Optimal design method for clamping force of constant force micro-gripper

Abstract

The present application relates to the field of optimal design of clamping force of constant force micro-gripper, and particularly relates to a kind of optimal design method of clamping force based on constant force micro-gripper.The present application aims at the technical problem that prior art cannot optimize the clamping force of constant force micro-gripper according to structural parameter optimization design variable, and adopts the method of establishing the combined response surface model between the clamping force of constant force micro-gripper and its structural parameter optimization design variable, realizes the optimization of the clamping force of constant force micro-gripper, compared with prior art, the present application solves the problem of low accuracy of response surface model obtained by single experimental design method, and improves the reliability of the optimal clamping force parameter of final output.

Description

Technical Field

[0001] This invention relates to the field of clamping force optimization design of constant force micro grippers, and more specifically to a clamping force optimization design method based on constant force micro grippers. Background Technology

[0002] As research in fields such as bioengineering, microelectromechanical systems, micron and nanotechnology, and optical engineering continues to evolve towards miniaturization, grippers, as end effectors in the process of manipulating objects, have promising applications in the processing and assembly of micromechanical parts, bioengineering, and optics.

[0003] In practical applications, micro grippers require strict control of clamping force. Researchers have designed many constant-force micro grippers that maintain a constant clamping force. The clamping force of a constant-force micro gripper does not increase with the increase of displacement. Existing technologies include embedded-drive constant-force micro grippers based on compliant amplification mechanisms, as well as multi-degree-of-freedom flexible micro grippers with adjustable constant force. However, these constant-force micro grippers only have constant force design from a structural perspective and have not established a nonlinear relationship between the clamping force of the constant-force micro gripper and its structural parameters. Therefore, the clamping force cannot be optimized through structural parameters.

[0004] Generally, the clamping force of a constant-force micro-gripper is designed to be F0. However, due to losses during the transmission of displacement and force, the actual clamping force F of the constant-force micro-gripper often differs from the designed value F0, necessitating optimization. Furthermore, to prevent damage to the object held by the constant-force micro-gripper due to excessive clamping force or detachment due to insufficient clamping force, it is desirable for the clamping force F to be as close as possible to the designed value F0. Therefore, it is necessary to establish a nonlinear relationship between the clamping force and its structural parameters, and to optimize the clamping force through these structural parameters. Most existing technologies use response surface methodology to construct this nonlinear relationship; however, only a single experiment is conducted when obtaining experimental sample points, resulting in insufficient accuracy of the established response surface model, leading to an inaccurate final constant-force micro-gripper. Summary of the Invention

[0005] This invention provides a clamping force optimization design method based on a constant force micro gripper, aiming to solve the technical problem that existing technologies cannot optimize the clamping force of a constant force micro gripper by optimizing design variables based on structural parameters.

[0006] This invention provides a clamping force optimization design method based on a constant force micro gripper, the clamping force optimization design method comprising the following steps:

[0007] S1. Establish the finite element model of the constant force micro-clamp;

[0008] S2. Using the structural parameters of the constant force micro-gripper as optimization design variables, and the clamping force of the constant force micro-gripper as a performance index, and the maximum stress of the constant force micro-gripper as a constraint index, the clamping force is defined as F, and the maximum stress as σ. max , ;

[0009] S3. The Box-Behnken Design is used to design experiments on the optimization design variables in step S2 to obtain the first experimental sample data. At the same time, ANSYS is used to calculate the first index data between the clamping force and the maximum stress corresponding to the first experimental sample data on the finite element model in step S1. Then, a first response surface model is constructed based on the experimental sample data and the index data.

[0010] S4. The optimization design variables in step S2 are used to perform experimental design using Central Composite Design to obtain the second experimental sample data. At the same time, the finite element model in step S1 is used to calculate the second index data between the clamping force and the maximum stress corresponding to the second experimental sample data using ANSYS. Then, a second response surface model is constructed based on the second experimental sample data and the index data.

[0011] S5. Combine the first response surface model with the second response surface model to obtain a combined response surface model;

[0012] S6. Determine whether the accuracy of the combined response model meets the preset accuracy requirement. If not, increase the number of optimization design variables and return to step S3 for repeated execution. If yes, execute step S7.

[0013] S7. Construct an optimization model based on the optimization design variables, and solve for the structural parameters using the Pareto genetic algorithm. Output the Pareto genetic algorithm solution set as the optimal structural parameters of the constant force micro gripper.

[0014] Furthermore, the structural parameters correspond to the first constant force module rod, the second constant force module rod, and the third constant force module rod with different lengths connected to the constant force adjustment block of the constant force micro gripper, including l1, l2, l3, l4, b1, b2, θ1, and θ2, where l1 represents the length of the first constant force module rod, l2 represents the length of the second constant force module rod, l3 represents the length of the constant force adjustment block, l4 represents the length of the third constant force module rod, b1 represents the width of the first constant force module rod, b2 represents the width of the second constant force module rod, θ1 represents the angle between the second constant force module rod and the constant force adjustment block, and θ2 represents the angle between the first constant force module rod and the constant force adjustment block.

[0015] Furthermore, the first response surface model satisfies:

[0016]

[0017]

[0018] Where, x i (i = 1...8), x j (j=1……8) are the components of the optimization design variables corresponding to the structural parameters, ε1, ε2, α0, α i α ii α ij ,β0,β i β ii β ij The coefficient is unknown.

[0019] Furthermore, the second response surface model satisfies:

[0020]

[0021]

[0022] Where, x i (i = 1...8), x j (j=1……8) are the components of the optimization design variables corresponding to the structural parameters, ε′1, ε′2, α′0, α′ i α′ ii α′ ij ,β′0,β′ i ,β′ ii ,β′ ij The coefficient is unknown.

[0023] Furthermore, the combined corresponding surface model satisfies:

[0024]

[0025]

[0026] in, k1, k2, k3, k4, k5, k6, k7, k8, k9, k 10 , m1, m2, m3, m4, m5, m6, m7, m8, m9, m 10 It is a constant.

[0027] Furthermore, the clamping force design value of the constant force microgripper is defined as F0, and the objective function of the clamping force optimization design method based on the constant force microgripper satisfies:

[0028]

[0029] Among them, F=f1(x)=f1(l1, l2, l3, l4, b1, b2, θ1, θ2), σ max =f2(x)=f2(l1, l2, l3, l4, b1, b2, θ1, θ2), σ s The yield strength of the material for the constant force micro gripper is given, where n0 is the safety factor.

[0030] The optimization model satisfies:

[0031] F(x)={|f1(x)-F0|, f2(x)}.

[0032] Furthermore, the structural parameters satisfy the following constraints:

[0033]

[0034] Furthermore, the parameters of the Pareto genetic algorithm in step S7 are set as follows: initial population size is 100, crossover probability is 0.2, mutation probability is 0.08, and maximum number of generations is 100.

[0035] The beneficial effects achieved by this invention are due to the adoption of a method for establishing a combined response surface model between the clamping force of a constant force micro-gripper and its structural parameter optimization design variables, which realizes the optimization of the clamping force of the constant force micro-gripper. Compared with the prior art, this invention solves the problem of low accuracy of the response surface model obtained by a single experimental design method and improves the reliability of the final output optimal clamping force parameters. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating the steps of the clamping force optimization design method based on a constant force micro-gripper provided in this embodiment of the invention.

[0037] Figure 2 This is a schematic diagram showing the position of the structural parameters provided in the embodiment of the present invention in the constant force micro clamp.

[0038] In the diagram, 1 represents the constant force module of the constant force micro-gripper; 1-1 represents constant force module rod I; 1-2 represents constant force module rod II; 1-3 represents constant force module rod III; 1-4 represents constant force module rod IV; 1-5 represents constant force module rod V; 1-6 represents constant force module rod VI; 1-7 represents constant force module rod VII; 1-8 represents constant force module rod VIII; 1-9 represents constant force adjustment block; θ1 represents the tilt angle between constant force module rod IV and constant force adjustment block; θ2 represents the tilt angle between constant force module rod II and constant force adjustment block; b1 represents the width of constant force module rod I; b2 represents the width of constant force module rod III; l1 represents the length of constant force module rod I; l2 represents the length of constant force module rod III; l3 represents the length of constant force adjustment block; and l4 represents the length of constant force module rod VI. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0040] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of the clamping force optimization design method based on a constant force micro-gripper provided in this embodiment of the invention, specifically including the following steps:

[0041] S1. Establish the finite element model of the constant force micro clamp.

[0042] S2. Using the structural parameters of the constant force micro-gripper as optimization design variables, and the clamping force of the constant force micro-gripper as a performance index, and the maximum stress of the constant force micro-gripper as a constraint index, the clamping force is defined as F, and the maximum stress as σ. max .

[0043] For details, please refer to Figure 2 , Figure 2 This is a schematic diagram showing the position of the structural parameters of the constant force micro-gripper provided in this embodiment of the invention. The constant force micro-gripper achieves the constant force clamping function due to the presence of a constant force module 1. The constant force mechanism 1 has a symmetrical structure. The structural parameters correspond to the first constant force module rod, the second constant force module rod, and the third constant force module rod with different lengths connected to the constant force adjustment block of the constant force micro-gripper, including l1, l2, l3, l4, b1, b2, θ1, and θ2. Specifically, l1 represents the length of the first constant force module rod, l2 represents the length of the second constant force module rod, l3 represents the length of the constant force adjustment block, and l4 represents the length of the third constant force module rod. Figure 2 In the constant force module, 1-1 and 1-2 both belong to the first constant force module rod with a length of l1, 1-3, 1-4, 1-7, and 1-8 both belong to the second constant force module rod with a length of l2, and 1-5 and 1-6 both belong to the third constant force module rod with a length of l4. b1 represents the width of the first constant force module rod, b2 represents the width of the second constant force module rod, θ1 represents the angle between the second constant force module rod and the constant force adjusting block, and θ2 represents the angle between the first constant force module rod and the constant force adjusting block.

[0044] Furthermore, the structural parameters satisfy the following constraints:

[0045]

[0046] S3. The Box-Behnken Design is used to design experiments on the optimization design variables in step S2 to obtain the first experimental sample data. At the same time, ANSYS is used to calculate the first index data between the clamping force and the maximum stress corresponding to the first experimental sample data on the finite element model in step S1. Then, a first response surface model is constructed based on the experimental sample data and the index data.

[0047] Box-Behnken Design is a type of responsive surface design.

[0048] Furthermore, the first response surface model satisfies:

[0049]

[0050]

[0051] Where, x i (i = 1...8), x j (j=1……8) are the components of the optimization design variables corresponding to the structural parameters, ε1, ε2, α0, α i α ii α ij ,β0,β i β ii β ij These are unknown coefficients. Specifically, in actual implementation, these unknown parameters can be calculated and solved using computational software.

[0052] S4. The optimization design variables in step S2 are used to perform experimental design using Central Composite Design to obtain second experimental sample data. At the same time, the finite element model in step S1 is used to calculate the second index data between the clamping force and the maximum stress corresponding to the second experimental sample data using ANSYS. Then, a second response surface model is constructed based on the second experimental sample data and the index data.

[0053] Central Composite Design is another type of responsive surface design. In this embodiment of the invention, two different methods are used to build the model for the structural parameters.

[0054] Furthermore, the second response surface model satisfies:

[0055]

[0056]

[0057] Where, x i (i = 1...8), x j (j=1……8) are the components of the optimization design variables corresponding to the structural parameters, ε′1, ε′2, α′0, α′ i α′ ii α′ ij ,β′0,β′ i ,β′ ii ,β′ ij The coefficient is unknown.

[0058] S5. Combine the first response surface model with the second response surface model to obtain a combined response surface model.

[0059] Furthermore, the combined corresponding surface model satisfies:

[0060]

[0061]

[0062] in, k1, k2, k3, k4, k5, k6, k7, k8, k9, k 10 , m1, m2, m3, m4, m5, m6, m7, m8, m9, m 10 It is a constant.

[0063] S6. Determine whether the accuracy of the combined response model meets the preset accuracy requirement. If not, increase the number of the optimized design variables and return to step S3 for repeated execution. If yes, execute step S7.

[0064] Specifically, within the parameter range constraints of the optimized design variables, an experimental design was conducted again to obtain sample data for verifying the optimized design variables. First, the clamping force F and maximum stress σ of the constant force micro-gripper corresponding to the sample data were calculated using ANSYS. max The response value was then used to calculate the clamping force F and maximum stress σ of the constant force microgripper corresponding to the test sample data, and then the constructed combined response surface model was used. max The response values ​​are compared between the two methods to determine whether the accuracy is met. If yes, proceed to step S7; otherwise, increase the number of optimization design variables used in each method and repeat steps S3, S4, and S5.

[0065] S7. Construct an optimization model based on the optimization design variables, and solve for the structural parameters using the Pareto genetic algorithm. Output the Pareto genetic algorithm solution set as the optimal structural parameters of the constant force micro gripper.

[0066] Specifically, the clamping force of the constant force micro-gripper is defined as F0. Since displacement and force are lost during transmission, the actual clamping force F of the constant force micro-gripper often differs from the design value F0. Therefore, optimization is necessary. To protect the object held by the constant force micro-gripper from damage due to excessive clamping force or from falling off due to insufficient clamping force, it is desirable for the clamping force F to be as close as possible to the design value F0. The objective function of the clamping force optimization design method based on the constant force micro-gripper satisfies:

[0067]

[0068] Among them, F=f1(x)=f1(l1, l2, l3, l4, b1, b2, θ1, θ2), σ max =f2(x)=f2(l1, l2, l3, l4, b1, b2, θ1, θ2), σ s The yield strength of the material for the constant force micro gripper is given, where n0 is the safety factor.

[0069] The optimization model satisfies:

[0070] F(x)={|f1(x)-F0|, f2(x)}.

[0071] The Pareto genetic algorithm is an algorithm used for multi-objective optimization. Furthermore, the parameters of the Pareto genetic algorithm in step S7 are set as follows: the initial population size is 100, the crossover probability is 0.2, the mutation probability is 0.08, and the maximum number of generations is 100.

[0072] The beneficial effects achieved by this invention are due to the adoption of a method for establishing a combined response surface model between the clamping force of a constant force micro-gripper and its structural parameter optimization design variables, which realizes the optimization of the clamping force of the constant force micro-gripper. Compared with the prior art, this invention solves the problem of low accuracy of the response surface model obtained by a single experimental design method and improves the reliability of the final output optimal clamping force parameters.

[0073] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0074] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0075] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0076] The embodiments of the present invention have been described above with reference to the accompanying drawings. The disclosed embodiments are merely preferred embodiments of the present invention. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many equivalent changes in form without departing from the spirit and scope of the claims of the present invention, and all such changes are within the protection scope of the present invention.

Claims

1. A method for optimizing the clamping force of a constant-force micro-gripper, characterized in that, The clamping force optimization design method includes the following steps: S1. Establish the finite element model of the constant force micro-clamp; S2. Using the structural parameters of the constant-force micro-gripper as optimization design variables, and the clamping force of the constant-force micro-gripper as a performance index, and the maximum stress of the constant-force micro-gripper as a constraint index, the clamping force is defined as F, and the maximum stress is... max , ; S3. The Box-Behnken Design is used to design experiments on the optimization design variables in step S2 to obtain the first experimental sample data. At the same time, ANSYS is used to calculate the first index data between the clamping force and the maximum stress corresponding to the first experimental sample data on the finite element model in step S1. Then, a first response surface model is constructed based on the experimental sample data and the index data. S4. The optimization design variables in step S2 are used to perform experimental design using Central Composite Design to obtain the second experimental sample data. At the same time, the finite element model in step S1 is used to calculate the second index data between the clamping force and the maximum stress corresponding to the second experimental sample data using ANSYS. Then, a second response surface model is constructed based on the second experimental sample data and the index data. S5. Combine the first response surface model with the second response surface model to obtain a combined response surface model; S6. Determine whether the accuracy of the combined response surface model meets the preset accuracy requirements. If not, increase the number of optimization design variables and return to step S3 for repeated execution. If yes, execute step S7. S7. Construct an optimization model based on the optimization design variables, and solve for the structural parameters using the Pareto genetic algorithm. Output the Pareto genetic algorithm solution set as the optimal structural parameters of the constant force micro gripper. The structural parameters correspond to the first, second, and third constant force module rods of different lengths connected to the constant force adjustment block of the constant force micro-gripper, including... l 1. l 2. l 3. l 4. b 1. b 2. , ,in, l 1 represents the length of the first constant force module rod. l 2 represents the length of the second constant force module rod. l 3 represents the length of the constant force adjusting block. l 4 represents the length of the third constant force module rod. b 1 represents the width of the first constant force module rod. b 2 represents the width of the second constant force module rod. This indicates the angle between the second constant force module rod and the constant force adjustment block. This indicates the angle between the first constant force module rod and the constant force adjustment block.

2. The clamping force optimization design method based on a constant force micro-gripper as described in claim 1, characterized in that, The first response surface model satisfies: ； in, , i =1……8、 , j =1……8 are the components of the optimization design variables corresponding to the structural parameters. , , , , , , , , , The coefficient is unknown.

3. The clamping force optimization design method based on a constant force micro-gripper as described in claim 2, characterized in that, The second response surface model satisfies: ； in, , i =1……8、 , j =1……8 are the components of the optimization design variables corresponding to the structural parameters. , , , , , , , , , The coefficient is unknown.

4. The clamping force optimization design method based on a constant force micro-gripper as described in claim 3, characterized in that, The combined corresponding surface model satisfies: ； in, , , , , , , , , , , k 1. k 2. k 3. k 4. k 5. k 6. k 7. k 8. k 9. k 10 , m 1. m 2. m 3. m 4. m 5. m 6. m 7. m 8. m 9. m 10 It is a constant.

5. The clamping force optimization design method based on a constant force micro-gripper as described in claim 4, characterized in that, The clamping force is defined as F, and the maximum stress is... max The clamping force of the constant force micro gripper is designed to be a fixed value. F 0, the objective function of the clamping force optimization design method based on the constant force micro gripper satisfies: ； in, F =f1( )=f1( l 1, l 2, l 3, l 4, b 1, b 2, , ), max =f2( )=f2( l 1, l 2, l 3, l 4, b 1, b 2, , ), s The yield strength of the material for the constant force micro gripper design n 0 represents the safety factor; The optimization model satisfies: F(x)={ ,f2( )}。 6. The clamping force optimization design method based on a constant force micro-gripper as described in claim 1, characterized in that, The structural parameters satisfy the following constraints: 。 7. The clamping force optimization design method based on a constant force micro-gripper as described in claim 1, characterized in that, The parameters of the Pareto genetic algorithm in step S7 are set as follows: initial population size is 100, crossover probability is 0.2, mutation probability is 0.08, and maximum number of generations is 100.

Images