363 results about "Materials design" patented technology

The invention belongs to the technical field of steel and iron material design, and discloses a stainless steel strength and toughness collaborative optimization method and system based on heterogeneous integration, and the method comprises the steps: constructing a database of stainless steel components, process parameters and mechanical properties; according to the database, establishing a stacked heterogeneous integration model; quantizing contribution weights of input variables of the stacked heterogeneous integrated model to mechanical properties based on an SHAP method, identifying key factors, and setting a multi-target strength and toughness collaborative optimization index to guide an optimization direction; optimal components and processes are screened according to SHAP analysis, a sample is prepared through vacuum melting, hot rolling and heat treatment, the performance is verified according to the ASTM standard, and a final optimization scheme is determined; and a cross-scale digital twinborn verification system is constructed, and collaborative optimization of alloy components, process and macroscopic performance is realized. According to the method, the problems of low efficiency and insufficient generalization ability of a single model of a traditional trial and error method are solved, and an efficient and explainable intelligent optimization scheme is provided for development of high-performance stainless steel.

The invention belongs to the technical field of high polymer material design and intelligent manufacturing, and discloses a machine learning driven epoxy resin based composite material thermal-mechanical property aided design method, which comprises the following steps: S1, data acquisition and feature construction; s2, performing feature screening; s3, constructing and training an interpretable prediction model; s4, carrying out reverse design and optimization; and S5, performing closed-loop verification and updating. According to the method, the quantitative relation of structure-process-performance is constructed through an interpretable machine learning model, and the contribution mechanism of each factor is revealed by means of SHAP analysis. And finally, reversely designing an optimal epoxy resin monomer structure and a matched curing process according to the performance target. The limitation of a traditional trial and error method is broken through, collaborative optimization of the material structure and the forming process can be achieved, and the development efficiency of the epoxy resin-based carbon fiber composite material is remarkably improved.

The invention discloses a special large language model for a solid waste cementing material and an innovative hypothesis generation method thereof, which realize accurate understanding and reasoning of information such as material composition, process parameters, mechanical properties and the like by constructing a special knowledge graph in the field of materials, combining experimental data vector retrieval and fusing a mixed retrieval enhanced generation technology, so that the innovative hypothesis generation of the solid waste cementing material is realized. And a scientific reasoning module is used for deducing a material design rule to generate a potential innovation hypothesis. The method effectively breaks through the problems of long development period and low innovation efficiency of traditional materials, and is suitable for the fields of low-carbon building material development and solid waste high-valued utilization.

The invention belongs to the technical field of high-performance composite materials, and discloses a carbon fiber reinforced thermoplastic composite material performance database, a construction method and application thereof, and the method comprises the following steps: S1, database structure construction; s2, experimental sample collection and data standardization; s3, feature engineering and variable reduction; s4, training a machine learning model; s5, constructing and verifying an adaptive model; and S6, data expansion and feedback optimization. According to the method, material performance prediction and formula parameter reverse design under target performance are realized through systematic acquisition and normalization processing of three types of data of material components, preparation process and performance characterization and building of a nonlinear mapping model among a material structure, a process and performance through a machine learning method. The database can be used for intelligently recommending a high-performance composite material combination scheme, is suitable for rapid screening and customized development of various thermoplastic composite materials, effectively reduces the research and development cost and development cycle, and improves the material design efficiency.

The invention discloses a micro proportional valve gradient composite coating and a preparation method thereof, and belongs to the technical field of micro proportional valve surface strengthening manufacturing. Aiming at the corrosion-resistant and wear-resistant requirements of the micro proportional valve under the working conditions of strong corrosion, multiple impurities and high pressure difference, the interface strengthening and corrosion-resistant and wear-resistant performance optimization of a metal matrix and a functional coating are combined, and metal / ceramic / polymer gradient material design, spraying parameter suitability research and a coating porosity / bonding strength control method are covered; the valve core and valve seat protection device is suitable for high-efficiency protection technical systems of valve cores and valve seats of precise instruments such as gas chromatographs and mass spectrometers.

The invention discloses a building composite phase change material intelligent matching and optimizing method based on a large language model, and belongs to the technical field of building energy saving and intelligent material design. Basic thermophysical property data, building environment parameter data and user demand data are collected; a deep reinforcement learning technology is utilized to construct an intelligent model based on a deep Q network strategy, and generated data samples are integrated into a thermophysical property database; outputting a candidate material combination recommendation scheme by adopting a large language model; evaluating the candidate material combination recommendation scheme by using the semantic tag vector and a sorting engine to obtain a performance evaluation result; generating a performance evaluation report according to the simulation model; a multi-agent negotiation algorithm is adopted, cross-regional thermal control performance is optimized, a multi-dimensional performance comparison diagram and tuning suggestions are generated, correction information of designers is recorded, optimization is carried out, and feedback is provided in a self-adaptive mode. According to the method, the building composite phase change material combination is screened, cross-regional thermal control is optimized, adaptive schemes and suggestions are output, and the self-adaptive optimization capability of the system is improved.

The invention belongs to the technical field of composite material design, and discloses a polyimide-based composite material design method and system based on experiment-machine learning collaborative optimization. The method comprises the following steps: S1, experimental database construction and machine learning performance modeling; S2, machine learning model construction and training; S3, model evaluation and performance index output; and S4, intelligent design of the polyimide-based composite material. The multi-objective performance collaborative optimization design and preparation of the polyimide-based ternary carbon heterostructure composite material are carried out by taking experimental data as a main material and machine learning as an auxiliary material. According to the method, an experiment-model-optimization closed-loop iterative design system is constructed based on an experiment-machine learning collaborative optimization method, the method has the advantages of high prediction precision, high optimization efficiency, good multi-target adaptability and the like, the development efficiency of the polyimide-based composite material is remarkably improved, and the development cost of the polyimide-based composite material is reduced. The method is suitable for intelligent design and large-scale application and popularization of the high-performance heat-conducting electromagnetic shielding material.

The invention relates to a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, which belongs to the technical field of metal material design and development, and comprises the following steps: calculating a stable high-strength deformation nickel-based superalloy system based on a first principle, and on this basis, calculating a deformation nickel-based superalloy system; constructing a deformed nickel-based superalloy component design space based on the knowledge of the nickel-based superalloy field; based on a deformed nickel-based superalloy component design space, the deformed nickel-based superalloy component design space is reduced by adopting an empirical formula in combination with thermodynamic high-throughput calculation to obtain the reduced deformed nickel-based superalloy component design space, and based on the reduced deformed nickel-based superalloy component design space, based on machine learning and a genetic algorithm, the deformation nickel-based superalloy component design space is obtained. And a deformed nickel-based high-temperature alloy reverse design model is established, and the deformed nickel-based high-temperature alloy with the target performance is screened. Compared with the prior art, the oriented development method for the high-strength and high-toughness nickel-based high-temperature alloy is achieved by fusing cross-scale calculation, domain knowledge constraint and machine learning reverse design.

The invention discloses a material performance prediction method and device based on a multi-machine learning regression model and a high-throughput experiment, a medium, a product and application thereof, and relates to the technical field of material design and optimization, and the method comprises the steps: obtaining preset material parameters; any performance is determined as the current performance; based on material components in the preset material parameters, determining target key features of the current performance under the preset material parameters; respectively inputting the process in the preset material parameters and the target key features of the current performance under the preset material parameters into a plurality of optimal performance prediction regression models corresponding to the current performance to obtain a plurality of sub-prediction values of the current performance under the preset material parameters; and based on the plurality of sub-predicted values of the current performance under the preset material parameters, determining a total predicted value of the current performance under the preset material parameters. The material performance prediction precision and the research and development efficiency are improved, and the research and development cost is reduced.

Embodiments relate to system and methods involving use of a technique for managing a database for producing a material composition having a thermodynamic phase. The technique can include: receiving a binary phase diagram for each material to be used as a component of a high-entropy alloy (HEA); using one or more active learning machine learning techniques for generating a feature, the feature including: a primary feature that is representative of a probability that an HEA will exhibit a solid solution phase and / or an intermetallic phase, and a physics-based feature that is representative of a factor related to formation of a desired intermetallic HEA phase; encoding the primary feature and the physics-based feature; generating an output representation of a HEA alloy composition and phase of a predicted materials composition; and selecting a HEA composition and phase that will meet a material design criterion.

The present invention relates to the technical field of concrete. Disclosed is a toughening design method for fiber-reinforced concrete. The method of the present invention changes the passive control characteristics of fibers on a concrete crack propagation process in conventional fiber-reinforced concrete. Starting from the origin of composite material design, firstly, an ultrahigh-ductility composite cementitious matrix is designed; and secondly, an aggregate is incorporated. On the basis of a specific combination of matrix and aggregate parameters, a composite material develops meso-scale multiple cracking and strain-hardening behaviors between aggregate particles during external loading. The fiber-reinforced concrete prepared by this method actively disperses brittle damage and failure within the material, and avoids unstable crack propagation caused by weak interfacial bonding between the aggregate and the matrix. Compared to conventional fiber-reinforced concrete design methods, the present invention more accurately establishes relationships between material compositions, microstructures and macroscopic mechanical response, and more efficiently enhances the toughness of the material.

The invention belongs to the technical field of composite material design, and discloses a polymer composite material design method based on LLM knowledge database construction and reasoning optimization, and the method comprises the following steps: S1, target performance setting; s2, constructing a knowledge database; s3, constructing a reverse design model; s4, constructing a performance verification module; and S5, performing multi-round test-verification design loop optimization. According to the method, an intelligent design method integrating target setting, semantic knowledge database construction, a design assistant, a performance verifier, a prompt engineering module and a language model material knowledge system is provided, closed-loop reasoning and feedback of structure-mechanism-performance are achieved through multiple rounds of DA-VM collaborative optimization, and the design efficiency is improved. The method has the advantages of high design efficiency, strong structure generation rationality, accurate performance prediction, knowledge evolvable and the like, and is suitable for intelligent rapid development of the high thermal conductivity-wave absorbing-shielding-mechanical integrated functional polymer composite material.

The invention discloses a metal material design method based on deep learning, and the method comprises the steps: S1, collecting real chemical component data, forming training samples, carrying out the data preprocessing of the training samples, increasing the number of the training samples through a data enhancement technology, and improving the expression capability of features through a self-attention mechanism; s2, designing a model architecture, wherein the model architecture comprises two basic elements, namely a generator and a discriminator; s3, in the model architecture, generating data similar to real chemical components through a generator, and measuring the similarity between the generated data and actual data by using an objective function; and S4, training the confrontation generation network to balance the generator and the discriminator. The metal material design method based on deep learning has the advantages of convenient operation in the material design process, low cost and convenient use.

The invention belongs to the technical field of functional ceramic materials, and provides a lead-free high-entropy relaxor ferroelectric material and a preparation method thereof.The raw materials of the lead-free high-entropy relaxor ferroelectric material comprise ceramic powder and oxide; the chemical general formula of the ceramic powder is (1-x) (Bi < 0.2 > Na < 0.2 > Ba < 0.2 > Sr < 0.2 > Ca < 0.2 >) TiO < 3-x > La (Mg < 1 / 2 > Zr < 1 / 2 >) O3. The ceramic powder is prepared through a high-entropy strategy, a slender ferroelectric hysteresis loop is obtained at the room temperature, the breakdown field strength of the material is obviously improved, meanwhile, high energy storage density and energy storage efficiency are obtained, the energy storage density reaches 13.5 J cm <-3 >, the energy storage efficiency is 94.6%, and the breakdown electric field reaches up to 750 kV cm <-1 >. According to the material design based on the high-entropy strategy, the relaxation characteristic of the material is remarkably improved, the dielectric loss of the ceramic is effectively reduced, and the practical application potential is shown.

The invention belongs to the technical field of concrete material design, and particularly relates to a data-driven concrete mix proportion multi-objective collaborative optimization method and system.The method comprises the steps that firstly, a concrete test data set is obtained, and the concrete test data set comprises concrete components, the curing age and the compressive strength; then, a BP neural network model is trained by utilizing the concrete test data set, and a multi-objective optimization model is constructed by taking cost minimization and carbon emission as objectives on the premise of preset compressive strength based on the trained BP neural network model; and finally, solving the multi-objective optimization model to obtain a Pareto optimal solution set, and outputting an optimal concrete mix proportion scheme under preset compressive strength. According to the method, concrete compressive strength constraint is set, only double-objective optimization needs to be considered, the calculation efficiency can be improved, meanwhile, the curing age of the concrete is taken into consideration during compressive strength prediction, and the accuracy of a compressive strength prediction result can be improved.

The invention discloses an interface enhancement gradient design method of an anti-impact aviation glass laminated structure, and relates to the technical field of composite material design, the method comprises the steps of glass material preparation, interface enhancement gradient design, lamination compounding, quality detection and performance testing, the glass material comprises a glass layer material, a middle layer material and a transition layer material, the laminated composite process comprises the steps of pre-strain treatment, pre-pressing treatment, hot-pressing combination and cooling demolding, and has the advantages that by arranging the transition layers with components and performance in gradient change, the content of reinforcing materials such as nanometer titanium dioxide particles and graphene nanosheets in each transition layer is gradually increased; and compared with a traditional uniform or simple layered structure, the gradient design has the advantage that the phenomenon of interface stress concentration caused by performance difference between the glass layer and the polymer intermediate layer is remarkably reduced.

The invention discloses an information acquisition method and device of an alloy material, electronic equipment and a medium. The method comprises the following steps: acquiring an information query instruction of the alloy material; analyzing the information query instruction through a specified large model to determine a current information query task; the information query task comprises at least one of a general information query task, an alloy performance prediction task and an alloy reverse design task; according to the information query task, calling a target model from a pre-constructed alloy field model library; and executing the information query task through the target model to obtain a target query result. Therefore, the general understanding, reasoning and interaction capabilities of the specified large model and the professional prediction precision and reverse optimization capability of the alloy field model are effectively combined, so that the efficiency, accuracy and intelligent level of alloy material design and discovery are improved, and the rich knowledge requirements of users on alloy material design are met.

The invention relates to an intelligent design method of a radiation shielding material based on cross-scale error feedback lock step, which comprises the following steps: determining a material component set and acquiring an atomic-scale graph structure of the material component set, and extracting intrinsic performance parameters such as heat conductivity and attenuation coefficient through an atomic-scale prediction module; and then, constructing a design variable vector in combination with a multi-physical field condition, calculating equivalent performance parameters of the composite material, establishing a cross-scale lock step mechanism, and triggering a molecular dynamics and Monte Carlo recalculation module to carry out correction when the deviation between a predicted value and a mesoscopic recalculation result exceeds a limit. Based on the correction parameters, a candidate material scheme is generated by using a multi-objective optimization algorithm; and performing gamma-ray and neutron shielding performance simulation on the candidate schemes through a Monte Carlo radiation transfer model accelerated by a deep neural network, and comparing with an experimental result to realize self-adaptive material design driven by cross-scale errors. According to the method, the design precision and efficiency of the radiation shielding material are remarkably improved.

The invention discloses a design method for improving the thermal performance of a composite material by regulating and controlling the cluster type and number. The composite material designed by the method consists of an inorganic filler and an organic polymer material matrix, wherein the inorganic filler is a high-thermal-conductivity material and is in a cluster form; and orderly construction of a heat conduction path in the system is realized by regulating and controlling the type and the number of the filler clusters, so that the composite thermal interface material with ultrahigh heat conductivity is obtained under the condition of lower filling volume fraction. The heat-conducting interface material prepared by the method has excellent ductility, can adapt to various thermal interface applications, effectively improves the thermal management efficiency of the device by virtue of the characteristics of ultrahigh heat-conducting property and low filling volume fraction, realizes energy consumption reduction and material saving, further improves the performance release of the device and prolongs the service life of the device.

The invention discloses a fracture toughness prediction method for a non-continuous bionic Brini-gang structure, and belongs to the field of bionic composite material design. The prediction method comprises the following steps: S1, acquiring geometric and material parameters of a discontinuous bionic Belii Vagina structure; s2, constructing a fracture mechanics theoretical model of the structure; s3, solving the opening displacement of the fracture tip of the bionic Belii gang structure by adopting a numerical method; and S4, calculating parameters such as a crack propagation resistance curve and fracture toughness according to the crack tip opening displacement. According to the fracture toughness prediction method of the non-continuous bionic Beliolian structure provided by the invention, fracture parameters such as crack tip opening displacement and a crack propagation resistance curve can be obtained by taking microscopic sizes and material parameters of fibers and a matrix of the structure as input, and the prediction method not only can reveal fracture characteristics and a toughening mechanism of the bionic Beliolian structure, but also can obtain fracture parameters such as fracture toughness of the bionic Beliolian structure. And the method can also be used for optimal design of a bionic Brini spine structure.

The invention provides a multi-objective optimization method for material performance, and belongs to the technical field of material science and artificial intelligence, and the process of the method provided by the invention takes a small sample learning model as a core, combines an active learning optimization strategy, and realizes quantitative optimization of multi-objective performance indexes in material research and development in an extreme service environment. According to the method, limited test data are fully utilized for learning, and the number of physical tests is remarkably reduced; through intelligent optimization search, the efficiency and the success rate of searching for the material design scheme meeting the multi-target performance requirement are improved, and the method has important significance in accelerating development of new materials in a severe environment.

The invention discloses a composite material four-nail connection structure strength prediction and bolt layout optimization method based on a physical information neural network, and belongs to the technical field of composite material design. The method comprises the following steps: carrying out simulation modeling and verification on the composite material four-nail connection structure; generating and expanding a basic data set; constructing and training a physical information neural network model; performing system calibration and verification on the prediction precision and generalization ability of the physical information neural network model by using experimental data; and constructing a composite material four-nail connection structure bolt layout automatic optimization framework, and outputting optimal bolt layout parameters. According to the method, the problem of physical unreasonable prediction of a pure data driving model in a data sparse region is solved, efficient collaborative optimization under geometric constraints of bolt spacing, edge distance and the like is realized, a complete closed loop from rapid strength evaluation to automatic layout optimization is realized, and the design efficiency and reliability of a composite material four-nail connection structure are improved.

The invention discloses a method and a system for predicting the tensile strength of a low-temperature wet saturated porous composite material laminated plate, and belongs to the technical field of civil aviation composite material design and evaluation, the method for predicting the tensile strength of the low-temperature wet saturated porous composite material laminated plate comprises the following steps: S1, estimating the tensile strength of the porous composite material laminated plate based on a stress field intensity method; s2, introducing a Tsai equation to fit a function relationship between the mechanical property parameters and the temperature and humidity environment parameters of the porous composite material; and S3, importing different temperature and humidity environment parameters into the function relationship to obtain the tensile strength of the porous composite material laminated plate in different temperature and humidity environments. On the basis of engineering estimation of the tensile strength of the porous composite material laminated plate based on a stress field intensity method, a Tsai equation is introduced to fit a function relationship between mechanical property parameters of a composite material and a temperature and humidity environment; and an engineering estimation technical scheme capable of predicting the tensile strength of the moisture-absorption saturated porous composite material laminated plate in the low-temperature environment is established.

The invention belongs to the technical field of intelligent material design, and particularly relates to a titanium alloy component design method based on a comparison generation method.The titanium alloy component design method comprises the four steps of data collection and processing, model architecture design, model training and component design, firstly, real chemical component data in the direction of a titanium alloy material is collected; secondly, two neural network models are constructed, the first model is used for generating corresponding chemical components according to the input titanium alloy performance parameters, the second model is used for comparing and evaluating the difference between the chemical components generated by the first model and the real chemical components, and then a training data set containing the real chemical components and the chemical components generated by the first model is used for analyzing the difference between the chemical components generated by the first model and the real chemical components; parameters of the first model are optimized through a loss function of the second model; and finally, generating new titanium alloy chemical components according to the input performance parameters. A comparative learning mechanism is adopted, the limitation that a traditional trial-and-error method is long in period and high in cost is broken through, and the problem that a traditional material design method depends on and monopolizes a single database is effectively avoided.

The invention discloses an interlayer superstructure electromagnetic-mechanical collaborative design method based on machine learning and a heuristic optimization algorithm, relates to the technical field of material design, and is used for solving the technical problems that the mechanical property of an interlayer superstructure obtained through existing design is poor, and the requirements for strength, stability and durability in practical application are difficult to meet. The collaborative design method comprises the following steps: setting an objective function of an interlayer superstructure, and determining the configuration of the interlayer superstructure to be designed according to the objective function; establishing a proxy model according to a machine learning algorithm, and integrating the proxy model into a heuristic optimization algorithm; generating geometric characteristic variables of the interlayer superstructure in batches; obtaining the mechanical property and the electromagnetic property of the interlayer superstructure; feeding back the mechanical property and the electromagnetic property to a heuristic optimization algorithm to obtain a numerical value of an objective function; and according to the numerical value of the target function, carrying out iterative updating on the geometric feature variables until an optimal solution is obtained, and completing the design.

The invention discloses a genetic algorithm-based high aspect ratio composite material wing structure profile optimization design method, and belongs to the field of aircraft structure design and optimization. The method specifically comprises the steps that firstly, loads needing to be borne by all structural sections in the spanwise direction of a wing box of a to-be-designed aircraft wing are calculated; aiming at a current structure section S, constructing an equivalent hexagonal section representing the S based on a selected airfoil profile, and adding a basic structure and a key check point; then, according to layout parameters and size design parameters of the structure section S, calculating geometric attributes corresponding to the S; calculating the normal stress and the shear stress of each key check point based on the load and the geometric attribute; the method comprises the following steps of: establishing a section size optimization model by taking the lightest structure weight as an optimization target and the allowable stress of material design as a constraint condition, and finally, iteratively solving to find an optimal section size parameter combination, and outputting corresponding weight and performance indexes. According to the method, the scheme of the high-aspect-ratio composite material wing in the overall design stage is rapidly balanced, and the design efficiency can be remarkably improved.

The invention provides a spherical hollow piezoelectric composite material and a preparation method thereof, and belongs to the technical field of piezoelectric materials. According to the method disclosed by the invention, the precise manufacturing of the complex three-dimensional structure of the piezoelectric composite material is realized, and the controllability of the structure can be realized; a 1-3 type (namely a two-phase piezoelectric composite material composed of one-dimensionally arranged piezoelectric phases and three-dimensionally communicated polymer phases) piezoelectric composite material is of a periodic array structure formed by embedding piezoelectric ceramic columns into a resin matrix, and precise arrangement and complex three-dimensional shape control of the ceramic columns are difficult to realize by a traditional process. According to the 3D photocuring printing technology, liquid photosensitive resin is cured layer by layer, so that the diameter, spacing and spatial distribution of piezoelectric materials (such as ceramic columns) can be accurately controlled, complex structures such as a spherical polymer hollow frame are formed, and the degree of freedom of material design is remarkably improved.

The present invention discloses a smart bio-inspired material design platform to satisfy multi-objective material design featuring complex microstructure for the future. the platform sets mechanical properties of a simulative material element via establishing a reduced model. A distribution of the simulative material element is simulated so as to output a material simulative parameter. A deep learning framework is combined in the platform for computing and evaluating an optimal material design that meets a target material parameter. Specifically, the reduced model can be based on data provided by any test of material mechanical properties, and the deep learning framework evaluates whether a biomimetic material design meets demand of the optimal target material parameter according to a standardized reward function model. The platform is applicable to multi-objective simulative material design, and is greatly potential for futuristic applications.

The invention discloses a multilayer wave-absorbing material design method, system, medium and equipment, and the method comprises the steps: building a multilayer wave-absorbing material model through electromagnetic simulation software, taking a radar cross-sectional area RCS as an optimization target, and controlling FEKO to be within a specified target frequency band; sampling electromagnetic parameters in a value range by adopting a Latin hypercube sampling algorithm, performing parameterized simulation on the multilayer wave-absorbing material model, extracting radar cross-sectional area (RCS) values to form a parameter-RCS data set, generating an electromagnetic parameter-RCS data set, training a convolutional neural network, and obtaining a multi-layer wave-absorbing material model; establishing a nonlinear mapping model from the electromagnetic parameters to a radar cross-sectional area (RCS); embedding a proxy model obtained by training into a Bayesian optimization framework, applying an expected improved function to guide a search space, automatically optimizing an electromagnetic parameter combination, and performing FEKO full-wave simulation verification; if the RCS value of the optimization result is lower than the preset threshold value, iteration continues until the performance requirement is met, and the optimal electromagnetic parameter combination is output.

This invention discloses a finite element design and analysis method for a novel structural piezoelectric composite material. The method includes the following steps: extracting the zero level set surface based on the spatial structural equation to create the geometric shapes of a 3D TPMS shell structure, including Schwarz P, Gyroid, Neovius, and Diamond structures; using Matlab to define the frame volume fraction of the geometric shapes and control the unit node information in the 3D TPMS shell structure model; generating the 3D TPMS shell structure model as an inp file, importing the inp file into Abaqus to generate hexahedral elements; using Rhino to smooth the surface of the hexahedral elements, exporting the STL file, and performing remeshing in Hypermesh to convert the hexahedral elements into tetrahedral elements; adding periodic boundary conditions to the tetrahedral element mesh and performing numerical simulation to obtain the TPMS shell structure composite material model. The novel TPMS shell structure piezoelectric composite material designed by this method significantly improves piezoelectric performance compared to existing piezoelectric composite materials.