Energy absorption box filling structure, energy absorption box and parameter value determination method of energy absorption box

Abstract

The invention provides an energy-absorbing box filling structure, an energy-absorbing box and a parameter value determining method thereof.The energy-absorbing box filling structure comprises a plurality of unit cell structures, the unit cell structures are arranged along the X axis, the Y axis and the Z axis, each unit cell structure comprises two concave hexagonal structures, and each concave hexagonal structure is provided with a plurality of convex hexagonal structures; each concave hexagonal structure comprises a top edge, a bottom edge, two first bevel edges and two second bevel edges, the two sides of the top edge are connected with the two first bevel edges respectively, the two sides of the bottom edge are connected with the two second bevel edges respectively, and the first bevel edges and the second bevel edges located on the same side are connected to form a concave part; wherein the two concave hexagonal structures are connected through the top edge and the bottom edge; connecting ribs are arranged at the sunken parts, and the adjacent unit cell structures on the X axis or the Y axis are connected through the connecting ribs. According to the energy absorption box, the energy absorption box has a good energy absorption effect, and meanwhile the requirement of lightweight design can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive safety, and particularly to an energy absorption box filling structure, an energy absorption box, and a method for determining its parameter values. Background Art

[0002] Automotive safety performance has always been one of the key topics of concern in the industry, aiming to improve automotive safety through energy absorption structures, collision warning, and intelligent assistance systems, etc.

[0003] An energy absorption box is an energy absorption device, usually installed between the front and rear bumpers and the vehicle body of an automobile. When a vehicle collides, the energy absorption box can absorb most of the collision energy through its own deformation, reducing the damage to the vehicle body and the occupants in the vehicle.

[0004] In the design of the energy absorption box in the related art, due to the small porosity of its internal filling structure, the overall mass of the energy absorption box is large, which cannot meet the requirements of lightweight design; moreover, its filling structure design is unreasonable, resulting in unreliable deformation characteristics and poor energy absorption effect. Summary of the Invention

[0005] In view of the above problems, an energy absorption box filling structure, an energy absorption box, and a method for determining its parameter values are provided to overcome or at least partially solve the above problems, including:

[0006] A plurality of unit cell structures, the plurality of unit cell structures are arranged along the X-axis, Y-axis, and Z-axis, and each unit cell structure includes two concave hexagonal structures, and each concave hexagonal structure includes:

[0007] A top side, a bottom side, two first slant sides, and two second slant sides, the two sides of the top side are respectively connected to the two first slant sides, the two sides of the bottom side are respectively connected to the two second slant sides, and the first slant side and the second slant side on the same side are connected to form a recessed part;

[0008] Wherein, the two concave hexagonal structures are connected by the top side and the bottom side; a connecting rib is arranged in the recessed part, and the unit cell structures adjacent to each other on the X-axis or the Y-axis are connected by the connecting rib.

[0009] Optionally, the unit cell structure is symmetric about a first plane formed by the X-axis and the Y-axis, a second plane formed by the X-axis and the Z-axis, and a third plane formed by the Y-axis and the Z-axis.

[0010] Optionally, the unit cell structures adjacent to each other on the Z-axis are connected by the top side and the bottom side.

[0011] Optionally, the number of the plurality of unit cell structures on the X-axis, the Y-axis, and the Z-axis is equal.

[0012] An energy absorption box, the energy absorption box includes the energy absorption box filling structure as described above.

[0013] A method for determining parameter values of an energy absorption box, applied to the energy absorption box as described above, includes:

[0014] Based on the target parameters of the energy absorption box, construct a multi-objective optimization model of the energy absorption box;

[0015] Solve the multi-objective optimization model to determine the target values of the target parameters, including:

[0016] Generate Sobol sequence random numbers to initialize the population; wherein, the population represents a set of possible values of the target parameters;

[0017] Generate a reverse population for the population, and respectively determine the individual fitness of the reverse population and the population;

[0018] If the individual fitness does not meet the preset value, update the population according to the preset escape energy factor, and return to the step of generating a reverse population for the population and determining the individual fitness of the reverse population and the population, and iterate until the individual fitness meets the preset value or the number of iterations reaches a threshold;

[0019] If the individual fitness meets the preset value, determine the target value in the population.

[0020] Optionally, the updating the population according to the preset escape energy factor includes:

[0021] Update the value of the escape energy factor according to the number of iterations;

[0022] Determine a target relational expression according to the updated value of the escape energy factor, and update the population according to the target relational expression;

[0023] Update the population using an adaptive mutation strategy according to the number of iterations.

[0024] Optionally, the target parameters include one or more of the length of the bottom side, the height of the unit cell structure, the material thickness of the unit cell structure, the material width of the unit cell structure, the distance between two concave parts of the concave hexagon structure, and the length of the connecting rib.

[0025] An electronic device, including a processor, a memory, and a computer program stored on the memory and capable of running on the processor, where when the computer program is executed by the processor, it implements the method for determining parameter values of the energy absorption box as described above.

[0026] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the method for determining the parameter values of the energy absorption box as described above is implemented.

[0027] The embodiments of the present invention have the following advantages: On the one hand, the embodiments of the present invention provide an energy absorption box filling structure. By connecting two concave hexagonal structures, the two concave hexagons showing negative Poisson's ratio characteristics can cooperate to absorb energy, which can effectively improve the performance of the energy absorption box. Moreover, by adding connecting ribs in the concave part, the structural strength is improved, and the porosity is also increased, so that while the energy absorption box has a good energy absorption effect, it can also meet the lightweight design requirements. On the other hand, the embodiments of the present invention also provide a method for determining the parameter values of the energy absorption box. By generating Sobol sequence random numbers to initialize the population, the distribution of population individuals is made more uniform, improving the reliability of the target value. And by generating the reverse population of the population, determining the individual fitness of the population and the reverse population, and then determining the target value in the population based on the individual fitness, the accuracy of the target value is effectively improved, further improving the energy absorption performance of the energy absorption box. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for the description of the present invention will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 is a schematic diagram of an energy absorption box filling structure provided by an embodiment of the present invention;

[0030] Figure 2 is a schematic diagram of a unit cell structure provided by an embodiment of the present invention;

[0031] Figure 3 is a three-view drawing of the arrangement of multiple unit cell structures provided by an embodiment of the present invention;

[0032] Figure 4 is a schematic diagram of the structure of a Japanese character-shaped cross-section energy absorption box in the related art;

[0033] Figure 5 is a comparison diagram of the force waveforms of the energy absorption box provided by an embodiment of the present invention and the Japanese character-shaped cross-section energy absorption box;

[0034] Figure 6 is a schematic diagram of the process of three-dimensional modeling of the energy absorption box provided by an embodiment of the present invention;

[0035] Figure 7It is a flowchart of steps of a method for determining parameter values of an energy absorption box provided by an embodiment of the present invention;

[0036] Figure 8 It is a flowchart of an improved Harris hawk algorithm provided by an embodiment of the present invention;

[0037] Figure 9 It is a flowchart of a method for multi-objective optimization of parameter values of an energy absorption box provided by an embodiment of the present invention. Detailed implementation manners

[0038] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0039] Referring to Figure 1 , a structural schematic diagram of an energy absorption box filling structure provided by an embodiment of the present invention is shown. The energy absorption box filling structure includes:

[0040] A plurality of unit cell structures, the plurality of unit cell structures are arranged along the X-axis, Y-axis, and Z-axis. Each unit cell structure includes two concave hexagonal structures. Each concave hexagonal structure includes:

[0041] A top side, a bottom side, two first slant sides, and two second slant sides. The two sides of the top side are respectively connected to the two first slant sides. The two sides of the bottom side are respectively connected to the two second slant sides. The first slant side and the second slant side on the same side are connected to form a concave part;

[0042] Among them, the two concave hexagonal structures are connected by the top side and the bottom side; a connecting rib is arranged in the concave part. The unit cell structures adjacent on the X-axis or the Y-axis are connected by the connecting rib.

[0043] The energy absorption box filling structure, also known as the inner core of the energy absorption box, is a key part for the energy absorption box to achieve the energy absorption function. When a vehicle collides, it absorbs and disperses the energy generated by the collision through its own deformation characteristics, reducing the energy transmitted to other parts of the vehicle, thereby protecting the vehicle occupants and the vehicle structure.

[0044] The unit cell structure is the basic component unit of the energy absorption box filling structure, with specific geometric shapes, mechanical properties, and arrangement methods inside the energy absorption box. These elements directly affect the energy absorption performance of the energy absorption box.

[0045] In this embodiment, as Figure 1As shown, multiple unit cell structures 1 are arranged along the X-axis, Y-axis, and Z-axis. Each unit cell structure 1 is as shown in Figure 2 shown. The unit cell structure 1 is composed of two concave hexagonal structures with the same shape, namely the concave hexagonal structure 11 and the concave hexagonal structure 12. Taking the concave hexagonal structure 11 as an example, each concave hexagonal structure includes:

[0046] a top edge 111, a bottom edge 112, two first slant edges 113, and two second slant edges 114. The top edge 111 and the bottom edge 112 are a set of parallel edges of the concave hexagonal structure 11. The two sides of the top edge 111 are respectively connected to the two first slant edges 113, and the two sides of the bottom edge 112 are respectively connected to the two second slant edges 114. The concave hexagonal structure 11 and the concave hexagonal structure 12 are respectively connected by their top edges and bottom edges;

[0047] The first slant edges 113 and the second slant edges 114 are in pairs and are respectively located on both sides of the top edge and the bottom edge. One end of the first slant edge 113 and the second slant edge 114 on the same side are connected and are inclined in opposite directions to form a recessed part. At the connection part between the first slant edge 113 and the second slant edge 114, that is, in the recessed part, a connecting rib 115 is provided. As shown in Figure 1 shown, the relative recessed parts of the adjacent unit cell structures 1 in the X-axis or Y-axis direction are connected by connecting ribs.

[0048] In some examples, the three-view effect diagrams of the arrangement of multiple unit cell structures of the energy absorption box filling structure are as shown in Figure 3 shown. It can be seen from the front view and the side view that the filling structure shows the characteristics of uniform periodic arrangement in the circumferential arrangement. Each unit cell structure will exhibit the negative Poisson's ratio deformation characteristics under the action of axial load, and the overall structure will also achieve the same deformation characteristics.

[0049] The concave hexagonal structure has the negative Poisson's ratio characteristic, that is, it expands laterally when in tension and contracts laterally when in compression. This enables it to produce a unique mechanical response when subjected to external forces, and can effectively enhance the stability and anti-deformation ability of the structure under complex loading conditions. In this embodiment, by connecting two concave hexagonal structures to form a unit cell structure, the force transmission in multiple directions of the connected multiple unit cell structures as a whole can be made more complex and diversified. When subjected to external forces, the force can be transmitted and shared between the two concave hexagons through the connection part, so as to absorb and transmit energy synergistically;

[0050] Moreover, by adding connecting ribs in the recessed part and connecting the adjacent unit cell structures in the X-axis or Y-axis direction through the connecting ribs, not only the stability of the overall structure is enhanced, but also the porosity is increased, so that the energy absorption box has a good energy absorption effect while meeting the lightweight design requirements.

[0051] In practical applications, the energy absorption box filling structure can be made of alloy materials with good energy absorption effect and light weight, such as aluminum alloy, to further reduce the weight of the energy absorption box. When the energy absorption box filling structure and the energy absorption box shell are made of 6061 aluminum alloy, the energy absorption box mass is 242g through the design of the energy absorption box filling structure provided in this embodiment, and the experimental measurement shows that the mass of the energy absorption box is 242g, which is compared with an unfilled Japanese-shaped cross-section energy absorption box in the related art (such as Figure 4 The weight of the 246g model (shown) is slightly lighter (using the same size shell design), which better meets the requirements of lightweight design without increasing the volume.

[0052] Moreover, by adopting the energy absorption box with the energy absorption box filling structure provided by the present invention, and carrying out compression simulation under the same working conditions as the above-mentioned energy absorption box with a Japanese-shaped cross-section, the bottom of the energy absorption box is fixed, and an impact load is applied to the upper part. The energy absorption box provided by the present invention will have obvious negative Poisson's ratio characteristics and shrink laterally, thereby achieving a better energy absorption effect. The force waveforms of the two are compared as follows: Figure 5 shown.

[0053] In some embodiments of the present invention, the unit cell structure is symmetrical about a first plane formed by the X-axis and the Y-axis, a second plane formed by the X-axis and the Z-axis, and a third plane formed by the Y-axis and the Z-axis.

[0054] In this embodiment, if Figure 2 As shown, the concave hexagonal structure 11 and the concave hexagonal structure 12 are both symmetrical structures, and the two are connected through the center points of their respective top edges and bottom edges, so that the unit cell structure as a whole is symmetrical about the first plane composed of the X-axis and the Y-axis (i.e., the XY plane), the second plane composed of the X-axis and the Z-axis (the XZ plane), and the third plane composed of the Y-axis and the Z-axis (the YZ plane). Through this symmetrical structure, the mechanical properties of the energy absorption box in all directions can be relatively consistent. When impacted by external forces, the stress can be evenly diffused throughout the structure along the XY, XZ, and YZ planes to avoid stress concentration in local areas, thereby improving the overall impact resistance of the structure and enabling the energy absorption box to absorb and dissipate energy more effectively.

[0055] In some embodiments of the present invention, adjacent unit cell structures on the Z axis are connected via the top edge and the bottom edge.

[0056] In this embodiment, if Figure 1 As shown, adjacent unit cell structures on the Z axis are connected by the top and bottom edges. In practical applications, this connection method can make the arrangement of the unit cell structure on the Z axis more compact, thereby forming a more stable energy absorption box filling structure, and can also better transmit the energy generated during the impact.

[0057] In some embodiments of the present invention, the number of the plurality of unit cell structures is equal on the X-axis, the Y-axis, and the Z-axis.

[0058] In practical applications, when the number of unit cell structures arranged on the X-axis, Y-axis, and Z-axis is equal (such as 5:5:5, etc.), the arrangement of the unit cell structures can be made more uniform. As a result, the energy absorption and buffering effects of the energy absorption box filling structure in all directions are basically the same, which can effectively resist impact loads from different directions and provide comprehensive protection for the structure.

[0059] An embodiment of the present invention further provides an energy absorption box, which includes the energy absorption box filling structure as described above.

[0060] In addition to the internal filling structure, the energy absorption box also has a housing (shell) to protect the internal structure. When the energy absorption box is subjected to an impact, the housing first bears the external force and then transfers the force to the internal energy absorption structure.

[0061] In some examples, the three-dimensional modeling process of the energy absorption box is as Figure 6 shown, which mainly includes: arranging the unit cell structure 1 along the X, Y, and Z axes respectively to obtain a 5*5*5 energy absorption box filling structure, and then filling the complete filling structure into the housing 2 to obtain the energy absorption box 3.

[0062] In practical applications, the energy absorption box filling structure can be processed by 3D printing and then filled into the interior of the energy absorption box body to improve the production efficiency of the energy absorption box.

[0063] Referring to Figure 7 , a flowchart of the steps of a method for determining the parameter values of an energy absorption box provided by an embodiment of the present invention is shown, which may specifically include the following steps:

[0064] Step 701, construct a multi-objective optimization model of the energy absorption box based on the target parameters of the energy absorption box;

[0065] The target parameters, that is, the various parameters of the energy absorption box structure to be determined, may specifically be the size of the unit cell structure, the size of the housing, etc. The specific values of these parameters will directly affect the actual energy absorption performance of the energy absorption box.

[0066] In some embodiments of the present invention, the target parameters include one or more of the length of the bottom side, the height of the unit cell structure, the material thickness of the unit cell structure, the material width of the unit cell structure, the distance between the two concave parts of the concave hexagon structure, and the length of the connecting rib.

[0067] In some examples, as Figure 2As shown, the target parameters may include one or more of the following: the length L1 of the bottom edge 112, the height H of the unit cell structure 1, the material thickness T of the unit cell structure, the material width W of the unit cell structure, the distance L2 between the two recessed parts, and the length L3 of the connecting rib 114.

[0068] By constructing a multi-objective optimization model of the energy absorption box, it aims to find the specific values of each target parameter that can achieve the best balance among multiple performance indicators of the energy absorption box, thereby further improving the energy absorption effect of the energy absorption box.

[0069] In some examples, the multi-objective optimization model can be as shown in Equation (1):

[0070]

[0071] Where Z is the target parameter, including: the length L1 of the bottom edge, the distance L2 between the two recessed parts in the concave hexagonal structure, the length L3 of the connecting rib, the height H of the unit cell structure, the material thickness T of the unit cell structure, and the material width W of the unit cell structure; the performance indicators include: specific energy absorption E1, average collision force F1, peak collision force F2, compression displacement S, and the mass M of the energy absorption box; M max , F2 max , S max are all preset thresholds corresponding to the performance indicators; E1(Z), F1(Z), M(Z), F2(Z), S(Z) represent the corresponding relationships (such as functional relationships, model relationships, etc.) between each performance indicator and the target parameter, and G(Z) is the objective function of the multi-objective optimization model.

[0072] In practical applications, the corresponding relationship between each performance indicator and the target parameter can specifically be a response surface model, and its construction method is as follows:

[0073] First, determine the value range of each target parameter. For example: L1 ∈ [14, 15], L2 ∈ [8, 9], L3 ∈ [7, 8], H ∈ [14, 15], W ∈ [1, 1.4], T ∈ [1, 1.4];

[0074] Then, based on the Optimal Latin Hypercube Design, select sample points within the above value range;

[0075] Furthermore, according to the selected sample points, successively establish finite element collision simulation models for different groups of sampling points, use analysis software (such as ABAQUS) to simulate the collision process of the energy absorption box, and record the effective simulation data;

[0076] Further, using the above simulation data, response surface models of various performance indexes specific energy absorption E1, average collision force F1, compression displacement S, peak collision force F2, and mass M are constructed respectively by the response surface method; exemplarily, the expression of the response surface model can be:

[0077]

[0078] where n is the number of design target parameters, x i and x j are inputs, and a i 、a ii 、a ij are undetermined coefficients of the second-order polynomial response surface model corresponding to each performance index.

[0079] Finally, the root mean square error (RMSE) and correlation coefficient (R2) are used to evaluate the prediction ability of the different response surface models constructed above. Among them, if the correlation coefficient R2 is greater than the preset target value X1 and the root mean square error RSME is greater than the preset target value X2, it means it is qualified and enters the next stage; if it is unqualified, the process of resampling the sample points and constructing a new response surface model is repeated until the above preset target values X1 and X2 are satisfied.

[0080] Step 702, solve the multi-objective optimization model to determine the target values of the target parameters, including:

[0081] Sub-step 7021, generate Sobol sequence random numbers to initialize the population; where the population represents the set of possible values of the target parameters;

[0082] In this embodiment, an improved algorithm based on the Harris hawk algorithm is proposed to solve the constructed multi-objective optimization model.

[0083] The Harris hawk algorithm simulates the behavior of a Harris hawk population preying on prey to obtain the optimal solution. The population is the set of possible values of the target parameters (algorithm strategy), that is, the set of possible solutions (or potential solutions, candidate solutions) of the multi-objective optimization model. Each individual in the population represents a possible value; the prey is the optimal solution.

[0084] The Harris hawk algorithm mainly includes an exploration stage, a transition stage, and an exploitation stage, which are specifically as follows:

[0085] In the exploration stage, the Harris hawk group randomly searches for prey (optimal solution) in the search space. At this time, the update formulas for the population individuals and prey positions are shown in Equation (3):

[0086]

[0087] Among them, t represents the number of algorithm iterations, q is the probability parameter for controlling policy selection, X rand is the position of an individual randomly selected from the population, r1, r2, r3, and r4 are random numbers, X prey,t is the position of the prey, X m,t is the average position of the population, and lb and ub are the lower and upper bounds of the search space.

[0088] In the transition stage, the algorithm decides whether to switch from the exploration stage to the exploitation stage according to the escape energy factor E of the prey. E is a key parameter used to control the behavior of the algorithm.

[0089] Exemplarily, the formula for the escape energy factor E can be shown as in Equation (4):

[0090]

[0091] Among them, E0 represents the initial value of the escape energy factor, t is the number of algorithm iterations, and T is the maximum number of algorithm iterations.

[0092] In the exploitation stage, the Harris hawk adopts different hunting strategies (i.e., position update formulas) to approach the optimal solution according to the energy and escape behavior of the prey.

[0093] Let the escape energy factor be E, and the random number generated when making a decision on the hunting strategy used is r. Specifically, it can include:

[0094] Soft siege (|E|≥0.5, r≥0.5):

[0095] X t+1 = ΔX t - E|JX prey,t - X t |, ΔX t = X prey,t - X t (5)

[0096] Hard siege (|E|<0.5, r≥0.5):

[0097] X t+1 = X prey,t - E|ΔX t |(6)

[0098] Progressive rapid dive soft siege (|E|≥0.5, r<0.5):

[0099]

[0100] Among them, the LF function is:

[0101]

[0102] Progressive Fast Dive Hard Siege (|E| < 0.5, r < 0.5):

[0103]

[0104] In sub-step 7021, the Sobol sequence random number refers to the random number generated based on the Sobol sequence. For the initial population, it means using the Sobol sequence random number as the initial value of each individual in the population. In this embodiment, due to the characteristic that such random numbers are evenly distributed in the multi-dimensional space, the aggregation phenomenon that may occur in random numbers is avoided, thereby improving the reliability of the algorithm results.

[0105] In some examples, initializing the population with Sobol sequence random numbers can be expressed as in Equation (10):

[0106] X i = Lb + S n × (Ub - Lb) (10)

[0107] where X i is an individual in the population, Lb and Ub are respectively the lower and upper bounds of the search space of the algorithm, and S n is the Sobol sequence random number.

[0108] Sub-step 7022: Generate a reverse population for the population, and respectively determine the individual fitness of the reverse population and the population;

[0109] The reverse population is a set of new individuals generated based on the individuals in the current population through specific reverse mapping rules. It is in a relative position to the individuals in the current population in the search space, aiming to expand the search range of the algorithm and increase the possibility of finding the global optimal solution.

[0110] Individual fitness reflects the quality of individuals in the population. The higher the individual fitness, the higher the quality of the individual and the closer it is to the optimal solution; the individual fitness of the population can intuitively reflect the quality of the population, and the individual fitness of the reverse population can be used as an individual reverse evaluation index for the population. In this embodiment, by generating the reverse population of the population and respectively determining the individual fitness of the population and the reverse population, the algorithm is prevented from falling into local optimal solutions, effectively improving the accuracy of the finally obtained target value.

[0111] In some examples, the elite reverse learning strategy can be used to generate the reverse population of the population, effectively increasing the diversity of population individuals and the quality of solutions, thereby avoiding premature convergence of the algorithm to a certain extent; at the same time, through the elite reverse learning strategy, elite individuals can also be determined, which represent the individuals close to the optimal solution in the explored search space, so as to assist in guiding other individuals to search in a better area with the elite individuals as a reference.

[0112] Specifically, the elite reverse learning strategy can be shown as in the following formula (11):

[0113]

[0114] where t is the number of algorithm iterations, k is a random number between 0 and 1; a(t) and b(t) are as shown in the following formulas (12) and (12):

[0115] a(t) = min(N1(t), N2(t), …, N p (t))(13)

[0116] b(t) = max(N1(t), N2(t), …, N p (t))(14)

[0117] Sub-step 7023, if the individual fitness does not meet the preset value, update the population according to the preset escape energy factor, and return to step 7022 for iterative execution until the individual fitness meets the preset value or the number of iterations reaches the threshold;

[0118] In sub-step 7023, if the individual fitness does not meet the preset value, that is, the individual fitness of the population and the individual fitness of the reverse population do not meet their corresponding preset values, it means that the obtained result is not close to the optimal solution or does not meet the accuracy requirements, and the population needs to be iteratively updated to promote the search process of the algorithm and make the algorithm gradually approach the optimal solution of the multi-objective optimization model.

[0119] In some embodiments of the present invention, the updating of the population according to the preset energy factor includes:

[0120] Sub-step 70231, update the value of the escape energy factor according to the number of iterations;

[0121] Specifically, in this embodiment, the following method is used to update the value of the escape energy factor E:

[0122] E = 2×(1 - (t / T) 1 / 3 ) 1 / 3 , E1 = E×(2×r6 - 1)(15)

[0123] where E1 is the value of the updated escape energy factor, t is the number of algorithm iterations, T is the maximum number of algorithm iterations, and r6 is a random number.

[0124] The escape energy factor E is slowly reduced in a non-linear manner to prevent the algorithm from falling into local search in the later stage of iteration.

[0125] Sub-step 70232: Determine the target relationship according to the updated value of the escape energy factor, and update the population according to the target relationship.

[0126] The target relationship is the strategy for updating the population, that is, the predation strategy. For example, the above formulas (5), (6), (7), and (9). By different values of the escape energy factor E and the generated random numbers, different strategies are selected to update the population.

[0127] Sub-step 70233: Update the population using an adaptive mutation strategy according to the iteration number.

[0128] The adaptive mutation strategy can mutate the individuals in the population, avoid the algorithm from converging prematurely and falling into the local optimal solution, thereby improving the optimization ability and convergence accuracy of the algorithm.

[0129] In specific implementation, the adaptive mutation strategy can be the Gaussian random walk strategy, and part of the population individuals can also be randomly selected for mutation operations by setting the individual mutation probability Mu. Mu is as follows:

[0130]

[0131] where t is the iteration number of the algorithm, T is the maximum iteration number of the algorithm, and η is a preset mutation factor.

[0132] The Gaussian random walk strategy can be specifically as follows:

[0133] x t+1 ={x t+1 |x t+1 =Gaussian(x t ,τ), r7≥Mu}(17)

[0134] τ = cos(π / 2×(t / T) 2 ×x t -X rand,t )(18)

[0135] where Gaussian(x t ,τ) represents a Gaussian distribution with x t as the expectation and τ as the standard deviation, and X rand,t is the position of the random individual in the population at the iteration number t.

[0136] For the convenience of those skilled in the art to understand, as Figure 8 shown, an embodiment of the present invention also provides a specific flow schematic diagram of an improved algorithm based on the Harris hawk algorithm. The main improvement points are as follows:

[0137] Initialize the population using Sobol random numbers, generate the reverse population, and calculate the individual fitness to improve the reliability and accuracy of the algorithm results; update the value of the escape ability factor according to Equation (15) and update the population using an adaptive mutation strategy to avoid premature convergence of the algorithm and falling into a local optimum.

[0138] Step 7024, if the individual fitness meets the preset value, determine the target value in the population.

[0139] In Step 7024, if the individual fitness meets the preset value, that is, the individual fitness of the population and the individual fitness of the reverse population both meet their corresponding preset values, it indicates that the obtained result is already the optimal solution or has reached the accuracy requirement. Then stop the iteration of the algorithm, determine the target value in the population and output it. For example, the optimal individual of the current population can be determined as the target value. The obtained target value is the specific value corresponding to each target parameter to be determined. In practical applications, these target values can be used as the specific parameter values during the production of the energy-absorbing box.

[0140] In some examples, a complete flowchart for constructing a multi-objective optimization model and solving the model using an improved Harris hawk algorithm is also provided. Specifically, as Figure 9 shown, it includes:

[0141] Determine the target parameters of the energy-absorbing box: the length L1 of the bottom side, the distance L2 between the two concave parts in the concave hexagon structure, the length L3 of the connecting rib, the height H of the unit cell structure, the material thickness T of the unit cell structure, the material width W of the unit cell structure; determine the performance indicators, including: specific energy absorption E1, average collision force F1, peak collision force F2, compression displacement S, and mass M of the energy-absorbing box.

[0142] Use optimal Latin hypercube sampling to establish the CAD and CAE models of the energy-absorbing box, and then through simulated collision, construct the response surface models E1(Z), F1(Z), M(Z), F2(Z), S(Z) of each performance indicator.

[0143] If the response surface model meets the accuracy requirement, enter the next process; if it does not meet the accuracy requirement, resample and establish the response surface model.

[0144] Based on the response surface model, construct a multi-objective optimization model for each target parameter of the energy-absorbing box.

[0145] Use the above-mentioned improved Harris hawk algorithm (MO-EMHHO) to solve the multi-objective optimization model.

[0146] If the optimization algorithm converges, end; if the optimization algorithm does not converge, continuously optimize the model.

[0147] In some examples, the pseudo-code for improving the Harris Hawk algorithm is also provided as follows:

[0148] Input: population size N, maximum number of iterations T, decision vector dimension dim, upper and lower bounds Ub and Lb, external archive capacity R, number of grid divisions G, etc.;

[0149] Initialize the population using Equation (10);

[0150] Determine the non-dominated solutions and initialize the external archive set;

[0151] While(t < T)

[0152] Generate the reverse population using the elite reverse learning mechanism, and calculate the individual fitness of the original population and its reverse population;

[0153] for j = 1:N

[0154] Update the escape factor according to Equation (15)

[0155] if |E| ≥ 1

[0156] Update the population according to Equation (3)

[0157] else if |E| < 1

[0158] if |E| ≥ 0.5 and r ≥ 0.5

[0159] Update the population according to Equation (5)

[0160] else if |E| < 0.5 and r ≥ 0.5

[0161] Update the population according to Equation (9)

[0162] else if |E| ≥ 0.5 and r < 0.5

[0163] Update the population according to Equation (7)

[0164] else if |E| < 0.5 and r < 0.5

[0165] Update the population according to Equation (6)

[0166] endif

[0167] endif

[0168] if individual mutation

[0169] Update the individual using the adaptive mutation strategy

[0170] end if

[0171] endfor

[0172] Determine the non-dominated solutions and update the external archive set Archive

[0173] if the external archive set is full

[0174] Use roulette wheel selection to filter out the redundant solutions in the external archive

[0175] end if

[0176] Detect the boundaries to prevent the individual positions from exceeding the constraint range

[0177] endwhile

[0178] return the updated external archive

[0179] Among them, the external archive is used to store the high-quality solutions found by the algorithm during the search process, and the solutions (objective values) can be selected from it according to actual needs.

[0180] The embodiments of the present invention have the following advantages: On the one hand, the embodiments of the present invention provide an energy-absorbing box filling structure. By connecting two concave hexagonal structures, the two concave hexagons showing the negative Poisson's ratio characteristic can cooperate to absorb energy, which can effectively improve the performance of the energy-absorbing box; moreover, by adding connecting ribs in the concave part, the structural strength is improved, and the porosity is also increased, so that while the energy-absorbing box has a good energy-absorbing effect, it can also meet the lightweight design requirements. On the other hand, the embodiments of the present invention also provide a method for determining the parameter values of an energy-absorbing box. By generating Sobol sequence random numbers to initialize the population, the distribution of population individuals is made more uniform, and the reliability of the objective value is improved; and, by generating the reverse population of the population, determining the individual fitness of the population and the reverse population, and then determining the objective value in the population based on the individual fitness, the accuracy of the objective value is effectively improved, and further the energy-absorbing performance of the energy-absorbing box is improved.

[0181] It should be noted that for the method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of the present invention are not limited by the described action sequence, because according to the embodiments of the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0182] Some embodiments of the present invention also provide an electronic device, which may include a processor, a memory, and a computer program stored on the memory and capable of running on the processor. When the computer program is executed by the processor, the above method for determining the parameter values of the energy-absorbing box is implemented.

[0183] Some embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the parameter value determination method of the energy absorption box as described above is implemented.

[0184] Some embodiments of the present invention also provide a computer program product, including a computer program, which implements the parameter value determination method of the energy absorption box as described above when executed by a processor.

[0185] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments.

[0186] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. And the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0187] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0188] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0189] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1means for the functions specified in one or more boxes.

[0190] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the process Figure 1 one process or more processes and / or boxes Figure 1 the functions specified in one or more boxes.

[0191] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in Figure 1 one process or more processes and / or boxes Figure 1 one box or more boxes.

[0192] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

[0193] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or terminal device including the above elements.

[0194] The above provides a detailed introduction to an energy-absorbing box filling structure, an energy-absorbing box and a method for determining its parameter values. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An energy absorption box filling structure, characterized in that: The energy absorption box filling structure comprises: A plurality of unit cell structures, wherein the plurality of unit cell structures are arranged along an X-axis, a Y-axis, and a Z-axis, each unit cell structure comprises two concave hexagonal structures, and each concave hexagonal structure comprises: A top edge, a bottom edge, two first oblique edges and two second oblique edges, the two sides of the top edge are respectively connected to the two first oblique edges, the two sides of the bottom edge are respectively connected to the two second oblique edges, and the first oblique edges and the second oblique edges on the same side are connected to form a concave portion; The two concave hexagonal structures are connected via the top edge and the bottom edge; the concave portion is provided with connecting ribs, and the adjacent unit cell structures on the X-axis or the Y-axis are connected via the connecting ribs.

2. The energy absorption box filling structure according to claim 1, characterized in that: The unit cell structure is symmetrical about a first plane formed by the X-axis and the Y-axis, a second plane formed by the X-axis and the Z-axis, and a third plane formed by the Y-axis and the Z-axis.

3. The energy absorption box filling structure according to claim 1, characterized in that: Adjacent unit cell structures on the Z axis are connected via the top edge and the bottom edge.

4. The energy absorption box filling structure according to claim 1, characterized in that: The number of the plurality of unit cell structures on the X-axis, the Y-axis, and the Z-axis is equal.

5. An energy absorption box, characterized in that: The energy absorption box comprises the energy absorption box filling structure according to any one of claims 1 to 4.

6. A method for determining parameter values ​​of an energy absorption box, characterized in that: The energy absorption box as claimed in claim 5 comprises: Based on the target parameters of the energy absorption box, a multi-objective optimization model of the energy absorption box is constructed; Solving the multi-objective optimization model to determine the target values ​​of the target parameters includes: Generate a Sobol sequence random number to initialize a population; wherein the population represents a set of possible values ​​of the target parameter; generating a reverse population for the population, and determining the individual fitness of the reverse population and the population respectively; If the individual fitness does not meet the preset value, the population is updated according to the preset escape energy factor, and the process returns to the step of generating a reverse population for the population, and determining the individual fitness of the reverse population and the population, and the process is iteratively executed until the individual fitness meets the preset value or the number of iterations reaches a threshold; If the individual fitness meets the preset value, the target value is determined in the population.

7. The method according to claim 6, characterized in that The updating of the population according to the preset escape energy factor comprises: According to the number of iterations, updating the value of the escape energy factor; Determining a target relationship according to the updated value of the escape energy factor, and updating the population according to the target relationship; According to the number of iterations, an adaptive mutation strategy is adopted to update the population.

8. The method according to claim 6 or 7, characterized in that: The target parameters include: one or more of the length of the bottom edge, the height of the unit cell structure, the material thickness of the unit cell structure, the material width of the unit cell structure, the distance between two recessed parts of the concave hexagonal structure and the length of the connecting rib.

9. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the method for determining the parameter value of the energy absorbing box according to any one of claims 6 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for determining parameter values ​​of an energy absorbing box according to any one of claims 6 to 8 is implemented.

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