19462 results about "Textile" patented technology

The invention discloses a continuous polyester fiber textile cloth that can be torn into pieces, processing equipment and a processing method. By ultrasonic hot-melting technology, the polyester fiber textile cloth is subjected to the high temperature generated by the high frequency generated by the action of a metal knife mold and an ultrasonic welding head, so that the thread of the loop layers and the base layer are melted in a line to form a thin line melting body that can be torn apart. The knife mold and the ultrasonic welding head are close to each other and resonate to generate heat. When they leave each other, the resonance disappears and the heat decreases, so as to realize the formation and temperature control of a linear high temperature zone.

The invention provides a textile defect positioning method, which comprises the following steps: according to an extracted defect feature map, fusing feature maps of different scales, extracting multi-scale defect features, and comprehensively depicting space distribution and combination information of defects; aiming at the condition that multiple flaws exist at the same time, a multi-task learning model is constructed, flaw detection, classification and positioning tasks are carried out at the same time, and a back propagation algorithm is adopted to optimize the multi-task learning model; positioning the defect area by adopting an image segmentation algorithm, obtaining the contour of the defect area according to the positioning result of the defect area and by adopting a Canny edge detection algorithm, and calculating the area and position information of the defect; and based on an evaluation result, a dynamic threshold adjustment algorithm is adopted, a defect judgment threshold is adjusted according to a production batch and a product type, and a threshold parameter is dynamically updated by analyzing historical data and a current production condition.

The invention provides a textile blending proportion defect detection method, which comprises the following steps: carrying out continuous image acquisition on a blended textile in a vibration state, and adjusting the exposure time and frame rate of camera equipment according to vibration frequency and amplitude characteristics to obtain a textile surface image sequence; the obtained textile surface image sequence is preprocessed, and warp and weft areas are segmented according to the surface texture features of the blended textile; based on the abnormal degree evaluation model, constructing a blending proportion flaw classifier, and training the classifier to identify different types of blending proportion flaws by using the warp and weft abnormal features, the fiber distribution features and the texture features as input; according to a blending proportion defect classifier, in combination with defect positioning requirements in a vibration environment, performing real-time tracking on detected different types of defect proportion areas, compensating image displacement caused by vibration, and ensuring that a defect detection result corresponds to an actual textile position.

The invention provides a textile defect detection method based on machine vision, and the method comprises the steps: obtaining a corresponding camera resolution parameter, an algorithm complexity parameter and a detection sensitivity parameter from a database according to a textile quality requirement, and carrying out the initialization setting of a machine vision system; performing feature extraction on the optimized textile surface image, extracting texture and morphological features related to wrinkle flaws, and constructing a feature spectrum capable of reflecting wrinkle features; identifying a suspected wrinkle flaw area in the characteristic spectrum, determining the contour of the flaw, and obtaining the position and range information of the wrinkle flaw; according to a pre-established wrinkle defect type database, performing mode matching on the quantized wrinkle characteristics, and if the matching degree exceeds a preset threshold value, determining the specific type of the wrinkle defect; and a support vector machine algorithm is adopted to classify the wrinkle degree quantitative indexes, wrinkle depth and density parameters are combined, different levels of wrinkle flaws are divided, and the wrinkle severity is evaluated.

The invention provides a textile woven structure anomaly detection method, which comprises the following steps: acquiring real-time tension monitoring data of warps and wefts in a jacquard textile weaving process, and establishing a tension distribution model in a normal weaving state according to the real-time tension monitoring data; if the classification result is in an abnormal state, spraying a pre-prepared chemical reagent on the surface of the abnormal position of the jacquard textile, and collecting spectral data of the surface of the textile after spraying; performing feature extraction and classification on the spectral data, and obtaining a comprehensive anomaly detection result by comparing a classification result with a tension position anomaly detection result; according to the comprehensive anomaly detection result, an abnormal area is accurately positioned and marked, and a sample in the abnormal area is cut and conveyed to a sample analysis device; and carrying out microstructure analysis on the cut sample, extracting fiber arrangement characteristics, judging the abnormal degree of fiber arrangement, and obtaining a microstructure abnormality evaluation result.

The invention relates, inter alia, to a metering device (10) for metering and supplying media via fluid lines (14a, 14b, 14c, 14e, 14d, 14e, 14f) to several target devices (12a, 12b, 12c), in particular to several commercial textile washing machines, wherein the metering device (10) comprises an inlet disc (16) with several inlet-side connections (18a, 18b, 18c, 18d, 18e, 18f, 18g, 18h), each of which can be connected to a container (11a, 11b, 11c), and an actuator (17) that is displaceable relative to the inlet disc (16) and which controls a plurality of through-channels (19a, 19b, 19c, 19e, 19f, 19g, 19h), wherein each through-channel (19a, 19b, 19c, 19e, 19f, 19g, 19h) is connectable to a target device (12a, 12b, 12c), wherein as a result of a displacement of the actuator (17) relative to the input disk (16) at least one of the through-channels (19a, 19b, 19c, 19e, 19f, 19g, 19h) is connected to one of the terminals (18a, 18b, 18c, 18d, 18e, 18f, 18g,18h) is communicable, wherein the dosing device (10) comprises several pumps (21a, 21b, 21c, 21d, 21e) that can be addressed by a control unit (20) of the dosing device (10), which are designed to extract medium (13a, 13b, 13c) from the container (11a, 11b, 11c) and to convey it to the target device (12a, 12b, 12c), and wherein each through-channel (19a, 19b, 19c, 19e, 19f, 19g, 19h) is assigned a pump (21a, 21b, 21c, 21d, 21e), each of which is located downstream of the through-channel (19a, 19b, 19c, 19e, 19f, 19g, 19h) is arranged.

The invention provides a textile color consistency detection method, which comprises the following steps: acquiring spectral data of a to-be-detected product, comprehensively scanning the surface of the product through multispectral imaging, and performing noise reduction and enhancement processing on scanning data by adopting an image processing algorithm to obtain high-precision color difference detection data; according to the optimal dyeing process parameters and the pretreatment scheme, numerical simulation is adopted to simulate the dyeing process, the diffusion process of the dye in the fiber is calculated through finite element analysis, and a dyeing uniformity prediction result is obtained; and comparing the prediction result with a color fastness test standard, judging whether the dyed fabric can meet the product quality requirement or not, if so, warehousing the batch of products, and if not, feeding back the prediction result to a dyeing process control system, and carrying out the next round of optimization iteration until the product quality requirement is met.

The invention relates to the technical field of textile machinery fault detection, and discloses a spinning machine fault detection method and system based on deep learning, and the method comprises the steps: collecting vibration, temperature and current signals through a plurality of sensors, constructing a data stream after principal component analysis and dimension reduction, employing a variational auto-encoder to reconstruct an error positioning abnormal data segment, and obtaining a spinning machine fault detection result. The method comprises the following steps: extracting local features of a multi-source signal in combination with a convolutional neural network, dynamically distributing feature weights by using an attention mechanism, screening time sequence key features through a forgetting gate, introducing an LSTM network to model a feature sequence dynamic relationship, completing fault category mapping based on a support vector machine, and triggering LSTM secondary classification calibration for samples with insufficient confidence. A closed-loop technical system of data dimension reduction, anomaly detection, feature optimization, time sequence modeling and classification verification is formed, and efficient analysis and accurate recognition of multi-modal fault features under complex working conditions are achieved.

The invention discloses an automatic textile color fastness rating method and system based on a CNN-Transform fusion model, and relates to the technical field of textile digital detection. And then, feature extraction and rating are carried out by using a CNN-Transform fusion model. After the features of the two are fused, weighting is carried out through an attention mechanism, classification is carried out through a full-connection layer, and the color fastness grade probability is determined through a Softmax function. Meanwhile, a cross entropy loss function is combined with a label smoothing technology, an Adam optimizer is utilized, and a learning rate attenuation strategy is matched to train the model. Finally, confidence evaluation and result correction are carried out on the rating result, automatic and high-precision textile color fastness rating is achieved, and the problems that an existing rating method is poor in accuracy, low in consistency, difficult to adapt to fine rating standards and the like are effectively solved.

The invention discloses a textile fabric tearing strength detection method and system, and relates to the technical field of fabric detection.The method comprises the steps that fabric microstructure, macromechanics and component data are synchronously collected through a multi-mode sensing system, and detection modes are dynamically switched based on a real-time recognition result; inputting the space-time correlated defect data into the physical constraint enhanced prediction model to generate a crack propagation trend and a tear strength prediction value; wherein the prediction model is fused with a Griffith crack propagation theory and a hidden Markov model, and double constraints of a material mechanics law and dynamic defect evolution are realized; a non-contact detection module and a flexible contact detection module work cooperatively, and a process optimization scheme is output in combination with an edge and cloud distributed architecture.

A lower-body garment comprises a left leg portion, a right leg portion and an intermediate portion connecting the left leg portion to the right leg portion. The intermediate portion has a groin covering portion adapted to extend over a groin area of a wearer. The groin covering portion has a 3D textile. The 3D textile has at least one opening. In use, the opening of the 3D textile is stretched and compressed by a walking movement of the wearer thereby creating air movement.

The invention discloses a textile fabric defect detection system, relates to the technical field of textile fabric defect detection, and is beneficial to comprehensively evaluating the appearance quality of textile fabrics by collecting the number of hair balls, a color difference value and the area of a color difference region in an external defect parameter collection process. The pilling phenomenon on the surface of the textile fabric can be accurately recognized by collecting the number of the hair balls, the overall appearance and the service life of the textile fabric can be affected by the hair balls, non-uniformity of the textile fabric in the dyeing process can be revealed through measurement of the color difference value, and the severity degree of the color difference problem can be determined through statistics of the area of the color difference region. In the external defect evaluation process, whether the textile fabric meets the quality standard or not can be objectively judged by analyzing the external defect evaluation index of the specified textile fabric, and in the structural defect analysis process, the structural performance of the textile fabric can be comprehensively known by collecting the fiber breaking strength, the fiber density and the porosity.

The invention discloses an automatic single-yarn strength tester and a test method, and relates to the technical field of textile detection equipment, the automatic single-yarn strength tester comprises a single-yarn strength tester used for testing breaking strength, breaking elongation and breaking strength indexes of a single yarn and fiber pure spinning or blended yarn; and the translation mechanism is used for translating a plurality of distributed yarns and carrying out continuous high-efficiency test operation. According to the automatic single-yarn strength tester, a connecting piece is pulled through a tension spring, then a fastening piece and a limiting block are driven to conduct limiting installation on a limiting groove in a rubber pad, the rubber pad can be rapidly replaced after being abraded, the yarn clamping stability is ensured, and the testing accuracy is ensured; the good friction force of the clamping face can be recovered by replacing the rubber pad, it is ensured that the single yarn is tightly fixed in the clamping mechanism, the single yarn cannot move due to unstable clamping in the tensile test process, it is ensured that the test can be conducted smoothly, and the result is accurate.

The invention discloses an automatic testing device and method for multi-directional tensile strength of textiles based on constant tension, and relates to the technical field of tensile strength detection.The automatic testing device comprises a rigid rack system, and a high-elastic fabric optical response mechanism is integrated on a double-stand-column portal frame of the rigid rack system; the fiber damage process is reflected through the light transmittance of light to the fabric and the light spot change; the high-elastic fabric optical response mechanism comprises a sealing light insulation assembly, a light source assembly and an imaging assembly. The light source assembly comprises pulse LED light source arrays arranged at equal intervals in the fabric stretching direction, a lampshade and a transverse movement driving unit. The problems that in the prior art, when the tensile strength of a fabric is detected, only the limit value when a sample is completely broken can be recorded, and microcracks generated by step-by-step failure of fibers in the stretching process cannot be captured are solved. The device has the advantages that when the tensile strength of the fabric is detected, the fiber fracture process in the fabric is directly reflected through the change of light spots formed by light penetrating through the fabric, and the like.

The present invention is used in the technical field of functional textile materials. Disclosed are a composite fiber-based protective material, and a preparation method therefor and the use thereof. The composite fiber-based protective material comprises a unidirectional moisture-guiding fiber membrane, a bacteriostatic sweat-absorbing layer and a waterproof moisture-permeable membrane, which are sequentially stacked, wherein the unidirectional moisture-guiding fiber membrane comprises a hydrophobic fiber membrane and a hydrophilic fiber membrane sandwiched between the hydrophobic fiber membrane and the bacteriostatic sweat-absorbing layer, and the average pore size of the hydrophobic fiber membrane is larger than the average pore size of the hydrophilic fiber membrane. The average pore size of the hydrophobic fiber membrane is 10-18 μm; and the average pore size of the hydrophilic fiber membrane is 1.0-2.3 μm. The composite fiber-based protective material is prepared from the unidirectional moisture-guiding fiber membrane, the bacteriostatic sweat-absorbing layer and the waterproof moisture-permeable membrane by means of a lamination process. In addition to waterproofness and moisture penetrability, the composite fiber-based protective material also has biodegradability and a bacteriostatic effect.

The invention relates to the technical field of fabric detection, in particular to a textile fabric tensile property detection device which comprises a base, a base is fixedly connected to the outer wall of the top end of the base, a top plate is fixedly connected to the inner wall of the top of the base, and a hydraulic rod is mounted on the outer wall of the top end of the top plate; one end of a piston rod of the hydraulic rod is fixedly connected with a clamping frame; according to the device, wrinkles can be treated in advance before fabric tensile property detection, the situation that the wrinkles exist due to improper operation affect detection data of the tensile property is prevented, the clamping frame arranged in the cross shape is matched with the four upper clamping plates and the four lower clamping plates, the fabric can be stretched in the upper, lower, left and right directions at the same time, and the detection data are more accurate; the clamping frame is controlled by the hydraulic rod to descend firstly, and then the threaded rod is driven to rotate under the linkage action of meshing of the limiting plate and the rack, so that the structural synchronism is higher, the detection convenience is enhanced, the data error is reduced, and the detection efficiency of the tensile property of the textile fabric is improved.

A system for recycling textiles includes a sorting module configured to automatically remove trims and to sort textiles into the fabric streams; a first decoloring system configured to receive the non-black and white cotton fabric stream and to provide a de-colored cotton fabric stream; a dry sanitization system configured to provide a sanitized cotton fabric stream and a sanitized polyester fabric stream; a second decoloring system configured to provide a semi de-colored cotton / polyester blend fabric stream; a fabric separation system configured to provide pure polyester fiber; a decoloring system configured to provide a de-colored polyester fabric stream; a cotton fiber recovery system configured to receive the de-colored or black or white and sanitized cotton fabric, and sanitized denim fabric streams; and a polyester fiber recovery system configured to receive the de-colored or black or white and sanitized polyester fabric streams.

The invention discloses a novel textile fabric tensile strength detection device, and relates to the technical field of cloth detection equipment. The device comprises a base, a pressing block is arranged at the top of the base, push plates are arranged on the two sides of the pressing block, and first pressure sensors are installed in the push plates; an adjusting assembly is arranged on the top of the base. A rotating shaft, a gear and a toothed plate of the adjusting assembly are matched with a hydraulic rod and a clamping plate of the clamping assembly, accurate clamping and fan-shaped stretching of the side edge of cloth are achieved, and the defect that a traditional device can only conduct horizontal tension detection is overcome; an adjusting plate, an adjusting groove and a moving shaft of the linkage assembly are driven by a screw rod and a moving sleeve of the driving assembly, so that the cloth generates fan-shaped deformation under the action of lateral tension, the tensile strength of the side edge of the cloth can be evaluated more comprehensively in combination with dual monitoring of a first pressure sensor and a second pressure sensor, the accuracy of detection data is remarkably improved, and the detection efficiency is improved. The device is especially suitable for testing high-strength fabrics with easy-to-tear side edges.

The present invention relates to the technical field of textile fabric detection, and in particular to a device for detecting the air permeability of textile fabrics, including a detection chassis, wherein the detection chassis is provided with an upper cavity and a lower cavity that are connected to each other, a load-bearing bracket is fixedly installed in the lower end of the lower cavity, and a driving motor is fixedly installed on the load-bearing bracket, the detection chassis is provided with a first limiting mechanism for fixing the fabric from the left and right sides through the driving motor, and a load-bearing bracket is symmetrically arranged in the upper cavity. The present invention arranges a ball screw driven to rotate by a driving motor in the detection chassis, and uses the ball screw and a lifting nut to pull the guide block and the guide rod; by arranging a load-bearing bracket that is symmetrical in front and back in the upper cavity, the fabric is clamped and limited from the front and back sides; a supercharger is used to infuse sulfur dioxide gas into the fabric fixed to different degrees, and by observing whether the magenta solution in the reaction tank fades, it is judged whether the sulfur dioxide gas has passed through the fabric.

The invention relates to the technical field of three-dimensional image reconstruction, in particular to a VR-based three-dimensional reconstruction method and system for textile culture heritage, and the method comprises the steps: obtaining a multi-view image collection sequence of the textile culture heritage, carrying out the geometric topological feature extraction of the multi-view image collection sequence, generating a three-dimensional point cloud geometric model, and carrying out the reconstruction of the three-dimensional point cloud geometric model. Performing multi-resolution texture feature mapping processing on the three-dimensional point cloud geometric model to generate an initial three-dimensional model fusing geometric topological features and multi-resolution texture features; performing dynamic geometric optimization processing on the initial three-dimensional model based on preset VR display parameters to generate a target three-dimensional model after surface continuity correction; and performing dynamic matching processing on the target three-dimensional model and a preset VR interaction algorithm to generate a VR visual model with a multi-dimensional interaction attribute, and transmitting the VR visual model to a VR display terminal to perform three-dimensional space rendering, so that the three-dimensional reconstruction and interaction intelligence degree of the textile culture heritage can be improved in combination with a VR technology.

The invention discloses a textile fabric polyester fiber detection method based on spectral imaging and curve fitting, and relates to the technical field of hyperspectral imaging analysis, and the method comprises the steps: carrying out the characteristic decomposition and principal component analysis of a reflectivity function after baseline correction, and obtaining a characteristic curve; extracting feature information of the hyperspectral image, and fusing the feature information of the hyperspectral image with the feature curve to generate a multi-dimensional feature vector; and constructing a hyperspectral fiber content detection model, inputting the multi-dimensional feature vector and the feature parameter vector into the hyperspectral fiber content detection model, and generating the polyester fiber content. Not only are the problems of background noise and drift interference solved, but also the multi-dimensional characteristics of the hyperspectral data are fully mined. The content of the polyester fibers in the waste textile fabrics can be efficiently detected on line. Finally, according to the method, the detection precision and reliability are remarkably improved, and an efficient, environment-friendly and accurate technical means is provided for resource utilization of the waste textile fabrics.

The invention provides a textile defect type identification method, and relates to the technical field of textile defect identification, and the method specifically comprises the steps: collecting original images of a plurality of textile samples, and carrying out the detailed marking; adjusting the original image to 224 * 224 pixels, constructing a CNN model, extracting feature maps of different convolutional layers, and generating a fusion feature vector in combination with the weights of the feature layers; performing pooling processing on the fused feature map to generate a single-channel space attention map, and obtaining a weighted feature map in combination with a channel attention weight; constructing a segmentation model, outputting a mask after training, and identifying a normal region and a defect region; analyzing and identifying a defect area and extracting related features through a connected component to form a feature set; and a comprehensive scoring coefficient is constructed based on the features, so that the textile defect type can be accurately identified. According to the method, the recognition capability on fine defects can be effectively enhanced, and finer deviations can be captured, so that the recognition rate is remarkably improved.

The invention provides a textile pattern dislocation identification method, which comprises the following steps of: performing geometric transformation analysis on extracted pattern contour information, calculating a relative position relationship of each part of a pattern, establishing a pattern moisture deformation model, and judging whether the pattern is dislocated or deformed by comparing the difference between the current pattern and a reference model; on the basis of the corrected pattern, adding a special waterproof mark on the surface of the wet textile, manufacturing a microscopic mark which does not influence the fabric performance, and capturing the position of the waterproof mark through infrared imaging equipment to serve as an auxiliary reference point for pattern positioning; and based on the waterproof mark position of the pattern, tracking the motion trail of the waterproof fabric, calculating the image distortion degree according to the motion vector field, correcting the moving waterproof mark fabric image in real time, and eliminating the influence of motion blur on waterproof mark identification.

The invention discloses a textile fabric wear resistance detection device, and relates to the technical field of textile fabric wear resistance detection, the textile fabric wear resistance detection device comprises a support mechanism I and a detection mechanism, a sampling mechanism is arranged on a support mechanism II, the detection mechanism is arranged on a support seat II, and an adjusting mechanism I and an adjusting mechanism II are arranged on the detection mechanism. The device has the advantages that the third supporting seat is matched with the sampling mechanism, so that an experiment can be performed without manually cutting experimental cloth into a specified size, the preparation time before the experiment is shortened, and the detection efficiency is improved; the detection mechanism is adjusted through the adjusting mechanism I, so that tests of the same test type but different test strengths can be conveniently carried out, and experiment results can be directly compared; the detection mechanism is adjusted through the adjusting mechanism II, so that tests with different test types and the same force as one group can be conveniently carried out, and experiment results can be directly compared; and through horizontal comparison or vertical comparison, a detection result which is more in line with a use scene is provided.

The invention relates to the field of spinning, and particularly discloses a curtain fabric with an antibacterial function and a preparation method thereof.The curtain fabric comprises a base fabric layer, an antibacterial layer and a surface layer, and the antibacterial layer is formed after the surface of the base fabric layer is coated with an antibacterial agent; the antibacterial agent at least comprises composite modified titanium dioxide particles loaded with nano-silver and an OPHB bio-based antibacterial stock solution; wherein the modified titanium dioxide particles are prepared by loading nano-silver on titanium dioxide and then modifying the titanium dioxide through polyethyleneimine and a silane coupling agent; the preparation method comprises the following steps: coating the surface of the base cloth layer with an antibacterial agent, and then drying to form the antibacterial layer; the side, provided with the antibacterial layer, of the base cloth layer is attached to the surface layer and subjected to hot press molding, and the curtain cloth with the antibacterial function is prepared and has the advantages that the curtain cloth with the good antibacterial effect is prepared, meanwhile, the cost is reduced, and the antibacterial durability is improved.

The invention relates to the technical field of biomechanical measurement and analysis, in particular to a home textile recommendation method and system based on child health data monitoring and analysis. The method comprises the following steps: collecting an original point cloud coordinate set of a sleeping posture of a child; extracting a cervical curvature parameter group according to the extracted cervical curvature parameter group of the skeleton key point spatial distribution diagram; performing cervical vertebra and body position analysis according to the cervical vertebra curvature parameter group to obtain cervical vertebra curvature-body position holographic data; collecting a static pressure baseline data set according to the cervical curvature-body position holographic data; performing body position-pressure correlation analysis according to the static pressure baseline data set to obtain a body position-pressure mapping relation table; and generating a pressure distribution index map according to the body position-pressure mapping relation table. According to the method, the physical sign data, the pressure distribution and the physiological curvature change of the children in the natural sleep state are comprehensively collected and analyzed, so that high-precision, whole-process and personalized healthy home textile recommendation is realized.

The invention relates to the technical field of polyester fibers, in particular to a high-strength wear-resistant polyester fiber and a preparation method thereof. The invention aims to solve the problem that the mechanical strength and wear resistance of the existing polyester fiber are difficult to consider at the same time. The preparation method comprises the following steps: carrying out esterification and polycondensation on terephthalic acid, ethylene glycol, hydroxyl-terminated polydimethylsiloxane and isophthalic acid to obtain modified PET; the preparation method comprises the following steps: respectively carrying out oxidation treatment on graphene and a multi-walled carbon nanotube, and then adding polyether amine for grafting; carrying out melt blending with PET slices to obtain nano master batches; carrying out melt blending on the chopped glass fibers and the PET slices to prepare micron master batches; and mixing the modified PET, the nano master batch and the micron master batch, carrying out ultrasonic dispersion, adding an auxiliary agent, and carrying out melt spinning, three-section stretching and three-section heat setting treatment to obtain the high-strength wear-resistant polyester fiber. Through the synergistic effect of the components and the process, the prepared fiber has excellent mechanical properties and wear resistance, and has wide application prospects in the fields of clothing, home textiles, industry and the like.

The invention belongs to the technical field of position tracking control of rigid multi-joint mechanical arms, and discloses a fuzzy self-adaptive visual servo control method and system for a multi-joint mechanical arm system for placing bobbins in a textile workshop, and the method comprises the steps: constructing an image error model according to the positions of feature points on an image plane; the fuzzy logic system is used for processing uncertain items in the system, the instruction filtering and fuzzy self-adaption technology is used for achieving tracking control over the expected trajectory of the system, the problem of calculation complexity is solved, and adverse effects caused by filtering errors are eliminated. In the process of changing the bobbins, the bobbins are placed on a creel through a multi-degree-of-freedom robotic arm, a clamping jaw at the tail end of the robotic arm is required to accurately grab the bobbins for feeding and discharging, and the requirement for accurate and rapid control of the robotic arm is met, so that research on visual servo control of the robotic arm is of great significance. The method has a good control effect, the problem of calculation complexity in the design process of the controller is solved, the trajectory tracking error is smaller, and the system robustness is higher.

The invention relates to the field of textile materials, and particularly discloses a graphene enhanced polyester yarn and a preparation method thereof.The preparation method comprises the following steps that silicon dioxide aerogel serves as a porous carrier to be loaded with graphene, and a graphene-porous carrier composite material is prepared; the graphene-porous carrier composite material is subjected to modification treatment through vinyl trimethoxy silane and 3-amino propyl triethoxy silane, and then a modified graphene composite material is prepared; reacting the modified graphene composite material, a styrene monomer, an acrylate monomer and an initiator to prepare modified graphene; mixing the prepared modified graphene with polyester chips, and then carrying out melt spinning to prepare graphene enhanced polyester yarns; the invention further discloses the graphene enhanced polyester yarn prepared by adopting the preparation method. The preparation method has the characteristics that the dispersion of the graphene in the polyester is improved, and the direct interface compatibility of the graphene and the polyester fiber is improved, so that the mechanical property of the polyester yarn is improved.

The invention relates to the field of textile materials, and provides an antibacterial breathable yarn obtained by blended spinning of polyester fibers, polyamide fibers, cotton fibers and elastic fibers. The polyester fiber is prepared through melt blending and spinning processes and comprises polyethylene glycol terephthalate slices, modified chitosan zinc oxide, an antioxidant 1010, a POE elastomer, polyether modified silicone oil and polyethylene glycol 200, and the modified chitosan zinc oxide is prepared by compounding chitosan cinnamyl aldehyde Schiff base and silane modified zinc oxide. The chitosan cinnamyl aldehyde Schiff base is prepared through a Schiff base condensation reaction of chitosan and cinnamyl aldehyde, and the silane modified zinc oxide is prepared from nano rod-shaped zinc oxide particles modified by isocyanate propyl triethoxy silane. The yarn has excellent antibacterial property, air permeability and flexibility and is further used for knitting antibacterial breathable socks, the knitting density of the sock bottom part is reduced to enhance the air permeability, the knitting density of the sock opening part is improved to improve the elasticity and the fitting degree, and the yarn has wide application value.