Multi-objective collaborative optimization method for structural parameters of strain gauge

Abstract

The invention discloses a multi-objective collaborative optimization method for structural parameters of a strain gauge, and particularly relates to the technical field of objective optimization. The method comprises the following steps: randomly extracting a plurality of structural characteristic parameter combinations by adopting an experimental design method to form a sample parameter set; performing random disturbance processing by using a Monte Carlo method to generate an extended parameter set; respectively establishing a sensitivity prediction model and a life attenuation model, and performing numerical simulation on the extended parameter set to obtain a multi-target comprehensive adaptability evaluation value of each parameter combination; constructing a parameter response surface based on the evaluation values, and performing iterative search in the parameter response surface by using a multi-target collaborative optimization algorithm to obtain an optimized parameter set; and finally, reconstructing the strain gauge structure according to the optimized parameter set, verifying the effects of the optimized parameters on improving the measurement precision and prolonging the service life through a simulation experiment, and providing a multi-target collaborative optimization scheme for the structural design of the strain gauge.

Description

Technical Field

[0001] The present invention relates to the technical field of target optimization, and more specifically, to a multi-objective collaborative optimization method for the structural parameters of a strain gauge. Background Art

[0002] In the design of traditional wire resistance strain gauges, key structural parameters such as the length of the sensitive grid wire, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer are usually designed based on process experience and a single objective. This results in parameter uncertainty in actual applications, thereby affecting the measurement accuracy and the device life. At the same time, due to the complex coupling effect between the structural parameters and the influence of random manufacturing errors, it is difficult for the existing technology to effectively balance the mutual constraints between the parameters while meeting the multi-objective requirements.

[0003] To solve the above problems, a technical solution is provided now. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-objective collaborative optimization method for the structural parameters of a strain gauge to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A multi-objective collaborative optimization method for the structural parameters of a strain gauge, comprising the following steps:

[0007] S1: According to the structural characteristic parameters of the strain gauge, randomly extract multiple groups of parameter combinations as a sample parameter set based on the experimental design method;

[0008] S2: Use the Monte Carlo method to perform random perturbation processing on the structural characteristic parameters in each sample parameter set to obtain an extended parameter set;

[0009] S3: According to the extended parameter set, respectively establish a sensitivity prediction model and a life attenuation model of the strain gauge;

[0010] S4: Perform collaborative simulation on the extended parameter set for the measurement accuracy sensitivity model and the life attenuation model to obtain the multi-objective comprehensive adaptability evaluation value of each group of parameter combinations;

[0011] S5: Construct a parameter response surface based on the multi-objective comprehensive adaptability evaluation value, and use the multi-objective collaborative optimization algorithm to search within the parameter response surface, and iteratively obtain the optimized parameter set of the structural characteristic parameters;

[0012] S6: Reconstruct the strain gauge structure according to the optimized parameter set, verify the improvement effect of the optimized parameters on the measurement accuracy and life of the strain gauge through simulation experiments, and output the optimized configuration result of the structural characteristic parameters of the strain gauge.

[0013] In a preferred embodiment, according to the structural characteristic parameters of the strain gauge, multiple groups of parameter combinations are randomly selected as the sample parameter set based on the experimental design method. Specifically:

[0014] According to the structural characteristic parameters of the strain gauge, the value ranges of each structural characteristic parameter are respectively set;

[0015] Based on the Latin hypercube sampling method in the experimental design method, within the value ranges of each structural characteristic parameter, multiple groups of parameter combinations that satisfy the uniform distribution are randomly selected to form the sample parameter set;

[0016] The structural characteristic parameters include the length of the sensitive grid wire, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer.

[0017] In a preferred embodiment, the Monte Carlo method is used to perform random perturbation processing on the structural characteristic parameters in each sample parameter set to obtain the extended parameter set. Specifically:

[0018] The Monte Carlo method is used to perform perturbation simulation on each group of structural characteristic parameters in the sample parameter set, and the perturbation range is determined based on the structural manufacturing tolerance range;

[0019] Multiple perturbed versions are generated for each group of perturbed structural characteristic parameters;

[0020] Each group of perturbed versions is combined with the sample parameter set to form the extended parameter set.

[0021] In a preferred embodiment, according to the extended parameter set, a sensitivity prediction model and a life attenuation model of the strain gauge are respectively established. Specifically:

[0022] The structural characteristic parameters in the extended parameter set are used as input variables, and based on the strain-resistance response relationship, a sensitivity prediction model corresponding to the measurement accuracy is established;

[0023] The structural characteristic parameters in the extended parameter set are used as input variables, and based on the number of fatigue loading cycles and the material property data, a life attenuation model is established.

[0024] In a preferred embodiment, a co-simulation of the measurement accuracy sensitivity model and the life attenuation model is performed on the extended parameter set to obtain the multi-objective comprehensive adaptability evaluation value of each group of parameter combinations. Specifically:

[0025] Each parameter combination in the extended parameter set is respectively input into the sensitivity prediction model and the life attenuation model for numerical simulation to obtain the measurement sensitivity value and the life prediction value of each group of parameter combinations;

[0026] The measurement sensitivity values and life prediction values corresponding to each set of parameter combinations are transformed and superimposed through linear weighted summation to obtain the multi-objective comprehensive adaptability evaluation value of each set of parameter combinations.

[0027] In a preferred embodiment, a parameter response surface is constructed based on the multi-objective comprehensive adaptability evaluation value, and a multi-objective collaborative optimization algorithm is used to search within the parameter response surface to iteratively obtain the optimized parameter set of the structural characteristic parameters. Specifically:

[0028] According to each set of parameter combinations in the extended parameter set and the corresponding multi-objective comprehensive adaptability evaluation values, a parameter response surface is constructed using the radial basis function interpolation method;

[0029] Through a multi-objective collaborative optimization search method combining the genetic algorithm and the particle swarm algorithm, iterative search is carried out within the parameter response surface, and the structural characteristic parameter combination that meets the optimization conditions is output as the optimized parameter set.

[0030] In a preferred embodiment, the strain gauge structure is reconstructed according to the optimized parameter set, and the improvement effects of the optimized parameters on the measurement accuracy and life of the strain gauge are verified through simulation experiments, and the optimized configuration result of the strain gauge structural characteristic parameters is output. Specifically:

[0031] According to the optimized parameter set, a three-dimensional finite element geometric model of the strain gauge is reconstructed;

[0032] In the three-dimensional finite element geometric model, the optimized values of the sensitive grid wire length, sensitive grid wire diameter, transition layer thickness, substrate thickness, and cover layer thickness are respectively assigned, and finite element numerical simulation is carried out;

[0033] During the numerical simulation process, by applying the standard load condition, the measurement sensitivity value and life value of the optimized strain gauge structure are obtained;

[0034] The measurement sensitivity value and life value of the optimized strain gauge structure are compared with the measurement sensitivity value and life prediction value obtained before optimization to verify and output the optimized configuration result of the strain gauge structural characteristic parameters.

[0035] The technical effects and advantages of a multi-objective collaborative optimization method for strain gauge structure parameters of the present invention:

[0036] By systematically analyzing key structural parameters such as the length of the sensitive grid wire, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer, the problems of reduced measurement accuracy and shortened service life caused by parameter uncertainty in traditional designs are effectively solved. Using the experimental design method and the Monte Carlo method, a uniformly distributed sample and an extended parameter set are obtained, providing a sufficient data basis for establishing a sensitivity prediction model and a life attenuation model; by using multi-objective comprehensive adaptability evaluation and parameter response surfaces, and combining the genetic algorithm and the particle swarm algorithm to cooperate with each other, the balanced optimization of multi-objective parameters is realized, so as to extend the service life of the strain gauge while ensuring high sensitivity. It not only overcomes the limitations brought by single-objective optimization, but also comprehensively considers manufacturing tolerances and the complex coupling effects between parameters, improves the overall stability and reliability of the strain gauge, and reduces the trial-and-error cost in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 FIG. is a schematic diagram of a multi-objective collaborative optimization method for the structural parameters of a strain gauge according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Embodiment

[0040] Figure 1 A multi-objective collaborative optimization method for the structural parameters of a strain gauge according to the present invention is given, which includes the following steps:

[0041] S1: According to the structural characteristic parameters of the strain gauge, randomly extract multiple groups of parameter combinations as a sample parameter set based on the experimental design method;

[0042] S2: Use the Monte Carlo method to perform random perturbation processing on the structural characteristic parameters in each sample parameter set to obtain an extended parameter set;

[0043] S3: According to the extended parameter set, establish a sensitivity prediction model and a life attenuation model of the strain gauge respectively;

[0044] S4: Perform collaborative simulation on the extended parameter set for the measurement accuracy sensitivity model and the life attenuation model to obtain the multi-objective comprehensive adaptability evaluation value of each group of parameter combinations;

[0045] S5: Construct a parameter response surface based on the multi-objective comprehensive adaptability evaluation value, and use the multi-objective collaborative optimization algorithm to search within the parameter response surface to iteratively obtain the optimized parameter set of the structural characteristic parameters;

[0046] S6: Reconstruct the strain gauge structure according to the optimized parameter set, verify the improvement effect of the optimized parameters on the measurement accuracy and life of the strain gauge through simulation experiments, and output the configuration result of the optimized structural characteristic parameters of the strain gauge.

[0047] Specifically, according to the structural characteristic parameters of the strain gauge, randomly extract multiple groups of parameter combinations as the sample parameter set based on the experimental design method, including:

[0048] According to the structural characteristic parameters of the strain gauge, set the value range of each structural characteristic parameter respectively; the structural characteristic parameters include the length of the sensitive grid wire, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the base, and the thickness of the covering layer;

[0049] Specifically, for the important geometric dimension parameters that affect the performance in the strain gauge, that is, the structural characteristic parameters, determine a numerical interval in which each structural characteristic parameter is allowed to vary. Among them, each value range refers to the minimum and maximum values that the structural characteristic parameter may take.

[0050] For example, if the length of the sensitive grid wire is an important geometric dimension parameter, according to the design requirements and manufacturing process, its value range may be set to 5 mm to 15 mm; similarly, the diameter of the sensitive grid wire can be set to 20 μm to 60 μm, and the thicknesses of the transition layer, base, and covering layer are respectively determined to have certain upper and lower limits according to the material strength and process control requirements.

[0051] The length of the sensitive grid wire represents the length of the grid wire part used for sensing, which affects signal transmission and sensitivity.

[0052] The diameter of the sensitive grid wire represents the thickness of the grid wire, which directly affects the resistance value and mechanical stability.

[0053] The thickness of the transition layer refers to the thickness of the transition region connecting the sensitive grid wire and the base, which plays a buffering and transmission role.

[0054] The thickness of the base is the thickness of the support layer, which is closely related to strain conduction and structural stability.

[0055] The thickness of the covering layer refers to the external covering layer used to protect the internal structure, and its thickness has a significant impact on environmental adaptability and durability.

[0056] During the design process, each parameter needs to undergo experiments and theoretical analysis to determine its reasonable value range and ensure their mutual cooperation to form a complete strain gauge structure. For example, during the optimization process, if it is found that increasing the grid wire length can improve the sensitivity but may cause a decrease in mechanical stability, then the length and other parameters must be comprehensively considered. The value range of each parameter will constitute the boundary conditions for subsequent sampling design to ensure that the sampling data is representative.

[0057] Based on the Latin hypercube sampling method in the experimental design method, within the value range of each structural characteristic parameter, multiple groups of parameter combinations that satisfy a uniform distribution are randomly selected to form a sample parameter set.

[0058] Specifically, the Latin hypercube sampling method is used to obtain representative and uniformly distributed structural characteristic parameter combinations within the set value range of each structural characteristic parameter, obtaining the basic data for optimization and simulation.

[0059] The Latin hypercube sampling method is a statistical sampling technique. By dividing the value range of each parameter into several intervals and randomly selecting a representative value within each interval, it ensures that the entire value range of each parameter is covered during sampling, thereby reducing the local aggregation problem caused by random sampling.

[0060] During the sampling process, each structural characteristic parameter is independently partitioned, and then the sampled values of each parameter are combined to form a sample parameter set.

[0061] Exemplarily, assume that the set value range for the diameter of the sensitive grid wire is from 20 to 60 microns. If it is divided into 8 small intervals with a width of 5 microns each, then 8 diameter candidate values are randomly selected within each interval; combined with the candidate values of other structural characteristic parameters to generate the final sample parameter set, and each combination represents a possible strain gauge structure configuration.

[0062] After the Latin hypercube sampling method, the obtained sample parameter set has the characteristic of uniform distribution within the entire design space, providing sufficient data support for establishing a sensitivity prediction model, a life decay model, and multi-objective optimization.

[0063] Specifically, the Monte Carlo method is used to perform random perturbation processing on the structural characteristic parameters in each sample parameter set, obtaining an extended parameter set, including:

[0064] For each set of structural characteristic parameters in the sample parameter set, the Monte Carlo method is used for perturbation simulation, and the perturbation range is determined based on the structural manufacturing tolerance range.

[0065] Specifically, for each combination of structural characteristic parameters obtained in the sample parameter set, random perturbation is introduced through the Monte Carlo method.

[0066] For example, if the set value of the length of the sensitive grid wire is 10 mm and the manufacturing tolerance range is determined to be ±5%, the perturbation range is 10 mm × (1 ± 0.05), that is, from 9.5 mm to 10.5 mm. Similarly, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer are all subject to similar random perturbations according to their respective manufacturing tolerance ranges.

[0067] Exemplarily, for a combination of structural feature parameters, if the original parameters are (10 mm, 40 μm, 10 μm, 100 μm, 30 μm), then multiple new combinations may be generated after random perturbation, such as (9.8 mm, 42 μm, 9.7 μm, 98 μm, 31 μm), etc.

[0068] This perturbation method takes into account the manufacturing errors existing in actual production and ensures that the optimization analysis can cover the actual uncertain factors.

[0069] Generate multiple perturbed versions for each set of perturbed structural feature parameters;

[0070] Combine each set of perturbed versions with the sample parameter set to form an extended parameter set;

[0071] Specifically, for each combination of structural feature parameters, after multiple perturbations by the Monte Carlo method, multiple different parameter versions will be generated, and these versions are collectively referred to as the extended parameter set.

[0072] Exemplarily, if a certain combination of structural feature parameters generates 20 different perturbed versions, then these 20 parameter combinations, together with the perturbed versions of other combinations of structural feature parameters and the sample parameter set, are combined to form an extended parameter set.

[0073] The extended parameter set contains more combinations of structural feature parameters with actual fluctuation characteristics than the sample parameter set, providing richer data support for subsequent model establishment and simulation.

[0074] Specifically, according to the extended parameter set, a sensitivity prediction model and a life attenuation model of the strain gauge are established respectively, including:

[0075] Take the structural feature parameters in the extended parameter set as input variables, and based on the relationship between strain and resistance response, establish a sensitivity prediction model corresponding to the measurement accuracy;

[0076] Specifically, use each set of structural feature parameters in the extended parameter set as input, and combine the relationship between strain and resistance response to establish a sensitivity prediction model for predicting the measurement accuracy (i.e., response sensitivity).

[0077] Exemplarily, there is a relational formula: Measurement sensitivity = Constant A × (reciprocal of the diameter of the sensitive grid wire) + Constant B × (linear ratio of the length of the sensitive grid wire) + …

[0078] Then, parameters such as Constant A and Constant B can be fitted through multivariate regression analysis, enabling the sensitivity prediction model to predict the measured sensitivity value using the input combination of structural feature parameters.

[0079] The structural feature parameters enable the prediction of the accuracy performance of the strain gauge under actual measurement by inputting a set of structural feature parameters, facilitating subsequent optimization.

[0080] Taking the structural feature parameters in the extended parameter set as input variables, based on the number of fatigue loading cycles and material property data, a life attenuation model is established;

[0081] Specifically, using the structural feature parameters in the extended parameter set as input, combined with the number of fatigue loading cycles and material property data, a life attenuation model capable of predicting the life attenuation trend of the strain gauge is established.

[0082] A formula model described in the following text can be adopted: Life prediction value = Initial life - Decay coefficient × Number of fatigue loading cycles.

[0083] Where the decay coefficient is a function depending on parameters such as the length and diameter of the sensitive grid wire. For example, if a certain set of structural feature parameters results in a smaller decay coefficient, the predicted life value of the strain gauge is higher under the same number of loading cycles.

[0084] The life attenuation model reflects the life attenuation of the strain gauge under different configurations of structural feature parameters under the action of fatigue and cyclic stress, and is an important basis for quantitatively evaluating the optimization direction.

[0085] Specifically, a co-simulation of the measurement accuracy sensitivity model and the life attenuation model is performed on the extended parameter set to obtain the multi-objective comprehensive adaptability evaluation value of each parameter combination, including:

[0086] Each parameter combination in the extended parameter set is respectively input into the sensitivity prediction model and the life attenuation model for numerical simulation to obtain the measurement sensitivity value and the life prediction value of each group of parameter combinations;

[0087] Specifically, each group of structural feature parameters in the extended parameter set is respectively input into the established sensitivity prediction model and life attenuation model, and two corresponding output values are obtained using numerical calculation methods:

[0088] For example, for the length of the sensitive grid wire, a coefficient for measuring the contribution degree of the length of the sensitive grid wire to the sensitivity is set, and the length of the sensitive grid wire is multiplied by the coefficient to obtain the sensitivity increment of the coefficient length part. Secondly, the reciprocal of the diameter of the sensitive grid wire is taken and then multiplied by the coefficient for measuring the influence of the diameter of the sensitive grid wire to obtain the correction amount of the diameter part of the sensitive grid wire. Subsequently, the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer are respectively multiplied by their corresponding coupling correction coefficients to form additional increments, and these additional increments are added in sequence. Finally, all the increments are added with the reference offset to comprehensively obtain the sensitivity measurement value.

[0089] For example, based on the material properties and environmental conditions, an initial life is given as a reference value. Considering the influence degrees of the length of the sensitive grid wire and the diameter of the sensitive grid wire on stress concentration, a decay factor associated with the stress level is set. At the same time, each of the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer is introduced into the decay correction associated with the number of fatigue loading cycles, so that the decay factor shows an increasing trend during cyclic loading. Finally, the product of the decay factor and the number of fatigue cycles is used as the life loss amount, and the life loss amount is subtracted from the initial life to obtain the life prediction value.

[0090] The obtained numerical results provide a quantitative basis for the performance evaluation of each combination of structural characteristic parameters and are the basic data for comprehensive evaluation.

[0091] By linearly weighted summation, the measured sensitivity values and life prediction values corresponding to each group of parameter combinations are transformed and superimposed to obtain the multi-objective comprehensive adaptability evaluation value of each group of parameter combinations.

[0092] Specifically, based on the multi-objective comprehensive adaptability evaluation value, a parameter response surface is constructed, and the multi-objective collaborative optimization algorithm is used to search within the parameter response surface, and the optimized parameter set of the structural characteristic parameters is obtained iteratively, including:

[0093] According to each group of parameter combinations in the extended parameter set and the corresponding multi-objective comprehensive adaptability evaluation value, the radial basis function interpolation method is used to construct the parameter response surface;

[0094] Specifically, using the data in the extended parameter set, that is, each group of parameter combinations and the corresponding comprehensive adaptability evaluation value, a mathematical model is constructed through the radial basis function interpolation method to describe the continuous distribution of the evaluation value in the multi-dimensional structural parameter space. Exemplarily, if there are several parameter points in the extended parameter set, each point corresponds to a comprehensive adaptability evaluation value. Using these points through the Gaussian radial basis function, a smooth response surface can be obtained within the parameter space, and this response surface enables an estimated comprehensive evaluation value to be obtained for any given parameter combination.

[0095] The parametric response surface provides a theoretical basis and computational platform for subsequent searching for the optimal parameter combination in the continuous domain, enabling the optimization search to no longer be limited to discrete data points.

[0096] Through a multi-objective collaborative optimization search method that combines the genetic algorithm and the particle swarm algorithm, iterative search is carried out within the parametric response surface, and the structural characteristic parameter combination that meets the optimization conditions is output as the optimized parameter set.

[0097] Specifically, on the parametric response surface, an optimization search method that combines the genetic algorithm and the particle swarm algorithm is used to iteratively find the structural characteristic parameter combination that can meet the optimization conditions (such as the maximum comprehensive evaluation value or meeting the design requirement threshold).

[0098] The genetic algorithm searches for the global optimal solution through operations such as encoding, crossover, and mutation; the particle swarm algorithm enhances local search through the collaborative movement of individuals in the population.

[0099] For example, after several iterations, the search method selects a set of parameter combinations whose corresponding comprehensive evaluation value on the response surface is higher than the set threshold and outputs it as the optimized parameter set.

[0100] The multi-objective collaborative optimization algorithm makes full use of the global search ability of the genetic algorithm and the fast local convergence ability of the particle swarm algorithm, making the finally output optimized parameter set more in line with the requirements of multi-objective balanced optimization in design.

[0101] Specifically, according to the optimized parameter set, the strain gauge structure is reconstructed, and the improvement effect of the optimized parameters on the measurement accuracy and life of the strain gauge is verified through simulation experiments, and the optimized strain gauge structural characteristic parameter configuration results are output, including:

[0102] According to the optimized parameter set, a three-dimensional finite element geometric model of the strain gauge is reconstructed.

[0103] Specifically, based on the obtained optimized parameter set, the three-dimensional geometric model of the strain gauge is reconstructed with the optimized structural characteristic parameters, and usually a finite element modeling software is used to construct a digital model under actual working conditions.

[0104] Exemplarily, if the length of the sensitive grid wire in the optimized parameter set is adjusted to 11 mm and the diameter is adjusted to 38 μm, and other parameters are updated accordingly, then in the finite element modeling, the dimensions of the corresponding parts in the three-dimensional geometric model of the strain gauge should be modified to these optimized values respectively.

[0105] The update of the three-dimensional geometry of the strain gauge ensures that the latest parameter configuration is considered during numerical simulation, thereby verifying the optimization effect.

[0106] In the three-dimensional finite element geometric model, the optimized values of the length of the sensitive grid wire, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer are respectively assigned, and finite element numerical simulation is carried out;

[0107] Specifically, in the updated three-dimensional finite element geometric model, the optimized values of the length of the sensitive grid wire, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer are respectively assigned to each key area, and then the three-dimensional finite element geometric model is simulated by using the finite element numerical calculation method.

[0108] After the finite element mesh is divided, the optimized dimensions are respectively input into the refined areas in the three-dimensional finite element geometric model to ensure that the structure and parameter design of the three-dimensional finite element geometric model are completely consistent in the simulation calculation.

[0109] In this way, it is ensured that the simulation results truly reflect the structural mechanics and resistance characteristics under the optimized scheme.

[0110] During the numerical simulation process, by applying the standard load conditions, the measurement sensitivity value and the life value of the optimized strain gauge structure are obtained;

[0111] Specifically, during the finite element simulation process, an external force or strain load is applied according to the standard load conditions, and the measurement sensitivity value and the life value of the optimized three-dimensional finite element geometric model under the standard load conditions are obtained through simulation calculation.

[0112] Exemplarily, the standard load conditions can be understood as under certain mechanical or thermal conditions, such as applying a certain standard stress, and the resistance change rate generated by the three-dimensional finite element geometric model after simulation is used as the measurement sensitivity; at the same time, the cyclic life under the standard load conditions is obtained according to the material fatigue theory.

[0113] By verifying the simulation performance of the optimized parameter combination under the standard load conditions, a reliable numerical proof is provided for the optimization result;

[0114] Specifically, the optimized measurement sensitivity value and life value obtained through finite element simulation are compared with the obtained sensitivity measurement value and life prediction value.

[0115] Exemplarily, if the simulation results show that the optimized measurement sensitivity is increased from the predicted 0.8 to 0.85, and the life value is increased from 20,000 cycles to 22,000 cycles, then this comparison proves the effectiveness of the optimization scheme.

[0116] Through the comparison and verification, a set of clear optimized configuration results verified by numerical simulation can be output to ensure that the entire strain gauge optimization scheme is reliable and has an implementation basis.

[0117] Compare the measured sensitivity value and lifespan value of the optimized strain gauge structure with the measured sensitivity value and lifespan prediction value obtained before optimization to verify and output the optimized configuration result of the strain gauge structure characteristic parameters.

[0118] All the above formulas are dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formula are set by those skilled in the art according to the actual situation.

[0119] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0120] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0121] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0122] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0123] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, in each embodiment of the present application, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0125] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0126] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0127] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-objective collaborative optimization method for the structural parameters of a strain gauge, characterized in that, It includes the following steps: S1: According to the structural characteristic parameters of the strain gauge, randomly extract multiple groups of parameter combinations as the sample parameter set based on the experimental design method; S2: Use the Monte Carlo method to perform random perturbation processing on the structural characteristic parameters in each sample parameter set to obtain the extended parameter set; S3: According to the extended parameter set, establish the sensitivity prediction model and the life attenuation model of the strain gauge respectively; S4: Perform co-simulation of the measurement accuracy sensitivity model and the life attenuation model on the extended parameter set to obtain the multi-objective comprehensive adaptability evaluation value of each group of parameter combinations; S5: Construct a parameter response surface based on the multi-objective comprehensive adaptability evaluation value, and use the multi-objective collaborative optimization algorithm to search within the parameter response surface, and iteratively obtain the optimized parameter set of the structural characteristic parameters; S6: Reconstruct the strain gauge structure according to the optimized parameter set, verify the improvement effect of the optimized parameters on the measurement accuracy and life of the strain gauge through simulation experiments, and output the configuration result of the optimized structural characteristic parameters of the strain gauge.

2. The multi-objective collaborative optimization method for the structural parameters of a strain gauge according to claim 1, wherein, S1 is specifically: According to the structural characteristic parameters of the strain gauge, set the value range of each structural characteristic parameter respectively; Based on the Latin hypercube sampling method in the experimental design method, randomly extract multiple groups of parameter combinations that satisfy the uniform distribution within the value range of each structural characteristic parameter to form the sample parameter set; The structural characteristic parameters include the length of the sensitive grid wire, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the substrate, and the thickness of the covering layer.

3. A multi-objective collaborative optimization method for the structural parameters of a strain gauge according to claim 2, characterized in that, S2 is specifically: Perform perturbation simulation on each group of structural characteristic parameters in the sample parameter set using the Monte Carlo method, and the perturbation range is determined based on the structural manufacturing tolerance range; Generate multiple perturbed versions for each group of perturbed structural characteristic parameters; Combine each group of perturbed versions with the sample parameter set to form the extended parameter set.

4. A multi-objective collaborative optimization method for strain gauge structure parameters according to claim 3, characterized in that, S3 is specifically: Take the structural characteristic parameters in the extended parameter set as input variables, and establish a sensitivity prediction model corresponding to the measurement accuracy based on the strain-resistance response relationship; Take the structural characteristic parameters in the extended parameter set as input variables, and establish a life attenuation model based on the number of fatigue loading cycles and material property data.

5. A multi-objective collaborative optimization method for the structural parameters of a strain gauge according to claim 4, characterized in that S4 is specifically: Input each parameter combination in the extended parameter set into the sensitivity prediction model and the life attenuation model respectively for numerical simulation to obtain the measurement sensitivity value and the life prediction value of each group of parameter combinations; Transform and superimpose the measurement sensitivity value and the life prediction value corresponding to each group of parameter combinations through linear weighted summation to obtain the multi-objective comprehensive adaptability evaluation value of each group of parameter combinations.

6. A multi-objective collaborative optimization method for strain gauge structure parameters according to claim 5, characterized in that S5 is specifically: Construct a parameter response surface using the radial basis function interpolation method according to each group of parameter combinations and the corresponding multi-objective comprehensive adaptability evaluation value in the extended parameter set; Through the multi-objective collaborative optimization search method combining the genetic algorithm and the particle swarm algorithm, perform iterative search within the parameter response surface, and output the combination of structural characteristic parameters that meet the optimization conditions as the optimized parameter set.

7. A multi-objective collaborative optimization method for the structural parameters of a strain gauge according to claim 6, characterized in that S6 is specifically: According to the optimized parameter set, reconstruct the three-dimensional finite element geometric model of the strain gauge; In the three-dimensional finite element geometric model, the optimized values of the length of the sensitive grid wire, the diameter of the sensitive grid wire, the thickness of the transition layer, the thickness of the substrate, and the thickness of the cover layer are respectively assigned, and finite element numerical simulation is carried out; During the numerical simulation process, by applying standard load conditions, the measured sensitivity value and the life value of the optimized strain gauge structure are obtained; The measured sensitivity value and the life value of the optimized strain gauge structure are compared with the measured sensitivity value and the life prediction value obtained before optimization, and the optimized configuration results of the strain gauge structure characteristic parameters are verified and output.

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