structure design drawing is obtained, three-dimensional position marking is conducted on a rusted part, and depth data is measured; setting laser initial parameters according to the environmental data; constructing a parameter updating network, inputting the depth data and the initial parameters for training, and generating laser adjustment parameters; rust removal is conducted on the rusted part according to the adjusting parameters and the path planning; and finally, acquiring the surface roughness of the cleaned part and optimizing network parameters. According to the method, the laser power, frequency and scanning speed are adjusted in a self-adaptive mode, and the accuracy and efficiency of laser rust removal are improved.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention relates to the technical field of machining precision control, and discloses an injection mold core machining precision control method and system.The method comprises the steps that a multi-point trigger type sensor is adopted for conducting data collection on the surface of a core of an injection mold, and core surface contour data, geometric dimension data and surface roughness data are obtained; inputting a feature extraction model, extracting fusion features of the surface of the mold core, and performing regional processing on the surface of the mold core to obtain regional polishing parameters; normalized error coefficient calculation is carried out, and a core surface quality distribution diagram is generated; calculating the compensation coefficient of each region to obtain a compensation optimization parameter matrix; and multi-axis linkage path planning is conducted, the surface of the mold core is divided into a plurality of grid units, the curvature value and the polishing track compensation value of each grid unit are calculated, a thermal deformation compensation polishing path is generated, and the automation level and the intelligent degree of mold core machining are improved.

The invention discloses a thermal barrier coating multi-dimensional terahertz nondestructive testing method and system based on an adaptive optimization mechanism, and the method comprises the steps: constructing a matrix sample library, and preparing thermal barrier coating samples with different thicknesses, porosities and uniformity; obtaining terahertz detection data of three dimensions of geometric dimension stability, microstructure integrity and material structure uniformity of the thermal barrier coating; establishing a multi-parameter correction model based on surface roughness, and performing feature extraction, fusion analysis and error compensation on the detection data by adopting a multi-modal integration algorithm; an adaptive optimization model is established through a deep learning algorithm, and continuous iterative optimization of the detection system is realized in combination with a feedback correction mechanism; the method is applied to an engine blade thermal barrier coating, an actual blade coating is detected, detection data are collected for system optimization, system parameters and models are updated, and system applicability and robustness are improved. The method can be widely applied to the field of nondestructive testing of coating structures such as aero-engine blade thermal barrier coatings.

The invention relates to the technical field of material removal in laser processing, in particular to a laser positioning stripping system and method for special-shaped neodymium iron boron waste, and the method comprises the following steps: S1, carrying out multispectral scanning on the surface of the special-shaped neodymium iron boron waste; s2, dynamically adjusting energy parameters of laser lift-off, and constructing an energy thickness matching model; s3, generating a fractal scanning path of the adaptive special-shaped surface based on the geometric contour data of the three-dimensional characteristic spectrum; s4, in-situ surface roughness detection is carried out in the laser lift-off process, a secondary finishing mode is triggered according to a detection result, and the safe lift-off allowance is dynamically corrected based on lift-off depth data; and S5, in-situ separation of the oxide layer fragments and the matrix particles is achieved through the hot air flow field in combination with the gas-solid separation dynamics principle, and matrix particle recovery is assisted and strengthened through the magnetic field. The laser energy, the scanning path and the thermal control strategy can be dynamically adjusted according to real-time data, so that material loss and waste are effectively avoided, and the processing efficiency and the recovery rate are improved.

The invention provides an intelligent control method for a floodlight illumination system, and the method comprises the steps: obtaining an environment surface roughness coefficient, spectral reflection distribution and spatial geometric change data, determining the wall surface material absorptivity and shielding region coordinates, and obtaining environment reflection characteristics and shielding distribution data; analyzing shielding boundary sharpness and shielding angle distribution according to the spatial geometric change and the coordinates of the shielding area, calculating a luminous flux distribution curve of a local dark area, and judging whether the influence range of the dark area exceeds a preset threshold range or not; calculating the illumination coverage integrity according to the updated light field distribution and polarization reflection characteristics, determining the boundary sharpness of a light beam, and obtaining a light field uniformity parameter; and according to the light field uniformity parameter and the shielding thermal radiation effect, calculating and optimizing light field parameters.

The invention provides a surface roughness measuring method and device based on a multispectral laser speckle image, and belongs to the field of surface quality visual inspection. Comprising the following steps: respectively acquiring speckle images formed by irradiating the surface of a workpiece with lasers of N wavelengths at different angles, and constructing a multispectral laser speckle data set; the speckle images of N wavelengths at a single angle are spliced according to channels and subjected to feature fusion, and then a multispectral laser speckle fusion image is generated; the fused images of the multiple angles form an image sequence, and single images in the sequence are subjected to multi-scale feature extraction and fusion to generate single-angle features; the multi-angle features generate global illumination information through a multi-angle feature aggregation module, and finally a roughness value is calculated through a multi-layer perceptron. Multi-spectral laser speckle coding surface features are adopted, multi-angle information is fused through the multi-angle feature aggregation neural network, and the relation between the light source wavelength, the incident angle and the surface three-dimensional shape and roughness is comprehensively considered, so that high-precision measurement is achieved.

The invention provides a surface quality monitoring method and system based on image-signal multi-modal data, and relates to the technical field of machining process detection, and the method comprises the steps: obtaining a workpiece surface image and a main shaft vibration acceleration signal in a milling process, inputting the workpiece surface image and the spindle vibration acceleration signal into a surface roughness classification model to obtain a machining state recognition result; wherein in the surface roughness classification model, shallow texture features of a workpiece surface image are extracted through an image processing module, and frequency domain features of a spindle vibration acceleration signal are extracted through wavelet transform and a frequency attention mechanism; and splicing projection features of the shallow texture features and the frequency domain features into a combined feature vector, carrying out dynamic weight distribution and fusion on the combined feature vector based on a self-adaptive fusion strategy of a gated attention mechanism to obtain weighted fusion features, inputting the weighted fusion features into a grade classifier, and outputting the grade of the surface roughness.

The invention belongs to the technical field of connectors and discloses a design method for a low-loss cable connector without resonance points, comprising: designing a spiral gradient groove structure on the surface of an inner conductor based on the impedance continuity principle, establishing a composite gradient impedance theoretical model of the connector, constructing a dielectric constant gradient distribution model of a double insulating layer, and setting a transition function between an inner layer and an outer layer; solving the S parameters and electric field distribution of the connector through electromagnetic-thermal coupling finite element simulation, optimizing the spiral angle θ and the gradient index n, establishing a quantitative model of the surface roughness of the conductor and high-frequency loss, and determining a threshold range of the surface roughness through calculation of the quantitative model; performing global optimization of structural parameters based on a multi-objective genetic algorithm, outputting a theoretical design parameter set and a report collection of performance verification, eliminating the resonance point problem of traditional connectors, and providing excellent low-loss performance in the full frequency band, which is particularly suitable for high-frequency and high-power transmission environments.

The invention is suitable for the technical field of performance detection, and provides a method for detecting the ablation resistance of a burning-resistant nozzle and an arc contact, and the method comprises the steps: collecting a temperature field matrix, an arc image sequence, an electrical parameter time sequence and a surface displacement curve of an ablation region, and generating a multi-modal data set; based on the data set, extracting spatial features of the ablation core region through a cavity convolution layer and a channel attention mechanism; inputting a preset bidirectional LSTM network, fusing thermal stress data of the COMSOL simulation model to drive gating weight update, and extracting time sequence evolution characteristics; generating a space-time fusion feature vector containing the temperature field gradient, the arc form change rate and the surface roughness evolution feature; inputting the space-time fusion feature vector, the thermal stress distribution data and the material phase change threshold parameter into a constructed ablation prediction model for training; the ablation prediction model after training is completed is optimized; and a performance prediction result is generated in real time by using the optimized ablation prediction model, so that the detection accuracy is effectively improved.

Disclosed in the present invention are a PET release film base film having low surface roughness and a preparation method therefor. The release film base film comprises three layers which are stacked: a core layer and two surface layers located on both sides of the core layer. The release film base film is obtained via three-layer co-extrusion by means of taking an auxiliary extrusion resin as a raw material of the two surface layers and a primary extrusion resin as a raw material of the core layer; the auxiliary extrusion resin is obtained by mixing an anti-blocking agent PET masterbatch and a PET base material in a mass ratio of 1:5-1:15; and the primary extrusion resin is a pure PET base material. The release film base film of the present invention has low surface roughness, low haze and excellent overall performance. Since large-particle benzoic acid-modified nano aluminum oxide has the refractive index very close to that of PET base films, the present invention can effectively avoid adverse visual impacts caused by a difference in the refractive index at interfaces between nanoparticles having large particle diameters and resin matrixes, thus effectively reducing the haze.

The invention provides a collaborative tool servo diamond turning method and system based on track pre-filtering, and the method comprises the steps: S1, integrating a fast tool shaft to a machine tool shaft in the cutting depth direction, and constructing a master-slave collaborative tool servo system; wherein the machine tool shaft serves as a main servo shaft of the master-slave cooperative tool servo system, and a low-frequency tool path obtained after reference tool path pre-filtering is executed; the fast cutter shaft is used as a slave servo shaft of the master-slave collaborative cutter servo system, and the deviation between the actual displacement of the master servo shaft and the reference cutter path is compensated in real time; s2, implementing a collaborative tool servo machining process based on a master-slave collaborative tool servo system; and S3, after machining is completed, surface contour data are obtained, and quantitative evaluation is conducted on a machining result through surface shape errors and surface roughness indexes.

The invention provides an intelligent semiconductor wafer spraying control method and system, and the method comprises the steps: obtaining a recommended cleaning time range of a wafer from a preset spraying time-wafer attribute association database according to the material and surface roughness of the wafer; the spraying parameters, obtained in the first three steps, of the sub-areas are integrated, a wafer surface cleaning scheme is formed, the cleaning scheme comprises the nozzle layout, the nozzle number, the spraying liquid components and the temperature of all the sub-areas, and the cleaning scheme is transmitted to a spraying control unit; after cleaning is completed, the surface image of the wafer is compared with a preset standard image, the similarity between the surface image of the wafer and the preset standard image is calculated, if the similarity is lower than a preset threshold value, the serial number and cleaning parameters of the wafer are recorded in an abnormal cleaning log, meanwhile, the wafer is conveyed to an unqualified product cache region, and the unqualified product cache region is used for cleaning the wafer. By means of spraying control, the cleaning efficiency and quality are improved, resource waste is reduced, and an advanced cleaning solution is provided for semiconductor manufacturing.

The invention provides a stamping part surface roughness detection method and system based on image recognition, and the method comprises the steps: building a surface contour database according to a metal square lattice standard model stamped by a texturing die, obtaining the actual contour data of the surface of a stamping part, comparing the difference between the actual contour and the metal square lattice standard model, and determining the surface roughness of the stamping part. The method comprises the following steps: obtaining a microscopic deformation feature distribution image, optimizing the microscopic deformation feature distribution image, calculating probability distribution of each pixel point belonging to real surface roughness or microscopic deformation, obtaining a continuous microscopic deformation area mask, calculating a deviation between an actual contour and an ideal surface as a corrected roughness value, and aiming at the corrected roughness value, obtaining a microscopic deformation feature distribution image; according to the characteristics of the stamping part, the surface is divided into a plurality of characteristic areas, when new stamping part surface data is input, the surface roughness grade is judged through the surface quality evaluation model, and the stamping part surface roughness detection method is beneficial to improving the stamping part surface roughness detection precision.

The invention discloses a numerical control machine tool efficient cutting method based on real-time surface roughness prediction. Firstly, acquisition and storage of internal information of an external sensor and a numerical control system machine tool are realized through data acquisition software developed by a numerical control machine tool type; the surface roughness of the part is predicted through data preprocessing, multi-source data feature extraction and fusion and a prediction model; and in the part machining process, data are collected and input into the model for real-time prediction and machining parameter adjustment, so that the cutting efficiency is improved on the premise of ensuring certain quality of the part, and the optimal parameters are stored in the knowledge base. The defects that the quality is uncertain in the part production process of the numerical control machine tool and manpower needs to be consumed for surface roughness detection after production is completed are effectively overcome, the knowledge base can provide optimal cutting parameters for production of various parts, and the method has great benefits on improving the production efficiency on the premise of ensuring certain quality of the parts.

The invention relates to the technical field of precision machining, and particularly discloses a complex curved surface precision machining method based on tool path optimization, which comprises the following steps: S1, acquiring a geometric model of a curved surface to be machined and tool kinetic parameters; s2, the optimal machining step length, the cutter roll angle and the feeding speed are calculated in real time according to the geometric features of the curved surface and the dynamic parameters of the cutter; s3, generating a self-adaptive tool path based on the dynamic optimization parameters in the step S2; s4, synchronously monitoring the cutting load in the machining process, and dynamically correcting cutter path parameters according to load feedback; and S5, outputting the optimized tool path instruction to the numerical control machine tool to execute machining. By calculating the Gaussian curvature and the average curvature of the curved surface in real time, dynamically adjusting the machining step length and the feeding speed, and automatically reducing the step length and reducing the feeding speed in a curvature sudden change area, the residual height of a steep area is controlled within a precise standard range, and the surface roughness is reduced by more than 30% compared with that of a traditional method; and the consistency of machining precision of each area of the complex curved surface is ensured.

The invention provides a robot milling surface roughness prediction method and system based on parallel convolution and coordinate attention mechanism feature fusion. According to the method, firstly, multi-source heterogeneous high-frequency signals in the milling process of a robot are synchronously collected, empty slice segments are eliminated and environmental noise is filtered out by utilizing a self-adaptive threshold value and a robust statistical method, and a standardized time sequence sample set is constructed; secondly, establishing a multi-source heterogeneous feature fusion roughness prediction model, and independently extracting deep features of cutting force and vibration signals by adopting a double-branch parallel convolution architecture; a coordinate attention mechanism is introduced, heterogeneous modal features are mapped into a virtual two-dimensional topological structure, the dynamic relative contribution degree of cutting force and vibration signals changing along with the machining state is captured through pooling aggregation along the modal dimension, and an attention weight map is generated to conduct dynamic weighting on the features. And finally, outputting a surface roughness predicted value through a regression network. According to the method, the problem that an existing fusion method neglects the dynamic dependency relationship between physical quantities is effectively solved, the anti-interference capability of the robot in the weak rigidity machining process is enhanced, and the precision and robustness of surface roughness prediction are remarkably improved. The method can be widely applied to robot complex component machining quality analysis and intelligent optimization, and has high efficiency and reliability.

The invention belongs to the field of metal materials, and particularly relates to a high-strength hot-rolled pickled steel plate for an automobile chassis and a production method thereof. The steel plate is mainly suitable for manufacturing automobile chassis structural parts. The steel comprises the following chemical components: C, Si, Mn, Al, V, Ni, Cu, Cr, Pt and Y, P is limited to be less than or equal to 0.015%, S is limited to be less than or equal to 0.005%, N is limited to be less than or equal to 0.006%, and the balance is Fe and inevitable impurities. The yield strength of the steel plate ranges from 498 MPa to 552 MPa, the tensile strength ranges from 608 MPa to 643 MPa, the transverse elongation A ranges from 33% to 38%, the steel plate is qualified after transverse cold bending by 180 degrees D = a, the surface quality is good, oxide scale stripes and chromatic aberration defects are avoided, the surface roughness Ra ranges from 1.10 micrometers to 1.35 micrometers, and the steel plate has excellent corrosion resistance.

The invention relates to the technical field of optic nerve detection, in particular to a plastic particle quality detection method and system based on optic nerves, and the method comprises the following steps: capturing a plastic particle image through an industrial camera, carrying out the balance processing of the image contrast, extracting the brightness and color features of the image, and removing the noise interference; and adjusting the image brightness and optimizing the particle edge to obtain a particle optimization image. According to the invention, the accuracy and automation level of plastic particle quality detection are greatly enhanced by using the visual neural network and the image processing technology, and the edge and shape features of the particles can be accurately extracted from a complex background by automatically adjusting the image contrast and brightness and applying an advanced edge detection algorithm. According to the method, the visual quality of the image is optimized, tiny flaws such as cracks and bubbles of the particles can be effectively recognized and analyzed, and more detailed data support can be provided compared with a traditional method by accurately calculating the surface roughness and texture uniformity of the particles.

The invention discloses an automobile body welding seam detection method and system based on machine vision, and particularly relates to the technical field of welding seam detection. The method comprises the following steps: capturing a welding seam image in real time through a high-definition camera, positioning a welding seam by using a template matching algorithm, calculating the width of the welding seam by using a dynamic window scanning technology, identifying a complex form area, extracting curvature change and surface roughness characteristics of the welding seam, and evaluating the adaptability of the algorithm to the complex form; classification processing is carried out according to the adaptability level, and the system automatically judges that a high-adaptability-level area is qualified; generating early warning and storing data in the low-adaptation-level region; the accuracy of a general adaptation level area is improved through a dynamic optimization algorithm, the detection precision is effectively improved, reworking and missing detection are reduced, and the adaptability of the system to various welding seam forms is improved.

The invention discloses a transparent material / metal dissimilar material ultrafast laser welding method based on a burst mode, and belongs to the technical field of laser micro-nano machining, and the transparent material / metal dissimilar material ultrafast laser welding method comprises the steps that the surface roughness and flatness of a pre-welded piece are treated through a mirror grinding and polishing technology, so that a sample piece reaches the optical contact condition, the pre-welded piece is in lap joint with a tool platform through a specially-made clamp, and the pre-welded piece is welded to the tool platform; when a visual Newton ring appears on a contact surface and an ultrafast laser focus is located at a material interface, using a burst mode under an ultrafast laser processing platform to increase the number of sub-pulses, welding a micro-area, equally dividing the energy of a single pulse into a plurality of pulses, and then emitting light in a pulse sequence mode, by using the burst mode containing multiple pulses, the heat accumulation effect can be achieved more easily, the ablation efficiency and the connection strength and stability of the transparent material and the metal heterogeneous material are improved while the machining rate is increased, and the method has wide application prospects.

The invention discloses a carbon fiber cloth reinforced damaged concrete structure detection and analysis method, and relates to the technical field of concrete structure health monitoring, and the method comprises the steps: extracting a high-risk point set from a three-dimensional damage distribution map, carrying out the weighted K-means clustering to construct a Voronoi subspace, calculating the failure probability, and dividing an emergency repair block set; generating a processing queue by using the repairing blocks according to priorities, planning a spiral path of the plasma spray gun by adopting a TSP algorithm, and generating a surface roughness distribution diagram in combination with laser confocal and white light interference detection; according to the roughness partition matching epoxy resin-carbon fiber dynamic matching scheme, a pressure thermodynamic diagram is generated through finite element simulation and electrode time-sharing electrification. Through fusion of the damage evolution graph and the digital twin model, the plasma etching parameters and the conductivity of the carbon fiber cloth are dynamically adjusted, so that the tensile strength of the repaired area is improved, and full-life-cycle health monitoring is realized.

The invention relates to the technical field of machining, in particular to an intelligent control system and method for sapphire substrate polishing, and the method comprises the following steps: S1, surface topography data acquisition: collecting surface roughness data and a three-dimensional topography distribution diagram of a sapphire substrate in real time; s2, generating characteristic parameters: extracting the surface waviness, the local recess depth, the bulge density, the lattice distortion index and the residual stress distribution gradient as the characteristic parameters; s3, dynamic parameter adjustment: generating a dynamic adjustment instruction of the polishing pressure, the rotating speed of the polishing disk and the supply rate of the polishing solution; s4, distribution optimization of the polishing solution: realizing uniform coverage of the polishing solution on the surface of the substrate; s5, stress real-time monitoring: generating a stress abnormity early warning signal; s6, outputting a closed-loop control instruction: feeding back a stress abnormity early warning signal to the adaptive control model, and iteratively correcting the dynamic adjustment instruction; according to the method, the risks of stress concentration and fatigue damage can be reflected in real time, and the safety and high efficiency of the polishing process are ensured.