Compliant amplification mechanism topology optimization method, device, equipment and storage medium

Abstract

The present application proposes a method, device, equipment and storage medium for topological optimization of a compliant amplifying mechanism, the method comprising: constructing a corresponding compliant amplifying mechanism model to be optimized according to an input parameter set; determining a design region and a non-design region of the compliant amplifying mechanism model to be optimized; discretizing the design region into a first number of first units, and discretizing the non-design region into a second number of second units; using an optimization criterion algorithm to iteratively optimize the relative density of the units to be optimized of the compliant amplifying mechanism model to be optimized with the goal of increasing the amplified displacement and reducing the parasitic displacement, and obtaining a target compliant amplifying mechanism model that meets the convergence condition, the units to be optimized include the first unit, the convergence condition includes making the relative parasitic displacement less than a set threshold, and the relative parasitic displacement is the absolute value of the ratio of the parasitic displacement at the output end to the amplified displacement. The present application achieves the purpose of increasing the amplified displacement and reducing the parasitic displacement by optimizing the relative density of the units in the design region.

Description

Technical Field

[0001] The present application relates to the field of precision positioning technology, and in particular to a method, device, equipment and storage medium for topological optimization of a compliant amplification mechanism. Background Art

[0002] Compliant mechanisms use their own elastic deformation to achieve the transmission and conversion of force and motion, and are often used in the field of precision positioning. In order to achieve micro-nano positioning, compliant precision positioning platforms are usually driven by piezoelectric ceramic drivers, but the stroke of piezoelectric ceramic drivers is small, usually only tens of microns. In order to increase the output displacement while achieving high precision, displacement amplification is required. Compliant amplification mechanisms can be used to achieve the transmission and amplification of output displacement and increase the motion range of the compliant mechanism. Therefore, the use of piezoelectric ceramic drivers and compliant amplification mechanisms together can enable precision positioning platforms to achieve high precision while meeting stroke requirements.

[0003] Common compliant amplification mechanisms include Scott-Russell amplification mechanism, bridge amplification mechanism and lever amplification mechanism, etc. In order to improve the performance of the mechanism, these primary amplification mechanisms can be appropriately combined to form a composite amplification mechanism, and the lever amplification mechanism is widely used in composite amplification mechanisms because of its simple structure and principle.

[0004] The compliant lever amplification mechanism is based on the lever amplification principle. The configuration and lever amplification principle of a typical compliant lever amplification mechanism are as follows: Figure 1 and Figure 2 As shown, is the amplified displacement required for output, and It is the parasitic displacement generated at the output end. It can be seen that the compliant lever amplification mechanism will generate parasitic displacement at the output end while amplifying the input displacement. In order to facilitate precise control, the ideal situation is to allow the amplification mechanism to output in a straight line, that is, to eliminate the parasitic displacement. Therefore, it is an important issue to deal with the parasitic displacement generated at the output end.

[0005] In order to solve the problem of parasitic displacement at the output end of the compliant lever amplification mechanism, the symmetrical arrangement of the compliant lever amplification mechanism can be adopted, and a flexible hinge and a straight beam are used to connect the output end in a direction perpendicular to the rod, thereby ensuring that the output end can output in a straight line and eliminate parasitic displacement. Its configuration and amplification principle are as follows: Figure 3 shown.

[0006] For the parasitic motion problem of compliant lever amplification mechanism, the traditional approach is to make a specific combination based on the known compliant mechanism configuration, such as Figure 3 and Figure 4As shown, the parasitic displacement at the output end of the compliant lever amplification mechanism is offset, thereby achieving the purpose of linear output. However, this form will increase the volume of the mechanism, which is not conducive to the miniaturization of the mechanism, and while offsetting the parasitic displacement, it will also lose part of the output displacement of the lever amplification mechanism, reducing the displacement amplification ratio of the mechanism. Summary of the invention

[0007] In order to solve the technical problem in the prior art that the output amplified displacement is reduced and the size of the compliant mechanism is increased in order to solve the parasitic displacement. The present application provides a method, device, equipment and storage medium for topological optimization of a compliant amplifying mechanism, the main purpose of which is to make the output end of the compliant amplifying mechanism have a larger amplified displacement while reducing the parasitic displacement of the output end and reducing the size of the compliant amplifying mechanism.

[0008] To achieve the above objectives, the present application provides a method for topology optimization of a compliant amplification mechanism, the method comprising:

[0009] Constructing a corresponding compliant amplification mechanism model to be optimized according to the input parameter set;

[0010] Determine the design area and non-design area of ​​the compliant amplification mechanism model to be optimized;

[0011] discretize the design area into a first number of first units, and discretize the non-design area into a second number of second units;

[0012] An optimization criterion algorithm is used to iteratively optimize the relative density of the unit to be optimized of the compliant amplifying mechanism model to be optimized with the goal of increasing the amplified displacement and reducing the parasitic displacement, so as to obtain a target compliant amplifying mechanism model that meets the convergence conditions, wherein the unit to be optimized includes the first unit, and the convergence conditions include making the relative parasitic displacement less than a set threshold, and the relative parasitic displacement is the absolute value of the ratio of the parasitic displacement at the output end of the amplifying mechanism model to be optimized to the amplified displacement.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides a topology optimization device for a compliant amplification mechanism, the device comprising:

[0014] A model building module, used for building a corresponding compliant amplification mechanism model to be optimized according to an input parameter set;

[0015] A partitioning module, used to determine the design area and non-design area of ​​the compliant amplification mechanism model to be optimized;

[0016] A discretization module, used for discretizing the design area into a first number of first units, and discretizing the non-design area into a second number of second units;

[0017] The optimization module is used to iteratively optimize the relative density of the to-be-optimized unit of the to-be-optimized compliant amplifying mechanism model by adopting an optimization criterion algorithm with the goal of increasing the amplified displacement and reducing the parasitic displacement, so as to obtain a target compliant amplifying mechanism model that meets the convergence condition, wherein the to-be-optimized unit includes the first unit, and the convergence condition includes making the relative parasitic displacement less than a set threshold, and the relative parasitic displacement is the absolute value of the ratio of the parasitic displacement at the output end of the to-be-optimized amplifying mechanism model to the amplifying displacement.

[0018] To achieve the above objectives, the present application also provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the compliant amplification mechanism topology optimization method as described in any of the above items are performed.

[0019] To achieve the above objectives, the present application also provides a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the processor executes the steps of the flexible amplification mechanism topology optimization method as described in any of the above items.

[0020] The topological optimization method, device, equipment and storage medium of the compliant amplifying mechanism proposed in this application are used to design a new type of compliant amplifying mechanism so that it can have a large output displacement while reducing the parasitic displacement at the output end. The topological optimization method is a conceptual design method that can find the optimal distribution of materials and obtain the optimal configuration under given design domains and constraints, so that the mechanism can achieve the optimal performance. This method is used in the design of compliant amplifying mechanisms to obtain a novel compliant amplifying mechanism with a high displacement amplification ratio and low parasitic motion. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the working principle of a typical compliant lever amplification mechanism in the prior art;

[0022] Figure 2 It is a schematic diagram of lever amplification in the prior art;

[0023] Figure 3 It is a structural schematic diagram of a symmetrically arranged configuration of a compliant lever amplification mechanism in the prior art;

[0024] Figure 4 It is a schematic diagram of the symmetrical arrangement of the lever amplification mechanism in the prior art;

[0025] Figure 5 It is a schematic diagram of a flow chart of a topology optimization method of a compliant amplification mechanism in one embodiment of the present application;

[0026] Figure 6 This is a construction diagram of a compliant amplification mechanism model to be optimized in one embodiment of the present application;

[0027] Figure 7 This is a construction diagram of a compliant amplification mechanism model to be optimized in a specific embodiment of the present application;

[0028] Figure 8 This is a result graph of the compliant amplification mechanism to be optimized in one embodiment of the present application;

[0029] Fig. 9 It is a displacement magnification ratio iteration diagram in one embodiment of the present application;

[0030] Fig.10 It is a parasitic displacement iteration diagram in one embodiment of the present application;

[0031] Fig.11 A three-dimensional diagram of a target compliance amplification mechanism in one embodiment of the present application;

[0032] Fig.12 A graph of a compliant amplification mechanism corresponding to different iteration times under the same set of input parameters in an embodiment of the present application;

[0033] Fig.13 This is a comparison effect diagram of the results of the compliant amplification mechanism output by different sets of input parameter sets in one embodiment of the present application;

[0034] Fig.14 This is a structural block diagram of a topology optimization device for a compliant amplification mechanism in one embodiment of the present application;

[0035] Fig.15 This is a block diagram of the internal structure of a computer device in one embodiment of the present application.

[0036] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme 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. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of this application. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] The topology optimization method of the compliant amplification mechanism provided in the present application can be applied to terminal devices, wherein the terminal devices can be, but are not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices.

[0039] Figure 5Schematic diagram of the process of topology optimization method of compliant amplification mechanism in one embodiment of the present application. Figure 5 The topology optimization method of the compliant amplification mechanism includes the following steps S100-S400.

[0040] S100: Constructing a corresponding compliant amplification mechanism model to be optimized according to the input parameter set.

[0041] Specifically, the compliant amplifying mechanism model to be optimized is a model corresponding to the compliant amplifying mechanism to be designed. This embodiment can be applied to design software installed in a terminal device, and the design software can be, for example, MATLAB software. The input parameter set is a modeling parameter provided by a designer to the design software, wherein the input parameter set includes structural parameters for constructing the compliant amplifying mechanism model and load parameters for applying force to the constructed compliant amplifying mechanism to be optimized.

[0042] The compliant amplifying mechanism models constructed with different structural parameters may be different. The structural parameters are used to determine the initial structure or configuration, input end and output end, etc. of the compliant amplifying mechanism model to be constructed and optimized.

[0043] Designers can provide multiple sets of different input parameter sets to construct different compliant amplification mechanism models to be optimized.

[0044] S200: Determine the design area and non-design area of ​​the compliant amplification mechanism model to be optimized.

[0045] Specifically, the region division parameter in the input parameter set determines how to divide the design region and non-design region of the compliant amplification mechanism model to be optimized. The design region is the region that needs to be optimized for relative density, and the relative density of the non-design region remains unchanged.

[0046] S300: discretizing the design area into a first number of first units, and discretizing the non-design area into a second number of second units.

[0047] Specifically, the compliant amplifying mechanism model to be optimized is a finite element model, and the finite element method is used to discretize the design area and the non-design area respectively to obtain the respective first unit and second unit.

[0048] The first unit and the second unit are both four-node units. The input parameter set includes a discrete parameter, which is used to specify the number of first units and second units into which the design region and the non-design region are discretized.

[0049] S400: Adopting an optimization criterion algorithm to iteratively optimize the relative density of the unit to be optimized of the compliant amplifying mechanism model to be optimized with the goal of increasing the amplified displacement and reducing the parasitic displacement, and obtaining a target compliant amplifying mechanism model that meets the convergence condition, wherein the unit to be optimized includes the first unit, and the convergence condition includes making the relative parasitic displacement less than a set threshold, and the relative parasitic displacement is the absolute value of the ratio of the parasitic displacement at the output end of the amplifying mechanism model to be optimized to the amplified displacement.

[0050] Specifically, the optimization criterion algorithm is used to solve the compliant amplifying mechanism model to be optimized with the goal of increasing the amplified displacement at the output end and reducing the parasitic displacement, so as to obtain the target relative density of the unit to be optimized. The relative density of the unit to be optimized of the target compliant amplifying mechanism model is the target relative density. The target compliant amplifying mechanism model meets the convergence condition.

[0051] In one embodiment, step S100 specifically includes:

[0052] The fixed end, input end and output end of the compliant amplification mechanism model to be optimized are determined according to the input parameter set, a first virtual spring is set at the input end, an input load is applied to the input end, and a second virtual spring is set at the output end, and a virtual load is applied to the output end.

[0053] Specifically, refer to Figure 6 , the lower left corner is determined as the fixed end, the location of input point A is the input end, and the location of target point B is the output end. Of course, Figure 6 This is only an exemplary display, and the actual relative positions of the input terminal, output terminal and fixed terminal are configured according to the designer.

[0054] A first virtual spring is provided at the input point A of the input end, and a second virtual spring is provided at the target point B of the output end, and both the first virtual spring and the second virtual spring have spring stiffness. Virtual springs are added at the input end and the output end to simulate the reaction force and gap between the compliant amplification mechanism and other components. An input load is also applied to the input point A of the input end, and the input load is the input force. A virtual load is also applied to the target point B of the output end, and the virtual load includes a first virtual load in the X-axis direction and a second virtual load in the Y-axis direction. The target point B can be the midpoint position of the output end but is not limited thereto.

[0055] Step S200 specifically includes:

[0056] The design area of ​​the compliant amplifying mechanism model to be optimized is determined as a rectangular area between the fixed end and the input end of the compliant amplifying mechanism model to be optimized, and the non-design area is determined as a straight beam between the input end and the output end of the compliant amplifying mechanism model to be optimized.

[0057] Specifically, refer to Figure 6, area 1 is determined as the design area, area 2 is determined as the non-design area, the fixed end is located at the lower left of the design area 1, the input end is located at the bottom of the design area 1, and the output end is located at the right side of the non-design area 2.

[0058] The positions of the fixed end, input end, output end, input point A, and target point B of the compliant amplifying mechanism model to be optimized in the present application are determined by the structural parameters in the input parameter set, and the design area and non-design area of ​​the compliant amplifying mechanism model to be optimized are specifically determined by the area division parameters in the input parameter set. The first virtual load and the second virtual load in the input load and virtual load are both load vectors, and their values ​​are determined by the load parameters in the input parameter set. The spring stiffness of the first virtual spring and the spring stiffness of the second virtual spring are determined by the first stiffness parameter in the input parameter set.

[0059] In one embodiment, step S400 specifically includes:

[0060] The relative parasitic displacement is less than the set threshold as the convergence condition, the objective function is constructed with the goal of increasing the amplified displacement and reducing the parasitic displacement, the volume ratio does not exceed the volume ratio threshold as the constraint condition, and the relative density of the unit to be optimized is used as the model parameter to be optimized to construct the mathematical model of the compliant amplification mechanism model, wherein the volume ratio is determined by the relative density of the unit to be optimized;

[0061] Obtaining an initial relative density value of the relative density of the unit to be optimized from the input parameter set as a value of the model parameter to be optimized at the initial iterative optimization node;

[0062] The relative density of the unit to be optimized is iteratively optimized through a mathematical model, and the target compliant amplification mechanism model that meets the convergence conditions is obtained.

[0063] Specifically, the amplified displacement is the output displacement of the output end of the compliant amplifying mechanism model to be optimized, and the parasitic displacement is the parasitic displacement of the output end of the compliant amplifying mechanism model to be optimized. The purpose of this application is to increase the amplified displacement of the output end and reduce the parasitic displacement as much as possible by optimizing the relative density of the unit to be optimized, that is, to make the displacement amplification ratio of the amplified displacement to the input displacement as large as possible, and at the same time, to make the absolute value of the ratio of the parasitic displacement to the amplified displacement (relative parasitic displacement) as small as possible.

[0064] In the variable density method of topology optimization, the relative density of the material determines the stiffness of the material. The input force (input load) and the stiffness of the material determine the displacement field generated under the input force. The amplified displacement and parasitic displacement can be determined by the displacement field. Under the same input force, the amplified displacement and parasitic displacement generated by different stiffness of the material are different. Moreover, the relative density of the material also determines the volume of the entire compliant amplifying mechanism model to be optimized, that is, it determines the volume ratio. Therefore, the mathematical model of this embodiment aims to increase the amplified displacement and reduce the parasitic displacement as much as possible, and uses the sensitivity of the objective function and the sensitivity of the constraint function as the solution direction (change direction) of the relative density of the unit to be optimized. The relative density of the unit to be optimized is changed with the volume ratio as the constraint condition to solve the target relative density that meets the convergence condition, and then obtain the target compliant amplifying mechanism model.

[0065] In one embodiment, the relative density of the unit to be optimized is iteratively optimized through a mathematical model to obtain a target compliant amplification mechanism model that meets the convergence condition, including:

[0066] Iterative optimization steps are executed cyclically through the mathematical model to iteratively optimize the relative density of the unit to be optimized until the convergence condition is met;

[0067] The iterative optimization steps include:

[0068] Obtain the value of the model parameter to be optimized at the current iterative optimization node as a candidate density value;

[0069] The amplified displacement and parasitic displacement at the output end of the current iterative optimization node are calculated through the mathematical model and the candidate density value;

[0070] According to the amplified displacement and parasitic displacement at the output end of the current iterative optimization node, it is judged whether the compliant amplified mechanism model to be optimized at the current iterative optimization node meets the convergence condition;

[0071] If the convergence condition is not met, the sensitivity corresponding to each unit to be optimized is calculated based on the candidate density value of the current iterative optimization node and the objective function;

[0072] According to the constraints and sensitivity of the mathematical model and the candidate density value of the current iterative optimization node, the values ​​of the model parameters to be optimized are updated to obtain the candidate density value of the next iterative optimization node.

[0073] Specifically, this embodiment obtains the final target relative density of the unit to be optimized by iterative optimization. Each iterative optimization is an iterative optimization node. The value of the relative density of the unit to be optimized at the first iterative optimization node is the initial relative density value in the input parameter set.

[0074] The units to be optimized in this embodiment include a first number of first units obtained by discretizing the design area, and a second number of second units in the non-design area are units that do not need to be optimized, and their relative density is a fixed value. The relative density value of the second unit is determined by the specified density value in the input parameter set. For example, the relative density value of the second unit can be set to 1 all the time. It is also possible to specify that the initial relative density value of the first unit is also 1.

[0075] According to the candidate density value of the unit to be optimized at the current iteration node, the unit stiffness matrix of the corresponding unit to be optimized at the current iteration node can be calculated, and according to the specified density value of the non-optimized unit, the unit stiffness matrix of the corresponding second unit can be calculated. Since the first unit and the second unit are both four-node units, the input point A is one of the four nodes of a unit to be optimized (the first unit). Since the first virtual spring is set at the input point A, and each unit stiffness matrix includes the node stiffness corresponding to each of the four nodes, the node stiffness of the input point A in the unit stiffness matrix of the unit to be optimized where the input point A is located needs to be added with the spring stiffness of the first virtual spring. The spring stiffness k of the first virtual spring is in is a parameter in the input parameter set. Specifically, the spring stiffness k of the first virtual spring in The value may be, for example, 1.

[0076] Similarly, the target point B is one of the four nodes of a second unit. Since the second virtual spring is set at the target point B, and each unit stiffness matrix includes the node stiffness corresponding to each of the four nodes, the node stiffness of the target point B in the unit stiffness matrix of the second unit where the target point B is located needs to be added with the spring stiffness of the second virtual spring. The spring stiffness k of the second virtual spring is out is a parameter in the input parameter set. Specifically, the spring stiffness k of the second virtual spring is out The value may be, for example, 0.0001.

[0077] The mathematical model also includes the functional relationship between the input load and the global stiffness matrix and displacement field. The global stiffness matrix includes the unit stiffness matrices of all first units and second units. Since the input load is a given value in the input parameter set, a current displacement field can be calculated at the current iteration node. According to the current displacement field, the displacement of each unit of the current iteration node can be obtained, and the amplified displacement and parasitic displacement of the output end of the current iteration node can be calculated by superposition according to the displacement of each unit.

[0078] Whether the convergence condition is met is determined based on the absolute value of the ratio of the parasitic displacement to the amplified displacement, i.e., the relative parasitic displacement, obtained by calculation. The convergence condition is specifically that the absolute value of the ratio of the parasitic displacement to the amplified displacement is less than or equal to a preset threshold. Of course, the stop condition of the iterative optimization of the present application may also include that the number of iterative optimizations reaches a preset number. That is, the iteration is stopped if the number of iterative optimizations reaches a preset number and the absolute value of the ratio of the parasitic displacement to the amplified displacement is less than or equal to any one of the preset thresholds.

[0079] If the current iteration node does not meet the convergence condition, it is necessary to enter the next iteration node. The value of the unit to be optimized at the next iteration node needs to be iteratively optimized based on the candidate density value of the current iteration node. Specifically, the sensitivity of each unit to be optimized is calculated according to the objective function and the constraint function, and the candidate density value of the next iteration node of the unit to be optimized is calculated according to the sensitivity of the unit to be optimized and the candidate density value of the unit to be optimized at the current iteration node. By analogy, the candidate density value of each unit to be optimized at the next iteration node can be obtained. Among them, the sensitivity is the derivation of the design variables by the objective function and the constraint function, and the sensitivity is the gradient, which determines the update direction of the relative density of the corresponding unit to be optimized.

[0080] In one embodiment, the objective function includes:

[0081]

[0082] in, is the amplified displacement generated at the output end, is the parasitic displacement generated at the output end, ω is the weight coefficient and is one of the parameters in the input parameter set;

[0083] Convergence conditions include:

[0084]

[0085] The constraints include:

[0086]

[0087] 0≤x min ≤x i ≤1,(i=1,2,3,…,n)

[0088] Among them, find:X={x1,x2,…,x n} T ∈R n , X is the set of values ​​of the relative density of the unit to be optimized, x i is the value of the relative density of the i-th unit to be optimized, g(x) is the constraint function, g(x) is the volume ratio; v iis the volume of the i-th unit, V0 is the initial design domain volume, V * is the volume ratio threshold, n is the total number of units to be optimized, x min is the minimum relative density;

[0089] st:Ku=F in

[0090] Ku x =F x

[0091] Ku y =F y

[0092] Where K is the global stiffness matrix and u is the input load F in The displacement field generated by the action, u x The first virtual load F at the output end x The displacement field under the action, u y The second virtual load F at the output end y The displacement field under the action, the global stiffness matrix K is determined by the relative density of the first unit and the second unit and the first spring stiffness corresponding to the first virtual spring and the second spring stiffness corresponding to the second virtual spring;

[0093] The sensitivity of the objective function is:

[0094]

[0095] The sensitivity of the constraint function is:

[0096]

[0097] Among them, k0 is the unit stiffness matrix of the corresponding unit to be optimized, and p is a given penalty factor in order to make the obtained image contour clearer.

[0098] Specifically, in each round of iterative optimization, the unit to be optimized has a candidate density value. The initial design domain volume V0 and volume ratio threshold V * are all given values ​​in the input parameter set. The constraint condition of this embodiment is that the ratio of the sum of the volumes of all units to be optimized to the volume of the initial design domain does not exceed the volume ratio threshold, and the relative density of each unit to be optimized does not exceed the minimum relative density. Therefore, the candidate density value of each unit to be optimized at the next iteration node can be determined based on the constraint condition, sensitivity and the candidate density value of the current iteration optimization node.

[0099] In one embodiment, the method further comprises:

[0100] Output a compliant amplifying mechanism result graph corresponding to at least one target compliant amplifying mechanism model, wherein each target compliant amplifying mechanism model is obtained by iteratively optimizing the corresponding compliant amplifying mechanism model to be optimized, and each compliant amplifying mechanism model to be optimized is constructed according to different input parameter sets.

[0101] Specifically, different groups of input parameter sets can construct different compliant amplification mechanisms. In the same group of input parameter sets, the target relative density is found by optimizing the relative density of the unit to be optimized, so that the displacement amplification ratio of the obtained target compliant amplification mechanism model is as large as possible. In this embodiment, different compliant amplification mechanism models to be optimized constructed by multiple groups of input parameter sets are iteratively optimized to obtain corresponding multiple target compliant amplification mechanism models, and the compliant amplification mechanism result graph of each target compliant amplification mechanism model is output, so that designers can select the optimal compliant amplification mechanism result graph that can be achieved. The target relative density of the unit to be optimized of this optimal compliant amplification mechanism result graph is the final relative density of each unit to be optimized in the design area of ​​the final compliant amplification mechanism.

[0102] The following is a specific implementation step to illustrate the working principle of this application:

[0103] See also Figure 7 Based on the typical compliant lever amplification mechanism configuration, a topology optimization model of the compliant amplification mechanism is established. Figure 7 For the specific construction diagram of the compliant amplification mechanism model, the total length of the design domain is 2L, the design area 1 is defined as a rectangular area with a length of 0.8L and a width of 0.4L, and the rigid area 2 is defined as a rectangular area with a length of 1.2L and a width of 0.2L.

[0104] Set the elastic modulus of the design domain material to E = 1, Poisson's ratio μ = 0.3, and discretize the design domain 1 into 40 × 80 four-node square elements with a relative density of x. i (0≤x min ≤x i ≤1), discretize the rigid region 2 into 20×120 four-node square elements, and set the relative density of the elements to 1. Fix the region with a length of 0.2L at the lower left corner of the design domain 1, and add F at the input point position 0.6L away from the midpoint of the fixed point. in = 1, and add a spring stiffness k in = 1, add F in the Y and X directions at the midpoint of the output end. y =1 and F x = 1, and add a spring stiffness k out = 0.0001 virtual output spring.

[0105] A mathematical model of topological optimization of the compliant amplification mechanism is established. In order to ensure that the mechanism has a large displacement amplification ratio and the parasitic displacement at the output end is small, the amplified displacement at the output end is Maximum and parasitic displacement at the output The minimum weighting is performed according to a certain proportion, so that the multi-objective optimization problem is transformed into a single-objective optimization problem, and the mathematical model of topology optimization of the compliant amplification mechanism is obtained as follows:

[0106] find:X={x1,x2,…,x n} T ∈R n

[0107]

[0108] st:Ku=F in

[0109] Ku x =F x

[0110] Ku y =F y

[0111]

[0112] 0≤x min ≤x i ≤1,(i=1,2,3,…,n)

[0113] Where: x i is the relative density of the material unit, which is the design variable of the entire optimization model, K is the global stiffness matrix, and u is the input load vector F in The displacement field generated by the action, u x is the virtual load vector F at the output end x The displacement field under the action, u y is the virtual load vector F at the output end y The displacement field under the action, ω is the weight coefficient, and the weight coefficient ω=0.4, is the amplified displacement generated at the output end, is the parasitic displacement generated at the output, v i is the volume of the i-th unit, V0 is the initial design domain volume, V * is the volume ratio, which is taken as 60%, n is the number of finite elements, x min It is the minimum unit relative density, usually taken as 0.001.

[0114] The sensitivity of the objective function and constraint conditions in the topology optimization problem of the compliant amplification mechanism is calculated and analyzed separately. The sensitivity of the objective function is:

[0115]

[0116] The sensitivity of the constraint function is:

[0117]

[0118] The optimization criterion method is used to solve the topology optimization problem of the compliant amplification mechanism, update the design variables, and determine whether the convergence conditions are met. If converged, the iteration is stopped to obtain the topology optimization result diagram. If not, the iteration is continued until the convergence conditions are met. The convergence conditions are Or the number of solution iterations reaches the set maximum value. Fig. 9 and Fig.10 Shown are the displacement amplification ratio iteration diagram and the parasitic displacement iteration diagram. Fig.12 The graph of the compliant amplifying mechanism corresponding to different iteration times under the same set of input parameters in one embodiment of the present application is shown in Figure 1. As the number of iterations increases, the graph of topological optimization gradually stabilizes, and finally a compliant amplifying mechanism graph that meets the convergence condition is obtained. The commercial software MATLAB is used to program and solve the topological optimization problem of the compliant amplifying mechanism, and the final result graph of the compliant amplifying mechanism is obtained, as shown in Figure 1. Figure 8 The outline of the final flexible magnifying mechanism result graph is extracted in MATLAB. After the engineers properly trim the edges and holes of the graph and complete the two-dimensional drawing, the two-dimensional drawing is imported into the three-dimensional modeling software. The three-dimensional model of the new flexible magnifying mechanism is drawn as shown in the figure. Fig.11 The three-dimensional model of the new compliant amplifying mechanism and the three-dimensional model of the typical compliant lever amplifying mechanism were imported into the commercial software ANSYS WORKBENCH for simulation, and the performance comparison results of the new compliant lever amplifying mechanism and the typical compliant lever amplifying mechanism were obtained as shown in Table 1.

[0119] Table 1

[0120]

[0121] By comparison, the new compliant amplifying mechanism obtained in the present application has a higher displacement amplification ratio and a lower parasitic displacement at the output end compared to the typical compliant lever amplifying mechanism in the prior art.

[0122] Fig.13 The flexible amplification mechanism diagram output by different sets of input parameter sets in one embodiment of the present application; Fig.13 In this embodiment, the weight coefficient ω and the spring stiffness k of the first virtual spring in the input parameter set are changed. in and the spring stiffness k of the second virtual spring outTaking the other parameters in the input parameter set as the same as an example, it is explained that the topology optimization effects of the compliant amplification mechanism model to be optimized constructed with different groups of input parameter sets are different.

[0123] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0124] Fig.14 This is a structural block diagram of a topology optimization device for a compliant amplification mechanism in an embodiment of the present application. Fig.14 , the device comprises:

[0125] A model building module 100 is used to build a corresponding compliant amplification mechanism model to be optimized according to an input parameter set;

[0126] A division module 200, for determining a design area and a non-design area of ​​the compliant amplification mechanism model to be optimized;

[0127] A discretization module 300 is used to discretize the design area into a first number of first units and discretize the non-design area into a second number of second units;

[0128] The optimization module 400 is used to iteratively optimize the relative density of the unit to be optimized of the flexible amplifying mechanism model to be optimized by using an optimization criterion algorithm with the goal of increasing the amplified displacement and reducing the parasitic displacement, so as to obtain a target flexible amplifying mechanism model that meets the convergence condition, wherein the unit to be optimized includes the first unit, and the convergence condition includes making the relative parasitic displacement less than a set threshold, and the relative parasitic displacement is the absolute value of the ratio of the parasitic displacement at the output end of the amplifying mechanism model to be optimized to the amplified displacement.

[0129] In one embodiment, the model building module 100 is specifically used to determine the fixed end, input end and output end of the compliant amplification mechanism model to be optimized according to the input parameter set, set a first virtual spring at the input end, apply an input load to the input end, set a second virtual spring at the output end, and apply a virtual load to the output end;

[0130] The division module 200 is specifically used to determine that the design area of ​​the flexible amplifying mechanism model to be optimized is the rectangular area between the fixed end and the input end of the flexible amplifying mechanism model to be optimized, and to determine that the non-design area is the straight beam between the input end and the output end of the flexible amplifying mechanism model to be optimized.

[0131] In one embodiment, the optimization module 400 specifically includes:

[0132] A mathematical model building module is used to build a mathematical model of a compliant amplification mechanism model with the relative parasitic displacement being less than a set threshold as a convergence condition, with the objective of increasing the amplified displacement and reducing the parasitic displacement as the goal, with the volume ratio not exceeding the volume ratio threshold as a constraint condition, and with the relative density of the unit to be optimized as the model parameter to be optimized, wherein the volume ratio is determined by the relative density of the unit to be optimized;

[0133] An initialization module, used to obtain an initial relative density value of the relative density of the unit to be optimized from the input parameter set as a value of the model parameter to be optimized at the initial iterative optimization node;

[0134] The iterative optimization module is used to iteratively optimize the relative density of the unit to be optimized through a mathematical model to obtain a target compliant amplification mechanism model that meets the convergence conditions.

[0135] In one embodiment, the iterative optimization module is specifically used to iteratively optimize the relative density of the unit to be optimized by looping the iterative optimization module until a convergence condition is met;

[0136] Among them, the loop iteration optimization module includes:

[0137] A density value acquisition unit, used to acquire the value of the model parameter to be optimized at the current iterative optimization node as a candidate density value;

[0138] A displacement calculation unit, used for calculating the amplified displacement and the parasitic displacement at the output end of the current iterative optimization node through a mathematical model and a candidate density value;

[0139] A convergence judgment unit, used to judge whether the compliant amplification mechanism model to be optimized at the current iterative optimization node meets the convergence condition according to the amplified displacement and the parasitic displacement at the output end of the current iterative optimization node;

[0140] A sensitivity calculation unit is used to calculate the sensitivity corresponding to each unit to be optimized according to the candidate density value of the current iterative optimization node and the objective function if the convergence condition is not met;

[0141] The model parameter updating unit is used to update the value of the model parameter to be optimized according to the constraint conditions and sensitivity of the mathematical model and the candidate density value of the current iterative optimization node to obtain the candidate density value of the next iterative optimization node.

[0142] In one embodiment, the objective function includes:

[0143]

[0144] in, is the amplified displacement generated at the output end, is the parasitic displacement generated at the output end, ω is the weight coefficient;

[0145] Convergence conditions include:

[0146]

[0147] The constraints include:

[0148]

[0149] 0≤x min ≤x i ≤1,(i=1,2,3,…,n)

[0150] Among them, find:X={x1,x2,…,x n} T ∈R n , X is the set of values ​​of the relative density of the unit to be optimized, x i is the value of the relative density of the i-th unit to be optimized, g(x) is the constraint function, g(x) is the volume ratio; v i is the volume of the i-th unit to be optimized, V0 is the volume of the initial design domain, V * is the volume ratio threshold, n is the total number of units to be optimized, x min is the minimum relative density;

[0151] st:Ku=F in

[0152] Ku x =F x

[0153] Ku y =F y

[0154] Where K is the global stiffness matrix and u is the input load F in The displacement field generated by the action, u x The first virtual load F at the output end x The displacement field under the action, u y The second virtual load F at the output end y The displacement field under the action, the global stiffness matrix K is determined by the relative density of the first unit and the second unit and the first spring stiffness corresponding to the first virtual spring and the second spring stiffness corresponding to the second virtual spring;

[0155] The sensitivity of the objective function is:

[0156]

[0157] The sensitivity of the constraint function is:

[0158]

[0159] Among them, k0 is the element stiffness matrix of the corresponding element to be optimized, and p is the penalty factor.

[0160] In one embodiment, the apparatus further comprises:

[0161] An output module is used to output a compliant amplification mechanism result graph corresponding to at least one target compliant amplification mechanism model, wherein each target compliant amplification mechanism model is obtained by iteratively optimizing the corresponding compliant amplification mechanism model to be optimized, and each compliant amplification mechanism model to be optimized is constructed according to different input parameter sets.

[0162] The meaning of "first" and "second" in the above modules / units is only to distinguish different modules / units, and is not used to define which module / unit has a higher priority or other limiting meanings. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of modules in this application is only a logical division, and there may be other division methods when implemented in actual applications.

[0163] For the specific definition of the compliant amplification mechanism topology optimization device, please refer to the definition of the compliant amplification mechanism topology optimization method mentioned above, which will not be repeated here. Each module in the above-mentioned compliant amplification mechanism topology optimization device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0164] Fig.15 1 is a block diagram of the internal structure of a computer device in one embodiment of the present application. The computer device may specifically be a terminal device. Fig.15As shown, the computer device includes a processor, a memory, a network interface, an input device and a display screen connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory includes a storage medium and an internal memory. The storage medium can be a non-volatile storage medium or a volatile storage medium. The storage medium stores an operating system and can also store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can implement the topology optimization method of the compliant amplification mechanism. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the storage medium. The internal memory can also store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the topology optimization method of the compliant amplification mechanism. The network interface of the computer device is used to communicate with an external server through a network connection. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the shell of the computer device, or an external keyboard, touchpad or mouse.

[0165] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions (such as a computer program) stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the method for topological optimization of a compliant amplification mechanism in the above embodiment are implemented, such as Figure 5 Steps S100 to S400 and other extensions of the method and related steps are shown. Alternatively, when the processor executes the computer-readable instructions, the functions of each module / unit of the topology optimization device for the compliant amplification mechanism in the above embodiment are realized, for example Fig.14 The functions of the modules 100 to 400 are shown in detail and will not be described again to avoid repetition.

[0166] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of a computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0167] The memory can be used to store computer-readable instructions and / or modules. The processor implements various functions of the computer device by running or executing the computer-readable instructions and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, video data, etc.), etc.

[0168] The memory may be integrated into the processor or provided separately from the processor.

[0169] Those skilled in the art will understand that Fig.15 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0170] In one embodiment, a computer-readable storage medium is provided on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the steps of the method for topology optimization of a compliant amplification mechanism in the above embodiment are implemented, such as Figure 5 Steps S100 to S400 and other extensions of the method and related steps are shown. Alternatively, when the computer-readable instructions are executed by the processor, the functions of each module / unit of the topology optimization device for the compliant amplification mechanism in the above embodiment are realized, for example Fig.14 The functions of the modules 100 to 400 are shown in detail and will not be described again to avoid repetition.

[0171] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0172] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, device, article or method including the element.

[0173] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. Through the description of the above implementation modes, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware, but in many cases the former is a better implementation mode. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, including a number of instructions to enable a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0174] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for topological optimization of a compliant amplification mechanism, characterized in that: The method comprises: Constructing a corresponding compliant amplification mechanism model to be optimized according to the input parameter set; Determine the design area and non-design area of ​​the compliant amplification mechanism model to be optimized; Discretize the design area into a first number of first units, and discretize the non-design area into a second number of second units; An optimization criterion algorithm is used to iteratively optimize the relative density of the unit to be optimized of the compliant amplifying mechanism model to be optimized with the goal of increasing the amplified displacement and reducing the parasitic displacement, so as to obtain a target compliant amplifying mechanism model that meets the convergence condition, wherein the unit to be optimized includes the first unit, and the convergence condition includes making the relative parasitic displacement less than a set threshold, and the relative parasitic displacement is the absolute value of the ratio of the parasitic displacement at the output end of the amplifying mechanism model to be optimized to the amplified displacement.

2. The method according to claim 1, characterized in that The step of constructing a corresponding compliant amplification mechanism model to be optimized according to the input parameter set includes: Determine the fixed end, input end and output end of the compliant amplification mechanism model to be optimized according to the input parameter set, set a first virtual spring at the input end, apply an input load to the input end, set a second virtual spring at the output end, and apply a virtual load to the output end; Determining the design area and the non-design area of ​​the compliant amplification mechanism model to be optimized includes: The design area of ​​the compliant amplifying mechanism model to be optimized is determined to be a rectangular area between the fixed end and the input end of the compliant amplifying mechanism model to be optimized, and the non-design area is determined to be a straight beam between the input end and the output end of the compliant amplifying mechanism model to be optimized.

3. The method according to claim 2, characterized in that The optimization criterion algorithm is used to iteratively optimize the relative density of the unit to be optimized of the compliant amplifying mechanism model to be optimized with the goal of increasing the amplified displacement and reducing the parasitic displacement, so as to obtain the target compliant amplifying mechanism model that meets the convergence condition, including: Taking the relative parasitic displacement being less than a set threshold as a convergence condition, building an objective function with the goal of increasing the amplified displacement and reducing the parasitic displacement, taking the volume ratio not exceeding the volume ratio threshold as a constraint condition, and building a mathematical model of the compliant amplification mechanism model with the relative density of the unit to be optimized as a model parameter to be optimized, wherein the volume ratio is determined by the relative density of the unit to be optimized; Acquire the initial relative density value of the relative density of the unit to be optimized from the input parameter set as the value of the model parameter to be optimized at the initial iterative optimization node; The relative density of the unit to be optimized is iteratively optimized through the mathematical model to obtain a target compliant amplification mechanism model that meets the convergence condition.

4. The method according to claim 3, characterized in that The iterative optimization of the relative density of the unit to be optimized by the mathematical model to obtain a target compliant amplification mechanism model that meets the convergence condition includes: cyclically executing an iterative optimization step through the mathematical model to iteratively optimize the relative density of the unit to be optimized until the convergence condition is met; Wherein, the iterative optimization step comprises: Obtaining the value of the model parameter to be optimized at the current iterative optimization node as a candidate density value; Calculating the amplified displacement and the parasitic displacement at the output end of the current iterative optimization node through the mathematical model and the candidate density value; Judging whether the compliant amplification mechanism model to be optimized of the current iterative optimization node satisfies the convergence condition according to the amplified displacement and the parasitic displacement at the output end of the current iterative optimization node; If the convergence condition is not met, the sensitivity corresponding to each unit to be optimized is calculated according to the candidate density value of the current iterative optimization node and the objective function; The values ​​of the model parameters to be optimized are updated according to the constraints of the mathematical model, the sensitivity and the candidate density value of the current iterative optimization node to obtain the candidate density value of the next iterative optimization node.

5. The method according to claim 3, characterized in that: The objective function includes: Among them, the is the amplified displacement generated at the output end, is the parasitic displacement generated at the output end, ω is the weight coefficient; The convergence conditions include: The constraints include: 0≤x min ≤x i ≤1,(i=1,2,3,...,n) Among them, find: X = {x1, x2, ..., x n } T ∈R n , X is the set of values ​​of the relative density of the unit to be optimized, x i is the value of the relative density of the i-th unit to be optimized, g(x) is the constraint function, g(x) is the volume ratio; v i is the volume of the i-th unit to be optimized, V0 is the volume of the initial design domain, V * is the volume ratio threshold, n is the total number of units to be optimized, x min is the minimum relative density; st: Ku=F in Stop x =F x Stop y =F y Where K is the global stiffness matrix and u is the input load F in The displacement field generated by the action, u x The first virtual load F at the output end x The displacement field under the action, u y The second virtual load F at the output end y The displacement field under the action of the global stiffness matrix K is determined by the relative density of the first unit and the second unit and the first spring stiffness corresponding to the first virtual spring and the second spring stiffness corresponding to the second virtual spring; The sensitivity of the objective function is: The sensitivity of the constraint function is: Among them, k0 is the element stiffness matrix of the corresponding element to be optimized, and p is the penalty factor.

6. The method according to claim 1, characterized in that The method further comprises: Output a compliant amplifying mechanism result graph corresponding to at least one target compliant amplifying mechanism model, wherein each target compliant amplifying mechanism model is obtained by iteratively optimizing the corresponding compliant amplifying mechanism model to be optimized, and each compliant amplifying mechanism model to be optimized is constructed according to different input parameter sets.

7. A topology optimization device for a compliant amplification mechanism, characterized in that: The device comprises: A model building module, used for building a corresponding compliant amplification mechanism model to be optimized according to an input parameter set; A partitioning module, used to determine the design area and non-design area of ​​the compliant amplification mechanism model to be optimized; A discretization module, used for discretizing the design area into a first number of first units, and discretizing the non-design area into a second number of second units; An optimization module is used to iteratively optimize the relative density of the to-be-optimized unit of the to-be-optimized compliant amplifying mechanism model with the goal of increasing the amplified displacement and reducing the parasitic displacement by using an optimization criterion algorithm, so as to obtain a target compliant amplifying mechanism model that meets a convergence condition, wherein the to-be-optimized unit includes the first unit, and the convergence condition includes making the relative parasitic displacement less than a set threshold, and the relative parasitic displacement is the absolute value of the ratio of the parasitic displacement at the output end of the to-be-optimized amplifying mechanism model to the amplified displacement.

8. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the processor performs the steps of the method for topology optimization of a compliant amplification mechanism as described in any one of claims 1 to 6.

9. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the processor executes the steps of the method for topology optimization of a compliant amplification mechanism as described in any one of claims 1-6.

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