Method for detecting performance of pearl wool product for packaging

Abstract

The invention relates to the technical field of material defect detection, in particular to a method for detecting the performance of a pearl wool product for packaging, which comprises the following steps: dropwise adding a fluorescent solution at a fluorescent mark point on the upper surface of a to-be-detected sample of the pearl wool product; acquiring a surface image of the lower surface of the to-be-detected sample in the process of applying the tensile force, and determining tension influence characteristic pixel points on the fluorescence display image; constructing a plurality of spatial fracture recognition vectors by acquiring coordinates of each fluorescent mark point and the corresponding tension dominant feature point, and judging whether the support structure of the to-be-detected sample has defects or not according to the first image recognition quantity; and determining a local defect area of the to-be-detected sample through a directivity analysis result of the space fracture recognition vector based on a judgment result that the second image recognition quantity meets a preset recognition condition. Furthermore, real-time monitoring of the dynamic fracture phenomenon of the internal non-crosslinked support structure in the tensile stress process is realized, and a local area with performance defects is accurately positioned.

Description

Technical Field

[0001] This invention relates to the field of material defect detection technology, and in particular to a method for testing the performance of pearl cotton products used in packaging. Background Technology

[0002] EPE foam, or polyethylene foam, is an environmentally friendly cushioning packaging material with a non-crosslinked closed-cell structure. Due to its excellent properties such as lightness, softness, cushioning and shock absorption, heat insulation, moisture resistance, and recyclability, it is widely used in the transport packaging of electronic products, precision instruments, glass and ceramics, and fresh food cold chain products. The uniformity of its internal non-crosslinked support structure and its structural stability under tensile stress are the core indicators determining the cushioning and protective performance of EPE foam products, directly affecting the safety of the packaged products during transportation and handling. Currently, performance testing of EPE foam products mainly focuses on mechanical property testing, primarily measuring tensile strength, elongation at break, resilience, and compression set. This type of testing only obtains the overall mechanical property parameters of the EPE foam product and cannot capture the uneven deformation of the internal non-crosslinked closed-cell structure during tensile stress. Using EPE foam products with localized structural defects can lead to packaging protection failure and damage to the packaged products.

[0003] For example, Chinese invention patent CN116046783A, published on May 2, 2023, discloses a three-dimensional imaging detection method for defects in fiber-reinforced polymer composite materials. The method involves immersing the fiber-reinforced polymer composite material in a fluorescent nanoparticle dispersion with a concentration of 0.25 mg / mL to 5 mg / mL to obtain a fiber-reinforced polymer composite material with defects selectively labeled by the fluorescent nanoparticles. After cleaning and drying, the composite material is placed under a microscope. The laser wavelength is adjusted to the position of the fluorescent nanoparticle absorption peak, and the composite material is moved along the laser propagation direction to scan and obtain optical slices at different thicknesses. Defect structure information is extracted from the optical slices, and the defect structure is reconstructed in three dimensions to obtain a three-dimensional image of the fiber-reinforced polymer composite material defect. Optical slices are then made along any plane in space from the three-dimensional image of the fiber-reinforced polymer composite material defect to analyze the three-dimensional structure of the defect.

[0004] Existing technologies cannot monitor the dynamic fracture phenomenon of internal non-crosslinked support structures in real time during tensile stress, cannot quantitatively characterize structural uniformity defects, and cannot accurately locate local areas with performance defects. Summary of the Invention

[0005] To address these issues, the present invention provides a method for testing the performance of pearl cotton products for packaging, thereby overcoming the problems of existing technologies that cannot monitor the dynamic fracture behavior of the internal non-crosslinked support structure in real time during tensile stress, cannot quantify structural uniformity defects, and cannot accurately locate local areas with performance defects.

[0006] To achieve the above objectives, the present invention provides a method for testing the performance of pearl cotton products for packaging, comprising: A sample of pearl cotton product to be tested is obtained, several fluorescent marking points are marked on the upper surface of the sample to be tested, and a predetermined amount of fluorescent solution is dropped at each of the fluorescent marking points. A tensile force is applied in a direction parallel to the surface of the sample to be tested, and several frames of surface images are continuously acquired on the lower surface of the sample to be tested. The fluorescence display image of each fluorescent marker point is determined according to the time sequence relationship of the fluorescence signal, and the tension-affected feature pixels are determined on the fluorescence display image. The coordinates of each fluorescent marker point and the corresponding tension-manifested feature point are obtained. Several spatial fracture identification vectors are constructed on the fluorescence imaging image. The first image recognition value is determined according to the magnitude of the spatial fracture identification vector to determine whether there are defects in the support structure of the sample under test. In response to the determination that the supporting structure of the sample under inspection has defects, the second image recognition value is determined based on the vector angle between any two spatial fracture recognition vectors; Based on the determination result that the second image recognition quantity meets the preset recognition conditions, the local defect area of ​​the sample to be inspected is determined according to the directional analysis result of the spatial fracture recognition vector.

[0007] Furthermore, the process of labeling several fluorescent points and adding fluorescent solution includes: In a Cartesian coordinate system parallel to the upper surface of the sample to be tested, several fluorescent markers are calibrated at equal intervals along the direction of tensile force application. Record the first coordinate of each fluorescent marker point in the Cartesian coordinate system. The drop points of the fluorescent solution on the surface of the sample to be tested are controlled to coincide with the first coordinates of each fluorescent marker point.

[0008] Furthermore, the process of determining the fluorescence imaging image of each fluorescent marker includes: Taking the start time of the application of tensile force as the zero point of time, surface images of the lower surface of the sample to be tested are continuously acquired at preset time intervals to form a sequence of surface image frames ordered by time sequence. Fluorescence signal identification is performed on each surface image in the surface image frame sequence, and the surface image frame in which the fluorescence signal is first identified within the identification range corresponding to the fluorescence marker is determined as the fluorescence display image corresponding to the fluorescence marker.

[0009] Furthermore, the process of determining the effect of tension on feature pixels includes: Within the fluorescence display image corresponding to each fluorescent marker point, determine the pixel point corresponding to the location where the fluorescence signal is first identified; The pixel is identified as the tension-dominant feature point corresponding to the fluorescent marker point.

[0010] Furthermore, the process of constructing several spatial fracture identification vectors includes: Determine the second coordinates of the tension dominant characteristic points corresponding to each fluorescent marker point in the Cartesian coordinate system; Several spatial fracture identification vectors are constructed using the first coordinate as the starting point of the vector and the second coordinate as the ending point of the vector.

[0011] Furthermore, the process of determining whether the support structure of the sample under inspection has defects based on the first image recognition value includes: Calculate the arithmetic mean of the magnitudes of all spatial fracture recognition vectors, and determine the arithmetic mean as the first image recognition value; The first image recognition value is compared with a preset first reference value; If the first image recognition value is greater than the first reference value, it is determined that the support structure of the sample under test has a defect.

[0012] Furthermore, the process of determining the second image recognition value includes: Calculate the vector angle between any two spatial fracture identification vectors, and determine the angle dataset composed of all vector angle values; Calculate the standard deviation of the included angle values ​​of the vectors within the included angle dataset, and determine the standard deviation as the second image recognition value.

[0013] Furthermore, the preset recognition condition is that the second image recognition value is less than or equal to a preset second reference value.

[0014] Furthermore, the process of determining the directional analysis results of the spatial fracture identification vector includes: Using the coordinate axes of a Cartesian coordinate system as a reference, determine the vector pointing angles of all spatial fracture identification vectors, and thus determine the angle range of the vector pointing angles.

[0015] Furthermore, the process of identifying local defect areas in the sample to be inspected includes: Determine the boundary lines of the two interval boundaries of the stated angle interval in the Cartesian coordinate system; The area where the spatial break identification vector between the two boundary lines falls is determined as the local defect area of ​​the sample to be inspected.

[0016] The beneficial effects of the technical solution shown in this application include: marking several fluorescent markers on the upper surface of the sample to be tested of pearl cotton products, and adding fluorescent solution to each marker; continuously acquiring several frames of surface images of the lower surface of the sample to be tested by applying tensile force, and determining tension-affected feature pixels on the fluorescent display images; constructing several spatial fracture identification vectors by acquiring the coordinates of each fluorescent marker and the corresponding tension-indicating feature points, and determining whether there are defects in the support structure of the sample to be tested based on the first image recognition value; and determining the local defect area of ​​the sample to be tested based on the determination result that the second image recognition value meets the preset recognition conditions through the directional analysis result of the spatial fracture identification vector. Furthermore, real-time monitoring of the dynamic fracture phenomenon of the internal non-crosslinked support structure during tensile stress is achieved, and the local area with performance defects is accurately located.

[0017] Furthermore, this invention acquires time-series images of the lower surface of pearl cotton products during the tensile stress process and accurately identifies fluorescence signals. It captures key image frames of the first fluorescence signal appearing on the lower surface of the product after the fluorescent solution leaks due to the fracture of the internal support structure of the pearl cotton. This allows the identification of the first time point and spatial location of the fluorescent solution penetrating from the upper surface to the lower surface of the product, thus realizing real-time monitoring of the dynamic fracture phenomenon of the internal non-crosslinked support structure during the tensile stress process.

[0018] Furthermore, relying on a unified Cartesian coordinate system, this invention transforms the leakage and displacement of fluorescent solution caused by the fracture of the internal support structure after the EPE foam product is subjected to tensile stress into quantifiable vector geometric features. By using vector geometric features, the leakage and displacement distance of fluorescent solution after the fracture of the internal support structure of EPE foam is transformed into the magnitude of a vector, and the direction of leakage and displacement is transformed into the direction of a vector, thus realizing the quantitative characterization of the fracture and displacement characteristics of the internal structure of EPE foam.

[0019] Furthermore, the modulus of the spatial fracture identification vector in this invention corresponds to the spatial straight-line distance between the first coordinate of the fluorescent marker point and the second coordinate of the tension dominant feature point. This distance directly quantifies the degree of leakage shift of the fluorescent solution at a single detection point due to the internal structural fracture. The larger the modulus, the more obvious the leakage shift after the structural fracture at that location, and the worse the uniformity of the support structure. Thus, it enables rapid determination of the overall defects of the support structure of pearl cotton products.

[0020] Furthermore, this invention relies on a unified Cartesian coordinate system benchmark to convert the pointing characteristics of each spatial fracture identification vector into quantifiable angle information. By integrating the pointing angles of all vectors, a unified angle range is defined, and this angle range is then mapped to a physical region within the coordinate system. This allows for the identification of concentrated distribution areas of defects in the internal support structure of pearl cotton products, enabling precise location of localized areas with performance defects. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the steps of a method for testing the performance of pearl cotton products for packaging according to an embodiment of the present invention; Figure 2 This is a diagram illustrating the steps of labeling several fluorescent markers and adding fluorescent solution in an embodiment of the present invention. Figure 3 This is a step diagram illustrating the process of determining whether the support structure of a sample under test has defects, according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the logic of determining whether the support structure of a sample under test has defects, according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0025] It should be understood that although the terms "first," "second," etc., may be used in this invention to describe various types of information, these information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information.

[0026] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Please see Figure 1The diagram illustrates the steps of a method for testing the performance of EPE foam products for packaging according to an embodiment of the present invention. The method includes: Step S100: Obtain a sample of pearl cotton product to be tested, mark a number of fluorescent markers on the upper surface of the sample to be tested, and add a predetermined amount of fluorescent solution to each of the fluorescent markers. In this invention, to ensure that when the internal support structure of the pearl cotton undergoes micro-cracks under tensile force, the fluorescent solution can penetrate downwards to the lower surface along the cracks under gravity and capillary action, and will not cause overall breakage due to excessive thinness or prevent the fluorescent solution from penetrating due to excessive thickness; optionally, the thickness of the pearl cotton product to be tested is 10mm.

[0028] In this invention, the fluorescent solution is an aqueous solution of a water-soluble fluorescent dye. Preferably, in this embodiment of the invention, the fluorescent solution is an aqueous solution of sodium fluorescein, the mass concentration of the fluorescent solution is 1.5 wt%, and the quantitative volume of the fluorescent solution added to each fluorescent marker is 10 μL.

[0029] Step S200: Apply tensile force in a direction parallel to the surface of the sample to be tested, continuously acquire several frames of surface images of the lower surface of the sample to be tested, determine the fluorescence display image of each fluorescent marker point according to the time sequence relationship of the fluorescence signal, and determine the tension-affected feature pixel points on the fluorescence display image. This invention uses a clamp to clamp the surface of the pearl cotton product, and then controls the clamp to stretch the sample to be tested at a uniform speed of 15 mm / min. The clamping and stretching control of the sample to be tested is a common tensile testing method. The specific device structure will not be described in detail here. The moment when the applied tensile force is terminated is when the fluorescent markers of the sample to be tested have found the corresponding tension-affected feature pixels.

[0030] Step S300: Obtain the coordinates of each fluorescent marker point and the corresponding tension-manifested feature point, construct several spatial fracture identification vectors on the fluorescence display image, and determine the first image recognition quantity based on the magnitude of the spatial fracture identification vectors to determine whether there are defects in the support structure of the sample under test. Step S400: In response to the determination result that there is a defect in the support structure of the sample to be inspected, the second image recognition quantity is determined based on the vector angle between any two spatial fracture recognition vectors. Step S500: Based on the determination result that the second image recognition quantity meets the preset recognition conditions, the local defect area of ​​the sample to be inspected is determined according to the directional analysis result of the spatial fracture recognition vector.

[0031] In this invention, after identifying a specific local defect area, manual sampling can be performed directly within that area, avoiding aimless random sampling. This ensures that the collected samples are all from core areas with structural inhomogeneity defects. After slicing and preparing the samples, the size of the closed-cell units, the pore wall thickness, and the porosity distribution are observed under a microscope. The uniformity of raw material mixing is detected through component analysis, thereby accurately identifying the specific causes of the uneven internal structure of the pearl cotton, such as inconsistent closed-cell sizes due to uneven temperature / pressure during the foaming process, differences in pore wall strength due to imbalanced raw material ratios, and local microcracks caused by stress concentration during the molding process. This enables refined control of the quality of pearl cotton products and improves the stability of product quality.

[0032] Specifically, please refer to Figure 2 The diagram illustrates the steps of calibrating several fluorescent markers and adding fluorescent solution according to an embodiment of the present invention. The process of calibrating several fluorescent markers and adding fluorescent solution includes: Step S101: In a Cartesian coordinate system parallel to the upper surface of the sample to be tested, mark a number of fluorescent markers at equal intervals along the direction of tensile force application. Step S102: Record the first coordinates of each fluorescent marker point in the Cartesian coordinate system. Step S103: Control the drop point of the fluorescent solution on the surface of the sample to be tested to coincide with the first coordinate of each fluorescent marker point.

[0033] In this invention, the spacing between adjacent fluorescent markers is 5cm-10cm, and optionally, the spacing between adjacent fluorescent markers is 10cm; the fluorescent markers are circular markers with a diameter of 3mm.

[0034] For example, a Cartesian coordinate system is established with the lower left corner of the sample to be tested as the origin, the positive X-axis as the direction parallel to the direction of the tensile force, and the positive Y-axis as the direction perpendicular to the direction of the tensile force and parallel to the surface of the pearl cotton. The coordinate unit of this Cartesian coordinate system is mm.

[0035] Understandably, by establishing a standardized planar coordinate benchmark, the precise spatial positioning of fluorescent markers and the location of fluorescent solution droplets can be achieved, providing a unified and traceable coordinate reference system for spatial tracing of the fluorescent solution leakage path during subsequent tensile stress. This equidistant calibration method allows the markers to form a uniform distribution of detection points along the direction of tensile force, comprehensively capturing the structural fracture characteristics of the pearl cotton product at different locations along this stress direction. Simultaneously, it records the first coordinate of each fluorescent marker within this coordinate system, achieving digital quantification of the spatial position of each marker. Furthermore, it controls the coincidence of the droplet point of the fluorescent solution on the sample surface with the first coordinate of each fluorescent marker, ensuring that the initial placement position of the fluorescent solution matches the coordinate position of the marker.

[0036] Specifically, the process of determining the fluorescence display image of each fluorescent marker includes: Taking the start time of the application of tensile force as the zero point of time, surface images of the lower surface of the sample to be tested are continuously acquired at preset time intervals to form a sequence of surface image frames ordered by time sequence. Fluorescence signal identification is performed on each surface image in the surface image frame sequence, and the surface image frame in which the fluorescence signal is first identified within the identification range corresponding to the fluorescence marker is determined as the fluorescence display image corresponding to the fluorescence marker.

[0037] In this invention, the first coordinate of each fluorescent marker point is taken as the center, and a circular area with a radius of 5cm is taken as the fluorescence signal recognition range of the fluorescent marker point.

[0038] In this invention, if the gray value of a pixel within the recognition range is greater than 200, it is determined that a fluorescent signal has been detected.

[0039] In this invention, a fluorescence imaging camera is used to acquire images of the lower surface of the sample under test; the preset time interval for continuous acquisition is 1 second; during the image acquisition process, an ultraviolet light source with a wavelength of 365nm-405nm is used to uniformly illuminate the lower surface of the sample to ensure accurate identification of the fluorescence signal.

[0040] Understandably, by acquiring time-series images of the lower surface of the pearl cotton product under tensile stress and accurately identifying the fluorescence signal, the key image frame that first shows a fluorescence signal on the lower surface of the product after the fluorescent solution leaks due to the fracture of the internal support structure of the pearl cotton can be captured, thereby locking in the first time point and spatial location of the fluorescent solution penetrating from the upper surface to the lower surface of the product.

[0041] The time-series image acquisition method in this invention can completely record the dynamic change of the fluorescence signal on the lower surface of the pearl cotton from zero to the presence of fluorescence during the tensile stress process. Subsequently, fluorescence signal identification is performed on each frame in the surface image frame sequence, and signal detection is only performed within the exclusive identification range corresponding to each fluorescent marker point. This can effectively avoid cross-interference of fluorescence signals from different fluorescent marker points, improve the targeting and accuracy of signal identification, and finally determine the surface image frame in which the fluorescence signal is first identified within the identification range corresponding to each fluorescent marker point as the fluorescence display image of that marker point. This image frame serves as the first visual record of the fluorescent solution penetrating to the lower surface, and the location of the fluorescence signal contained therein is the first point where the fluorescent solution leaks after the internal structure of the pearl cotton breaks under the tensile force.

[0042] Specifically, the process of determining the effect of tension on feature pixels includes: Within the fluorescence display image corresponding to each fluorescent marker point, determine the pixel point corresponding to the location where the fluorescence signal is first identified; The pixel is identified as the tension-dominant feature point corresponding to the fluorescent marker point.

[0043] Understandably, this invention precisely pinpoints the pixel-level spatial location where the fluorescent solution first penetrates to the lower surface of the sample and displays a fluorescent signal after the internal support structure of the pearl cotton breaks due to tensile force. It performs refined pixel identification on the fluorescence display image corresponding to each fluorescent marker point. The fluorescent signal pixel point directly corresponds to the first location where the fluorescent solution penetrates from the upper surface of the pearl cotton through the internal fracture to the lower surface. This pixel point is identified as the tension-manifested feature point corresponding to the fluorescent marker point. This allows for the capture of the initial location of fluorescent solution leakage caused by the breakage of the internal support structure of the pearl cotton under tensile tension with pixel-level precision, accurately reflecting the actual situation of the tensile breakage of the corresponding support structure within the pearl cotton.

[0044] Specifically, the process of constructing several spatial fracture identification vectors includes: Determine the second coordinates of the tension dominant characteristic points corresponding to each fluorescent marker point in the Cartesian coordinate system; Several spatial fracture identification vectors are constructed using the first coordinate as the starting point of the vector and the second coordinate as the ending point of the vector.

[0045] This invention relies on a unified Cartesian coordinate system to transform the leakage and displacement of fluorescent solution caused by the fracture of the internal support structure after the EPE foam product is subjected to tensile stress. The internal non-crosslinked closed-cell structure is dynamically transformed into quantifiable vector geometric features. By accurately representing the spatial correlation between the initial drop position and the leakage position of the fluorescent solution through the start and end points of the vector, a quantitative description of the direction and distance of the fracture displacement of the internal structure of EPE foam is achieved. By using vector geometric features, the leakage displacement distance of the fluorescent solution after the fracture of the internal support structure of EPE foam is transformed into the magnitude of the vector, and the direction of the leakage displacement is transformed into the direction of the vector, thus realizing the quantitative characterization of the fracture displacement characteristics of the internal structure of EPE foam.

[0046] Specifically, please refer to Figure 3 as well as Figure 4 As shown, Figure 3 This is a diagram illustrating the steps of determining whether the support structure of a sample under test has defects, according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the logic of determining whether the support structure of a sample under inspection has defects according to an embodiment of the present invention. The process of determining whether the support structure of a sample under inspection has defects based on the first image recognition value includes: Step S301: Calculate the arithmetic mean of the magnitudes of all spatial fracture recognition vectors, and determine the arithmetic mean as the first image recognition value; Step S302: Compare the first image recognition value with a preset first reference value; Step S303: If the first image recognition value is greater than the first reference value, it is determined that the support structure of the sample under test has a defect; if the first image recognition value is less than or equal to the first reference value, it is determined that the support structure of the sample under test does not have a defect.

[0047] In this invention, the value of the preset first reference quantity is obtained from the statistical results of the preliminary test. The same specification and qualified pearl cotton products are subjected to no less than 5 repeated tests in advance, and the arithmetic mean L1 of the spatial fracture identification vector magnitude obtained from the preliminary test is calculated. Preferably, the first reference quantity = δ × L1, where δ is the value factor of the first reference quantity, and the value range of δ is [1.1, 1.2]. In this invention, the value factor δ of the first reference quantity can be 1.15.

[0048] Those skilled in the art will understand that the core protective performance of EPE foam relies on its uniform non-crosslinked closed-cell support structure. Qualified EPE foam products have uniformly sized closed-cell units, intact pore wall structures, and uniform spatial distribution. When a tensile force is applied in a direction parallel to the surface of the EPE foam, the uniform closed-cell structure will produce consistent elastic deformation along the direction of force. The pore wall fracture of the internal closed cells will be in a uniform micro-crack state. At this time, the fluorescent solution dropped at the fluorescent marker point on the upper surface will, under the action of gravity and capillary action, penetrate downwards perpendicularly to the upper and lower surfaces of the EPE foam along the uniform micro-cracks of the closed-cell structure, without any horizontal offset component. Correspondingly, in a Cartesian coordinate system parallel to the surface of the EPE foam, the second coordinate of the tension-manifested feature point will coincide with the first coordinate of the fluorescent marker point, and the magnitude of the spatial fracture identification vector is 0.

[0049] When the local support structure of EPE foam has uniform defects, such as uneven size of closed-cell units, uneven thickness of pore walls, or local micropores and cracks, the closed-cell structure in the defect area will undergo non-uniform fracture due to uneven stress under tensile force. The resulting cracks will deviate from the direction perpendicular to the surface, exhibiting tilting or displacement along the plane. During the permeation process, the fluorescent solution will diffuse along these non-uniform tilted cracks, and its leakage path will generate a horizontal offset component on a plane parallel to the EPE foam surface. Ultimately, the leakage point on the lower surface of the product and the fluorescent marking point on the upper surface will form a spatial position difference in the planar coordinate system. The magnitude of this position difference is quantified by the modulus of the spatial fracture identification vector.

[0050] The modulus of the spatial fracture identification vector in this invention corresponds to the spatial straight-line distance between the first coordinate of the fluorescent marker point and the second coordinate of the tension dominant feature point. This distance directly quantifies the degree of leakage shift of the fluorescent solution at a single detection point due to the internal structural fracture. The larger the modulus, the more obvious the leakage shift after the structural fracture at that location, and the worse the uniformity of the support structure. Thus, it enables rapid determination of the overall defects of the support structure of pearl cotton products.

[0051] Specifically, the process of determining the second image recognition value includes: Calculate the vector angle between any two spatial fracture identification vectors, and determine the angle dataset composed of all vector angle values; Calculate the standard deviation of the included angle values ​​of the vectors within the included angle dataset, and determine the standard deviation as the second image recognition value.

[0052] It is understandable that the direction of the spatial fracture identification vector directly corresponds to the leakage offset direction of the fluorescent solution at each detection point due to the internal structural fracture. The angle between any two vectors reflects the degree of difference in the leakage offset direction between the two detection points. By calculating the angle values ​​between all pairs of spatial fracture identification vectors, an angle dataset containing all directional difference information is formed, which can completely cover the interrelationship features of the offset directions at each detection point. The second image recognition quantity reflects the overall dispersion and consistency of the leakage offset directions at all detection points. The smaller the standard deviation, the more consistent the direction of each vector is, indicating that the structural fracture leakage offset directions at each detection point of the pearl cotton are highly correlated, reflecting a clear directional clustering feature of defects in the internal support structure. The larger the standard deviation, the more dispersed the direction of each vector is, indicating that the leakage offset directions at each detection point are not significantly correlated, and the structural defects are randomly distributed. Thus, the spatial distribution characteristics of defects in the pearl cotton support structure are accurately quantified.

[0053] Specifically, the preset recognition condition is that the second image recognition value is less than or equal to the preset second reference value.

[0054] The smaller the second image recognition value in this invention, the more consistent the direction of each spatial fracture recognition vector tends to be. This reflects that the internal structural fracture leakage offset of each detection point of the pearl cotton product under tensile force is oriented in the same direction. This indicates that the internal support structure defects of the product are affected by the local defect area and form a clustered fracture feature with obvious directionality. Such a situation has the prerequisite of locating the local defect area through vector orientation analysis.

[0055] In this invention, the value of the preset second reference quantity is calculated based on prior experimental calculations. At least 10 tests are conducted on pearl cotton products of the same specification that have passed inspection, and the standard deviation d1 of the vector angle value obtained from the tests is calculated. Preferably, the second reference quantity = β × d1, where β is the value factor of the second reference quantity, and the value range of β is [1.05, 1.15]. In this invention, the value factor β of the second reference quantity can be 1.1. Preferably, the value of the second reference quantity is 15°.

[0056] In this invention, if the second image recognition value is greater than the preset second reference value, the support structure defect of the sample to be inspected is determined to be randomly distributed, and there is no need to perform directional analysis on the spatial fracture recognition vector.

[0057] Specifically, the process of determining the directional analysis results of the spatial fracture identification vector includes: Using the coordinate axes of a Cartesian coordinate system as a reference, determine the vector pointing angles of all spatial fracture identification vectors, and thus determine the angle range of the vector pointing angles.

[0058] Using the positive X-axis of the Cartesian coordinate system as the reference for angle calculation, the angle between each spatial fracture identification vector and the positive X-axis is calculated in a counterclockwise direction. This angle is the pointing angle of the vector, and the value range of the pointing angle is 0°-360°.

[0059] For example, the pointing angles of all spatial fracture identification vectors are determined, and the angle interval is defined by taking the minimum pointing angle as the lower boundary of the angle interval and the maximum pointing angle as the upper boundary of the angle interval.

[0060] Specifically, the process of identifying local defect areas in the sample to be inspected includes: Determine the boundary lines of the two interval boundaries of the stated angle interval in the Cartesian coordinate system; The area where the spatial break identification vector between the two boundary lines falls is determined as the local defect area of ​​the sample to be inspected.

[0061] In this invention, the origin of the Cartesian coordinate system is taken as the starting point of the ray, and the lower boundary angle and the upper boundary angle of the angle interval are taken as the ray directions, respectively. Two rays originating from the origin are drawn, and these two rays are the two boundary lines of the angle interval. The acute angle region between the two boundary lines is the local defect region of the sample to be inspected. The acute angle region is the physical range of the smallest included angle corresponding to the angle interval, and is the core aggregation area to which all spatial fracture identification vectors point.

[0062] This invention uses the coordinate axes of a Cartesian coordinate system parallel to the surface of the pearl cotton sample as the angle calibration benchmark to determine the vector pointing angles of all spatial fracture identification vectors. The distribution range of all pointing angles is integrated to delineate a vector pointing angle interval. This interval completely covers the concentrated distribution range of leakage offset directions at each detection point, intuitively reflecting the overall directional characteristics of leakage offset due to internal structural fractures in the pearl cotton. Subsequently, the two boundaries of this angle interval are transformed into boundary lines within a Cartesian coordinate system, achieving precise mapping from the angle interval to the physical coordinate system region. The coordinate system region where all spatial fracture identification vectors fall between the two boundary lines is then identified as the local defect region of the sample. Since the vectors within this region all exhibit leakage offset in the direction corresponding to the angle interval, it indicates that the pearl cotton in this region is affected by local support structure defects, resulting in a uniformly directional fracture under tensile force. This represents a concentrated distribution area of ​​internal support structure defects. This achieves precise localization of defective areas.

[0063] The present invention also provides a computer-readable storage medium for storing computer program code, which, when run on a computer, causes the computer to execute the above-described related method steps to implement the performance testing method for packaging pearl cotton products provided in the above embodiments.

[0064] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for testing the performance of pearl cotton products for packaging, characterized in that, include: A sample of pearl cotton product to be tested is obtained, several fluorescent marking points are marked on the upper surface of the sample to be tested, and a predetermined amount of fluorescent solution is dropped at each of the fluorescent marking points. A tensile force is applied in a direction parallel to the surface of the sample to be tested, and several frames of surface images are continuously acquired on the lower surface of the sample to be tested. The fluorescence display image of each fluorescent marker point is determined according to the time sequence relationship of the fluorescence signal, and the tension-affected feature pixels are determined on the fluorescence display image. The coordinates of each fluorescent marker point and the corresponding tension-manifested feature point are obtained. Several spatial fracture identification vectors are constructed on the fluorescence imaging image. The first image recognition value is determined according to the magnitude of the spatial fracture identification vector to determine whether there are defects in the support structure of the sample under test. In response to the determination that the supporting structure of the sample under inspection has defects, the second image recognition value is determined based on the vector angle between any two spatial fracture recognition vectors; Based on the determination result that the second image recognition quantity meets the preset recognition conditions, the local defect area of ​​the sample to be inspected is determined according to the directional analysis result of the spatial fracture recognition vector.

2. The method for testing the performance of EPE foam products for packaging according to claim 1, characterized in that, The process of labeling several fluorescent points and adding fluorescent solution includes: In a Cartesian coordinate system parallel to the upper surface of the sample to be tested, several fluorescent markers are calibrated at equal intervals along the direction of tensile force application. Record the first coordinate of each fluorescent marker point in the Cartesian coordinate system. The drop points of the fluorescent solution on the surface of the sample to be tested are controlled to coincide with the first coordinates of each fluorescent marker point.

3. The method for testing the performance of EPE foam products for packaging according to claim 2, characterized in that, The process of determining the fluorescence imaging image of each fluorescent marker includes: Taking the start time of the application of tensile force as the zero point of time, surface images of the lower surface of the sample to be tested are continuously acquired at preset time intervals to form a sequence of surface image frames ordered by time sequence. Fluorescence signal identification is performed on each surface image in the surface image frame sequence, and the surface image frame in which the fluorescence signal is first identified within the identification range corresponding to the fluorescence marker is determined as the fluorescence display image corresponding to the fluorescence marker.

4. The method for testing the performance of EPE foam products for packaging according to claim 3, characterized in that, The process of determining the effect of tension on feature pixels includes: Within the fluorescence display image corresponding to each fluorescent marker point, determine the pixel point corresponding to the location where the fluorescence signal is first identified; The pixel is identified as the tension-dominant feature point corresponding to the fluorescent marker point.

5. The method for testing the performance of EPE foam products for packaging according to claim 4, characterized in that, The process of constructing several spatial fracture identification vectors includes: Determine the second coordinates of the tension dominant characteristic points corresponding to each fluorescent marker point in the Cartesian coordinate system; Several spatial fracture identification vectors are constructed using the first coordinate as the starting point of the vector and the second coordinate as the ending point of the vector.

6. The method for testing the performance of EPE foam products for packaging according to claim 5, characterized in that, The process of determining whether there are defects in the support structure of the sample under inspection based on the first image recognition value includes: Calculate the arithmetic mean of the magnitudes of all spatial fracture recognition vectors, and determine the arithmetic mean as the first image recognition value; The first image recognition value is compared with a preset first reference value; If the first image recognition value is greater than the first reference value, it is determined that the support structure of the sample under test has a defect.

7. The method for testing the performance of EPE foam products for packaging according to claim 6, characterized in that, The process of determining the second image recognition value includes: Calculate the vector angle between any two spatial fracture identification vectors, and determine the angle dataset composed of all vector angle values; Calculate the standard deviation of the included angle values ​​of the vectors within the included angle dataset, and determine the standard deviation as the second image recognition value.

8. The method for testing the performance of EPE foam products for packaging according to claim 1, characterized in that, The preset recognition condition is that the second image recognition value is less than or equal to the preset second reference value.

9. The method for testing the performance of EPE foam products for packaging according to claim 8, characterized in that, The process of determining the directional analysis results of the spatial fracture identification vector includes: Using the coordinate axes of a Cartesian coordinate system as a reference, determine the vector pointing angles of all spatial fracture identification vectors, and thus determine the angle range of the vector pointing angles.

10. The method for testing the performance of EPE foam products for packaging according to claim 9, characterized in that, The process of identifying local defect areas in a sample to be inspected includes: Determine the boundary lines of the two interval boundaries of the stated angle interval in the Cartesian coordinate system; The area where the spatial break identification vector between the two boundary lines falls is determined as the local defect area of ​​the sample to be inspected.

Images