A device and method for testing the elasticity of nonwoven fabrics

Abstract

This invention belongs to the field of nonwoven fabric elasticity testing technology, specifically a nonwoven fabric elasticity testing device and method, including a frame, with a conveying device and a testing device mounted on the upper surface of the frame. This nonwoven fabric elasticity testing device and method, through the inclusion of a conveying device, achieves automation and precision in fabric clamping and conveying. Specifically, the clamping component adopts a structure combining a fixed clamping plate and a movable clamping plate. The movable clamping plate can precisely adjust its clamping position under the action of a drive mechanism. Simultaneously, the movable clamping plate, through the rolling cooperation of rollers and a limiting track, can move smoothly along a preset trajectory during clamping, facilitating fabric clamping. Furthermore, the mounting frame is slidably connected to the movable slider via a connecting rod, ensuring the alignment accuracy of the upper and lower clamping components and facilitating disassembly and maintenance. The conveying component uses a combination of a conveyor belt and a slide rail, enabling the clamping components to be transported smoothly and accurately to the testing station.

Description

Technical Field

[0001] This invention relates to the field of nonwoven fabric elasticity testing technology, and in particular to a nonwoven fabric elasticity testing device and testing method. Background Technology

[0002] Nonwoven fabrics, as sheet materials made by arranging fibers in a oriented or random manner through physical or chemical methods, occupy an important position in fields such as medical and health care, packaging, geotextiles, and clothing linings due to their short production process, low cost, and wide application. Elasticity is one of the key mechanical properties of nonwoven fabrics, directly affecting their dimensional stability, fit, and durability during use. Therefore, accurate and efficient testing of the elastic properties of nonwoven fabrics is of great significance for quality control, product development, and process optimization.

[0003] Currently, the industry mainly relies on universal testing machines or dedicated electronic fabric strength testers to test the elasticity of fabrics. The basic principle is to clamp the two ends of a pre-cut standard strip sample between upper and lower clamps, and apply a tensile load to the sample by the uniform upward movement of the upper clamp. At the same time, force and displacement sensors record the force and elongation during the tensile process, and finally calculate parameters such as breaking strength, elongation at break, and elastic recovery rate.

[0004] However, existing testing devices and methods have several significant technical shortcomings when dealing with non-woven fabrics, a special material, leading to discrepancies between test results and actual conditions:

[0005] 1. Inadequate clamping and fixing can easily lead to sample slippage or damage.

[0006] Nonwoven fabrics are composed of fiber webs, and their surface often possesses a certain degree of fluffiness and a low coefficient of friction. Traditional testing devices typically use flat or toothed metal clamps, tightened pneumatically or manually. In practice, if the clamping force is too weak, the sample easily slips off the clamp during stretching, leading to test failure or inaccurate recording of the effective clamping distance. If the clamping force is too strong, it can damage or even break the fibers at the clamp, causing fracture to occur at the clamp edge rather than the center of the sample—not a true fracture behavior of the material itself. Furthermore, uneven clamping pressure distribution results in inconsistent stress along the sample's width, easily causing stress concentration and fracture at the clamp. This "inadequate fixation" problem directly affects the accuracy and repeatability of the test results.

[0007] Second, uneven stress leads to distorted strain distribution.

[0008] Ideally, tensile testing requires the specimen to withstand uniform uniaxial stress within the gauge length. However, due to limitations in the alignment accuracy of the mechanical structure and the non-uniformity of the specimen itself, existing devices often struggle to guarantee perfect alignment. More importantly, traditional testing methods calculate strain solely based on the relative displacement of the upper and lower clamps, assuming uniform deformation throughout the gauge length. However, for loosely structured, highly anisotropic nonwoven fabrics, the slippage and rearrangement of the fiber network during stretching can lead to severe "necking," where deformation concentrates in a weak area of ​​the specimen, while deformation in other areas is minimal. This non-uniform strain distribution is simply averaged as overall elongation, making the calculated elastic modulus and other parameters unable to accurately reflect the material's constitutive relationship.

[0009] Third, the detection mode is singular and lacks multi-dimensional data support.

[0010] Traditional elasticity testing typically outputs only a force-displacement curve, providing only a macroscopic mechanical response. However, the elastic behavior of nonwoven fabrics is closely related to the evolution of their microstructure, such as changes in fiber orientation, slippage between fiber bundles, disintegration of tangles, and breakage of individual fibers. Existing methods cannot simultaneously observe these microstructural changes during stretching, nor can they capture the acoustic emission signals that indicate the onset of fiber damage. Therefore, when a decrease in macroscopic force is detected, irreversible structural damage may have already occurred within the material. The macroscopic curve alone cannot accurately define the limits of elastic deformation and the damage initiation point, leading to an overly optimistic assessment of elastic recovery capabilities.

[0011] IV. Environmental factors are overlooked, affecting test consistency.

[0012] Temperature and humidity are important factors affecting the mechanical properties of polymer fiber materials. Current tests are mostly conducted in laboratory environments with normal temperature and humidity. However, the fluctuations in environmental temperature and humidity during different batches of tests have not been effectively recorded and used for data correction. For fibers with strong hygroscopicity (such as viscose fiber and cotton fiber) or temperature-sensitive synthetic fibers (such as polypropylene and polylactic acid), ignoring environmental compensation will introduce significant testing errors, making the test results from different times and locations incomparable. Summary of the Invention

[0013] Given that existing nonwoven fabric elasticity testing technologies generally suffer from problems such as unreliable clamping, uneven force distribution, limited testing dimensions, and lack of compensation for environmental interference, making it difficult to meet the technical requirements of modern nonwoven fabric production for high precision, high repeatability, and multi-dimensional quality evaluation, this invention proposes a nonwoven fabric elasticity testing device and testing method.

[0014] The present invention proposes a non-woven fabric elasticity testing device, which includes a frame, and a conveying device and a testing device are provided on the upper surface of the frame;

[0015] The conveying device includes a conveying component, a clamping component, and a driving mechanism. The conveying component drives the clamping component to move. The clamping component includes a fixed clamping plate and a movable clamping plate. After the movable clamping plate moves, it clamps the fabric. The driving mechanism drives the movable clamping plate to move.

[0016] The detection device includes clamping jaws, which clamp the clamping component;

[0017] It also includes a detection system, which comprises a main control module, a multi-dimensional sensing unit, a dynamic analysis unit, an intelligent evaluation unit, a detection execution unit, and a user interaction module.

[0018] Preferably, the conveying assembly is mounted on the outer surface of the frame, and the conveying assembly comprises a conveyor belt assembly and a slide rail.

[0019] Preferably, the clamping component further includes a movable slider, the outer surface of which is slidably inserted into the inner wall of the slide rail of the conveying assembly, and the outer surface of which is fixedly installed to the outer surface of the conveyor belt of the conveying assembly via a connector. Mounting brackets are provided on the outer surfaces of both the upper and lower movable sliders. The lower surface of the upper mounting bracket is slidably inserted into the upper surface of the upper movable slider via a connecting rod, and the lower mounting bracket is fixedly installed on the outer surface of the lower movable slider.

[0020] Preferably, the outer surface of the mounting frame is fixedly installed on the outer surface of the fixed clamping plate, a lifting frame is slidably inserted into the outer surface of the mounting frame, the lower surface of the mounting frame is rotatably connected to a drive gear ring via a bearing, a lifting screw is threadedly connected to the inner wall of the drive gear ring, one end of the lifting screw is fixedly installed on the lower surface of the lifting frame, the upper surface of the lifting frame is slidably connected to the lower surface of the movable clamping plate via a guide rail, a pressure sensor is installed inside the movable clamping plate, a limit rail is fixedly installed on the outer surface of the mounting frame, and a roller is rotatably connected to the outer surface of the movable clamping plate, the outer surface of the roller is rollingly connected to the inner wall of the limit rail;

[0021] A hydraulic cylinder is fixedly mounted on the outer surface of the frame, and a drive motor is slidably connected to the outer surface of the frame. The outer surface of the drive motor is fixedly mounted to one end of the piston rod of the hydraulic cylinder, and a drive gear is fixedly mounted on one end of the output shaft of the drive motor. The drive gear meshes with the drive gear ring.

[0022] Preferably, the detection device further includes a support frame, on the outer surface of which a lifting hydraulic cylinder is fixedly installed. One end of the piston rod of the lifting hydraulic cylinder is fixedly installed with one end of the clamping claw. A handle is fixedly installed on the upper surface of the mounting frame above. After the clamping claw clamps the handle, it drives the mounting frame to rise.

[0023] Preferably, the multi-dimensional sensing unit includes a visual acquisition module, a deformation tracking module, an acoustic monitoring module, a thickness detection module, and an environmental compensation module.

[0024] The vision acquisition module consists of two hyperspectral industrial cameras fixedly mounted on the frame. The two hyperspectral industrial cameras are located on both sides of the fabric and are used to acquire the surface spectral image sequence of the fabric during the stretching process to capture changes in fiber orientation and evolution of microstructure.

[0025] The deformation tracking module includes multiple laser displacement sensor arrays, which are fixedly installed on the side of the frame and the outer surface of the support frame, respectively. Their detection ends are respectively aligned with different areas of the fabric surface and the upper surface of the mounting frame, for real-time acquisition of multi-point strain distribution of the fabric and upward displacement data of the mounting frame.

[0026] The acoustic monitoring module includes an acoustic emission sensor, which is installed on the outer surface of the fixed clamping plate and close to the fabric clamping area, and is used to collect acoustic emission signals generated by fiber breakage and slippage during fabric stretching.

[0027] The thickness detection module includes a laser thickness sensor, which is fixedly installed on the outer surface of the frame and is used to non-contactly measure the thickness change of the fabric before and during stretching.

[0028] The environmental compensation module includes a temperature and humidity sensor and a barometric pressure sensor, which are installed on the outer surface of the frame to collect temperature, humidity and barometric pressure data of the detected environment as the basis for compensation for elastic parameter correction.

[0029] Preferably, the dynamic analysis unit includes a multi-source data fusion processor, a strain field reconstruction module, a damage evolution analysis module, and a dynamic response feature extraction module;

[0030] The multi-source data fusion processor is used to receive and synchronously process hyperspectral image data, multi-point displacement data, acoustic emission signals, thickness change data and environmental compensation data from the multi-dimensional sensing unit to establish a spatiotemporally aligned multidimensional dataset.

[0031] The strain field reconstruction module reconstructs the full-field strain distribution of the fabric during the entire stretching process based on digital image correlation algorithms and multi-point displacement data, and identifies strain concentration areas and deformation non-uniformity.

[0032] The damage evolution analysis module combines the intensity and frequency characteristics of acoustic emission signals with changes in fiber orientation in hyperspectral images to analyze the initiation, accumulation, and expansion process of microscopic damage inside the fabric in real time.

[0033] The dynamic response feature extraction module extracts dynamic elastic response features from displacement-time curves and acoustic emission waveforms, including instantaneous elastic modulus, damping characteristics, and stress relaxation rate.

[0034] Preferably, the intelligent evaluation unit includes a multi-scale elasticity model, an adaptive algorithm library, a comprehensive quality evaluation module, and a decision output module;

[0035] The multi-scale elastic model is based on the fusion of deep learning and physical mechanism modeling. It correlates macroscopic displacement data, microscopic structural evolution and acoustic emission characteristics to establish elastic constitutive relations from fiber scale to sample scale, and predicts the nonlinear elastic behavior, anisotropic characteristics and fracture toughness of the fabric.

[0036] The adaptive algorithm library stores elasticity evaluation algorithms applicable to fabrics with different weights and fiber compositions. It can automatically select the optimal algorithm combination based on the fabric type identified by hyperspectral image and perform Bayesian optimization using historical detection data.

[0037] The comprehensive quality evaluation module integrates macroscopic elastic parameters, microstructural stability indicators, damage accumulation index, and dynamic response characteristics to output a multidimensional quality score. Macroscopic elastic parameters include elastic modulus, elongation at break, and elastic recovery rate; microstructural stability indicators include fiber orientation change rate and damage accumulation index; and dynamic response characteristics include damping ratio and stress relaxation time.

[0038] The decision output module generates test conclusions based on the comprehensive evaluation results and preset process standards, and issues grading instructions to the sorting mechanism.

[0039] Preferably, the detection execution unit includes a multi-dimensional loading control module, a clamping pressure adaptive module, a multi-point synchronous triggering module, and an anomaly self-diagnosis module;

[0040] The multi-dimensional loading control module is electrically connected to the lifting hydraulic cylinder, the pushing hydraulic cylinder and the drive motor. It not only controls the stretching speed, but also realizes multiple loading modes such as cyclic loading and stepped loading to simulate the stress situation of the fabric in actual use.

[0041] The clamping pressure adaptive module is electrically connected to the pressure sensor inside the moving clamping plate. It automatically adjusts the clamping pressure according to the real-time detected fabric thickness and clamping status to prevent slippage or injury and ensure detection repeatability.

[0042] The multi-point synchronous triggering module is electrically connected to the laser displacement sensor array, the hyperspectral industrial camera, and the acoustic emission sensor. It triggers the synchronous acquisition of multi-source data based on the displacement increment fed back by the displacement sensor to ensure the consistency of spatiotemporal resolution.

[0043] The anomaly self-diagnosis module monitors the working status and data rationality of each sensor in real time. When an abnormal data or equipment failure is detected, it automatically pauses the detection and issues an alarm message to prompt the operator to check.

[0044] The user interaction unit includes a local / remote monitoring terminal, a parameter setting and strategy selection interface, and a data management and report generation module.

[0045] The local / remote monitoring terminal is used to display images captured by the industrial camera, displacement-time curves, elastic parameter calculation results, and system operating status in real time, and to issue warnings when data exceeds limits.

[0046] The parameter setting and strategy selection interface allows users to input fabric type, thickness, and testing standards, and select testing modes (such as constant speed stretching and cyclic stretching).

[0047] The data management and report generation module automatically stores the raw data and results of each test and generates a test report that includes elasticity curves, feature values, and test conclusions.

[0048] The present invention provides a detection method for a nonwoven fabric elasticity testing device, comprising the following steps:

[0049] S1: Initialization and Parameter Setting

[0050] The user inputs the type, weight, fiber composition, and testing standards of the fabric to be tested through the user interaction module, selects the loading mode (constant speed stretching, cyclic loading, or stepped loading), and sets the safety threshold and abnormal alarm parameters. The main control module sends the parameters to the intelligent evaluation unit and the detection execution unit, and wakes up the multi-dimensional sensing unit to enter standby mode.

[0051] S2: Automatic clamping and adaptive voltage regulation

[0052] The conveyor belt of the conveyor assembly moves the clamping component to the fabric loading station. One end of the fabric is placed against the outer surface of the fixed clamping plate. The hydraulic cylinder in the upper drive mechanism is activated, pushing the drive gear on the drive motor to approach and mesh with the drive gear ring. The drive motor then starts, and the rotation of the drive gear drives the drive gear ring to rotate, which in turn drives the lifting screw to rise. After the lifting frame rises on the mounting frame, the moving clamping plate moves along the track shape of the limit track via rollers. The moving clamping plate approaches the fixed clamping plate, and one end of the fabric is brought into contact with the fabric through the convex texture. The lower end of the fabric is placed on the fixed clamping plate below. The movement of the moving clamping plate below completes the clamping of both ends of the fabric. The clamping pressure adaptive module dynamically adjusts the clamping pressure based on the feedback from the pressure sensor integrated in the moving clamping plate and the fabric thickness measured in real time by the laser thickness sensor, ensuring no slippage and no pinching damage. The actual clamping force data is stored in a temporary cache.

[0053] S3: Transport to the testing station

[0054] The conveyor assembly moves the clamping component holding the fabric along the slide rail to directly below the detection device. Once it is confirmed to be in place, the conveyor belt stops. At this time, the environmental compensation module records the current temperature, humidity, and air pressure data for subsequent correction.

[0055] S4: Tensile loading and synchronous acquisition of multi-source data

[0056] The lifting hydraulic cylinder drives the clamping jaws to descend and clamp the handle located on the upper mounting frame; according to the preset loading mode, the multi-dimensional loading control module controls the lifting hydraulic cylinder to rise at a set speed or acceleration to stretch the fabric.

[0057] At the same time, the multi-point synchronous triggering module uses the displacement increment fed back by the laser displacement sensor array as the trigger signal to synchronously start the following acquisition tasks:

[0058] Two hyperspectral industrial cameras continuously acquire surface spectral image sequences from both sides of the fabric; a laser displacement sensor array monitors the displacement of multiple feature points on the fabric surface and the mounting bracket in real time, generating multi-point strain data; an acoustic emission sensor acquires acoustic emission signals generated by fiber breakage and slippage during the stretching process; a laser thickness sensor continuously measures the fabric thickness change; all data have a unified timestamp and are aligned in real time by a multi-source data fusion processor.

[0059] S5: Dynamic Analysis and Feature Extraction

[0060] The dynamic analysis unit processes the synchronized multi-source data: the strain field reconstruction module reconstructs the strain distribution of the entire fabric field based on digital image correlation algorithms and multi-point displacement data, and identifies strain concentration areas; the damage evolution analysis module integrates the intensity and frequency characteristics of acoustic emission signals with changes in fiber orientation in hyperspectral images to analyze the initiation, accumulation, and expansion process of micro-damage; the dynamic response feature extraction module extracts dynamic features such as instantaneous elastic modulus, damping characteristics, and stress relaxation rate from displacement-time curves and acoustic emission waveforms.

[0061] S6: Intelligent Assessment and Quality Scoring

[0062] The intelligent evaluation unit calls upon a multi-scale elastic model to correlate macroscopic displacement, microstructural evolution, and acoustic emission characteristics, establishing an elastic constitutive relationship from fiber to sample, and predicting the nonlinear elastic behavior, anisotropy, and fracture toughness of the fabric. The adaptive algorithm library automatically matches the optimal evaluation algorithm based on the fabric type identified by the hyperspectral image and performs Bayesian optimization by combining historical data. The comprehensive quality evaluation module integrates macroscopic elastic parameters (elastic modulus, elongation at break, elastic recovery rate), microstructural stability indicators (fiber orientation change rate, damage accumulation index), and dynamic response characteristics (damping ratio, stress relaxation time) to output a multidimensional quality score.

[0063] S7: Decision Output and Hierarchical Sorting

[0064] The decision output module compares the quality score with the preset process standard, generates the test conclusion (such as Grade A, Grade B, unqualified), and sends the instruction to the downstream sorting mechanism to achieve automatic grading; the user interaction module displays the strain cloud map, damage evolution curve, elastic parameters and final report in real time, and all test data are automatically stored in the database to support traceability and statistical analysis.

[0065] S8: Reset and Self-Check for Abnormalities

[0066] After the stretching is completed, the clamping jaws release the handle, and the lifting hydraulic cylinder resets; the conveying component moves the clamping part back to the loading station, ready for the next inspection; throughout the process, the abnormal self-diagnosis module continuously monitors the status of each sensor and the rationality of the data. Once the data is found to be out of tolerance or the equipment is faulty, the process is immediately paused and an audible and visual alarm is issued to prompt manual intervention.

[0067] The beneficial effects of this invention are as follows:

[0068] 1. By setting up a conveying device, the automation and precision of fabric clamping and conveying are achieved. Specifically, the clamping component adopts a structure that combines a fixed clamping plate and a movable clamping plate. The movable clamping plate can precisely adjust its clamping position under the action of the drive mechanism. At the same time, the movable clamping plate can move smoothly along a preset trajectory during the clamping process through the rolling cooperation of rollers and limit rails, which facilitates the clamping of fabric. In addition, the mounting frame is slidably connected to the movable slider through the plug rod, which not only ensures the centering accuracy of the upper and lower clamping components, but also facilitates disassembly and maintenance. The conveying component adopts a combination of conveyor belt and slide rail. Through the sliding connection of the movable slider and the slide rail and the precise drive of the conveyor belt, the clamping components can be smoothly and accurately transported to the inspection station, providing a reliable guarantee for continuous and automated inspection and solving the technical problems of unreliable clamping and uneven force distribution that are common in existing non-woven fabric elasticity testing technologies.

[0069] 2. By setting up a detection device and system, the clamping jaws, driven by a lifting hydraulic cylinder, precisely grasp the mounting frame to achieve stable tensile loading. Simultaneously, the detection system integrates a hyperspectral camera, a laser displacement sensor array, an acoustic emission sensor, and a thickness sensor. During the stretching process, it synchronously acquires fabric surface images, multi-point strain, acoustic emission signals, and thickness change data, constructing a spatiotemporally aligned multidimensional dataset. The dynamic analysis unit uses digital image correlation algorithms to reconstruct the full-field strain distribution, identify strain concentration areas, and, combined with acoustic emission characteristics, track the evolution of fiber micro-damage in real time, overcoming the limitations of traditional detection methods that can only obtain force-displacement curves. The intelligent evaluation unit integrates macroscopic elastic parameters, microstructural indicators, and dynamic response characteristics... This technology integrates multiple scales and adaptive algorithms to output multi-dimensional quality scores, enabling a comprehensive and objective evaluation of fabric elasticity performance. The detection execution unit supports multiple loading modes to simulate actual working conditions, and the clamping pressure is adaptively adjusted according to the thickness detection. Multi-point synchronous triggering ensures high-precision synchronization of multi-source data, effectively solving technical problems in existing technologies such as single detection dimension, distorted strain distribution, and difficulty in defining damage initiation points. It significantly improves the accuracy, repeatability, and comprehensiveness of information in nonwoven fabric elasticity detection, and addresses the problems of single detection dimension and uncompensated environmental interference in existing nonwoven fabric elasticity detection technologies, which are insufficient to meet the requirements of modern nonwoven fabric production for high precision, high repeatability, and multi-dimensional quality evaluation. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of a nonwoven fabric elasticity testing device proposed in this invention;

[0071] Figure 2 This is a perspective view of the conveying component structure of a nonwoven fabric elasticity testing device proposed in this invention;

[0072] Figure 3This is a perspective view of the mounting frame structure of a nonwoven fabric elasticity testing device proposed in this invention;

[0073] Figure 4 This is a perspective view of the hydraulic cylinder structure for a nonwoven fabric elasticity testing device proposed in this invention.

[0074] Figure 5 This is a perspective view of the lifting screw structure of a nonwoven fabric elasticity testing device proposed in this invention;

[0075] Figure 6 This is a perspective view of the movable clamping plate structure of a nonwoven fabric elasticity testing device proposed in this invention;

[0076] Figure 7 This is a perspective view of the fixed clamping plate structure of the nonwoven fabric elasticity testing device proposed in this invention;

[0077] Figure 8 This is a perspective view of the industrial camera structure of a nonwoven fabric elasticity testing device proposed in this invention.

[0078] Figure 9 This is a flowchart of the detection system for a nonwoven fabric elasticity testing device proposed in this invention.

[0079] In the diagram: 1. Frame; 2. Conveying assembly; 3. Moving slider; 31. Mounting frame; 32. Fixed clamping plate; 33. Lifting frame; 34. Drive gear ring; 35. Lifting screw; 36. Moving clamping plate; 37. Pressure sensor; 38. Limiting track; 39. Roller; 4. Pushing hydraulic cylinder; 41. Drive motor; 42. Drive gear; 5. Support frame; 51. Lifting hydraulic cylinder; 52. Clamping jaw; 53. Handle; 6. Industrial camera; 61. Laser displacement sensor array; 62. Acoustic emission sensor; 63. Laser thickness sensor; 64. Temperature and humidity sensor; 65. Air pressure sensor. Detailed Implementation

[0080] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0081] Reference Figures 1-9 A nonwoven fabric elasticity testing device includes a frame 1, with a conveying device and a testing device disposed on the upper surface of the frame 1.

[0082] like Figures 2-7As shown, the conveying device includes a conveying assembly 2, a clamping component, and a driving mechanism. The conveying assembly 2 drives the clamping component to move. The clamping component includes a fixed clamping plate 32 and a movable clamping plate 36. After the movable clamping plate 36 moves, it clamps the fabric. The driving mechanism drives the movable clamping plate 36 to move. The conveying assembly 2 is installed on the outer surface of the frame 1. The conveying assembly 2 consists of a conveyor belt assembly and a slide rail. The conveyor belt assembly includes a conveyor belt, a drive wheel, and a motor that drives the drive wheel to rotate.

[0083] Specifically, the clamping components also include a movable slider 3. The outer surface of the movable slider 3 is slidably inserted into the inner wall of the slide rail of the conveying component 2. The outer surface of the movable slider 3 is fixedly installed to the outer surface of the conveyor belt of the conveying component 2 through a connector. Mounting brackets 31 are provided on the outer surfaces of both the upper and lower movable slider 3. The lower surface of the upper mounting bracket 31 is slidably inserted into the upper surface of the upper movable slider 3 through a connecting rod. The lower mounting bracket 31 is fixedly installed on the outer surface of the lower movable slider 3. This split design ensures the alignment accuracy of the upper and lower clamping components and facilitates disassembly and maintenance. The connecting rod structure allows the upper mounting bracket 31 to rise, which is convenient for stretching the fabric for elasticity testing.

[0084] Specifically, the outer surface of the mounting frame 31 is fixedly installed on the outer surface of the fixed clamping plate 32. A lifting frame 33 is slidably inserted into the outer surface of the mounting frame 31. A drive gear ring 34 is rotatably connected to the lower surface of the mounting frame 31 via a bearing. A lifting screw 35 is threadedly connected to the inner wall of the drive gear ring 34. One end of the lifting screw 35 is fixedly installed on the lower surface of the lifting frame 33. The upper surface of the lifting frame 33 is slidably connected to the lower surface of the movable clamping plate 36 via a guide rail. A pressure sensor 37 is installed inside the movable clamping plate 36. The movable clamping plate is formed by the clamping plate being fixedly installed to a support plate via the pressure sensor 37. The support plate is slidably connected to the lifting frame 33 to monitor the clamping force in real time. A limit rail 38 is fixedly installed on the outer surface of the mounting frame 31. A roller 39 is rotatably connected to the outer surface of the movable clamping plate 36. The outer surface of the roller 39 is rotatably connected to the inner wall of the limit rail 38 to ensure that the movable clamping plate 36 can move during the lifting process, conform to the fabric, and complete the clamping work.

[0085] A hydraulic cylinder 4 is fixedly mounted on the outer surface of the frame 1. A drive motor 41 is slidably connected to the outer surface of the frame 1. The outer surface of the drive motor 41 is fixedly mounted to one end of the piston rod of the hydraulic cylinder 4. A drive gear 42 is fixedly mounted on one end of the output shaft of the drive motor 41, and the drive gear 42 meshes with the drive gear ring 34. During operation, the hydraulic cylinder 4 drives the drive motor 41 to move horizontally, causing the drive gear 42 to mesh with or disengage from the drive gear ring 34. When the two are meshed, the drive motor 41 rotates, causing the drive gear ring 34 to rotate, which in turn causes the lifting screw 35 to move up and down through threaded transmission, thereby driving the lifting frame 33 and the moving clamping plate 36 to rise and fall, thus achieving the clamping or release of the fabric. In this way, the two sets of drive mechanisms drive multiple sets of clamping components.

[0086] like Figure 8 As shown, the detection device includes a clamping jaw 52, ​​which clamps the component.

[0087] Specifically, the testing device also includes a support frame 5, on the outer surface of which a lifting hydraulic cylinder 51 is fixedly installed. One end of the piston rod of the lifting hydraulic cylinder 51 is fixedly installed with one end of the clamping claw 52. A handle 53 is fixedly installed on the upper surface of the upper mounting frame 31. After the clamping claw 52 clamps the handle 53, it drives the mounting frame 31 to rise, thereby achieving the stretching loading of the fabric.

[0088] like Figure 9 As shown, it also includes a detection system, which comprises a main control module, a multi-dimensional sensing unit, a dynamic analysis unit, an intelligent evaluation unit, a detection execution unit, and a user interaction module.

[0089] Specifically, the multi-dimensional sensing unit includes a visual acquisition module, a deformation tracking module, an acoustic monitoring module, a thickness detection module, and an environmental compensation module.

[0090] The vision acquisition module consists of two hyperspectral industrial cameras 6 fixedly mounted on the frame 1. The two hyperspectral industrial cameras 6 are located on both sides of the fabric and are used to acquire surface spectral image sequences of the fabric during the stretching process, capturing changes in fiber orientation and evolution of microstructure. Hyperspectral imaging technology can acquire spectral information at every point on the fabric surface. By analyzing changes in spectral characteristics, the orientation, aggregation state, and microscopic damage of the fibers can be identified.

[0091] The deformation tracking module includes multiple laser displacement sensor arrays 61, fixedly mounted on the side of the frame 1 and the outer surface of the support frame 5. The laser displacement sensor detection ends on the outer surface of the support frame 5 are aligned with the upper surface of the mounting frame 31 to collect real-time upward displacement data of the mounting frame 31. The multiple laser displacement sensors located on the sides are respectively aligned with different areas on both sides of the fabric to collect real-time multi-point strain distribution of the fabric. This multi-point deployment method can comprehensively capture the deformation of the fabric during the stretching process, overcoming the deficiency of traditional single-point displacement sensors in obtaining strain distribution.

[0092] The acoustic monitoring module includes an acoustic emission sensor 62, which is mounted on the outer surface of the fixed clamping plate 32 and close to the fabric clamping area. It is used to collect acoustic emission signals generated by fiber breakage and slippage during fabric stretching. Acoustic emission technology is a dynamic non-destructive testing method that can monitor the damage evolution process inside materials in real time. When fibers break or slip, they release elastic waves. The acoustic emission sensor 62 captures these signals and converts them into electrical signals for analysis.

[0093] The thickness detection module includes a laser thickness sensor 63, which is fixedly installed on the outer surface of the frame 1 and used for non-contact measurement of fabric thickness changes before and during stretching. The environmental compensation module includes a temperature and humidity sensor 64 and a pressure sensor 65, which are installed on the outer surface of the frame 1 and used to collect temperature, humidity, and pressure data of the detection environment as a basis for compensation for elastic parameter correction.

[0094] Specifically, the dynamic analysis unit includes a multi-source data fusion processor, a strain field reconstruction module, a damage evolution analysis module, and a dynamic response feature extraction module. The multi-source data fusion processor is used to receive and synchronously process hyperspectral image data, multi-point displacement data, acoustic emission signals, thickness change data, and environmental compensation data from the multi-dimensional sensing unit to establish a spatiotemporally aligned multidimensional dataset.

[0095] The strain field reconstruction module, based on digital image correlation algorithms and multi-point displacement data, reconstructs the full-field strain distribution of the fabric during the entire tensile process. The basic principle of the digital image correlation algorithm is to calculate the displacement and strain fields by tracking the positional changes of the speckle pattern on the fabric surface before and after deformation; for any point on the fabric surface... The position after deformation is Then the displacement components at that point are:

[0096] , ;in, Dots on the fabric surface exist Displacement components in the direction, Dots on the fabric surface exist Displacement components in the direction, The coordinates of the point before deformation, These are the coordinates of the corresponding points after deformation.

[0097] Based on the displacement field, each strain component can be calculated; for small deformation cases, the normal strain... , and shear strain The calculation formula is:

[0098] , , ;in, for The positive strain in the direction, for The positive strain in the direction, , For displacement components, , It is a partial derivative operator.

[0099] For large deformations in nonwoven fabrics, the Green-Lagrange strain tensor is used for description:

[0100] , , ;in, , , For the Green-Lagrange strain tensor components.

[0101] Using the above algorithm, the strain field reconstruction module can generate a strain distribution cloud map of the fabric, which intuitively displays the strain concentration area and deformation non-uniformity, providing an important basis for evaluating the elastic performance of the fabric.

[0102] The damage evolution analysis module combines the intensity and frequency characteristics of acoustic emission signals with changes in fiber orientation in hyperspectral images to analyze the initiation, accumulation, and propagation of microscopic damage within the fabric in real time. Characteristic parameters of the acoustic emission signals include amplitude, energy, ring count, rise time, and duration. The relationship between the cumulative count of acoustic emission events and the degree of damage can be described by the following formula:

[0103] ;in, for The cumulative number of acoustic emission events at any given time. for The occurrence rate of acoustic emission events at any given time. For time, The integral variable represents the time when a acoustic emission event occurred in the past.

[0104] Damage variables can be defined based on the energy characteristics of acoustic emission signals. :

[0105] ;in, To accumulate acoustic emission energy, The total acoustic emission energy is estimated when the structure completely breaks apart.

[0106] By combining hyperspectral image analysis, fiber orientation variation parameters can be extracted. For the orientation distribution of fibers in nonwoven fabrics, orientation parameters are typically used. describe:

[0107] ,in, These are fiber orientation parameters, describing the directionality of fiber alignment. It is the angle between the fiber orientation and the stretching direction.

[0108] The dynamic response feature extraction module extracts dynamic elastic response features from displacement-time curves and acoustic emission waveforms, including instantaneous elastic modulus, damping characteristics, and stress relaxation rate. For the stress-strain relationship during tensioning, a differential constitutive equation is used. Considering the viscoelastic properties of nonwoven fabrics, their stress response can be expressed as:

[0109] ,in, for Stress at any moment for The ability to adapt to changing circumstances and record the degree of historical distortion. For strain rate, At the current stress response moment, For time variables The differential, To indicate from a past moment up to the current moment The relaxation modulus during this time interval.

[0110] For a step strain input, the relaxation modulus function can be approximated as a Prony series:

[0111] ,in, For relaxation modulus function, To balance the modulus, For the first The modulus of a relaxation mode. For the first A period of relaxation, The total number of relaxation modes, It is an exponential function.

[0112] The instantaneous elastic modulus is defined as the stress-strain ratio at the initial loading moment:

[0113] ,in, For instantaneous elastic modulus, for Stress at any moment for Adaptability at all times.

[0114] Specifically, the intelligent evaluation unit includes a multi-scale elasticity model, an adaptive algorithm library, a comprehensive quality evaluation module, and a decision output module;

[0115] The intelligent evaluation unit includes a multi-scale elasticity model, an adaptive algorithm library, a comprehensive quality evaluation module, and a decision output module. The multi-scale elasticity model is based on a fusion of deep learning and physical mechanisms, linking macroscopic displacement data, microstructural evolution, and acoustic emission characteristics to establish elastic constitutive relationships from the fiber scale to the sample scale. The model employs a Physical Information Neural Network (PINN) architecture, and its loss function includes data fitting terms and physical constraint terms.

[0116] ;in, Total loss function, This is the data fitting term (mean square error between predicted and measured values). This refers to the physical constraint terms (physical equation residuals). These are weighting coefficients used to balance data and physical constraints.

[0117] This model can predict the nonlinear elastic behavior, anisotropic characteristics, and fracture toughness of fabrics. For the elastic behavior of anisotropic materials, the generalized Hooke's law is used to describe it:

[0118] ;in, These are second-order stress tensor components. These are the components of the fourth-order stiffness tensor. These are the components of the second-order strain tensor. , , , The index (values ​​1, 2, 3).

[0119] The adaptive algorithm library stores elasticity evaluation algorithms suitable for fabrics with different basis weights and fiber compositions. It can automatically select the optimal algorithm combination based on the fabric type identified by hyperspectral image recognition and perform Bayesian optimization using historical detection data. The goal of Bayesian optimization is to find the optimal combination of algorithm parameters. This makes the performance indicators maximize:

[0120] ;in, For algorithm parameters, For parameter space.

[0121] A probabilistic surrogate model of the objective function is established using Gaussian process regression, and the selection of the next set of parameters is guided by the acquisition function (e.g., the desired improvement in EI).

[0122] ;in, To improve the acquisition function, For mathematical expectation, This represents the current optimal objective function value.

[0123] The comprehensive quality evaluation module integrates macroscopic elastic parameters, microstructural stability indices, damage accumulation index, and dynamic response characteristics to output a multidimensional quality score. Macroscopic elastic parameters include elastic modulus. Elongation at break Elastic recovery rate Microstructure stability indicators include fiber orientation change rate. and damage accumulation index Dynamic response characteristics include damping ratio and stress relaxation time ;

[0124] Overall score The calculation adopts the weighted comprehensive evaluation method:

[0125] ;in - These are the weighting coefficients, and , This is the reference standard value for the elastic modulus. This is the reference standard value for elongation at break. This is the reference standard value for elastic recovery rate. This is the reference standard value for the damping ratio. This is the reference standard value for stress relaxation time.

[0126] The decision output module generates test conclusions based on the comprehensive evaluation results and preset process standards, and issues grading instructions to the sorting mechanism; the grading standards can be set as follows: ≥90 points is Grade A, 75≤ <90 points is Grade B, 60 ≤ <75 points is Grade C. A score of <60 is considered unqualified.

[0127] Specifically, the detection execution unit includes a multi-dimensional loading control module, a clamping pressure adaptive module, a multi-point synchronous triggering module, and an anomaly self-diagnosis module. The multi-dimensional loading control module is electrically connected to the lifting hydraulic cylinder 51, the pushing hydraulic cylinder 4, and the drive motor 41. It not only controls the stretching speed but also realizes various loading modes such as cyclic loading and stepped loading to simulate the stress conditions of the fabric in actual use.

[0128] The clamping pressure adaptive module is electrically connected to the pressure sensor 37 inside the moving clamping plate, and automatically adjusts the clamping pressure based on the real-time detected fabric thickness and clamping status. The control algorithm uses PID control:

[0129] ;in, Pressure deviation, To exert pressure on the target, for Real-time pressure measurement This is the proportionality coefficient. The integral coefficient is... is the differential coefficient.

[0130] Target clamping pressure According to the thickness of the fabric And material properties are determined:

[0131] ;in, Based on clamping pressure, This is the thickness compensation coefficient. For reference thickness.

[0132] The multi-point synchronous triggering module is electrically connected to the laser displacement sensor array 61, the hyperspectral industrial camera 6, and the acoustic emission sensor 62, and triggers the module based on the displacement increments fed back by the displacement sensors in the laser displacement sensor array 61. Trigger multi-source data synchronous collection; when Reaching the preset threshold At that time, the system sends a synchronization trigger signal to ensure that each sensor collects data under the same deformation state, thus ensuring the consistency of spatiotemporal resolution;

[0133] The anomaly self-diagnosis module monitors the working status and data rationality of each sensor in real time. When anomalies or equipment malfunctions are detected, it automatically pauses the detection and issues an alarm message to prompt the operator to check.

[0134] The user interaction unit includes a local / remote monitoring terminal, a parameter setting and strategy selection interface, and a data management and report generation module;

[0135] The local / remote monitoring terminal is used to display images captured by industrial cameras, displacement-time curves, elastic parameter calculation results, and system operating status in real time, and to issue warnings when data exceeds limits.

[0136] The parameter setting and strategy selection interface allows users to input fabric type, thickness, and testing standards, and select the testing mode (such as constant speed stretching or cyclic stretching).

[0137] The data management and report generation module automatically stores the raw data and results of each test and generates a test report that includes elasticity curves, feature values, and test conclusions.

[0138] The present invention provides a detection method for a nonwoven fabric elasticity testing device, comprising the following steps:

[0139] S1: Initialization and Parameter Setting

[0140] The user inputs the type, weight, fiber composition, and testing standards of the fabric to be tested through the user interaction module, selects the loading mode (constant speed stretching, cyclic loading, or stepped loading), and sets the safety threshold and abnormal alarm parameters. The main control module sends the parameters to the intelligent evaluation unit and the detection execution unit, and wakes up the multi-dimensional sensing unit to enter standby mode.

[0141] S2: Automatic clamping and adaptive voltage regulation

[0142] The conveyor belt of the conveyor assembly 2 moves the clamping component to the fabric feeding station; one end of the fabric is attached to the outer surface of the clamping plate 32, and the hydraulic cylinder 4 in the upper drive mechanism is activated, pushing the drive gear 42 on the drive motor 41 to approach and mesh with the start drive gear ring 34, thus starting the drive motor 41. The drive motor 41 drives the drive gear ring 34 to rotate through the rotation of the drive gear 42, which in turn drives the lifting screw 35 to rise, pushing the lifting frame 33 to rise on the mounting frame 31, and then moving the clamping plate 3. 6. After rising along the track shape of the limiting track 38 via the roller 39, the moving clamping plate 36 approaches the fixed clamping plate 32, bringing one end of the fabric into contact with the fabric through the convex texture, and placing the lower end of the fabric on the fixed clamping plate 32 below. The movement of the lower moving clamping plate 36 completes the clamping of both ends of the fabric. The clamping pressure adaptive module dynamically adjusts the clamping pressure based on the feedback from the pressure sensor 37 integrated in the moving clamping plate 36, combined with the fabric thickness measured in real time by the laser thickness sensor 63. This ensures no slippage or pinching, and stores the actual clamping force data in a temporary cache.

[0143] S3: Transport to the testing station

[0144] The conveyor assembly 2 moves the clamping component holding the fabric along the slide rail to directly below the detection device. Once it is confirmed to be in place, the conveyor belt stops. At this time, the environmental compensation module records the current temperature, humidity and air pressure data for subsequent correction.

[0145] S4: Tensile loading and synchronous acquisition of multi-source data

[0146] The lifting hydraulic cylinder 51 drives the clamping jaw 52 to descend and clamp the handle 53 located on the upper mounting frame 31; according to the preset loading mode, the multi-dimensional loading control module controls the lifting hydraulic cylinder 51 to rise at a set speed or acceleration to stretch the fabric.

[0147] At the same time, the multi-point synchronous triggering module uses the displacement increment fed back by the laser displacement sensor array 61 as the trigger signal to synchronously start the following acquisition tasks:

[0148] Two hyperspectral industrial cameras 6 continuously acquire surface spectral image sequences from both sides of the fabric; a laser displacement sensor array 61 monitors the displacement of multiple feature points on the fabric surface and the mounting bracket 31 in real time, generating multi-point strain data; an acoustic emission sensor 62 acquires acoustic emission signals generated by fiber breakage and slippage during the stretching process; a laser thickness sensor 63 continuously measures the change in fabric thickness; all data have a unified timestamp and are aligned in real time by a multi-source data fusion processor.

[0149] S5: Dynamic Analysis and Feature Extraction

[0150] The dynamic analysis unit processes the synchronized multi-source data: the strain field reconstruction module reconstructs the strain distribution across the entire fabric field based on digital image correlation algorithms and multi-point displacement data, identifying areas of concentrated strain; the damage evolution analysis module integrates the intensity and frequency characteristics of acoustic emission signals with changes in fiber orientation in hyperspectral images to analyze the initiation, accumulation, and expansion processes of microscopic damage; and the dynamic response feature extraction module extracts the instantaneous elastic modulus and damping ratio from the displacement-time curve and acoustic emission waveform. Stress relaxation rate Dynamic features, etc.

[0151] S6: Intelligent Assessment and Quality Scoring

[0152] The intelligent evaluation unit utilizes a multi-scale elastic model to correlate macroscopic displacement, microstructural evolution, and acoustic emission characteristics, establishing an elastic constitutive relationship from fiber to sample to predict the fabric's nonlinear elastic behavior, anisotropy, and fracture toughness. The adaptive algorithm library automatically matches the optimal evaluation algorithm based on the fabric type identified by the hyperspectral image and performs Bayesian optimization using historical data. The comprehensive quality evaluation module integrates macroscopic elastic parameters (elastic modulus, etc.). Elongation at break Elastic recovery rate Microstructure stability index (fiber orientation change rate) Damage Cumulative Index and dynamic response characteristics (damping ratio) Stress relaxation time ), outputting multi-dimensional quality scores.

[0153] S7: Decision Output and Hierarchical Sorting

[0154] The decision output module will score the quality. The system compares the data with preset process standards, generates test results (such as Grade A, Grade B, or Unqualified), and sends instructions to the downstream sorting mechanism to achieve automatic grading. The user interaction module displays strain cloud diagrams, damage evolution curves, elastic parameters, and the final report in real time. All test data is automatically stored in the database, supporting traceability and statistical analysis.

[0155] S8: Reset and Self-Check for Abnormalities

[0156] After the stretching is completed, the clamping jaws 52 release the handle 53, and the lifting hydraulic cylinder 51 resets; the conveying assembly 2 moves the clamping parts back to the loading station to prepare for the next inspection; throughout the process, the abnormal self-diagnosis module continuously monitors the status of each sensor and the rationality of the data. Once the data is found to be out of tolerance or the equipment is faulty, the process is immediately suspended and an audible and visual alarm is issued to prompt manual intervention.

[0157] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A nonwoven fabric elasticity detection device comprising a frame (1), characterized in that: The upper surface of the frame (1) is provided with a conveying device and a detection device; The conveying device includes a conveying component (2), a clamping component, and a driving mechanism. The conveying component (2) drives the clamping component to move. The clamping component includes a fixed clamping plate (32) and a movable clamping plate (36). After the movable clamping plate (36) moves, it clamps the fabric. The driving mechanism drives the movable clamping plate (36) to move. The clamping component also includes a movable slider (3), and mounting brackets (31) are provided on the outer surfaces of both the upper movable slider (3) and the lower movable slider (3). The outer surface of the mounting bracket (31) is fixedly installed on the outer surface of the fixed clamping plate (32). A lifting bracket (33) is slidably inserted into the outer surface of the mounting bracket (31). A drive gear ring (34) is rotatably connected to the lower surface of the mounting bracket (31) through a bearing. A lifting screw (35) is threadedly connected to the inner wall of the drive gear ring (34). One end of the lifting screw (35) is fixedly installed on the lower surface of the lifting bracket (33). The upper surface of the lifting bracket (33) is slidably connected to the lower surface of the movable clamping plate (36) through a guide rail. A pressure sensor (37) is installed inside the movable clamping plate (36). A limit rail (38) is fixedly installed on the outer surface of the mounting bracket (31). A roller (39) is rotatably connected to the outer surface of the movable clamping plate (36). The outer surface of the roller (39) is tumbledly connected to the inner wall of the limit rail (38). A push hydraulic cylinder (4) is fixedly installed on the outer surface of the frame (1), and a drive motor (41) is slidably connected to the outer surface of the frame (1). The outer surface of the drive motor (41) is fixedly installed with one end of the piston rod of the push hydraulic cylinder (4), and a drive gear (42) is fixedly installed on one end of the output shaft of the drive motor (41). The drive gear (42) meshes with the drive gear ring (34). The detection device includes a clamping jaw (52) for clamping the clamping component; The detection device also includes a support frame (5), on the outer surface of which a lifting hydraulic cylinder (51) is fixedly installed. One end of the piston rod of the lifting hydraulic cylinder (51) is fixedly installed with one end of the clamping claw (52). A handle (53) is fixedly installed on the upper surface of the mounting frame (31) above. After the clamping claw (52) clamps the handle (53), it drives the mounting frame (31) to rise. It also includes a detection system, which comprises a main control module, a multi-dimensional perception unit, a dynamic analysis unit, an intelligent evaluation unit, a detection execution unit, and a user interaction module; The multidimensional sensing unit includes a visual acquisition module, a deformation tracking module, an acoustic monitoring module, a thickness detection module, and an environmental compensation module.

2. The nonwoven fabric elasticity testing device according to claim 1, characterized in that: The conveying assembly (2) is installed on the outer surface of the frame (1), and the conveying assembly (2) consists of a conveyor belt assembly and a slide rail.

3. The nonwoven fabric elasticity testing device according to claim 2, characterized in that: The outer surface of the movable slider (3) is slidably inserted into the inner wall of the slide rail of the conveying assembly (2). The outer surface of the movable slider (3) is fixedly installed to the outer surface of the conveyor belt of the conveying assembly (2) through a connector. The lower surface of the upper mounting bracket (31) is slidably inserted into the upper surface of the upper movable slider (3) through a plug rod. The lower mounting bracket (31) is fixedly installed on the outer surface of the lower movable slider (3).

4. The nonwoven fabric elasticity testing device according to claim 3, characterized in that: The multi-dimensional sensing unit includes a visual acquisition module, a deformation tracking module, an acoustic monitoring module, a thickness detection module, and an environmental compensation module; The vision acquisition module consists of two hyperspectral industrial cameras (6) fixedly installed on the frame (1). The two hyperspectral industrial cameras (6) are located on both sides of the fabric and are used to acquire the surface spectral image sequence of the fabric during the stretching process to capture the changes in fiber orientation and the evolution of microstructure. The deformation tracking module includes multiple laser displacement sensor arrays (61), which are fixedly installed on the side of the frame (1) and the outer surface of the support frame (5), respectively. Their detection ends are respectively aligned with different areas of the fabric surface and the upper surface of the mounting frame (31) to collect multi-point strain distribution of the fabric and upward displacement data of the mounting frame (31) in real time. The acoustic monitoring module includes an acoustic emission sensor (62), which is installed on the outer surface of the fixed clamping plate (32) and attached to the vicinity of the fabric clamping area, for collecting acoustic emission signals generated by fiber breakage and slippage during fabric stretching; The thickness detection module includes a laser thickness sensor (63), which is fixedly installed on the outer surface of the frame (1) and is used to non-contactly measure the thickness change of the fabric before and during stretching. The environmental compensation module includes a temperature and humidity sensor (64) and a pressure sensor (65), which are installed on the outer surface of the frame (1) to collect temperature, humidity and pressure data of the detection environment as the basis for compensation for elastic parameter correction.

5. The nonwoven fabric elasticity testing device according to claim 4, characterized in that: The dynamic analysis unit includes a multi-source data fusion processor, a strain field reconstruction module, a damage evolution analysis module, and a dynamic response feature extraction module. The multi-source data fusion processor is used to receive and synchronously process hyperspectral image data, multi-point displacement data, acoustic emission signals, thickness change data and environmental compensation data from the multi-dimensional sensing unit to establish a spatiotemporally aligned multidimensional dataset. The strain field reconstruction module reconstructs the full-field strain distribution of the fabric during the entire stretching process based on digital image correlation algorithms and multi-point displacement data, and identifies strain concentration areas and deformation non-uniformity. The damage evolution analysis module combines the intensity and frequency characteristics of acoustic emission signals with changes in fiber orientation in hyperspectral images to analyze the initiation, accumulation, and expansion process of microscopic damage inside the fabric in real time. The dynamic response feature extraction module extracts dynamic elastic response features from displacement-time curves and acoustic emission waveforms, including instantaneous elastic modulus, damping characteristics, and stress relaxation rate.

6. The nonwoven fabric elasticity testing device according to claim 5, characterized in that: The intelligent evaluation unit includes a multi-scale elasticity model, an adaptive algorithm library, a comprehensive quality evaluation module, and a decision output module. The multi-scale elastic model is based on the fusion of deep learning and physical mechanism modeling. It correlates macroscopic displacement data, microscopic structural evolution and acoustic emission characteristics to establish elastic constitutive relations from fiber scale to sample scale, and predicts the nonlinear elastic behavior, anisotropic characteristics and fracture toughness of the fabric. The adaptive algorithm library stores elasticity evaluation algorithms applicable to fabrics with different weights and fiber compositions. It can automatically select the optimal algorithm combination based on the fabric type identified by hyperspectral image and perform Bayesian optimization using historical detection data. The comprehensive quality evaluation module integrates macroscopic elastic parameters, microstructural stability indicators, damage accumulation index, and dynamic response characteristics to output a multidimensional quality score. Macroscopic elastic parameters include elastic modulus, elongation at break, and elastic recovery rate; microstructural stability indicators include fiber orientation change rate and damage accumulation index; and dynamic response characteristics include damping ratio and stress relaxation time. The decision output module generates test conclusions based on the comprehensive evaluation results and preset process standards, and issues grading instructions to the sorting mechanism.

7. The nonwoven fabric elasticity testing device according to claim 6, characterized in that: The detection execution unit includes a multi-dimensional loading control module, a clamping pressure adaptive module, a multi-point synchronous triggering module, and an abnormal self-diagnosis module; The multi-dimensional loading control module is electrically connected to the lifting hydraulic cylinder (51), the pushing hydraulic cylinder (4) and the drive motor (41). It not only controls the stretching speed, but also realizes multiple loading modes such as cyclic loading and stepped loading to simulate the stress situation of the fabric in actual use. The clamping pressure adaptive module is electrically connected to the pressure sensor (37) inside the movable clamping plate (36). It automatically adjusts the clamping pressure according to the real-time detected fabric thickness and clamping status to prevent slippage or injury and ensure detection repeatability. The multi-point synchronous triggering module is electrically connected to the laser displacement sensor array (61), the hyperspectral industrial camera (6) and the acoustic emission sensor (62). It triggers the synchronous acquisition of multi-source data based on the displacement increment fed back by the displacement sensor to ensure the consistency of spatiotemporal resolution. The anomaly self-diagnosis module monitors the working status and data rationality of each sensor in real time. When an abnormal data or equipment failure is detected, it automatically pauses the detection and issues an alarm message to prompt the operator to check. The user interaction module includes a local / remote monitoring terminal, a parameter setting and strategy selection interface, and a data management and report generation module. The local / remote monitoring terminal is used to display images captured by the industrial camera, displacement-time curves, elastic parameter calculation results, and system operating status in real time, and to issue warnings when data exceeds limits. The parameter setting and strategy selection interface allows users to input fabric type, thickness, and testing standards, and select the testing mode; The data management and report generation module automatically stores the raw data and results of each test and generates a test report that includes elasticity curves, feature values, and test conclusions.

8. A method for testing the elasticity of nonwoven fabrics using a nonwoven fabric elasticity testing device as described in claim 7, characterized in that: S1: Initialization and parameter settings: The user inputs the type, weight, fiber composition, and testing standards of the fabric to be tested through the user interaction module, selects the loading mode (constant speed stretching, cyclic loading, or stepped loading), and sets the safety threshold and abnormal alarm parameters. The main control module sends the parameters to the intelligent evaluation unit and the detection execution unit, and wakes up the multi-dimensional sensing unit to enter standby mode. S2: Automatic clamping and adaptive voltage regulation: The conveyor belt of the conveyor assembly (2) drives the clamping component to move to the fabric feeding station; one end of the fabric is attached to the outer surface of the fixed clamping plate (32), the hydraulic cylinder (4) in the upper drive mechanism is started, the drive gear (42) on the drive motor (41) is pushed close to the start drive gear ring (34) and meshes with it, the drive motor (41) is started, the drive motor (41) drives the drive gear ring (34) to rotate through the rotation of the drive gear (42), and then drives the lifting screw (35) to rise, pushing the lifting frame (33) to rise on the mounting frame (31), and then the moving clamping plate (36) moves through the rollers (39) After rising along the track shape of the limiting track (38), the moving clamping plate (36) moves close to the fixed clamping plate (32), and one end of the fabric is brought into contact with the fabric through the convex texture. The lower end of the fabric is placed on the fixed clamping plate (32) below. By moving the moving clamping plate (36) below, the two ends of the fabric are clamped. The clamping pressure adaptive module dynamically adjusts the clamping pressure according to the feedback from the pressure sensor (37) integrated in the moving clamping plate (36) and the fabric thickness measured in real time by the laser thickness sensor (63) to ensure no slippage and no pinching. The actual clamping force data is stored in the temporary cache. S3: Transport to the testing station: The conveyor assembly (2) moves the clamping component holding the fabric along the slide rail to directly below the detection device. After confirming that it is in place, the conveyor belt stops. At this time, the environmental compensation module records the temperature, humidity and air pressure data of the current environment for subsequent correction. S4: Tensile loading and synchronous acquisition of multi-source data: The lifting hydraulic cylinder (51) drives the clamping jaws (52) to descend and clamp the handle (53) located on the upper mounting frame (31); according to the preset loading mode, the multi-dimensional loading control module controls the lifting hydraulic cylinder (51) to rise at a set speed or acceleration to stretch the fabric; at the same time, the multi-point synchronous triggering module uses the displacement increment fed back by the laser displacement sensor array (61) as the trigger signal to synchronously start the following acquisition tasks: Two hyperspectral industrial cameras (6) continuously acquire surface spectral image sequences on both sides of the fabric; a laser displacement sensor array (61) monitors the displacement of multiple feature points on the fabric surface and the mounting bracket (31) in real time, generating multi-point strain data; an acoustic emission sensor (62) acquires acoustic emission signals generated by fiber breakage and slippage during the stretching process; a laser thickness sensor (63) continuously measures the change in fabric thickness; all data have a unified timestamp and are aligned in real time by a multi-source data fusion processor. S5: Dynamic Analysis and Feature Extraction The dynamic analysis unit processes the synchronized multi-source data: The strain field reconstruction module reconstructs the strain distribution of the entire fabric field based on digital image correlation algorithms and multi-point displacement data, and identifies strain concentration areas; the damage evolution analysis module integrates the intensity and frequency characteristics of acoustic emission signals with changes in fiber orientation in hyperspectral images to analyze the initiation, accumulation, and expansion process of micro-damage; the dynamic response feature extraction module extracts instantaneous elastic modulus, damping characteristics, and stress relaxation rate dynamic features from displacement-time curves and acoustic emission waveforms. S6: Intelligent Assessment and Quality Scoring The intelligent evaluation unit calls a multi-scale elastic model to correlate macroscopic displacement, microstructure evolution and acoustic emission characteristics, establish an elastic constitutive relationship from fiber to sample, and predict the nonlinear elastic behavior, anisotropy and fracture toughness of the fabric. The adaptive algorithm library automatically matches the optimal evaluation algorithm based on the fabric type identified by the hyperspectral image and performs Bayesian optimization by combining historical data; the comprehensive quality evaluation module integrates macroscopic elastic parameters, microstructural stability indicators, and dynamic response characteristics to output a multidimensional quality score; among which, macroscopic elastic parameters include elastic modulus, elongation at break, and elastic recovery rate, microstructural stability indicators include fiber orientation change rate and damage accumulation index, and dynamic response characteristics include damping ratio and stress relaxation time; S7: Decision Output and Hierarchical Sorting: The decision output module compares the quality score with the preset process standard, generates the test conclusion: Grade A, Grade B, or unqualified, and sends the instruction to the downstream sorting mechanism to achieve automatic grading; the user interaction module displays the strain cloud map, damage evolution curve, elastic parameters and final report in real time, and all test data are automatically stored in the database, supporting traceability and statistical analysis; S8: Reset and self-check for abnormalities: After the stretching is completed, the clamping jaws (52) release the handle (53), and the lifting hydraulic cylinder (51) resets; the conveying component (2) moves the clamping parts back to the loading station to prepare for the next test; throughout the process, the abnormal self-diagnosis module continuously monitors the status of each sensor and the rationality of the data. Once the data is found to be out of tolerance or the equipment is faulty, the process is immediately suspended and an audible and visual alarm is issued to prompt manual intervention.

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