6453 results about "Textile" patented technology

The invention provides a method for recovering a BHET monomer from a waste polyester textile alcoholysis solution, and is characterized in that the recovery method comprises the following steps: (1) filtering: mixing the waste polyester textile alcoholysis solution with water under a stirring condition, and filtering at 55-70 DEG C; and (2) cooling crystallization: under a stirring condition, carrying out primary cooling on the filtrate obtained by filtering in the step (1) from 55-70 DEG C to 45-55 DEG C, then adding a BHET seed crystal, then carrying out secondary cooling to 5-15 DEG C, and maintaining the temperature for 20-60 min to obtain the BHET monomer. The mixed solution cooling crystallization method is developed on the basis of complexity of components of the waste polyester textile fabric, the problems that in the conventional production process, a BHET crystal product is low in quality, prone to scaling, difficult to filter and dry and the like are solved, and the obtained BHET product is large in particle size, narrow in particle size distribution, high in purity, good in fluidity and high in stability.

The invention relates to a textile product defect identification method based on improved YOLOv11. The method comprises the following steps: acquiring a textile product defect image data set; performing pretreatment; dividing into a training set and a verification set; the method comprises the following steps: introducing MConv into a YOLOv11 backbone network, adding a CCIAP module behind a C2PSA module, and applying BiFPN in a path aggregation network; performing prediction through YOLO Head to obtain N prediction feature maps; the overall loss of the network is calculated, and network parameters are optimized through back propagation; predicting the verification set image through a network to output AP values of various categories; repeating the above steps to obtain a trained YOLOv11 network; and detecting the test image or video by using the trained detector to obtain a detection result. According to the method, the MConv is introduced into the YOLOv11 network to enlarge the receptive field, the CCIAP module is added behind the C2PSA to improve the feature extraction capability, and the BiFPN is applied to the Neck layer to enhance the feature fusion capability, so that the target detection precision is improved and the real-time detection of textile product flaws is realized under the condition that the reasoning speed is not influenced.

A textile fabric is provided, including: a main body and at least one film layer. The main body includes two surfaces opposite to each other, and each of the two surfaces has a surface texture being concave and convex. The at least one film layer is coveringly disposed on at least one of the two surfaces, and each said film layer is concave and convex corresponding to the surface texture. Therefore, the at least one film layer has an appearance corresponding to the surface texture, and the textile fabric has good toughness, is waterproof and is not easily damaged, which solves the problems of insufficient structural strength and poor durability of a conventional textile fabric.

A textile fabric is provided, including: a main body and a film layer. The main body includes two surfaces opposite to each other, and the film layer is coveringly disposed on one of the two surfaces. A side of the film layer opposite to the main body includes at least one first surface portion and at least one second surface portion, and a glossiness of the at least one first surface portion is different from a glossiness of the at least one second surface portion.

The present application discloses a shapewear garment comprising an upper, middle, and lower portion, along with an abdominal compression panel. The upper portion wraps around the wearer's chest and upper back. The middle portion, positioned below the upper, comprises an abdominal and lower back covering. The lower portion extends around the wearer's front crotch area and buttocks. The abdominal compression panel, combined with the abdominal covering, has a higher modulus of elasticity than the other portions. A bust support area on the upper portion's outer surface features greater textile tension than surrounding areas.

The invention provides a textile cloth defect real-time detection method and system based on multi-modal feature fusion, and the method comprises the steps: collecting visual image data, infrared thermal imaging data and ultrasonic acoustic data of textile cloth through a multi-sensor array, and forming multi-modal input; performing time sequence alignment and noise filtering preprocessing on the multi-modal data to eliminate motion artifacts and environmental interference; a parallel feature extraction module is used for extracting texture features from the visual data, extracting temperature distribution features from the thermal imaging data and extracting acoustic impedance features from the acoustic data. By deeply fusing complementary information of three modes of vision, thermal imaging and ultrasonic wave, the detection capability is improved, the vision mode captures surface texture details, the thermal imaging mode reveals thermodynamic anomalies related to friction and materials, the ultrasonic wave mode perceives subcutaneous structure defects, hidden flaws which cannot be recognized by a single mode can be found, and the detection efficiency is improved. Therefore, the omission ratio is greatly reduced, and flaw types are distinguished more accurately.

The invention relates to the technical field of textile detection, in particular to a computer-aided textile color fastness prediction method, which comprises the following steps: acquiring multi-source data before and after a textile color fastness test through computer control equipment, including digital images, reflection spectrum data, process parameters and dye characteristic component data, carrying out pretreatment; extracting color difference features and spectrum similarity features, and screening by adopting a dimension reduction algorithm to form a core feature vector; a machine learning algorithm is adopted to construct a color fastness prediction model, parameters are optimized in combination with cross validation and an early stop mechanism, model fine tuning is executed, and a final optimization model is obtained; and outputting a color fastness grade prediction result, and carrying out spectrum similarity secondary verification. According to the method, objective, efficient and high-precision prediction of the color fastness grade of the textile is realized, and the problems of high subjectivity and low efficiency of traditional manual evaluation are solved.

The invention provides a textile surface defect detection method and system based on image features, and relates to the technical field of textile surface defect detection.The method comprises the steps that an RGB image of a to-be-detected textile is obtained and zoomed to 512 * 512 pixels, and then at least five semantic tags are output through a semantic segmentation model; the method comprises the following steps: adopting hyperspectral imaging with the wavelength of 400-1700 nm and the resolution of 10 nm, calculating the pixel reflectivity through a reflectivity formula, comparing the reflectivity of 5-8 characteristic wavebands, marking as a pollutant if the average deviation exceeds a threshold value, matching the type and position of a stain with a spectral angular distance, and after a standard sample and a semantic region are converted into an LAB color space, extracting a minimum rotation enclosing rectangle and dividing a grid, invalid pixels are filled with 3 * 3 neighborhood effective pixel weighted mean values, and a grid LAB mean value is calculated. A grid pollutant pixel corresponding to an image to be detected is filled with a standard sample in a mean value mode, then channels A and B are clustered through a sliding window, a pilling area is preliminarily judged according to the standard deviation of a channel L, and the pilling area is confirmed by combining the structured light three-dimensional point cloud and comparing with a threshold value.

A textile fabric is provided, including: a main body, a film layer and a covering layer. The main body includes two surfaces opposite to each other, and each of the two surfaces has a surface texture being concave and convex. The film layer is coveringly disposed on one of the two surfaces and is concave and convex corresponding to the surface texture, and the covering layer is coveringly disposed on the other of the two surfaces. Therefore, the film layer has an appearance corresponding to the surface texture, and the textile fabric has good toughness, is waterproof and is not easily damaged, and the covering layer provides comfortable feeling of touching.

Athletic and / or work wear garment can have textile regions of differing friction to facilitate maintaining a grip between high friction textile regions and objects pressed against the high friction textile regions. In some embodiments, the high friction textile regions can facilitate carrying objects. In some embodiments, the garment can be a shirt. In some embodiments, the garment can be pants. In some embodiments, the garment can be an undergarment and the high friction textile regions can reduce shifting of an outer garment in contact with the undergarment.

The invention relates to the technical field of intelligent manufacturing and management of textiles and garments, and discloses an automatic sewing and cutting management system for underwear processing. The system comprises a central control module, a material conveying and positioning sub-module, a machine vision acquisition sub-module, a multi-axis linkage sewing mechanical arm sub-module and a cutting execution sub-module. According to the system, image data of raw materials and semi-finished products are collected through a machine vision collection sub-module before sewing and after sewing respectively, the initial deformation quantity and the secondary deformation quantity are calculated through a central control module, then a dynamic sewing path and an actual cutting path are generated, and a mechanical arm is controlled to execute self-adaptive sewing and accurate cutting; a sensing-decision-execution closed-loop intelligent control strategy is adopted, the problem of deformation accumulation of the high-elasticity fabric in the processing process is effectively solved, the precision and efficiency of underwear production and the material utilization rate are further improved, and small-batch multi-variety flexible automatic production is achieved.

The invention provides a waterproof and windproof warm-keeping fabric and a production process thereof, and relates to the technical field of textile fabrics, the waterproof and windproof warm-keeping fabric comprises a protective surface layer, a windproof temperature-locking middle layer and a skin-friendly warm-keeping inner layer which are sequentially arranged from outside to inside, and the three layers are compounded through a gradient hot melt adhesive; the protective surface layer is a graphene oxide modified polyester fiber fabric treated by a composite hydrophobic finishing liquid; the windproof temperature-locking middle layer sequentially comprises a non-swelling environment-friendly TPU film, a polytetrafluoroethylene microporous film and a Cs0. 33WO3 modified PA6 / 66 hollow fiber net from outside to inside, and the three are laminated and compounded into an integrated structure through hot pressing; and the skin-friendly warm-keeping inner layer is a bamboo pulp fiber-cotton-skin-friendly antibacterial polyester fiber blended fabric loaded with phase change microcapsules. The fabric disclosed by the invention integrates excellent waterproof, windproof and warm-keeping properties, is suitable for outdoor and cold-proof clothes, and has both functions and comfort.

The invention relates to the field of textile detection, and discloses a fabric flame retardance analysis method based on infrared thermal imaging sensing data, which comprises the following steps: performing non-contact temperature measurement on a fabric sample in a controlled heat shock disturbance field, acquiring an infrared imaging signal sequence, and performing energy projection rearrangement on the infrared imaging signal sequence; based on the discontinuous change of the energy flow density in the primary thermal behavior field, establishing a boundary disturbance model of a local overheating region, and comparing the morphological difference between a thermal expansion path and a radiation energy attenuation region; mapping the heat potential difference of each region in the critical response diagram into an energy constraint state inside the material, and deducing time bias distribution of energy conduction; introducing a spectrum equalization function under time offset distribution, and executing asymmetric compensation on local abnormal points of the infrared response data; and based on the decomposition threshold dynamic trajectory, calculating a flame-retardant response equilibrium rate and an energy flow retention rate, and forming a flame-retardant performance evaluation index. The method has the advantage of improving the accuracy of flame retardant detection.

The invention relates to the technical field of textile material and fiber processing, in particular to a high-elasticity moisture-absorption breathable acetate fabric and a preparation method thereof. The invention discloses a high-elasticity moisture-absorption breathable acetate fabric and a preparation method thereof.The acetate fabric takes a bio-based degradable thermoplastic polyester elastomer as an island and acetate as a sea, a homothermal double-hole structure is embedded in the acetate fabric, and the surface of the acetate fabric is covered with a polyglutamic acid and chitosan oligosaccharide self-repairing hydrophilic layer; the preparation method comprises the following steps: pre-burying alkali-resistant microspheres in a marine facies through solid island-solution sea double-path spinning of a bio-based degradable thermoplastic polyester elastomer and acetate, synchronously forming groove-closed pore double channels through simultaneous heating one-step alkali etching-baking, and finally forming a dynamic reversible hydrogen bond network through polyglutamic acid and chitosan oligosaccharide, so as to obtain the marine-based degradable alkali-resistant hydrogel. A bio-based finishing layer with a self-repairing function is obtained, and the finally obtained fabric has the characteristics of high elasticity, breathability, lasting moisture absorption, greenness and degradability.

The invention discloses a novel textile material with eiderdown wrapped by rainbow yarn nanofibers and a preparation method. The novel textile material comprises skeleton fibers located at the axis, an eiderdown component with a heat preservation effect and a rainbow yarn nanofiber reinforcing layer wrapping the outer surface of the eiderdown component. The wrapping reinforcing layer is of a three-dimensional network structure formed by continuous or quasi-continuous nanofibers in a winding, interweaving and lap joint mode, so that stable compounding and structural shaping of down feather components and skeleton fibers are achieved under the condition that the down feather components and the skeleton fibers do not depend on adhesive fixation. The preparation method comprises the following steps: cleaning, drying and opening down for pretreatment; performing melt differential electrostatic spinning on the polymer melt under a high-voltage electrostatic field to form rainbow yarn nanofibers; the down flocs are introduced into the coating area through the framework fibers, so that the rainbow yarn nanofibers are deposited and wound to form a coating reinforcing layer; after being gathered and shaped and twisted into yarns, the yarns can be continuously dragged and wound. The novel textile material has spinnability, structural stability and long-acting thermal insulation performance, and is suitable for spinning, weaving and knitting processing.

The invention relates to the technical field of production matching recommendation, in particular to a textile fabric online production recommendation system based on a knowledge graph, and the system comprises a knowledge graph construction module which is used for integrating and storing multi-dimensional information of textile fabrics, and constructing a textile fabric knowledge graph; the user demand receiving module is used for receiving cloth demand information input by a user, wherein the cloth demand information comprises cloth types and performance requirements; the recommendation algorithm module is used for analyzing the matching degree between the user demand and the cloth characteristics through a matching algorithm based on the knowledge graph, and recommending an optimal cloth production scheme for the user; and the scene analysis module is used for collecting related environment parameters of an expected application scene of a user, constructing an influence model between cloth performance and the environment parameters, evaluating performance of recommended cloth under a demand environment condition, and optimizing cloth recommendation according to an evaluation result. And the user can obtain the cloth recommendation which most meets the requirements.

The invention discloses a textile fabric production on-line monitoring system based on intelligent manufacturing, and relates to the technical field of textile intelligent detection. By obtaining a textile microstructure image sequence, a tension disturbance signal and infrared thermal imaging data, an energy-structure coupling multi-dimensional feature matrix is constructed, a defect candidate area is extracted in combination with a textile weave structure map, and the defect of the textile fabric production on-line monitoring system is obtained. Constructing a perturbation evolution path set; a dynamic tension response model is introduced to fit a tension energy curved surface, and spatial probability distribution of structural instability points is output; in combination with loom state parameters, a long-short-term memory recurrent neural network is constructed, and abnormal score calculation and dynamic threshold comparison are achieved; when the score value exceeds the limit, triggering a high-frequency image sampling and enhanced recognition process, extracting micro-scale defect features and completing classification and recognition; outputting a defect visual thermodynamic diagram to realize real-time early warning of defects; the method has the advantages of high identification precision, fast early warning response and low false alarm rate, and is suitable for intelligent quality monitoring in a complex weaving scene.

The invention belongs to the technical field of functional finishing of textiles, and particularly relates to microcapsule emulsion with a pH indicating function, preparation of the microcapsule emulsion and application of the microcapsule emulsion in a textile finishing agent. The invention provides a preparation method of a microcapsule emulsion with a pH indication function, which comprises the following steps: reacting isocyanate with polyol to obtain a polyurethane prepolymer, mixing and dissolving the polyurethane prepolymer with a pH indicator solution, an acrylate monomer and an initiator to obtain an oil phase, and taking an aqueous solution of a polymer emulsifier as a water phase; then mixing and emulsifying the oil phase and the water phase, adding a chain extender, heating to carry out chain extension reaction, cross-linking to a certain degree to form a polyurethane-polyurea-polyacrylate hybrid shell layer on an oil-water interface, and coating a core material pH indicator to obtain the microcapsule emulsion with the pH indication function. According to the invention, the pH indicator is coated in the form of microcapsules for the first time, and industrial production and processing can be carried out.

A 3-D honeycomb textile can include a textured yarn. The textile can exhibit a heat-shrinkage rate from about 10% to about 60%. The textile can be configured to reversibly change its dimensions under application of stress. Prosthetic valves can have the disclosed textiles as a sealing member. Additionally, prosthetic valves can the disclosed textiles as cushion materials. In addition, methods of making the disclosed textiles and prosthetic valves are described.

The invention belongs to the technical field of path planning, and particularly relates to a multi-AGV textile workshop material distribution path dynamic planning method and system, and the method comprises the following steps: S1, obtaining the position and speed of each AGV, and the Lidar and visual data of the environment, and recognizing and classifying the barriers, such as textile equipment, raw material stacks, workers and the like, in the environment through a semantic segmentation network; and fusing the position information of the Lidar point cloud and the semantic category information of the obstacle, constructing an asymmetric probabilistic risk potential field for different categories of obstacles, and ensuring that the risk potential field gradient of a region corresponding to a worker is the highest. According to the method, the AGV can accurately distinguish different obstacles such as working personnel and equipment by utilizing the semantic segmentation network and constructing the asymmetric risk potential field, so that the operation safety in a complex man-machine co-fusion environment is improved, and particularly, the safety of the personnel is guaranteed.

An embroidery frame arrangement assignable to a sewing or embroidery machine and on which an at least sectionally tubular or bag-like textile structure is clampable in order to provide a flat, single-layer first partial region of the textile structure to be embroidered. The embroidery frame arrangement has first and second frame parts which are fixable together and / or second and third frame parts which are fixable together. The tubular or bag-like section of the textile structure is fixable between the first and second frame parts or between the second and third frame parts when the second frame part engages with the first frame part and / or when the third frame part engages with the second frame part. Consequently, the flat, single-layer first partial region of the textile structure to be embroidered is provided on the second frame part and surrounded thereby or is provided on the third frame part and surrounded thereby.

The invention relates to the technical field of automatic quality inspection of textiles, in particular to a flexible textile quality inspection system and device based on deep learning. The quality inspection system provided by the invention comprises a detection module which collects data and applies the data to machine training and machine quality inspection, a reasoning module which is in communication connection with the detection module and performs reasoning judgment by using the data collected by the detection module and determines a defect type, and a verification module which manually samples and rechecks a reasoning judgment result of the reasoning module, and the learning module feeds back a manual sampling rechecking result of the checking module to the reasoning module for optimization training learning. The multi-modal sensing and deep learning reasoning module is utilized to realize efficient automatic identification of multiple types of defects on the surface and in the fabric, the detection precision is improved, the missing detection rate and the false detection rate are remarkably reduced, and dynamic optimization of the model is realized through man-machine collaborative sampling inspection of the verification module and in combination with feedback correction and a continuous self-learning mechanism of the learning module. And the system detection capability is continuously improved along with use.

A shoe or other footwear has a non-removable multi-layer foot-support member (FSM) embedded within the sole. The uppermost layer is a breathable, stretchable textile configured for direct contact with the foot, offering softness and elasticity. Directly beneath it is a containment layer made of elastic fabric partially infused or impregnated with silicone, which stabilizes and restrains the granular contents below. The central layer includes one or two or three sealed chambers filled with a cured mixture of silicone and polymeric granules, forming a deformable medium that yields under pressure and mimics natural granular terrain or sand. Beneath this is a synthetic textile layer that restricts lateral expansion of the granular mixture, and preserves chamber geometry during use. The bottom component is a semi-rigid base that structurally supports and anchors all overlying layers.

The invention provides a textile fabric drapability measuring method and system capable of showing warp and weft anisotropy. The method comprises the following steps: aligning the centers of square textile fabric samples and covering the square textile fabric samples on a square supporting platform in parallel; collecting a stable top view image of the sample drooping due to self weight; processing the image to obtain overhanging contour information; respectively determining the overhang spacing along the warp direction and the weft direction of the sample as characteristic values based on the contour; and independently evaluating the longitude and latitude drapability according to the characteristic values. According to the invention, the to-be-tested textile fabric can form a stable draping form with clear direction characteristics, independent draping quantitative indexes in warp and weft directions can be respectively obtained through single test, the fabric warp and weft anisotropy can be directly represented, the test repeatability is good, the operation is simple and convenient, and a reliable basis is provided for clothing fabric parameter calibration.

The invention discloses a lasting aromatic lyocell fiber of a biological enzyme immobilized temperature-sensitive microcapsule and a preparation method of the lasting aromatic lyocell fiber, and relates to the technical field of polyester fabrics. The method comprises the steps that firstly, a temperature-sensitive microcapsule of a three-layer structure is prepared, an inner core contains aromatic substances of lavender essential oil, ambrota ether and rosin glycerin ester, a middle layer enables N-isopropylacrylamide monomers to be polymerized to form PNIPAM temperature-sensitive hydrogel, and a shell is a nanocellulose crystal-polydopamine compound; then spinning by taking the NMMO aqueous solution containing the microcapsules as an inner layer solution and taking the NMMO aqueous solution containing the eucalyptus pulp as an outer layer solution by adopting a coaxial micro-fluidic spinning technology; and finally carrying out crosslinking treatment through a biological enzyme coagulating bath, wherein the coagulating bath contains an NMMO aqueous solution, horse radish peroxidase and a hydrogen peroxide solution with gradient concentration. The aromatic lyocell fiber prepared by the invention has excellent temperature-sensitive aroma release performance and washing fastness, and solves the technical problems that microcapsules in traditional aromatic textiles are easy to break and fall off and aromatic substances are easy to volatilize.

A textile fabric is provided, including: a main body and at least one membrane. The main body includes two surfaces opposite to each other, and each of the two surfaces has a surface texture being concave and convex. The at least one membrane is coveringly partially disposed on one of the two surfaces and is concave and convex according to the surface texture. Therefore, the at least one membrane has an appearance corresponding to the weaving pattern, and the woven textile has good toughness, is waterproof and is not easily damaged, which solves the problems of a conventional woven textile such as insufficient structural strength and poor durability.

A helmet comprises a crown and a neckband having a body. Right attachment point and left attachment point attach the neckband to the lateral parts of the crown. The body is installed movable between a use position where the body is salient from the crown and a stowed position where the body is installed in the cavity. The inner face of the body is facing the rear inner face of the crown in the stowed position. The body is of curved shape and moves between the positions by turning elastically. The body is connected to the right attachment point and the left attachment point by a flexible attachment formed by a flexible textile element or a strip of elastomer material. The flexible attachment is elastically deformable in three-dimensional manner.

A textile fabric is provided, including: a main body and a first film layer. The main body includes two surfaces opposite to each other and at least one hollow hole penetrating through the two surfaces. The first film layer is disposed on one of the two surfaces and covers the at least one hollow hole. Therefore, a shape of the at least one hollow hole is changeable to meet different design requirements, and the first film layer can fix the at least one hollow hole and prevent edges of the main body at the at least one hollow hole from becoming unraveled so that the textile fabric has a three-dimensional surface texture and is tough and easy to be processed.

The invention provides a textile defect detection method and device, and the method comprises the steps: inputting a detection image of a textile into a preset appearance detection model, the preset appearance detection model comprises a plurality of encoders and decoders, and the decoders comprise a plurality of expansion convolution branches; converting the detection image into an activation probability feature, a first cooperation feature and a second cooperation feature based on a plurality of encoders; in the decoder, channel shuffling and segmentation are carried out on the activation probability feature, the first cooperation feature and the second cooperation feature to obtain a first sensing feature, expansion convolution is carried out on the first sensing feature through a plurality of expansion convolution branches to obtain a second sensing feature, and the second sensing feature is spliced to obtain a flaw detection result.

The invention relates to the technical field of textile bio-manufacturing and industrial process automation control, in particular to a bio-based fabric production temperature and humidity intelligent regulation and control system which comprises a data acquisition unit used for acquiring temperature and humidity sensor values in a fermentation curing workshop in real time and reading current heating power of an actuator and a fan rotating speed instruction; the reference state reconstruction unit is used for receiving the input state vector and generating an interference-free reference vector; the disturbance simulation unit is used for generating a theoretical damaged state vector representing a specific fault mode; the double-track difference unit is used for generating a real difference feature vector and a theoretical difference feature vector; the coupling regulation and control unit is used for calculating a similarity value of the real difference feature vector and the theoretical difference feature vector in a number domain space; according to the method, non-ideal factor interference is effectively eliminated, and the physical interpretability and numerical traceability of the reference data are ensured.