53878 results about "Compositermes" patented technology

The invention relates to the technical field of compression molding of carbon fiber prepregs, in particular to a partitioned pressurization control method and system for compression molding of the carbon fiber prepregs and a molding process. S2, initial pressure calculation; s3, gradient pressurization control; s4, dynamic pressure adjustment; and S5, a global pressure maintaining stage: when the resin curing degree alpha reaches 0.6-0.8, switching to a full-screen pressure maintaining mode, and maintaining the pressure of each region to be 90%-95% of the current maximum pressure value until curing is completed. The invention provides a zoning pressurization control method and system for compression molding of carbon fiber prepreg and a molding process, and realizes the compression molding method and system with accurate zoning pressure regulation and control and directional discharge of volatile components. And by integrating intelligent sensing, multi-channel hydraulic control and a data driving optimization algorithm, the limitation of a traditional process is broken through, and a technical support is provided for efficient forming of the high-performance carbon fiber composite material.

The invention relates to a method for establishing and optimizing a multi-scale prediction model for constitutive parameters of a fiber reinforced composite material. Comprising the following steps: constructing a fiber-matrix microscopic RVE model, applying a boundary condition and a load, determining a failure criterion, and calculating equivalent mechanical parameters of a fiber bundle; a fiber bundle-matrix mesoscopic RVE model is generated, fiber bundle equivalent mechanical parameters serve as material attributes of fiber bundles in the mesoscopic RVE, a viscoelastic model is adopted for a matrix, and fiber reinforced material unit cell equivalent mechanical parameters are calculated; constructing a macroscopic fiber reinforced composite material finite element model, endowing a macroscopic model with unit cell equivalent mechanical parameters, defining an orthotropic material card, simulating an explosion impact process by adopting an explicit dynamics algorithm, outputting structural displacement, a damage area and stress distribution, and comparing calculation and test data. And correcting the microscopic RVE model and the mesoscopic RVE model through reverse optimization iteration to obtain a final macroscopic prediction model. The explosion-proof performance of the fiber composite structure is remarkably improved.

A composite can include cellulose fiber and foam binding the cellulose fiber. A method for manufacturing a composite can comprise mixing a plurality of ingredients to form a pre-foam mixture, foaming the pre-foam mixture to produce a foam, mixing the foam with cellulose fiber to form a composite material, and curing the composite material.

The present invention relates to a sodium-ion battery material, and specifically relates to a sodium ferric phosphate pyrophosphate composite material, and a preparation process, evaluation method and process optimization method therefor and the use thereof. The sodium ferric phosphate pyrophosphate composite material has a spinel-type NaFePO4 phase; and in an XRD diffraction pattern obtained by subjecting the sodium ferric phosphate pyrophosphate composite material to XRD diffraction using CuKα radiation, there are a characteristic diffraction peak T of sodium ferric phosphate pyrophosphate and a characteristic diffraction peak O of spinel-type NaFePO4 at 2θ of 33.6±0.2° and 32.8±0.2°, respectively, the peak intensities thereof are respectively IT and IO, and the diffraction peak intensity ratio Ir is equal to IT / IO, wherein 6.19≤Ir≤13.

The invention discloses a macroscopic progressive damage intelligent evaluation method driven by multi-scale microscopic damage observation, and the method comprises the steps: selecting a plurality of representative materials to carry out a microscopic mechanical test, and observing the damage evolution process under different loading conditions through MicroCT to obtain a microscopic mechanical test result; simulating a crack state based on a micro-mechanical model to reproduce a micro-mechanical test result, and performing material parameter correction of simulation calculation by taking test data as a constraint to obtain a micro-numerical modeling calculation result; and determining a representative volume element (RVE) scale based on a microscopic numerical modeling calculation result, and describing a macroscopic damage behavior by utilizing a continuous damage mechanical model so as to obtain evolution relation data of a macroscopic damage variable, stress and strain. According to the method, the damage behaviors of metal and composite material structures under complex working conditions can be accurately predicted, and the safety and reliability of aircraft structures can be improved.

The invention relates to the technical field of intelligent control, in particular to a composite material forming process intelligent control system which comprises a micro-scale deformation detection module, a stress offset correction module, a temperature gradient regulation and control module, an interlayer curing balance module and a forming intelligent control execution module. According to the method, a deformation rate sudden change area is accurately positioned through micro-scale detection, and stress concentration is found in advance, so that stress adjustment is more targeted, the local stress overload influence is reduced, real-time pressure feedback is combined with target pressure offset analysis to optimize a loading mode, uniform stress in the curing process is ensured, and the defect risk is reduced; high-precision temperature gradient monitoring and thermal diffusion rate analysis improve temperature control precision and improve interlayer temperature equilibrium, curing rate change rate calculation and heat conduction path optimization dynamically adjust a heat dissipation channel and promote uniform curing, stress-temperature distribution equilibrium degree analysis is combined with a deformation trend to optimize heating and pressure parameters, and uniform curing is achieved. And the forming stability and the quality consistency are improved.

The invention belongs to the technical field of gel materials, and particularly relates to aerogel powder for polyolefin modification and a preparation method and application thereof. The preparation method of the aerogel powder comprises the following steps: (1) preparing aramid nanofiber dispersion liquid; (2) preparing a polypyrrole coated aramid nanofiber dispersion liquid; (3) mixing polyamide acid and the polypyrrole coated aramid nanofiber dispersion liquid, and then carrying out partial imidization and solvent exchange to prepare hydrogel; and (4) carrying out freeze drying, thermal imidization and crushing to obtain the aerogel powder for polyolefin modification. The aerogel powder is a porous solid material formed by forming one-dimensional composite fibers from aramid nanofibers and polypyrrole and combining the one-dimensional composite fibers with polyimide resin. The aerogel powder and polyolefin resin are compounded to obtain the polyolefin resin composite material with high strength and antistatic property, and the polyolefin resin composite material can be widely applied to the fields of pipes, plates, grouting equipment parts, grouting bags and the like.

The invention belongs to the technical field of fiber materials, and particularly relates to a modified aramid nanofiber as well as a preparation method and application thereof. The preparation method comprises the following steps: (1) preparing an aramid nanofiber dispersion liquid; (2) modifying the aramid nanofiber by using POSS; and (3) carrying out in-situ polymerization on polypyrrole on the surface of the aramid nanofiber. POSS (polyhedral oligomeric silsesquioxane) has a relatively large volume exclusion effect, the template effect of long-chain alkyl quaternary ammonium salt can be influenced by scattered adhesion of POSS on the surface of aramid nanofiber, uneven polymerization of pyrrole monomers is caused, and formation of a rough structure on the surface of the fiber is facilitated. According to the invention, the polypyrrole-coated aramid nanofiber with a coarse structure is used as a polyolefin modified material, so that the problem of low interfacial force existing in a common filler is solved, and the polyolefin resin composite material with high strength and antistatic property is obtained and can be widely applied to the fields of pipes, plates, grouting equipment parts, grouting bags and the like.

The invention belongs to the technical field of aerogel materials, and particularly relates to high-performance antistatic aerogel for 3D printing as well as a preparation method and application thereof. According to the invention, inorganic hollow nanoparticles are selected as a nucleating modifier for in-situ polymerization of aniline monomers in a polyamide solution, so that polyaniline-coated inorganic hollow nanoparticles are uniformly dispersed in polyamide, and the aerogel composite material with polyamide as a continuous phase and polyaniline-coated inorganic hollow nanoparticles as a conductive and reinforced phase is formed. The use of the hollow inorganic nanoparticles avoids the problem that the solid inorganic nanoparticles lose the characteristics of the inorganic nanoparticles due to high specific gravity and high surface energy, and easy agglomeration or adsorption with other substances in the preparation process of the solid inorganic nanoparticles. The inorganic hollow nanoparticles are adopted as a nucleating modifier, so that the problems of agglomeration of solid nanoparticles and dispersion in a polyamide matrix are well solved.

The invention discloses a dynamic optimization method and device for an automatic fiber placement path of a composite material and a medium, and belongs to the technical field of automatic fiber placement of composite materials. The method comprises the steps that a three-dimensional model of a component to be laid is obtained, and curved surface curvature distribution characteristics and fiber laying angle constraints are obtained based on the three-dimensional model; processing the curved surface curvature distribution characteristics based on a path planning algorithm to generate an initial fiber placement path; on the basis of the tension mapping table and the temperature compensation coefficient, the fiber placement head is driven to execute reverse pre-tension control; acquiring actual fiber trend data according to a preset sampling period based on a polarization laser polarization instrument, and extracting a fiber angle deviation of the actual fiber trend data; processing the fiber angle deviation and the resin viscoelasticity data based on a Bayesian optimization algorithm so as to dynamically correct the laying path and generate an updating instruction; and adjusting the spatial pose and tension of the fiber placement head according to the updating instruction. Based on the method, dynamic multi-target fiber placement path optimization and real-time feedback control are realized.

The invention belongs to the technical field of polymer preparation, and particularly relates to a curved-surface polyaniline modified 3D printing polyurethane material and application thereof. According to the preparation method, an ice crystal hard template method and a polyether glycol soft template method are combined, so that the microstructure of polyaniline is adjusted, and the curved polyaniline with regular morphology is prepared. The curved-surface arc-shaped structure has a large specific surface area, can form more contact sites with resin when being directly melt-blended with the resin, is beneficial to construction of a conductive path, and is not easy to agglomerate due to the curved-surface structure. The polyaniline with the arc-shaped structure is of a three-dimensional space structure and is directly blended with the resin to form a randomly arranged conductive network structure, so that the composite material is not easy to break when being impacted, and can be better embedded between the resin to form a stress transfer channel, and a complex three-dimensional network structure is constructed; the mechanical property and the antistatic capability of the product are improved.

The invention provides a cold-rolled sheet shape multivariable cooperative control optimization method and system. The method comprises the steps that rolling tension set value parameters are determined on the basis of a dynamic coupling model so as to be matched with dynamic tension fluctuation in the high-speed strip steel rolling process of a cold-rolled sheet, the magnetic conductivity change in the strip steel rolling process is detected through the magnetoelastic effect, and a magnetic conductivity gradient map is generated; residual stress distribution data are inverted based on the magnetic conductivity gradient map, and target axial roll shifting control parameters influencing the roll gap shape are generated; and multivariable cooperative matching is conducted on the rolling tension set value parameter and the target axial roll shifting control parameter, a matching result is obtained, the rolling tension and the roll gap shape are adjusted, and strip steel deviation and fluctuation caused by dynamic coupling of the composite material tension and the cold-rolled sheet shape in the cold-rolled sheet high-speed strip steel rolling process are restrained. According to the technical scheme, through cooperative control of the dynamic coupling model and the magnetic conductivity gradient map, deviation and fluctuation of the strip steel are effectively restrained, and the shape stability and rolling precision of the cold-rolled plate are improved.

The invention discloses a high-temperature-resistant epoxy resin composite material and a preparation method thereof, and relates to the technical field of composite materials. The high-temperature-resistant epoxy resin composite material is prepared from the following raw materials in parts by weight: 70 to 80 parts of modified epoxy resin, 8 to 15 parts of high-temperature-resistant modifier, 15 to 30 parts of curing agent, 3 to 8 parts of nano reinforcing filler, 1 to 3 parts of coupling agent, 3 to 6 parts of carbon fiber, 2 to 5 parts of nano titanium dioxide, 5 to 10 parts of organic silicon rubber, 1 to 3 parts of conductive agent, 3 to 7 parts of flame retardant and 2 to 4 parts of zinc borate. The high-temperature-resistant epoxy resin composite material has outstanding advantages and strong thermal stability, and can bear high temperature; mechanical performance is excellent, and firmness and durability are achieved; the self-repairing capability is achieved, and the service cycle can be prolonged; the anti-permeability is good, and gas and liquid can be effectively blocked; the surface is smooth, the friction coefficient is low and the application prospect is wide.

The invention relates to the technical field of model construction, in particular to a method for constructing an effective dielectric constant model of a carbon fiber composite material. The method comprises the following steps: collecting a sample, carrying out electromagnetic orthogonal frequency sweep analysis, generating a spectrum distribution map, obtaining internal field distribution mapping data through boundary topology recombination, carrying out interval correlation analysis on the internal field data, generating a spatial distribution function, and calibrating a phase resonance point to obtain a frequency characteristic spectrum. Performing field region classification and interactive coupling evaluation according to the frequency characteristic spectrum, deducing a node link structure, performing internal and external field interactive tensor projection and constraint embedding processing based on the node link structure, constructing a composite field model, generating a dielectric frequency dispersion curve through dielectric characteristic inversion, and performing modeling based on frequency dispersion curve data to obtain an effective dielectric constant model. According to the invention, the more accurate construction method of the carbon fiber composite material effective dielectric constant model is realized.

The invention discloses a mechanical property prediction method of a composite material based on an improved sparrow algorithm-random forest, and belongs to the technical field of composite material property prediction, and the method comprises the following steps: carrying out finite element multi-scale simulation on a carbon fiber composite material to obtain an elastic constant matrix; connecting the elastic constant matrix material to a macroscopic model for macroscopic simulation to obtain sample data; a random forest RF model is constructed in Matlab software; an improved sparrow search algorithm is introduced into a random forest RF model to construct a CFSSA-SF prediction model; training a CFSSA-SF prediction model by using the training set data; and inputting test set data into the CFSSA-SF prediction model to obtain a prediction result. According to the method, the finite element method is adopted to carry out multi-scale modeling, the improved sparrow algorithm is combined with the random forest to construct the fusion CFSSA-RF prediction model, and the mechanical properties of the carbon fiber composite material under different parameters are effectively predicted.

The invention discloses spherical aluminum oxide and a silicone rubber heat conduction composite material thereof, and belongs to the technical field of functional polymer materials. The spherical aluminum oxide is subjected to surface modification through an amphiphilic fluorosilicone heat conduction interface synergist; comprising the following raw materials in parts by weight: 100-200 parts of vinyl silicone oil, 1-6 parts of side hydrogen-containing silicone oil, 1-6 parts of end hydrogen-containing silicone oil, 0.2-2 parts of a platinum catalyst, 0.01-0.1 part of an inhibitor and 2500-6000 parts of amphiphilic fluorine-silicon heat-conducting interface synergistic spherical aluminum oxide powder. Through surface modification of spherical aluminum oxide, the prepared silicone rubber heat-conducting composite material realizes synergistic gain of heat conduction, insulation and flexibility, and can meet the requirements of multiple fields such as power battery pack heat dissipation gaskets, high-frequency circuit packaging and wearable equipment.

The invention relates to the technical field of low-dielectric heat-conducting functional materials, in particular to a nano silicone rubber composite material as well as a preparation method and application thereof. According to the preparation method, a modified vinyl silsesquioxane coupling agent is taken as a bridge, and boron nitride nanosheets, silicon carbide nanowires and the modified vinyl silsesquioxane coupling agent are covalently connected through hydrolysis reaction, so that a heterostructure is constructed, and the nano heat-conducting filler is obtained; the preparation method comprises the following steps: dispersing a nano heat-conducting filler in vinyl silicone oil, and constructing a covalent cross-linked network through addition cross-linking reaction and curing treatment to obtain the nano composite silicone rubber material. According to the processing method, the filling amount, the dielectric constant and the dielectric loss can be reduced, meanwhile, the heat conduction capacity is improved, and the problems that in the prior art, the dielectric constant and the dielectric loss are low due to simple mixing of the boron nitride nanosheets and the silicon carbide nanowires are solved.

The invention discloses a silicon-carbon negative electrode material, porous carbon and a preparation method, the porous carbon is prepared by mixing ammonium fluoride and a pre-carbonized material to prepare a premix; and putting the premix in an inert atmosphere, and carrying out pre-activation, drying, carbonization, activation and crushing treatment to obtain the composite material. And uniformly depositing the nano silicon particles into the porous carbon skeleton through a mature CVD (Chemical Vapor Deposition) process to obtain the silicon-carbon composite material. Fluorine and nitrogen co-doping modification can increase the defect degree of a carbon skeleton, efficiently relieve mechanical stress accompanied by lithium storage volume expansion of the silicon negative electrode in a micro scale, provide abundant lithium ion transport and mass transfer channels and enhance the conductivity of porous carbon, and ammonium fluoride is used as a supplementary activator besides water vapor, so that the porosity of the porous carbon is further improved, and the conductivity of the porous carbon is improved. An effective space is provided for uniform deposition of silicon, the fluorine-containing carbon layer can induce formation of a high-stability SEI film rich in LiF on the surface of the nano silicon material, and the interface stability and coulombic efficiency of the silicon negative electrode are further improved.

The invention relates to the technical field of high-pressure resin transfer molding (HP-RTM), in particular to an intelligent forming system and equipment based on thermal-mechanical coupling carbon fiber composites.The intelligent forming method comprises the steps that real-time temperature field and strain field data are collected through a partition temperature control mold, thermal-mechanical coupling state parameters are obtained through signal filtering and abnormal value removing processing, and the thermal-mechanical coupling state parameters are obtained through a thermal-mechanical coupling module; an ultrasonic probe array is combined to detect internal defect distribution to evaluate a forming quality index, process parameters are dynamically compensated according to the quality index, and a deep learning model is constructed to optimize a forming process. According to the invention, real-time monitoring and closed-loop control of multi-physics field coupling can be realized, the defect rate is obviously reduced, the debugging period is shortened, the production efficiency is improved, and an intelligent solution is provided for carbon fiber composite material forming.

The present application belongs to the technical field of material processing, and relates to a mold-free mirror image filament laying method and a mold-free mirror image filament laying system. The method lays a filament ribbon in a mirror image symmetry, without a mold, thereby improving production flexibility and efficiency. The method acquires the temperature and pressure of the filament ribbon in real time, calculates the output power of a heater and the modification value of a filament laying end motion mechanism path by using temperature and pressure differences, and optimizes controller parameters through reinforcement learning, so as to realize stable control of the temperature and pressure. The system comprises a hardware part and a control system. The hardware part includes pressure and heat source components. The control system adjusts through real-time feedback to ensure the quality of filament laying. The present application solves the problem that the temperature and pressure are difficult to accurately control in the process of mold-free mirror image filament laying, and improves the performance of composite products.

The invention discloses a method for testing mechanical properties of a carbon fiber reinforced composite material based on image recognition, and relates to the technical field of material tests.The method comprises the steps that a carbon fiber sample of a fiber laying layer is prepared, the width tolerance is controlled through laser cutting, and speckle patterns are sprayed on the surface; training a U-Net + + neural network based on the collected macro / micro image, and analyzing the dynamic change of the fiber angle in real time; fusing the acquired macro / micro image with the dynamic change of the fiber angle, and constructing a constitutive equation of dynamic fiber orientation distribution; and importing the constitutive equation into a finite element model, iteratively optimizing the fiber-matrix interface strength parameters through inversion calculation, performing blind test verification on the sample by using the optimized finite element model, and outputting a cross-scale correlation test report. According to the method, an interface strength parameter is subjected to inversion iteration optimization through a finite element model, a macro-micro strain field, a fiber slip angle and an interface damage parameter are integrated through a cross-scale correlation test report, and full-chain data support is provided for a composite material structure.

The invention discloses a polypropylene composite material based on rare earth element modification and a preparation method and application thereof, and belongs to the technical field of medical instruments, and the polypropylene composite material comprises the following raw materials in parts by weight: 100 parts of medical grade PP, 3-8 parts of a rare earth organic complex, 5-10 parts of nano-hydroxyapatite, 2-5 parts of a maleic anhydride grafted PP compatilizer and 0.3-0.5 part of an antioxidant, the invention also provides specific preparation steps and application thereof. According to the invention, oxide + salt bimorphological rare earth and an organic ligand are complexed to form a rare earth organic complex, so that the problem of poor dispersity of inorganic rare earth is solved, and meanwhile, the material is endowed with antibacterial and antistatic functions; through a melt blending-in-situ fiber forming process, the rare earth complex forms gradient distribution in a polypropylene matrix, the surface layer is enriched with antibacterial components, and the inner layer is enhanced in mechanical property, so that a low-cost solution with high strength, antibacterial property and biocompatibility is provided for high-end medical instruments, and the market blank is filled.

The invention provides a cracking equipment data processing method combined with digital twinning, which comprises the following steps: acquiring initial state data and environmental data of a carbon fiber composite material in a cracking furnace, and performing time synchronization processing to obtain a time consistency data set; extracting pyrolysis characteristic parameters of interaction between the carbon fiber bundle and the matrix from the spatial registration data set, and calculating a reaction rate and pyrolysis behavior change of a microscopic scale of a fiber matrix interface to obtain a pyrolysis characteristic parameter set; if a deviation exists between the prediction result and a preset target carbon powder quality index, determining a heating power distribution adjustment amount and a change trend of an air inlet valve opening degree based on a deviation degree, and obtaining a control parameter adjustment set of the cracking furnace; and carrying out constraint and smoothing processing on the control parameter adjustment set to obtain a control set value, formatting the control set value into a closed-loop feedback instruction, and updating the closed-loop feedback instruction to a cracking furnace control system to obtain adjusted pyrolysis operation state data.

Systems and methods for forming an oxidation protection system on a composite structure are provided. In various embodiments, the oxidation protection system comprises a boron-glass layer formed on the composite substrate and a silicon-glass layer formed over the boron-glass layer. Each of the boron-glass layer and the silicon-glass layer include a glass former and a glass modifier. The boron-glass layer includes a boron compound comprising a mixture of boron carbide (B4C) powder and cubic boron nitride (BN) powder.

The invention discloses a positive electrode material for a zinc-bromine flow battery and a preparation method and application thereof, and belongs to the technical field of zinc-bromine flow batteries, the preparation method comprises the following steps: crushing and sieving an active carbon source, adding the active carbon source into a graphene oxide aqueous dispersion, stirring, centrifuging, removing supernate, drying, mixing with an activating agent, and activating in an inert atmosphere; washing to be neutral, drying and sieving to obtain a graphene / activated carbon composite material; and attaching the graphene / activated carbon composite material to a carbon-plastic bipolar plate to obtain the positive electrode material for the zinc-bromine flow battery. A graphene / activated carbon composite system is combined with a high-conductivity network, graded holes and surface functional groups, so that the reaction kinetics bottleneck of a traditional carbon material Br2 is broken through.

This application relates to an EPP material and a folding mat using this EPP material, which relates to the field of EPP material technology. The EPP material is formed into a plate by welding EPP particles, and the surface of the plate is coated with a hydrophobic antibacterial paint. The raw materials for preparing the EPP particles include 80-100 parts by weight of polypropylene resin, 1-3 parts by weight of a nucleating agent, 6-8 parts by weight of a modified graphene composite material, 5-10 parts by weight of a flame retardant, 5-15 parts by weight of a reinforcing agent, and 0.5-1.5 parts by weight of a surfactant. The raw materials for preparing the modified graphene composite material include hydrophobic modified graphene oxide, hydrophobic antibacterial nano-zinc oxide, and a modified ammonium polyphosphate coupling agent. The folding mat using this EPP material includes an EPP material, a surface layer, and a sewing thread, and the surface layer and the EPP material are fixedly connected by the sewing thread. This application has the effect of improving the flame retardancy, waterproofness, and antibacterial properties of EPP material, and the resulting tatami mat is environmentally friendly, comfortable, and has a good user experience.

This application relates to a test device applied to a coupled environment of stress, wear, and corrosion, and a quantitative evaluation method, and belongs to the field of failure analysis of materials. The test device includes a stress-loading mechanism configured to load a stress on a sample, a corrosion mechanism configured to conduct an electrochemical corrosion test on the sample, and a wear mechanism configured to wear a sample. The test device applied to a coupled environment of stress, wear, and corrosion of this application can establish a wear-stress-corrosion coupled action test process, and can investigate service behaviors of various metallic materials and metal matrix composites under conditions close to actual service conditions to obtain key behavior data under a multi-factor coupled action, quantitatively analyze a wear, a corrosion, a wear-corrosion interaction, a stress-wear interaction.

A disclosed leaky coaxial resonant sensor system and methods. In use, the system includes at least one split-ring resonator (SRR) embedded within a material of the component. The at least one SRR is formed from a composite material. Additionally, the at least one SRR is configured to resonate at a first frequency in response to an interrogation signal from a leaky coaxial cable antenna. In some aspects, each SRR may resonate at a first frequency in response to an electromagnetic ping when the composite material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the composite material is in a second state. A resonant frequency of the composite material may be based on physical characteristics of the composite material.

The invention relates to a composite material garbage sorting method based on a hyper-spectrum technology and an AI classification model. The method comprises the following steps that S1, multi-modal data collection is conducted; s2, the collected multi-modal data are preprocessed and fused, and combined features of the multi-modal data are obtained; s3, constructing a classification model based on the multi-modal neural network model, and performing classification according to the joint features; s4, based on a classification result, using a SegFormer deep segmentation network to identify junk components at a pixel level, and realizing accurate segmentation of a plurality of material composition objects; s5, target tracking of the garbage trajectory is kept through a target tracking algorithm; and S6, on the basis of the target tracking result in the S5, the position of the garbage on the conveying belt is predicted in real time, and the sorting action is precisely executed according to the classification and segmentation result. According to the invention, the complex composite material garbage can be efficiently and accurately treated.

The invention discloses a textile production defect detection method and system based on image processing, and belongs to the field of textile defect detection. According to the method, the point cloud data is generated by collecting the surface deformation stripes, and the penetrability reflection signal containing the sound wave propagation time is obtained through the multi-frequency ultrasonic probe. Denoising the point cloud data and then positioning a surface deformation candidate area based on a curvature gradient and a normal vector deflection angle; and an internal abnormal region is determined by separating and extracting a reflection wave amplitude value and a time delay parameter through an ultrasonic frequency band. And matching the surface point cloud coordinates with the ultrasonic propagation time by adopting an iterative nearest point algorithm to establish feature association between the normal vector deflection angle and the reflected wave amplitude. And dynamically distributing fusion weights for the surface features and the internal features according to the curvature gradient change rate and the amplitude attenuation rate to generate a multi-modal defect distribution diagram. And geometric shape and reflection intensity parameters are extracted and matched with a preset defect template to realize type and position judgment, and the method can be suitable for production defect detection of the thick textile with a multi-layer composite material structure.