Aluminum honeycomb cell expansion CAE model parameter calibration method based on machine vision

Abstract

The embodiment of the invention discloses an aluminum honeycomb cell expansion CAE model parameter calibration method based on machine vision. The method comprises the steps that initial model parameters are selected and substituted into a target aluminum honeycomb cell expansion CAE model, a rigidity-displacement characteristic curve and a strain cloud picture are obtained through calculation, then a rigidity criterion in the rigidity-displacement characteristic curve is determined based on a stable deformation interval, the strain cloud picture is extracted based on machine vision image standard normalization processing to determine a deformation mode criterion, and the deformation mode criterion is determined. And finally, continuously iterating the model parameters of the target aluminum honeycomb cell expansion CAE model through the rigidity criterion and the deformation mode criterion until both the rigidity criterion and the deformation mode criterion meet preset requirements. According to the automatic calibration steps, manual calibration intervention is not needed, calibration of the aluminum honeycomb cell expansion CAE model can be completed by using a small number of iteration steps, the model calibration time is saved, an aluminum honeycomb block crushing deformation mode in the calibration process is quantified in an image machine vision mode, and the calibration process efficiency meets the actual test requirement.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to the technical field of model parameter calibration, and in particular, to a machine vision-based aluminum honeycomb cell expansion CAE model parameter calibration method. Background Art

[0002] As the requirements for collision safety simulation accuracy in vehicle development are increasing, the aluminum honeycomb structure in the deformable digital barrier CAE model no longer uses traditional solid unit simulation, but instead uses more accurate honeycomb shell unit modeling technology. Directly using the explicit finite element method to carry out full-scale simulation of the honeycomb core block is prone to deformation instability, contact anomalies, and negative slip energy due to the large unit scale, which affects the stability of the CAE calculation process and the confidence of the results. Therefore, it is necessary to start from the perspective of cell-level model cognition, and use the "cell method" and "expanded cell equivalent method" to "expand the cell" of the aluminum honeycomb structure to simulate the basic structural characteristics of the aluminum honeycomb. For the aluminum honeycomb expanded cell CAE equivalent model, it is necessary to calibrate the parameters of the CAE model. The existing calibration method often adopts the traditional calibration process of manual trial and error, which not only cannot track the results in real time, but also takes a long time to calibrate, the process is inefficient, and the crushing deformation mode of the aluminum honeycomb block cannot be quantified during the calibration process. Summary of the invention

[0003] The embodiment of this specification provides a method for calibrating parameters of an aluminum honeycomb cell expansion CAE model based on machine vision, and its technical solution is as follows:

[0004] In a first aspect, an embodiment of the present specification provides a method for calibrating parameters of an aluminum honeycomb cell expansion CAE model based on machine vision, the method comprising:

[0005] Select the initial model parameters and substitute them into the target aluminum honeycomb expanded cell CAE model to calculate and obtain the stiffness-displacement characteristic curve and the strain cloud diagram, wherein the model parameters include an average material thickness parameter and a material thickness difference parameter, wherein the average material thickness parameter is used to characterize the average value of the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge in the target aluminum honeycomb expanded cell CAE model, and the material thickness difference parameter is used to characterize the difference between the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge;

[0006] Determining a stiffness criterion in the stiffness-displacement characteristic curve based on a stable deformation interval, wherein the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion;

[0007] Extracting deformation mode criterion of the strain cloud map by image standard normalization processing based on machine vision;

[0008] The model parameters of the target aluminum honeycomb expanded cell CAE model are continuously iterated based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet preset requirements.

[0009] In a second aspect, a machine vision-based aluminum honeycomb expansion CAE model parameter calibration device is provided, the device comprising:

[0010] An acquisition module is used to select initial model parameters and substitute them into the target aluminum honeycomb expanded cell CAE model to calculate and obtain the stiffness-displacement characteristic curve and the strain cloud diagram, wherein the model parameters include an average material thickness parameter and a material thickness difference parameter, wherein the average material thickness parameter is used to characterize the average value of the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge in the target aluminum honeycomb expanded cell CAE model, and the material thickness difference parameter is used to characterize the difference between the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge;

[0011] A determination module, used to determine a stiffness criterion in the stiffness-displacement characteristic curve based on a stable deformation interval, wherein the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion;

[0012] An extraction module, used for extracting the deformation mode criterion of the strain cloud map through image standard normalization processing based on machine vision;

[0013] An iteration module is used to continuously iterate the model parameters of the target aluminum honeycomb expanded cell CAE model based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet preset requirements.

[0014] In a third aspect, an electronic device is provided, including a device processor and a memory;

[0015] The device processor is connected to the memory;

[0016] The memory is used to store executable program code;

[0017] The device processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0018] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a device processor, cause the computer or the device processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0019] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least:

[0020] In one or more embodiments of the present specification, the initial model parameters are first selected and substituted into the target aluminum honeycomb cell expansion CAE model to calculate the stiffness-displacement characteristic curve and strain cloud map, and then the stiffness criterion in the stiffness-displacement characteristic curve is determined based on the stable deformation interval, and the strain cloud map is extracted based on the image standard normalization processing of machine vision to determine the deformation mode criterion, and finally the model parameters of the target aluminum honeycomb cell expansion CAE model are continuously iterated through the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion meet the preset requirements. The above automatic calibration steps do not require manual calibration intervention, and the calibration of the aluminum honeycomb cell expansion CAE model can be completed with a small number of iteration steps, which saves the model calibration time, and quantifies the crushing deformation mode of the aluminum honeycomb block during the calibration process through image machine vision, so that the efficiency of its calibration process meets the actual test requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of the system architecture of a machine vision-based aluminum honeycomb cell expansion CAE model parameter calibration method provided in an embodiment of this specification;

[0023] Figure 2 A flowchart of a method for calibrating parameters of an aluminum honeycomb cell expansion CAE model based on machine vision provided in an embodiment of this specification;

[0024] Figure 3 A schematic diagram of the structure of a machine vision-based aluminum honeycomb cell expansion CAE model parameter calibration device provided in an embodiment of this specification;

[0025] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0027] The terms "first", "second", "third", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0028] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present specification. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described with respect to some examples may be combined in other examples.

[0029] See also Figure 1 , Figure 1 A schematic diagram of the system architecture of a machine vision-based aluminum honeycomb cell expansion CAE model parameter calibration method provided in an embodiment of this specification is shown.

[0030] like Figure 1 As shown, the system architecture of the parameter calibration method of the aluminum honeycomb expanded cell CAE model may at least include a terminal 10 , a server 20 and a network 30 .

[0031] The terminal 10 includes but is not limited to electronic devices such as smart phones, desktop computers, tablet computers, laptops, smart speakers, digital assistants, smart wearable devices, etc., and may also be software running on the above electronic devices, such as applications, etc. Optionally, the operating system running on the electronic device may include but is not limited to Android system, IOS system, Linux, Windows, etc. Optionally, the terminal 10 provides a parameter calibration service to the user, and the terminal 10 may obtain a parameter calibration instruction of an application program interface and send a parameter calibration request to the server 20.

[0032] The server 20 can provide background services for the terminal 10. According to the parameter calibration request sent by the terminal 10, the server 20 will obtain a series of parameter calibration instructions, and the server 20 transmits the parameter calibration instructions to other terminals 10 through the network 30. Specifically, the server 20 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0033] The network 30 is used to provide a medium for a communication link between the terminal 10 and the server 20. The network 30 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0034] In addition, it should be noted that Figure 1 What is shown is only one system provided by the present disclosure. In practical applications, other systems may also be included, for example, more terminals may be included.

[0035] In the embodiments of the present specification, the terminal 10 and the server 20 may be directly or indirectly connected via wired or wireless communication, which is not limited in the present disclosure.

[0036] See next Figure 2 , Figure 2 The overall flow chart of a machine vision-based aluminum honeycomb cell expansion CAE model parameter calibration method provided in an embodiment of the present specification is shown. The aluminum honeycomb cell expansion CAE model parameter calibration method can be used in the server 20.

[0037] like Figure 2 As shown, the parameter calibration method of the aluminum honeycomb expanded cell CAE model may at least include the following steps:

[0038] Step 201: Select initial model parameters and substitute them into the target aluminum honeycomb expanded cell CAE model to calculate and obtain the stiffness-displacement characteristic curve and strain cloud diagram.

[0039] In the embodiment of the present specification, the required stiffness criterion is determined by substituting the initial model parameters into the target aluminum honeycomb cell expansion CAE model, and the stiffness-displacement characteristic curve calculated by the simulation software is used to determine the required stiffness criterion, and the deformation mode criterion is determined by obtaining the strain cloud diagram calculated by the simulation software. Therefore, when calibrating the parameters of the aluminum honeycomb cell expansion CAE model, in order to iteratively optimize the model parameters through the stiffness criterion and the deformation mode criterion, it is necessary to first obtain the stiffness-displacement characteristic curve and the strain cloud diagram of the target aluminum honeycomb cell expansion CAE model after simulation calculation.

[0040] Among them, generally speaking, the greater the difference in human eye visual perception of a certain image under different deformation modes, the higher the accuracy of its deformation mode judgment. Compared with the strain cloud map and the displacement cloud map, the human eye visual difference of the strain cloud map is more significant, so it is more reasonable to use the strain cloud map to judge the deformation mode.

[0041] Step 202: Determine a stiffness criterion in the stiffness-displacement characteristic curve based on a stable deformation interval.

[0042] Wherein, the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion.

[0043] In the embodiments of this specification, in order to ensure that the stiffness characteristics of the aluminum honeycomb expansion cell CAE model with determined model parameters meet the established design goals in each region, the wide stable deformation range of the stiffness characteristic bandwidth can be determined according to the stiffness design requirements, and in the calibration process of the aluminum honeycomb expansion cell CAE model, as many intervals of its stiffness-displacement characteristic curve as possible should fall within the stable deformation range. However, since the stiffness-displacement curve of the aluminum honeycomb expansion cell CAE model has a linear rising section and oscillations during the entire crushing process, it is usually difficult to make its displacement domain stiffness values ​​fall within the stable deformation range. Therefore, it is necessary to select the stiffness-displacement characteristic curve obtained in the stable deformation range to determine the corresponding stiffness criterion. Among them, the stiffness criterion includes the average stiffness criterion and the stiffness characteristic criterion.

[0044] In one possible implementation, the stiffness criterion in the stiffness-displacement characteristic curve is determined based on the stable deformation interval, and the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion, including:

[0045] Determine the upper and lower limits of stiffness and the length of the qualified interval corresponding to the stable deformation interval;

[0046] Based on the integral difference algorithm, the average stiffness criterion corresponding to the stiffness upper and lower limits is determined, and the stiffness characteristic criterion corresponding to the length of the standard-reaching interval is determined.

[0047] In the embodiment of this specification, the upper and lower stiffness limits L1 and L2 in the stiffness-displacement characteristic curve and the length of the standard interval L can be determined by the stable deformation interval. Then, the average stiffness criterion σ corresponding to the upper and lower stiffness limits is determined by the integral difference algorithm. a , the specific calculation formula is as follows:

[0048]

[0049] Among them, L1 and L2 are the lower limit and upper limit of the stable deformation interval of the stiffness-displacement curve, and L is the length of the standard interval.

[0050] Furthermore, the stiffness characteristic criterion λ corresponding to the qualified interval length is determined by the following formula:

[0051]

[0052] Step 203: extracting the deformation mode criterion of the strain cloud map by image standard normalization processing based on machine vision.

[0053] In the embodiments of the present specification, in order to further obtain the deformation mode criterion and ensure that the digitized results of the strain cloud map at different deformation moments are comparable, the obtained strain cloud map can be first subjected to standard normalization extraction, and after obtaining the normalized curve, the curve data can be converted into the deformation mode criterion through the curve integration method or other geometric transformation methods.

[0054] In one possible implementation, the machine vision-based image standard normalization process extracts the deformation mode criterion of the strain cloud map, including:

[0055] Performing image standardization processing on the strain cloud map to obtain a standard strain cloud map;

[0056] Based on the RGB principle of machine vision, image data of the standard strain cloud map is normalized and extracted to obtain a normalized average distribution curve;

[0057] The target closed area ratio in the normalized average distribution curve is determined to obtain a deformation mode criterion.

[0058] In the embodiments of this specification, in order to ensure that the digitized results of the strain cloud map at different deformation moments are comparable, it is necessary to first perform image standardization on the acquired strain cloud map, and use standard fixed interception parameters to process the strain cloud map to obtain a standard strain cloud map. Then, each pixel in the standard strain cloud map can be parsed into a combination of three channel data: red (R), green (G) and blue (B). Therefore, in order to extract the image data of the standard strain cloud map, the image data of the standard strain cloud map can be normalized and extracted by the RGB principle to obtain a normalized average distribution curve. Further. By analyzing and observing the normalized average distribution curves under the same crush amount in different deformation modes, the areas enclosed by the two curves on the left and right sides and the horizontal axis are significantly different, so the area ratio of the target closed area in the normalized average distribution curve can be determined to obtain the deformation mode criterion.

[0059] In one possible implementation, performing image standardization processing on the strain cloud map to obtain a standard strain cloud map includes:

[0060] Determining standard interception parameters, wherein the standard interception parameters include color gradient, interception viewing angle, interception range and pixel size;

[0061] The strain cloud diagram is standardized and intercepted based on the standard interception parameters to obtain a standard strain cloud diagram.

[0062] In the embodiment of the present specification, when the strain cloud map is subjected to image standardization processing, the standard interception parameters may be determined first, which may include color gradient, interception angle, interception range and pixel size. Optionally, the color gradient uses the maximum cloud map color gradient value and the minimum cloud map color gradient value of the solidified strain cloud map, the interception angle may be fixed to the strain cloud map front view angle, the interception range is the aluminum honeycomb block cloud map area at the initial deformation moment as the boundary, does not include blank areas, and the pixel size may be set to 387x963. Then, the strain cloud map is standardized and intercepted by determining the standard interception parameters to obtain a standard strain cloud map.

[0063] In one possible implementation, the image data of the standard strain cloud map is normalized and extracted based on the RGB principle of machine vision to obtain a normalized average distribution curve, including:

[0064] Extracting the R channel matrix in the standard strain cloud map based on the RGB principle of machine vision;

[0065] The R channel matrix is ​​normalized and extracted based on the column average method, and the extraction result and the number of data rows form a normalized average distribution curve.

[0066] In the embodiments of the present specification, it is known from the observation of the historical standard strain cloud map that the pixels in the high strain area tend to be red, while the pixels in the low strain area tend to be blue. It can be seen from this that when the RGB principle is used for image data, in theory, the R channel data of the pixels in the high strain area is relatively high, and the B channel data is relatively low. Therefore, the R channel matrix in the standard strain cloud map can be extracted by the RGB principle first. Then, the data of the R channel matrix is ​​averaged along the column by the column average method, and the average value after normalization extraction is used as the ordinate, and the corresponding number of data rows is used as the abscissa, and a normalized average distribution curve is constructed in the rectangular coordinate system.

[0067] In one possible implementation, determining the target closed area ratio in the normalized average distribution curve to obtain a deformation mode criterion includes:

[0068] Determine a target closed area enclosed by the normalized average distribution curve and the horizontal axis of the coordinate, wherein the target closed area includes a first target closed area and a second target closed area, wherein the first target closed area is a closed area enclosed by the normalized average distribution curve and the vertical axis, and the second target closed area is a closed area enclosed by the normalized average distribution curve and a line parallel to the maximum vertical axis;

[0069] Determine a first area corresponding to the first target closed area and a second area corresponding to the second target closed area based on a curve integration method;

[0070] The ratio of the second area to the first area is determined to obtain a deformation mode criterion.

[0071] In the embodiments of the present specification, when determining the area ratio of the target closed area in the normalized average distribution curve, the vertical axis can be used as the left dividing line, the parallel line of the maximum vertical axis as the right dividing line, the horizontal axis of the coordinate as the bottom dividing line, and the normalized average distribution curve as the upper dividing line in the normalized average distribution curve diagram, and two target closed areas can be marked in the normalized average distribution curve diagram, namely the first target closed area and the second target closed area. Among them, the first target closed area is the left closed area, and the second target closed area is the right closed area. Then, the first area S1 corresponding to the first target closed area and the second area S2 corresponding to the second target closed area are determined by the curve integration method. It can be seen from the historical test records that the normalized average distribution curve has a significant difference in the area enclosed by the horizontal axis on the left and right sides, and the first area S1 is much larger than the second area S2. Further, the determined second area S2 is calculated as a ratio with the first area S1, and the ratio result is used as the deformation mode criterion γ.

[0072] Among them, the specific calculation formula of the deformation mode criterion γ is as follows:

[0073]

[0074] Step 204: continuously iterate the model parameters of the target aluminum honeycomb expanded cell CAE model based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet preset requirements.

[0075] Among them, the model parameters include an average material thickness parameter and a material thickness difference parameter. The average material thickness parameter is used to characterize the average value of the transverse edge material thickness of the unit cell at the crushed end and the transverse edge material thickness of the unit cell at the support end in the target aluminum honeycomb expanded cell CAE model, and the material thickness difference parameter is used to characterize the difference between the transverse edge material thickness of the unit cell at the crushed end and the transverse edge material thickness of the unit cell at the support end.

[0076] In the embodiments of the present specification, when automatically calibrating the model parameters of the target aluminum honeycomb cell expansion CAE model, the CAE model with the same working conditions can be submitted for calculation after adjusting the average material thickness parameters and the material thickness difference parameters each time, and the corresponding obtained stiffness criterion and deformation mode criterion are compared with the preset stiffness requirements and deformation mode requirements respectively. As long as one of them does not meet the requirements, the model parameters will continue to be adjusted until both the stiffness criterion and the deformation mode criterion meet the preset requirements, thereby completing the automatic calibration of the aluminum honeycomb cell expansion CAE model parameters.

[0077] Among them, the average material thickness parameter is the average value of the transverse edge material thickness of the unit cell at the crushing end and the transverse edge material thickness of the unit cell at the supporting end in the target aluminum honeycomb expanded cell CAE model, and the material thickness difference parameter is the difference between the transverse edge material thickness of the unit cell at the crushing end and the transverse edge material thickness of the unit cell at the supporting end.

[0078] In one possible implementation, the model parameters of the target aluminum honeycomb expanded cell CAE model are continuously iterated based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet preset requirements, including:

[0079] Performing a first iteration on the average material thickness parameter based on the average stiffness criterion until the average stiffness criterion meets the average stiffness preset requirement;

[0080] Performing a second iteration on the material thickness difference parameter based on the deformation mode criterion until the deformation mode criterion meets the preset requirements of the deformation mode;

[0081] The average material thickness parameter and the material thickness difference parameter are iterated for the third time based on the stiffness characteristic criterion and the deformation mode criterion until the stiffness characteristic criterion and the deformation mode criterion both meet the preset stiffness characteristic requirements and the preset deformation mode requirements.

[0082] In the embodiment of the present specification, when iterating the model parameters of the target aluminum honeycomb cell expansion CAE model, it is necessary to first determine the average stiffness preset requirements, stiffness characteristic preset requirements, and deformation mode preset requirements. Among them, the stiffness-displacement characteristic curve obtained by multiple quasi-static crush tests on the target aluminum honeycomb block can be obtained to obtain the average stiffness characteristic distribution and performance bandwidth in the stable deformation range of the curve, and the required average stiffness preset requirements and stiffness characteristic preset requirements are determined. Then, by selecting the strain cloud map in the CAE calculation results obtained by multiple load gradient change schemes, the deformation mode criterion change range is obtained through image standard normalization processing based on machine vision, and the required deformation mode preset requirements are determined.

[0083] Next, the model parameters of the target aluminum honeycomb expanded cell CAE model are continuously iterated through the stiffness criterion and deformation mode criterion determined in real time. When both the stiffness characteristics and the deformation mode meet the target preset requirements, the following formula can be used to determine whether the average stiffness criterion meets the average stiffness preset requirements:

[0084] σ m -ε≤σ a ≤σ m +ε

[0085] Among them, σ m is the median stiffness bandwidth, ε is the error term. Optionally, σ m The average material thickness parameter can be taken as 0.0905, and ε can be taken as 5%. The first iteration is performed on the average material thickness parameter until the average stiffness criterion σa Meet the preset average stiffness requirements.

[0086] Next, the deformation mode criterion is compared with the preset deformation mode criterion upper limit value γ0, and the following formula can be used to determine whether the deformation mode criterion meets the preset deformation mode requirement:

[0087] γ≤γ0

[0088] Optionally, γ0 can be 0.15. When the deformation mode criterion is greater than the upper limit value γ0 of the deformation mode criterion, the material thickness difference parameter is iterated for the second time until the deformation mode criterion γ meets the above deformation mode preset requirement.

[0089] Furthermore, the determined stiffness characteristic criterion is compared with the preset stiffness characteristic criterion upper limit value λ0, and the following formula can be used to determine whether the stiffness characteristic criterion meets the preset stiffness characteristic requirement:

[0090] λ≤λ0

[0091] Among them, optionally, λ0 can be taken as 0.9.

[0092] When the deformation mode criterion is greater than the deformation mode criterion upper limit value γ0 or the stiffness characteristic criterion is greater than the stiffness characteristic criterion upper limit value λ0, the material thickness difference parameter is iterated for the third time until the deformation mode criterion γ meets the above deformation mode preset requirements and the stiffness characteristic criterion λ meets the above stiffness characteristic preset requirements.

[0093] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] See next Figure 3 , Figure 3 The schematic diagram of the structure of a parameter calibration device for an aluminum honeycomb cell expansion CAE model provided in the embodiment of this specification is shown. It should be noted that: Figure 3 The parameter calibration device of the aluminum honeycomb expansion cell CAE model shown is used to implement the present application Figure 2 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 2 The embodiment shown.

[0095] like Figure 3As shown, the parameter calibration device of the aluminum honeycomb cell expansion CAE model may at least include:

[0096] An acquisition module 301 is used to select initial model parameters and substitute them into a target aluminum honeycomb expanded cell CAE model to calculate and obtain a stiffness-displacement characteristic curve and a strain cloud diagram;

[0097] A determination module 302, for determining a stiffness criterion in the stiffness-displacement characteristic curve based on a stable deformation interval, wherein the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion;

[0098] An extraction module 303 is used to extract the deformation mode criterion of the strain cloud map based on the image standard normalization processing of machine vision;

[0099] The iteration module 304 is used to continuously iterate the model parameters of the target aluminum honeycomb expanded cell CAE model based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion meet the preset requirements. The model parameters include an average material thickness parameter and a material thickness difference parameter. The average material thickness parameter is used to characterize the average value of the transverse edge material thickness of the crushed end unit cell and the transverse edge material thickness of the support end unit cell in the target aluminum honeycomb expanded cell CAE model. The material thickness difference parameter is used to characterize the difference between the transverse edge material thickness of the crushed end unit cell and the transverse edge material thickness of the support end unit cell.

[0100] In one possible implementation, the determination module 302 is specifically configured to:

[0101] Determine the upper and lower limits of stiffness and the length of the qualified interval corresponding to the stable deformation interval;

[0102] Based on the integral difference algorithm, the average stiffness criterion corresponding to the stiffness upper and lower limits is determined, and the stiffness characteristic criterion corresponding to the length of the standard-reaching interval is determined.

[0103] In one embodiment, the extraction module 303 is specifically used for:

[0104] Performing image standardization processing on the strain cloud map to obtain a standard strain cloud map;

[0105] Based on the RGB principle of machine vision, image data of the standard strain cloud map is normalized and extracted to obtain a normalized average distribution curve;

[0106] The target closed area ratio in the normalized average distribution curve is determined to obtain a deformation mode criterion.

[0107] In one possible implementation, the extraction module 303 is further configured to:

[0108] Determining standard interception parameters, wherein the standard interception parameters include color gradient, interception viewing angle, interception range and pixel size;

[0109] The strain cloud diagram is standardized and intercepted based on the standard interception parameters to obtain a standard strain cloud diagram.

[0110] In one possible implementation, the extraction module 303 is further configured to:

[0111] Extracting the R channel matrix in the standard strain cloud map based on the RGB principle of machine vision;

[0112] The R channel matrix is ​​normalized and extracted based on the column average method, and the extraction result and the number of data rows form a normalized average distribution curve.

[0113] In one possible implementation, the extraction module 303 is further configured to:

[0114] Determine a target closed area enclosed by the normalized average distribution curve and the horizontal axis of the coordinate, wherein the target closed area includes a first target closed area and a second target closed area, wherein the first target closed area is a closed area enclosed by the normalized average distribution curve and the vertical axis, and the second target closed area is a closed area enclosed by the normalized average distribution curve and a line parallel to the maximum vertical axis;

[0115] Determine a first area corresponding to the first target closed area and a second area corresponding to the second target closed area based on a curve integration method;

[0116] The ratio of the second area to the first area is determined to obtain a deformation mode criterion.

[0117] In one possible implementation, the iteration module 304 is specifically configured to:

[0118] Performing a first iteration on the average material thickness parameter based on the average stiffness criterion until the average stiffness criterion meets the average stiffness preset requirement;

[0119] Performing a second iteration on the material thickness difference parameter based on the deformation mode criterion until the deformation mode criterion meets the preset requirements of the deformation mode;

[0120] The average material thickness parameter and the material thickness difference parameter are iterated for the third time based on the stiffness characteristic criterion and the deformation mode criterion until the stiffness characteristic criterion and the deformation mode criterion both meet the preset stiffness characteristic requirements and the preset deformation mode requirements.

[0121] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (Field-Programmable Gate Array, FPGA), an integrated circuit (Integrated Circuit, IC), etc.

[0122] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0123] See next Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0124] like Figure 4 As shown, the electronic device 400 may include: at least one device processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0125] The communication bus 402 may be used to realize the connection and communication among the above-mentioned components.

[0126] The user interface 403 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0127] The network interface 404 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0128] Among them, the device processor 401 may include one or more processing cores. The device processor 401 uses various interfaces and lines to connect various parts within the entire electronic device 400, and executes various functions and processes data of the electronic device 400 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the device processor 401 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The device processor 401 can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the device processor 401, and it can be implemented separately through a chip.

[0129] The memory 405 may include RAM or ROM. Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 405 may optionally be at least one storage device located away from the aforementioned device processor 401. As Figure 4 As shown, the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0130] Specifically, the device processor 401 can be used to call the parameter calibration application of the aluminum honeycomb cell expansion CAE model stored in the memory 405, and specifically perform the following operations:

[0131] Select the initial model parameters and substitute them into the target aluminum honeycomb expanded cell CAE model to calculate and obtain the stiffness-displacement characteristic curve and the strain cloud diagram, wherein the model parameters include an average material thickness parameter and a material thickness difference parameter, wherein the average material thickness parameter is used to characterize the average value of the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge in the target aluminum honeycomb expanded cell CAE model, and the material thickness difference parameter is used to characterize the difference between the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge;

[0132] Determining a stiffness criterion in the stiffness-displacement characteristic curve based on a stable deformation interval, wherein the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion;

[0133] Extracting deformation mode criterion of the strain cloud map by image standard normalization processing based on machine vision;

[0134] The model parameters of the target aluminum honeycomb expanded cell CAE model are continuously iterated based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet preset requirements.

[0135] As an optional embodiment of the present specification, the stiffness criterion in the stiffness-displacement characteristic curve is determined based on the stable deformation interval, and the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion, including:

[0136] Determine the upper and lower limits of stiffness and the length of the qualified interval corresponding to the stable deformation interval;

[0137] Based on the integral difference algorithm, the average stiffness criterion corresponding to the stiffness upper and lower limits is determined, and the stiffness characteristic criterion corresponding to the length of the standard-reaching interval is determined.

[0138] As an option of the embodiment of this specification, the image standard normalization processing based on machine vision to extract the deformation mode criterion of the strain cloud map includes:

[0139] Performing image standardization processing on the strain cloud map to obtain a standard strain cloud map;

[0140] Based on the RGB principle of machine vision, image data of the standard strain cloud map is normalized and extracted to obtain a normalized average distribution curve;

[0141] The target closed area ratio in the normalized average distribution curve is determined to obtain a deformation mode criterion.

[0142] As an option of the embodiment of this specification, the step of performing image standardization processing on the strain cloud map to obtain a standard strain cloud map includes:

[0143] Determining standard interception parameters, wherein the standard interception parameters include color gradient, interception viewing angle, interception range and pixel size;

[0144] The strain cloud diagram is standardized and intercepted based on the standard interception parameters to obtain a standard strain cloud diagram.

[0145] As an option of the embodiment of this specification, the image data of the standard strain cloud map is normalized and extracted based on the RGB principle of machine vision to obtain a normalized average distribution curve, including:

[0146] Extracting the R channel matrix in the standard strain cloud map based on the RGB principle of machine vision;

[0147] The R channel matrix is ​​normalized and extracted based on the column average method, and the extraction result and the number of data rows form a normalized average distribution curve.

[0148] As an optional embodiment of the present specification, the determining the target closed area ratio in the normalized average distribution curve to obtain the deformation mode criterion includes:

[0149] Determine a target closed area enclosed by the normalized average distribution curve and the horizontal axis of the coordinate, wherein the target closed area includes a first target closed area and a second target closed area, wherein the first target closed area is a closed area enclosed by the normalized average distribution curve and the vertical axis, and the second target closed area is a closed area enclosed by the normalized average distribution curve and a line parallel to the maximum vertical axis;

[0150] Determine a first area corresponding to the first target closed area and a second area corresponding to the second target closed area based on a curve integration method;

[0151] The ratio of the second area to the first area is determined to obtain a deformation mode criterion.

[0152] As an optional embodiment of the present specification, the model parameters of the target aluminum honeycomb expanded cell CAE model are continuously iterated based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet the preset requirements, including:

[0153] Performing a first iteration on the average material thickness parameter based on the average stiffness criterion until the average stiffness criterion meets the average stiffness preset requirement;

[0154] Performing a second iteration on the material thickness difference parameter based on the deformation mode criterion until the deformation mode criterion meets the preset requirements of the deformation mode;

[0155] The average material thickness parameter and the material thickness difference parameter are iterated for the third time based on the stiffness characteristic criterion and the deformation mode criterion until the stiffness characteristic criterion and the deformation mode criterion both meet the preset stiffness characteristic requirements and the preset deformation mode requirements.

[0156] The embodiments of this specification also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0157] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0158] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0159] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0160] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0162] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program code.

[0163] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0164] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for calibrating parameters of aluminum honeycomb cell expansion CAE model based on machine vision, characterized in that: The method comprises: Select the initial model parameters and substitute them into the target aluminum honeycomb expanded cell CAE model to calculate and obtain the stiffness-displacement characteristic curve and the strain cloud diagram, wherein the model parameters include an average material thickness parameter and a material thickness difference parameter, wherein the average material thickness parameter is used to characterize the average value of the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge in the target aluminum honeycomb expanded cell CAE model, and the material thickness difference parameter is used to characterize the difference between the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge; Determining a stiffness criterion in the stiffness-displacement characteristic curve based on a stable deformation interval, wherein the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion; Extracting deformation mode criterion of the strain cloud map by image standard normalization processing based on machine vision; The model parameters of the target aluminum honeycomb expanded cell CAE model are continuously iterated based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet preset requirements.

2. The method according to claim 1, characterized in that The stiffness criterion in the stiffness-displacement characteristic curve is determined based on the stable deformation interval, wherein the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion, including: Determine the upper and lower limits of stiffness and the length of the qualified interval corresponding to the stable deformation interval; Based on the integral difference algorithm, the average stiffness criterion corresponding to the stiffness upper and lower limits is determined, and the stiffness characteristic criterion corresponding to the length of the standard-reaching interval is determined.

3. The method according to claim 1, characterized in that The machine vision-based image standard normalization process extracts the deformation mode criterion of the strain cloud map, including: Performing image standardization processing on the strain cloud map to obtain a standard strain cloud map; Based on the RGB principle of machine vision, image data of the standard strain cloud map is normalized and extracted to obtain a normalized average distribution curve; The target closed area ratio in the normalized average distribution curve is determined to obtain a deformation mode criterion.

4. The method according to claim 3, characterized in that The step of performing image standardization processing on the strain cloud map to obtain a standard strain cloud map includes: Determining standard interception parameters, wherein the standard interception parameters include color gradient, interception viewing angle, interception range and pixel size; The strain cloud diagram is standardized and intercepted based on the standard interception parameters to obtain a standard strain cloud diagram.

5. The method according to claim 3, characterized in that: The image data of the standard strain cloud map is normalized and extracted based on the RGB principle of machine vision to obtain a normalized average distribution curve, including: Extracting the R channel matrix in the standard strain cloud map based on the RGB principle of machine vision; The R channel matrix is ​​normalized and extracted based on the column average method, and the extraction result and the number of data rows form a normalized average distribution curve.

6. The method according to claim 3, characterized in that The step of determining the target closed area ratio in the normalized average distribution curve to obtain a deformation mode criterion includes: Determine a target closed area enclosed by the normalized average distribution curve and the horizontal axis of the coordinate, wherein the target closed area includes a first target closed area and a second target closed area, wherein the first target closed area is a closed area enclosed by the normalized average distribution curve and the vertical axis, and the second target closed area is a closed area enclosed by the normalized average distribution curve and a line parallel to the maximum vertical axis; Determine a first area corresponding to the first target closed area and a second area corresponding to the second target closed area based on a curve integration method; The ratio of the second area to the first area is determined to obtain a deformation mode criterion.

7. The method according to claim 1, characterized in that The continuously iterating the model parameters of the target aluminum honeycomb expanded cell CAE model based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet the preset requirements includes: Performing a first iteration on the average material thickness parameter based on the average stiffness criterion until the average stiffness criterion meets the average stiffness preset requirement; Performing a second iteration on the material thickness difference parameter based on the deformation mode criterion until the deformation mode criterion meets the preset requirements of the deformation mode; The average material thickness parameter and the material thickness difference parameter are iterated for the third time based on the stiffness characteristic criterion and the deformation mode criterion until the stiffness characteristic criterion meets the preset stiffness characteristic requirement and the deformation mode criterion meets the preset deformation mode requirement.

8. A machine vision-based aluminum honeycomb expansion cell CAE model parameter calibration device, characterized in that: The device comprises: An acquisition module is used to select initial model parameters and substitute them into the target aluminum honeycomb expanded cell CAE model to calculate and obtain the stiffness-displacement characteristic curve and the strain cloud diagram, wherein the model parameters include an average material thickness parameter and a material thickness difference parameter, wherein the average material thickness parameter is used to characterize the average value of the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge in the target aluminum honeycomb expanded cell CAE model, and the material thickness difference parameter is used to characterize the difference between the material thickness of the crushed end unit cell transverse edge and the material thickness of the support end unit cell transverse edge; A determination module, used to determine a stiffness criterion in the stiffness-displacement characteristic curve based on a stable deformation interval, wherein the stiffness criterion includes an average stiffness criterion and a stiffness characteristic criterion; An extraction module, used for extracting the deformation mode criterion of the strain cloud map through image standard normalization processing based on machine vision; An iteration module is used to continuously iterate the model parameters of the target aluminum honeycomb expanded cell CAE model based on the stiffness criterion and the deformation mode criterion until the stiffness criterion and the deformation mode criterion both meet preset requirements.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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