A method for detecting the bending strength of fiberboard

Abstract

The present invention discloses a method for detecting the anti-bending strength of a fiberboard, which relates to the technical field of mechanical stress testing. The method includes: arranging a loading mechanism above the fiberboard; wherein, the loading mechanism includes a loading rod that can reciprocate in the vertical direction; starting the loading mechanism to drive the flexible loading head to move downward so as to approach and contact the fiberboard; after the flexible loading head contacts the fiberboard, the loading rod continues to drive the flexible loading head to move downward at a preset first non-linear velocity curve; simultaneously recording the bending deformation amount of the fiberboard and the pressure distribution detected by a plurality of pressure sensing units; when the bending deformation amount of the fiberboard reaches a first preset proportional value of the thickness of the fiberboard, pausing the loading process, and determining the preliminary anti-bending strength situation of the fiberboard according to the pressure distribution detected by the plurality of pressure sensing units; determining the anti-bending strength value of the fiberboard according to the preliminary anti-bending strength situation of the fiberboard.

Description

Technical Field

[0001] This application relates to the technical field of mechanical stress testing, and more particularly, to a method for detecting the bending strength of fiberboard. Background Art

[0002] Fiberboard is a type of artificial board made by interweaving fiber materials and relying on their inherent adhesive properties. During the production process, adhesives and / or additives can be added as needed. Its fiber raw materials are widely sourced, including wood fibers, other plant fibers, as well as polyester fibers, recycled fibers, etc. This type of board has excellent texture, retaining both the physical and mechanical properties of wood while solving problems such as large differences in longitudinal and transverse strength caused by the single arrangement direction of wood fibers, and has strong dimensional stability. The surface of the fiberboard is flat, without knots, and can be made into large-sized, different thicknesses, and various density boards, so it has a wide range of applications and performs well in furniture manufacturing, mattress production, building decoration, vehicle and ship manufacturing, packaging, and other fields.

[0003] Due to the wide range of uses of fiberboard, the stability of its mechanical properties is crucial. Fiberboards with different raw materials, manufacturing processes, and application fields have significant differences in their mechanical properties.

[0004] Therefore, there is an urgent need for a method for detecting the bending strength of fiberboard to evaluate the bending strength of fiberboard after production, thereby ensuring its performance and safety in practical applications. Summary of the Invention

[0005] The embodiments of this application provide a method for detecting the bending strength of fiberboard to solve the above technical problems.

[0006] This application provides a method for detecting the bending strength of fiberboard, including: placing the fiberboard on the support member of the detection device;

[0007] Setting a loading mechanism above the fiberboard; wherein, the loading mechanism includes a loading rod that can reciprocate in the vertical direction; a flexible loading head is connected to the lower end of the loading rod; the flexible loading head is composed of a plurality of telescopic airbags, and a pressure sensing unit is provided on the surface of each airbag, and the pressure sensing unit is used to detect the pressure distribution on the fiberboard in real time;

[0008] Starting the loading mechanism to drive the flexible loading head to move downward by the loading rod to approach and contact the fiberboard;

[0009] After the flexible loading head contacts the fiber board, the loading rod continues to drive the flexible loading head to move downward at a preset first non-linear velocity curve to apply a first pressure to the fiber board; meanwhile, the bending deformation amount of the fiber board and the pressure distribution detected by multiple pressure sensing units are recorded; wherein, the first non-linear velocity curve is a curve that first accelerates and then decelerates.

[0010] When the bending deformation amount of the fiber board reaches a first preset proportional value of the thickness of the fiber board, the loading process is paused, and the preliminary flexural strength condition of the fiber board is determined according to the pressure distribution detected by multiple pressure sensing units.

[0011] Determine the flexural strength value of the fiber board according to the preliminary flexural strength condition of the fiber board.

[0012] Further, the determining the flexural strength value of the fiber board according to the preliminary flexural strength condition of the fiber board includes:

[0013] Apply a preset restoring force to the fiber board in a direction opposite to the preliminary flexural deformation direction to make the fiber board return to the initial state; then, based on a preset second non-linear velocity curve, apply a second pressure to the fiber board until the bending deformation amount of the fiber board reaches a second preset proportional value of the thickness of the fiber board; wherein, the second non-linear velocity curve is a curve that first accelerates and then decelerates.

[0014] Repeat the loading process and the restoring process multiple times.

[0015] Determine the flexural strength value of the fiber board according to the pressure distribution detected by multiple pressure sensing units during multiple loading processes and the preliminary flexural strength condition of the fiber board.

[0016] Further, the detection device further includes an optical fiber sensor array; the optical fiber sensor array is electrically connected to the data processing unit of the detection device; while the loading mechanism is arranged above the fiber board, the optical fiber sensor array is arranged below the fiber board.

[0017] Use the optical fiber sensor array to monitor the internal stress distribution of the fiber board in real time during multiple loading processes and restoring processes.

[0018] The determining the flexural strength value of the fiber board according to the pressure distribution detected by multiple pressure sensing units during multiple loading processes and the preliminary flexural strength condition of the fiber board includes:

[0019] According to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, combined with the pressure distribution detected by multiple pressure sensing units and the preliminary flexural strength of the fiberboard, calculate the flexural strength distribution map of the fiberboard;

[0020] Evaluate the flexural strength value of the fiberboard according to the flexural strength distribution map of the fiberboard.

[0021] Further, the calculating the flexural strength distribution map of the fiberboard according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, combined with the pressure distribution detected by multiple pressure sensing units and the preliminary flexural strength of the fiberboard includes:

[0022] According to the preliminary flexural strength of the fiberboard, the characteristics and stress characteristics of the multi-layer composite structure of the fiberboard, determine the stress-strain relationship equation of the fiberboard under the action of a bending load, and the deformation coordination conditions of each layer;

[0023] Conduct a superposition correlation analysis on the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes and the pressure distribution detected by multiple pressure sensing units;

[0024] According to the results of the superposition correlation analysis, the stress-strain relationship equation and the deformation coordination conditions of each layer, establish a relationship model between the stress and pressure at multiple positions on the fiberboard;

[0025] Using the stress-pressure relationship model, combined with the material parameters and geometric parameters of each layer of the fiberboard, calculate the flexural strength distribution map of the fiberboard;

[0026] Among them, the material parameters of each layer of the fiberboard include elastic modulus, Poisson's ratio and density; the geometric parameters of each layer of the fiberboard include the thickness of each layer, the fiber arrangement direction and the distribution of the adhesive.

[0027] Further, the determining the stress-strain relationship equation of the fiberboard under the action of a bending load and the deformation coordination conditions of each layer according to the preliminary flexural strength of the fiberboard, the characteristics and stress characteristics of the multi-layer composite structure of the fiberboard includes:

[0028] Using a random sequential adsorption algorithm, a representative volume element model of the fiberboard is generated according to the preliminary flexural strength of the fiberboard, the characteristics of the multi-layer composite structure of the fiberboard, and the mechanical properties, including: determining the shape, size, and distribution of the fibers according to the preliminary flexural strength of the fiberboard, the characteristics of the multi-layer composite structure of the fiberboard, and the mechanical properties, randomly selecting the starting point and direction of the fibers, and gradually depositing the fibers into the representative volume element model until a preset volume fraction is reached; verifying the generated representative volume element model through finite element analysis; where the fibers are at the position with orientation The deposition probability is calculated based on the following formula:

[0029] ;

[0030] where is the deposition probability of the fiber at the position with orientation ; is the distance from the position to the nearest deposited fiber; is the characteristic length of fiber deposition, characterizing the randomness of fiber deposition; is the polar angle of the fiber; is the azimuth angle of the fiber; and represent the target fiber orientation; is the standard deviation of the fiber orientation; is the normalization constant, used to ensure that the sum of the probability distribution is 1; represents the exponential function;

[0031] Based on the representative volume element model, using the Monte Carlo algorithm, the mechanical properties under different fiber orientations and distributions are simulated to determine the stress-strain relationship equation of the fiberboard under bending load;

[0032] Based on the representative volume element model, combined with the preliminary flexural strength of the fiberboard, the deformation coordination conditions of each layer of the fiberboard are determined using the genetic algorithm-sequential quadratic programming algorithm; where the optimization objective function of the genetic algorithm-sequential quadratic programming algorithm is ;

[0033] ;

[0034] where is the stress of the th layer; is the target stress; is the strain of the th layer; is the Strain of the layer; is the number of layers; is the initial strain; is the regularization parameter used to balance the weights of the target stress and the initial strain; is the interlayer coordination weight used to coordinate the deformations between layers.

[0035] Furthermore, the superimposed correlation analysis of the internal stress distribution monitored by the fiber optic sensor array and the pressure distribution detected by multiple pressure sensing units during multiple loading processes includes:

[0036] Using a convolutional neural network, based on the internal stress distribution monitored by the fiber optic sensor array and the pressure distribution detected by multiple pressure sensing units, to determine the feature map of the internal stress distribution and the feature map of the pressure distribution;

[0037] Align and superimpose the feature map of the internal stress distribution and the feature map of the pressure distribution;

[0038] Based on the superimposed result, combining the stress-strain relationship equation of the fiber board and the deformation coordination conditions of each layer, to determine the result of the superimposed correlation analysis;

[0039] Among them, the alignment and superimposition of the feature map of the internal stress distribution and the feature map of the pressure distribution includes:

[0040] Perform feature fusion on the feature map of the internal stress distribution and the feature map of the pressure distribution to obtain the initial fusion feature;

[0041] Through a multi-scale channel attention module, extract the local channel attention feature and the global channel attention feature of the initial fusion feature;

[0042] Input the local channel attention feature and the global channel attention feature into an iterative attention feature fusion module for iterative feature fusion;

[0043] Among them, the aligned feature map is ;

[0044] ;

[0045] Among them, means finding the feature map that minimizes ; is the feature map of the internal stress distribution; is the feature map of the pressure distribution; is the feature map of the pressure distribution; represents the feature map and the feature map of the internal stress distribution The mean square error between; represent the feature map and the feature map of the pressure distribution The mean square error between; represent the feature map of the attention loss; is the weight coefficient, which is used to balance the contributions of the feature map of the internal stress distribution and the feature map of the pressure distribution; is the attention loss weight.

[0046] Furthermore, the support member includes two support seats that can move relative to each other in the horizontal direction, and each support seat is provided with an arc-shaped groove that matches the thickness of the fiber board;

[0047] A driving motor is provided at the bottom of the support seat, and the driving motor is used to automatically adjust the distance between the two support seats according to the length of the fiber board to be detected and the detection requirements; wherein, the driving motor is electrically connected to the control unit of the detection device.

[0048] Furthermore, the loading mechanism further includes an internal environment simulation system; the internal environment simulation system adjusts the temperature, humidity, and air pressure during the detection in real time according to the preset environmental parameters to simulate the usage conditions of the fiber board in different environments.

[0049] Furthermore, the airbag of the flexible loading head is filled with an intelligent gel material with temperature compensation function, and the intelligent gel material can automatically adjust its own hardness and elasticity at different temperatures.

[0050] Furthermore, the inner surface of the arc-shaped groove is coated with a layer of nano-lubricating material to reduce the friction between the fiber board and the support seat.

[0051] Based on the embodiments provided in this application, a flexible loading head is adopted, which is composed of multiple telescopic airbags, and a pressure sensing unit is provided on the surface of each airbag. This design can detect the pressure distribution on the fiberboard in real time. Compared with the traditional single-point loading method, it can more comprehensively and accurately reflect the stress distribution state of the fiberboard during the loading process. This accurate pressure distribution detection ability helps to more accurately evaluate the bending strength of the fiberboard and avoid misjudgment caused by local stress concentration. The movement of the loading rod adopts a preset first non-linear velocity curve, that is, it accelerates first and then decelerates. This non-linear loading method can better approximate the dynamic loading conditions that the fiberboard may encounter during actual use. Compared with the traditional uniform loading, it can more truly reflect the mechanical property changes of the fiberboard at different loading stages, thereby improving the reliability and practicality of the detection results. During the detection process, by recording the bending deformation amount and the pressure distribution of the fiberboard, when the bending deformation amount of the fiberboard reaches the first preset proportional value of its thickness, the loading process can be paused, and the preliminary bending strength of the fiberboard can be determined according to the pressure distribution. This dynamic evaluation method can judge the bending strength of the fiberboard in advance before it is completely damaged, avoid irreversible damage to the fiberboard caused by overloading, and improve the safety and efficiency of the detection at the same time. The detection method of the present invention is applicable to fiberboards with different raw materials, manufacturing processes and application fields. Since there are significant differences in the mechanical properties of fiberboards, the flexible loading head and the non-linear loading method can better adapt to the characteristics of different fiberboards, provide accurate bending strength evaluations for various types of fiberboards, and have wide applicability. By recording and analyzing the pressure distribution in real time and combining the dynamic loading process, the present invention can quickly and accurately determine the bending strength value of the fiberboard. This efficient and accurate detection method can significantly improve the efficiency of fiberboard quality detection, reduce the detection cost, and provide more powerful technical support for the production, application and quality control of fiberboards. The detection method of the present invention can effectively evaluate the performance and safety of the fiberboard in actual use. Through accurate bending strength detection, it can ensure that the fiberboard can withstand the expected load during applications in furniture manufacturing, building decoration, vehicle and ship manufacturing and other fields, avoid potential safety hazards caused by insufficient strength, and thus enhance the overall application value and market competitiveness of the fiberboard. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and the illustrative embodiments and descriptions thereof are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0053] Figure 1 is a flowchart of an optional method for detecting the bending strength of a fiberboard according to an embodiment of the present application;

[0054] Figure 2 Flow chart of another alternative method for detecting the flexural strength of fiberboard according to an embodiment of the present application.

[0055] The realization of the object of the present invention, its functional features and advantages will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] Optionally, as Figure 1 shown, the present application provides a method for detecting the flexural strength of fiberboard, including:

[0058] S101, placing the fiberboard on the supporting member of the detecting device;

[0059] S102, arranging a loading mechanism above the fiberboard; wherein, the loading mechanism includes a loading rod that can reciprocate in the vertical direction; a flexible loading head is connected to the lower end of the loading rod; the flexible loading head is composed of a plurality of telescopic air bags, and a pressure sensing unit is arranged on the surface of each air bag, and the pressure sensing unit is used to detect the pressure distribution on the fiberboard in real time;

[0060] Among them, the loading mechanism can be arranged above the midpoint position of the fiberboard, but is not limited thereto;

[0061] S103, starting the loading mechanism to drive the flexible loading head to move downward to approach and contact the fiberboard;

[0062] S104, after the flexible loading head contacts the fiberboard, the loading rod continues to drive the flexible loading head to move downward at a preset first non-linear velocity curve to apply a first pressure to the fiberboard; meanwhile, record the bending deformation amount of the fiberboard and the pressure distribution detected by a plurality of pressure sensing units; wherein, the first non-linear velocity curve is a curve that first accelerates and then decelerates;

[0063] In this embodiment, the acceleration of the acceleration section of the first non-linear velocity curve can be 0.1 m / s² to 0.5 m / s²; the deceleration of the deceleration section of the first non-linear velocity curve can be 0.2 m / s² to 1 m / s².

[0064] S105. When the bending deformation of the fiberboard reaches the first preset proportional value of the thickness of the fiberboard, pause the loading process, and determine the preliminary bending strength of the fiberboard according to the pressure distribution detected by multiple pressure sensing units.

[0065] In this embodiment, the first preset proportional value may include but is not limited to 15%-25%.

[0066] S106. Determine the bending strength value of the fiberboard according to the preliminary bending strength of the fiberboard.

[0067] Based on the embodiments provided in this application, a flexible loading head is adopted, which is composed of multiple telescopic airbags, and each airbag surface is provided with a pressure sensing unit. This design can detect the pressure distribution on the fiberboard in real time. Compared with the traditional single-point loading method, it can more comprehensively and accurately reflect the stress distribution state of the fiberboard during the stress process. This accurate pressure distribution detection ability helps to more accurately evaluate the bending strength of the fiberboard and avoid misjudgment caused by local stress concentration. The movement of the loading rod adopts a preset first non-linear velocity curve, that is, the way of accelerating first and then decelerating. This non-linear loading method can be closer to the dynamic loading situation that the fiberboard may encounter in actual use. Compared with the traditional uniform loading, it can more truly reflect the mechanical property changes of the fiberboard at different loading stages, thereby improving the reliability and practicability of the detection results. During the detection process, by recording the bending deformation of the fiberboard and the pressure distribution, when the bending deformation of the fiberboard reaches the first preset proportional value of its thickness, the loading process can be paused, and the preliminary bending strength of the fiberboard can be determined according to the pressure distribution. This dynamic evaluation method can judge the bending strength of the fiberboard in advance before it is completely damaged, avoid irreversible damage to the fiberboard caused by overloading, and improve the safety and efficiency of the detection at the same time. The detection method of the present invention is applicable to fiberboards with different raw materials, manufacturing processes and application fields. Since the mechanical properties of fiberboards vary greatly, the flexible loading head and non-linear loading method can better adapt to the characteristics of different fiberboards, provide accurate bending strength evaluation for various types of fiberboards, and have wide applicability. By recording and analyzing the pressure distribution in real time and combining with the dynamic loading process, the present invention can quickly and accurately determine the bending strength value of the fiberboard. This efficient and accurate detection method can significantly improve the efficiency of fiberboard quality detection, reduce the detection cost, and provide more powerful technical support for the production, application and quality control of fiberboards. The detection method of the present invention can effectively evaluate the performance and safety of the fiberboard in actual use. Through accurate bending strength detection, it can ensure that the fiberboard can withstand the expected load in applications such as furniture manufacturing, building decoration, vehicle and ship manufacturing, etc., avoid potential safety hazards caused by insufficient strength, and thus enhance the overall application value and market competitiveness of the fiberboard.

[0068] Further, determine the flexural strength value of the fiberboard according to the preliminary flexural strength condition of the fiberboard, including:

[0069] Apply a preset restoring force to the fiberboard in a direction opposite to the preliminary flexural deformation direction to make the fiberboard return to its initial state; then, based on a preset second non-linear velocity curve, apply a second pressure to the fiberboard until the amount of flexural deformation of the fiberboard reaches a second preset proportional value of the thickness of the fiberboard; wherein, the second non-linear velocity curve is a curve that first accelerates and then decelerates;

[0070] In this embodiment, when the fiberboard is subjected to the first pressure, it will bend downward. This bending direction can be regarded as a vector pointing to the concave surface of the fiberboard. For example, if the fiberboard is subjected to a downward pressure in the middle, the fiberboard will bend downward to form a concave shape. To make the fiberboard return to its initial flat state, a force in a direction opposite to the preliminary flexural deformation direction needs to be applied. This force is called the restoring force. The direction of the restoring force should be upward, opposite to the bending direction of the fiberboard, to offset the bending deformation and make the fiberboard return to its initial flat state.

[0071] In this embodiment, the acceleration of the acceleration section of the second non-linear velocity curve can be 0.1 m / s² to 0.5 m / s²; the deceleration of the deceleration section of the second non-linear velocity curve can be 0.2 m / s² to 1 m / s².

[0072] Repeat the loading process and the restoring process multiple times;

[0073] Determine the flexural strength value of the fiberboard according to the pressure distribution detected by multiple pressure sensing units during multiple loading processes and the preliminary flexural strength condition of the fiberboard.

[0074] After each loading-restoring process ends, the surface cleaning unit of the detection device will automatically clean the surface of the fiberboard to remove dust, impurities, and possible tiny fiber scraps on the surface to ensure the accuracy of the next loading process. The surface cleaning unit includes an ultrasonic cleaning head and an electrostatic adsorption device. The ultrasonic cleaning head is used to loosen the attachments on the surface of the fiberboard, and the electrostatic adsorption device is used to adsorb the loosened attachments to achieve efficient cleaning of the surface of the fiberboard.

[0075] Further, the detection device further includes an optical fiber sensor array; the optical fiber sensor array is electrically connected to the data processing unit of the detection device; while arranging the loading mechanism above the fiberboard, arrange the optical fiber sensor array below the fiberboard;

[0076] Using an optical fiber sensor array, the internal stress distribution of the fiberboard during multiple loading processes and the recovery process is monitored in real time; among them, the data processing unit calculates the bending strength distribution map of the fiberboard through a finite element analysis algorithm based on the monitoring data of the optical fiber sensor array and in combination with the pressure values of the pressure sensing unit, providing a more detailed reference basis for the quality assessment of the fiberboard;

[0077] Optionally, as Figure 2 shown, the bending strength value of the fiberboard is determined according to the pressure distribution detected by multiple pressure sensing units during multiple loading processes and the preliminary bending strength of the fiberboard, including:

[0078] S201, calculate the bending strength distribution map of the fiberboard according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, in combination with the pressure distribution detected by multiple pressure sensing units and the preliminary bending strength of the fiberboard;

[0079] S202, evaluate the bending strength value of the fiberboard according to the bending strength distribution map of the fiberboard.

[0080] Furthermore, calculating the bending strength distribution map of the fiberboard according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, in combination with the pressure distribution detected by multiple pressure sensing units and the preliminary bending strength of the fiberboard, includes:

[0081] According to the preliminary bending strength of the fiberboard, the characteristics and mechanical properties of the multi-layer composite structure of the fiberboard, considering the mechanical property differences of its various layers and the interaction of the interfaces between layers; based on the classical laminated plate theory and the elastic mechanics theory, determine the stress-strain relationship equation of the fiberboard under bending load and the deformation coordination conditions of each layer;

[0082] Perform a superposition correlation analysis on the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes and the pressure distribution detected by multiple pressure sensing units; when performing the superposition correlation analysis, for the dynamic change relationship between internal stress and pressure distribution under non-linear velocity curve loading; for example, during the acceleration stage of the loading rod, the pressure detected by the pressure sensing unit increases rapidly, and the internal stress monitored by the optical fiber sensor array also rises rapidly accordingly; while during the deceleration stage, the changes in pressure and internal stress gradually tend to be stable;

[0083] According to the results of the superposition correlation analysis, the stress-strain relationship equation and the deformation coordination conditions of each layer, establish a relationship model between stress and pressure at multiple positions on the fiberboard;

[0084] Using the relationship model between stress and pressure, combined with the material parameters and geometric parameters of each layer of the fiberboard, calculate the bending strength distribution map of the fiberboard;

[0085] Among them, the material parameters of each layer of the fiberboard include elastic modulus, Poisson's ratio, and density; the geometric parameters of each layer of the fiberboard include the thickness of each layer, the fiber arrangement direction, and the distribution of the adhesive.

[0086] Furthermore, according to the preliminary bending strength of the fiberboard, the characteristics and mechanical properties of the multi-layer composite structure of the fiberboard, determine the stress-strain relationship equation of the fiberboard under the action of bending load, and the deformation coordination conditions of each layer, including:

[0087] Using the random sequential adsorption algorithm, generate a representative volume element model of the fiberboard according to the preliminary bending strength of the fiberboard, the characteristics and mechanical properties of the multi-layer composite structure of the fiberboard, including:

[0088] According to the preliminary bending strength of the fiberboard, the characteristics and mechanical properties of the multi-layer composite structure of the fiberboard, determine the shape, size, and distribution method of the fibers, randomly select the starting point and direction of the fibers, and gradually deposit the fibers into the representative volume element model until the preset volume fraction is reached; verify the generated representative volume element model through finite element analysis to ensure that it can accurately reflect the microstructure and mechanical properties of the fiberboard; among them, the fiber is at the position with orientation The deposition probability is calculated based on the following formula:

[0089] ;

[0090] Among them, is the deposition probability of the fiber at the position with orientation ; is the distance from the position to the nearest deposited fiber; is the characteristic length of fiber deposition, characterizing the randomness of fiber deposition; is the polar angle of the fiber; is the azimuth angle of the fiber; and represent the target fiber orientation; is the standard deviation of fiber orientation; is the normalization constant, used to ensure that the sum of the probability distribution is 1; represents the exponential function;

[0091] Among them, the random sequential adsorption algorithm deposits fibers into the three-dimensional model in a random order until the required volume fraction is reached;

[0092] Based on the representative volume element model, using the Monte Carlo algorithm, simulate the mechanical properties under different fiber orientations and distributions to determine the stress-strain relationship equation of the fiberboard under bending load;

[0093] Based on the representative volume element model, combined with the preliminary flexural strength of the fiberboard, use the genetic algorithm-sequential quadratic programming algorithm to determine the deformation coordination conditions of each layer of the fiberboard. According to the multi-layer composite structure characteristics of the fiberboard, construct a finite element model including the material properties and geometric parameters of each layer; Use the genetic algorithm-sequential quadratic programming algorithm to optimize the multi-layer structure of the fiberboard, adjust parameters such as fiber angle and layer thickness to improve the mechanical properties and determine the deformation coordination conditions of each layer under bending load; Among them, the optimization objective function of the genetic algorithm-sequential quadratic programming algorithm is ;

[0094] ;

[0095] Among them, is the stress of the th layer; is the target stress; is the strain of the th layer; is the strain of the th layer; is the number of layers; is the initial strain; is the regularization parameter, used to balance the weights of the target stress and the initial strain; is the interlayer coordination weight, used to coordinate the deformation between layers.

[0096] In this embodiment, the specific values of the regularization parameter and the interlayer coordination weight can be adjusted according to the application scenario and actual requirements of the fiberboard. Exemplarily:

[0097] In the aerospace field, the fiberboard usually needs to have high flexural strength and good interlayer coordination to cope with extreme mechanical environments. In this case: the regularization parameter can take relatively large values, such as 0.5 - 1.0, to ensure a higher matching degree between the target stress and the actual stress, thus ensuring the performance of the fiberboard under high-strength loads. The interlayer coordination weight can take 0.3 - 0.5 to ensure the deformation coordination between layers and avoid failure modes such as interlayer delamination.

[0098] In the automotive industry, the fiberboard needs to balance cost and processing efficiency while ensuring a certain strength. Therefore, the regularization parameter It can take values from 0.3 to 0.5 to balance the weights of the target stress and the initial strain and ensure the performance of the fiberboard under dynamic loads. Interlayer coordination weight It can take values from 0.2 to 0.3 to ensure the coordination of interlayer deformations and avoid cost increase caused by over-optimization.

[0099] In building and bridge engineering, fiberboards usually need to have good durability and overall stability. Regularization parameter It can take values from 0.2 to 0.4 to ensure uniform stress distribution in the fiberboard during long-term use. Interlayer coordination weight It can take values from 0.1 to 0.2 to ensure the deformation coordination between layers and avoid local stress concentration.

[0100] For general industrial applications, the performance requirements for fiberboards are relatively low, and smaller parameter values can be used. Regularization parameter It can take values from 0.1 to 0.3 to simplify the calculation and ensure basic stress matching at the same time. Interlayer coordination weight It can take values below 0.1 to reduce the computational complexity.

[0101] In summary, the regularization parameter Generally has a selection range between 0.1 and 1.0, and the specific value depends on the requirements for the matching degree of the target stress and the initial strain. Interlayer coordination weight Generally has a selection range between 0.1 and 0.5, and the specific value depends on the requirements for the coordination of interlayer deformations.

[0102] In practical applications, the parameters can be optimized and adjusted through a combination of experimental verification and numerical simulation to ensure that the flexural strength and overall performance of the fiberboard meet the actual requirements.

[0103] Furthermore, a superposition correlation analysis is performed on the internal stress distribution monitored by the fiber optic sensor array and the pressure distribution detected by multiple pressure sensing units during multiple loading processes, including:

[0104] Using a convolutional neural network, according to the internal stress distribution monitored by the fiber optic sensor array and the pressure distribution detected by multiple pressure sensing units, determine the feature map of the internal stress distribution and the feature map of the pressure distribution;

[0105] Align and superimpose the feature map of the internal stress distribution and the feature map of the pressure distribution;

[0106] Based on the superposition result, combined with the stress-strain relationship equation of the fiberboard and the deformation coordination conditions of each layer, determine the result of the superposition correlation analysis;

[0107] Among them, aligning and superimposing the feature map of the internal stress distribution and the feature map of the pressure distribution includes:

[0108] Performing feature fusion on the feature map of the internal stress distribution and the feature map of the pressure distribution to obtain an initial fused feature;

[0109] Extracting the local channel attention feature and the global channel attention feature of the initial fused feature through a multi-scale channel attention module;

[0110] Inputting the local channel attention feature and the global channel attention feature into an iterative attention feature fusion module for iterative feature fusion;

[0111] Among them, the aligned feature map is ;

[0112] ;

[0113] Among them, means finding the feature map that minimizes ; is the feature map of the internal stress distribution; is the feature map of the pressure distribution; represents the mean square error between the feature map and the feature map of the internal stress distribution ; represents the mean square error between the feature map and the feature map of the pressure distribution ; represents the attention loss of the feature map ; is the weight coefficient used to balance the contributions of the feature map of the internal stress distribution and the feature map of the pressure distribution; is the attention loss weight.

[0114] In this embodiment, the specific values of the weight coefficient and the attention loss weight can be adjusted according to the application scenario and actual requirements of the fiberboard. Exemplarily:

[0115] In the aerospace field, the requirements for detection accuracy and reliability are extremely high, and it is necessary to ensure the accuracy and stability of feature fusion. The weight coefficient can take values from 0.7 to 0.9 to pay more attention to the contribution of the feature map of the internal stress distribution and ensure the accuracy of the stress distribution. The attention loss weight can take values from 0.1 to 0.3 to appropriately increase the weight of the attention loss and ensure the robustness of feature fusion.

[0116] In the automotive industry, detection efficiency and cost control are relatively important, and a balance needs to be achieved between precision and efficiency. The weight coefficient can take values from 0.5 to 0.7 to balance the contributions of the internal stress distribution and the pressure distribution. The attention loss weight can take values from 0.05 to 0.1 to appropriately reduce the weight of attention loss and thus reduce the computational complexity.

[0117] In construction and bridge engineering, the detection coverage and integrity are more critical, and the comprehensiveness of feature fusion needs to be ensured. The weight coefficient can take values from 0.3 to 0.5 to pay more attention to the contribution of the pressure distribution feature map. The attention loss weight can take values from 0.01 to 0.05 to reduce the weight of attention loss and ensure the extensiveness of feature fusion.

[0118] In general industrial applications, the versatility and adaptability of detection are more important, and the parameters can be relatively loose. The weight coefficient can take values from 0.2 to 0.4 to simplify the calculation. The attention loss weight can take values below 0.01 to further reduce the computational complexity.

[0119] In summary, the value range of the weight coefficient is generally between 0.2 and 0.9, and the specific value depends on the degree of emphasis on the internal stress distribution and the pressure distribution feature map. The attention loss weight has a value range generally between 0.01 and 0.3, and the specific value depends on the weight requirement for attention loss.

[0120] In practical applications, the parameters can be optimized and adjusted through a combination of experimental verification and numerical simulation to ensure the accuracy and reliability of the fiberboard bending strength detection.

[0121] In this embodiment, feature alignment: Use image processing techniques (such as interpolation, registration, etc.) to align the feature maps of the internal stress distribution and the pressure distribution to the same size and spatial position to ensure their spatial consistency. Initial feature fusion: Perform initial feature fusion on the aligned feature maps of the internal stress distribution and the pressure distribution to obtain initial fusion features. Multi-scale channel attention module: Extract local channel attention features and global channel attention features of the initial fusion features through the multi-scale channel attention module.

[0122] Among them, the multi-scale channel attention module can include two branches, one for extracting local channel attention and the other for extracting global channel attention. The specific implementation can refer to the following steps: Local channel attention: Use a small convolutional kernel (such as 3x3) to extract local features, and then extract local channel attention features through a channel attention mechanism (such as the SE module). Global channel attention: Use a large convolutional kernel (such as 7x7) or global average pooling to extract global features, and then extract global channel attention features through a channel attention mechanism.

[0123] Iterative attention feature fusion module: Input the local channel attention features and global channel attention features into the iterative attention feature fusion module for iterative feature fusion.

[0124] Among them, the iterative attention feature fusion module gradually optimizes the feature fusion result by alternately integrating the initial feature fusion and the attention module.

[0125] Furthermore, the support component includes two support seats that can move relative to each other in the horizontal direction, and each support seat is provided with an arc-shaped groove that matches the thickness of the fiberboard.

[0126] A driving motor is provided at the bottom of the support seat. The driving motor is used to automatically adjust the distance between the two support seats according to the length of the fiberboard to be detected and the detection requirements, so that the fiberboard can be stably placed on the support seat; among them, the driving motor is electrically connected to the control unit of the detection device.

[0127] Furthermore, the loading mechanism further includes an internal environment simulation system; the internal environment simulation system adjusts the temperature, humidity, and air pressure during the detection in real time according to the preset environmental parameters to simulate the use of the fiberboard in different environments, so as to more comprehensively and accurately evaluate the bending strength of the fiberboard.

[0128] In this embodiment, the temperature adjustment range is -20°C - 60°C, the humidity adjustment range is 10% - 90%RH, and the air pressure adjustment range is 0.05MPa - 0.2MPa.

[0129] Furthermore, the airbag of the flexible loading head is filled with an intelligent gel material with temperature compensation function. The intelligent gel material can automatically adjust its own hardness and elasticity at different temperatures to ensure that the pressure exerted by the loading head on the fiberboard is uniform and stable at different environmental temperatures.

[0130] Furthermore, the inner surface of the arc-shaped groove is coated with a layer of nano-lubricating material to reduce the friction between the fiberboard and the support seat.

[0131] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall similarly be included within the patent protection scope of the present invention.

Claims

1. A method for detecting the bending strength of fiberboard, characterized in that, Including: Placing the fiberboard on the support member of the detection device; Setting a loading mechanism above the fiberboard; wherein, the loading mechanism includes a loading rod that can reciprocate in the vertical direction; a flexible loading head is connected to the lower end of the loading rod; the flexible loading head is composed of a plurality of telescopic airbags, and a pressure sensing unit is provided on the surface of each airbag, and the pressure sensing unit is used to detect the pressure distribution on the fiberboard in real time; Starting the loading mechanism to drive the flexible loading head to move downward by the loading rod to approach and contact the fiberboard; After the flexible loading head contacts the fiberboard, the loading rod continues to drive the flexible loading head to move downward at a preset first non-linear velocity curve to apply a first pressure to the fiberboard; meanwhile, record the bending deformation amount of the fiberboard and the pressure distribution detected by a plurality of pressure sensing units; wherein, the first non-linear velocity curve is a curve that first accelerates and then decelerates; When the bending deformation amount of the fiberboard reaches the first preset proportional value of the thickness of the fiberboard, pause the loading process, and determine the preliminary flexural strength of the fiberboard according to the pressure distribution detected by a plurality of pressure sensing units; Repeating the loading process and the recovery process multiple times to make the fiberboard return to the initial state, and calculating the flexural strength distribution map of the fiberboard according to the internal stress distribution monitored by the fiber optic sensor array during multiple loading processes, combined with the pressure distribution detected by a plurality of pressure sensing units and the preliminary flexural strength of the fiberboard, including: Determining the stress-strain relationship equation of the fiberboard under the action of a bending load and the deformation coordination conditions of each layer according to the preliminary flexural strength of the fiberboard, the characteristics and stress characteristics of the multi-layer composite structure of the fiberboard; Performing superposition correlation analysis on the internal stress distribution monitored by the fiber optic sensor array during multiple loading processes and the pressure distribution detected by a plurality of pressure sensing units; Establishing a relationship model between the stress and pressure at multiple positions on the fiberboard according to the results of the superposition correlation analysis, the stress-strain relationship equation, and the deformation coordination conditions of each layer; Using the stress-pressure relationship model, combined with the material parameters and geometric parameters of each layer of the fiberboard, to calculate the flexural strength distribution map of the fiberboard; Wherein, the material parameters of each layer of the fiberboard include elastic modulus, Poisson's ratio, and density; the geometric parameters of each layer of the fiberboard include the thickness of each layer, the fiber arrangement direction, and the distribution of the adhesive; Evaluating the flexural strength value of the fiberboard according to the flexural strength distribution map of the fiberboard.

2. The fiberboard flexural strength detection method according to claim 1, characterized in that, The determining the flexural strength value of the fiberboard according to the preliminary flexural strength of the fiberboard includes: Apply a preset restoring force to the fiberboard in a direction opposite to the initial anti-bending deformation direction; then, based on a preset second non-linear velocity curve, apply a second pressure to the fiberboard until the amount of bending deformation of the fiberboard reaches a second preset proportional value of the thickness of the fiberboard; wherein, the second non-linear velocity curve is a curve that first accelerates and then decelerates.

3. The fiberboard flexural strength detection method according to claim 2, wherein, The fiber optic sensor array is electrically connected to the data processing unit of the detection device; while arranging the loading mechanism above the fiberboard, arrange the fiber optic sensor array below the fiberboard. Utilize the fiber optic sensor array to monitor in real time the internal stress distribution of the fiberboard during multiple loading processes and recovery processes.

4. The fiberboard flexural strength detection method according to claim 3, wherein According to the preliminary anti-bending strength condition of the fiberboard, the characteristics and stress characteristics of the multi-layer composite structure of the fiberboard, determine the stress-strain relationship equation of the fiberboard under the action of a bending load, and the deformation coordination conditions of each layer, including: Using the random sequential adsorption algorithm, a representative volume element model of the fiberboard is generated according to the preliminary flexural strength of the fiberboard, the characteristics of the multi-layer composite structure of the fiberboard, and the stress characteristics, including: determining the shape, size, and distribution of the fibers according to the preliminary flexural strength of the fiberboard, the characteristics of the multi-layer composite structure of the fiberboard, and the stress characteristics, randomly selecting the starting point and direction of the fibers, and gradually depositing the fibers into the representative volume element model until a preset volume fraction is reached; verifying the generated representative volume element model through finite element analysis; wherein, the fibers are at the position with an orientation The deposition probability is calculated based on the following formula: ； wherein, is the probability of fiber deposition at position with orientation ; is the distance from position to the nearest already deposited fiber; is the characteristic length of fiber deposition, characterizing the randomness of fiber deposition; is the polar angle of the fiber; is the azimuthal angle of the fiber; and represent the target fiber orientation; is the standard deviation of fiber orientation; is the normalization constant, used to ensure that the sum of the probability distribution is 1; represents the exponential function; Based on the representative volume element model, use the Monte Carlo algorithm to simulate the mechanical properties under different fiber orientations and distributions to determine the stress-strain relationship equation of the fiberboard under the action of a bending load. Based on the representative volume element model, combined with the preliminary flexural strength of the fiberboard, the deformation coordination conditions of each layer of the fiberboard are determined by using the genetic algorithm-sequential quadratic programming algorithm; among them, the optimization objective function of the genetic algorithm-sequential quadratic programming algorithm is ; ； Among them, is the stress of the th layer; is the target stress; is the strain of the th layer; is the strain of the th layer; is the number of layers; is the initial strain; is the regularization parameter, used to balance the weights of the target stress and the initial strain; is the interlayer coordination weight, used to coordinate the deformations between layers.

5. The fiberboard flexural strength detection method according to claim 4, wherein The superimposed correlation analysis of the internal stress distribution monitored by the fiber optic sensor array during multiple loading processes and the pressure distribution detected by multiple pressure sensing units includes: Use a convolutional neural network to determine the feature map of the internal stress distribution and the feature map of the pressure distribution according to the internal stress distribution monitored by the fiber optic sensor array and the pressure distribution detected by multiple pressure sensing units. Align and superimpose the feature map of the internal stress distribution and the feature map of the pressure distribution. Based on the superimposed result, combine the stress-strain relationship equation of the fiberboard and the deformation coordination conditions of each layer to determine the result of the superimposed correlation analysis. Wherein, the alignment and superimposition of the feature map of the internal stress distribution and the feature map of the pressure distribution include: Perform feature fusion on the feature map of the internal stress distribution and the feature map of the pressure distribution to obtain an initial fusion feature. Extract the local channel attention feature and the global channel attention feature of the initial fusion feature through a multi-scale channel attention module. Input the local channel attention feature and the global channel attention feature into an iterative attention feature fusion module for iterative feature fusion. Among them, the aligned feature map is ; ； Among them, represents finding the feature map that minimizes ; is the feature map of the internal stress distribution; is the feature map of the pressure distribution; represents the mean square error between the feature map and the feature map of the internal stress distribution ; represents the mean square error between the feature map and the feature map of the pressure distribution ; represents the attention loss of the feature map ; is the weight coefficient used to balance the contributions of the feature map of the internal stress distribution and the feature map of the pressure distribution; is the attention loss weight.

6. The method for detecting the anti-bending strength of a fiberboard according to claim 1, wherein: The support member includes two support seats that can move relative to each other in the horizontal direction, and each support seat is provided with an arc-shaped groove that matches the thickness of the fiberboard. A driving motor is provided at the bottom of the support seat, and the driving motor is used to automatically adjust the distance between the two support seats according to the length of the fiberboard to be detected and the detection requirements; wherein, the driving motor is electrically connected to the control unit of the detection device.

7. The method for detecting the anti-bending strength of a fiberboard according to claim 1, wherein: The loading mechanism further includes an internal environment simulation system; the internal environment simulation system adjusts the temperature, humidity, and air pressure during the detection in real time according to preset environmental parameters to simulate the usage of the fiberboard under different environments.

8. The method for detecting the flexural strength of a fiberboard according to claim 1, wherein: The airbag of the flexible loading head is filled with an intelligent gel material with temperature compensation function, and the intelligent gel material can automatically adjust its own hardness and elasticity at different temperatures.

9. The method for detecting the flexural strength of a fiberboard according to claim 6, wherein: The inner surface of the arc-shaped groove is coated with a layer of nano-lubricating material to reduce the friction between the fiberboard and the support seat.

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