146 results about "Material Design" patented technology

The invention provides a multi-agent system for water quality purification material development, and belongs to the technical field of computer systems based on specific calculation models. The system comprises a data extraction agent, a material design agent, a material evaluation and screening agent, a synthesis method generation agent, a material characterization agent, a working condition matching agent, an effect verification agent, a mechanism mining agent and a material field knowledge base. According to the system, through cooperative work of a plurality of intelligent agents, a development report including structure information, an evaluation report, a synthesis scheme, a characterization scheme, a working condition matching scheme, an effect verification report and a deep mechanism analysis report of a water quality purification material for realizing a target task of the water quality purification material is directly generated according to the target task of the water quality purification material; the method does not depend on manpower and computing power resources, and saves time and labor.

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 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 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 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 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 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.

This invention discloses a rapid modeling method for large-size composite structures with variable thickness solid meshes based on a yarn model, belonging to the field of finite element modeling of fiber-reinforced composite materials. Its core process includes: for a large-size component S to be analyzed, generating yarn models for each ply partition using composite material design software and batch-dividing triangular meshes; similarly, extracting smooth surface profiles from the three-dimensional geometry of the overall component S and generating a structured shell mesh as the base carrier; then, using a voxel hash-accelerated spatial Boolean intersection algorithm to automatically identify the element sets of each ply partition between each yarn mesh and the overall shell mesh, generating a stepped solid mesh through normal offset, ensuring interface compatibility through node merging, and finally assigning material properties and generating an analysis model that can be directly submitted for solution. This invention significantly reduces manual workload and error risk, providing an efficient and reliable preprocessing solution for the curing deformation and strength analysis of composite material structures.

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 relates to the field of environment-friendly packaging boxes, and discloses a multi-modal data fusion environment-friendly packaging box intelligent design auxiliary system which comprises a packaging full-life-cycle heterogeneous atlas database module, a design constraint parameter analysis module, a topological variation index engine module, a compliance and multi-objective optimization module and a parameterization scheme generation module. According to the method, a time dimension is introduced through a topological variation index engine, instantaneous stress in a folding process is calculated in combination with a nonlinear viscoelastic model, physical evolution of a structure is simulated in a virtual design stage, and the fracture risk is predicted; meanwhile, multi-objective optimization is carried out by utilizing a Hash mask mechanism based on laws and regulations and a pruning algorithm, and a compliance design scheme containing production process parameters is output, so that the problems of lack of physical simulation and low compliance verification efficiency in environmental protection material design are solved, and the physical feasibility and the production yield of the design scheme are improved.

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 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.

The invention relates to the technical field of computer-aided material design, in particular to a material chemical formula generation method based on performance sensitivity self-adaptive stratified sampling, which comprises the following steps: inputting an element list; querying a performance sensitivity knowledge base to obtain a sensitivity level and an adjustment factor; analyzing the composite elements, extracting multi-dimensional element features, and quantitatively calculating element combination complexity; based on the element number, the sensitivity and the complexity, a corresponding sampling strategy is adaptively selected, and the sampling amount is dynamically allocated; generating a chemical formula and de-weighting; and outputting a chemical formula list and full-process metadata. Through innovation of resource allocation driven by performance sensitivity, composite element atomic-scale analysis, feature space clustering and the like, on the premise that sampling representativeness and chemical rationality are guaranteed, the calculation efficiency is improved by dozens of times to hundreds of times, the response time is reduced to the second level or the minute level, and the method has the advantages of being traceable, extensible and high in universality and has wide application prospects. And the material screening and discovery process is effectively accelerated.

The invention discloses a microstructure mapping modeling and mechanical simulation method suitable for a two-phase structure alloy, and the method comprises the steps: carrying out the self-adaptive median filtering denoising and Retinex enhancement of a metallographic diagram, so as to improve the gray scale comparison of a matrix and a second phase; training a U-Net + + segmentation model by using the enhanced image to complete pixel-level phase region extraction; a two-phase geometric model is established according to the segmentation result, a finite element feature data set is obtained through multi-physics field coupling calculation, lossless fusion and topological consistency verification are conducted on the finite element feature data set and a prior model, and a fusion model is obtained; simulation parameters are set based on actual working conditions, mechanical simulation is executed, and mechanical property parameters and response curves are output. The method can accurately reflect the mechanical behaviors of the two-phase structure alloy under different working conditions, provides a scientific basis for material design and performance optimization, has high universality, and can be widely applied to performance analysis and evaluation of various types of alloys.

The invention discloses a material physical property calculation and simulation method and system based on a first principle, and relates to the field of material physical property calculation and simulation, and the method comprises the following operation steps: S1, atomic-scale structure modeling and initial parameter setting; s2, structure optimization and energy minimization are carried out; s3, electronic structure calculation and intrinsic physical property extraction; s4, performing multi-scale coupling and mesostructure simulation; s5, simulating a multi-physics coupling process; s6, data-driven intelligent optimization is carried out; s7, carrying out multi-dimensional visual verification and interaction; and S8, carrying out full life cycle model iteration and knowledge base construction. According to the material physical property calculation and simulation method and system based on the first principle, full-process simulation from an atomic scale to a macroscopic material is achieved, atomic-scale structure modeling, multi-scale coupling and macroscopic process simulation are covered, physical properties and behaviors of the material under different scales can be deeply revealed, and the material physical property calculation and simulation method and system based on the first principle are provided for the purpose of improving the material physical property calculation and simulation efficiency. And more complete and accurate information can be provided for material design and process optimization.

The disclosure relates generally to methods and systems for automated design of materials and the manufacturing process for desired properties. Conventional automated materials design techniques do not perform an integrated design of (i) a material composition and (ii) their manufacturing processing steps. The present disclosure addresses this gap by using a multi-agent setup for automated design, wherein a distinct Reinforcement learning (RL) agent is used to mirror the composition selection (CS) and various sequential manufacturing process steps (PS) involved in its manufacturing route. The distinct RL agents learn from both past design data and computational models (empirical / analytical / physics-based models) representing the design process. The present disclosure also integrates other important parameters such as manufacturability, ESG norms, cost, process energy etc. and their relative importance into the design decision making process of the RL agents by expressing them as reward components upon which the RL agents are trained.

The invention discloses a material design method, device and equipment based on positive and negative interaction and a medium, and the method comprises the steps: collecting and sorting the formula process, structural characteristics, property indexes, performance expressions and other data of a material, training a positive prediction model through the data, and enabling the data to predict the property of the material according to the formula process parameters; a reverse optimization algorithm is introduced, possible formula process combinations are reversely deduced or generated according to target properties, the performance of the combinations is evaluated through a forward prediction model, and iterative optimization is performed until the target properties are achieved or a preset threshold value is met, so that the problem that a traditional experimental method needs to be subjected to multi-batch design, test inspection and repeated adjustment is solved; the technical problems of time and labor consumption, low efficiency, difficulty in quickly responding to market and application requirements, extremely high calculation cost for multi-factor strong correlation analysis, and difficulty in realizing quantitative, precise and high-efficiency optimization design in the prior art are solved.

The invention discloses a large model collaborative material multi-objective interactive design and decision-making method, relates to the field of alloy material design optimization, and aims to realize efficient design and multi-objective decision-making of a complex system through a closed-loop process of knowledge graph construction, dynamic agent model optimization, evolutionary algorithm search and large model real-time feedback. The method comprises the steps of constructing a knowledge graph from cross-domain knowledge and interacting with a large model, automatically adapting to a machine learning model and constructing a proxy model library, performing multi-objective optimization by using an evolutionary algorithm, and recommending an optimal design scheme through large model fine tuning and a feedback mechanism. Through the method, the limitation of traditional optimization is broken through, the design efficiency, precision and optimization efficiency are improved, the effectiveness of the design scheme in engineering feasibility and multi-target balance is ensured, and the method has remarkable application value.

The invention discloses a wave-absorbing material design method based on neural network guidance, and belongs to the field of intelligent material design. The method comprises the following steps: selecting actually measured magnetic conductivity frequency dispersion data of a magnetic material as a boundary condition, constructing a parameter space containing magnetic conductivity, dielectric constant, material thickness and frequency, and calculating a reflection loss RL value through a transmission line model; gPU acceleration tensor operation is adopted to generate a three-dimensional parameter space, and discretization processing is carried out on the parameter space; implementing a double-task screening strategy to extract an optimal dielectric constant combination and an effective region boundary; constructing a neural network model with physical constraints, and inputting thickness and frequency prediction dielectric constant parameters; and generating solution set distribution to guide material design through inversion verification. A physical model and deep learning are fused, an Einstein summation method and a broadcast mechanism are adopted to realize hundred million-level parameter parallel calculation, the problem of multi-parameter coupling optimization is solved, the physical rationality of output parameters is ensured through a softplus activation function, and the design efficiency and precision of the magnetic wave-absorbing material are remarkably improved.

The invention discloses a twin crystal nucleation position and variant selection prediction method based on machine learning, and belongs to the crossing field of material science and machine learning. The method comprises the following steps: acquiring microscopic structure data of a deformation material through electron back scattering diffraction, and constructing a multi-dimensional data set containing crystal grain parameters, crystal boundary parameters and deformation mechanism parameters by taking crystal grains and a certain crystal boundary combination as an analysis unit; and then modeling and predicting the twin crystal nucleation position and variant selection respectively by adopting a hierarchical machine learning modeling strategy. According to the method, the twinning behavior in the polycrystalline material can be efficiently and accurately predicted, and theoretical support and a technical path are provided for high-performance metal material design and plastic deformation mechanism research.

The invention provides a copper-based composite material performance prediction method based on a space-time attention mechanism, and the method comprises the steps: obtaining a microstructure diagram and stress-strain data through molecular dynamics simulation, extracting topological features through a diagram attention network, and dynamically adjusting the parameters of a time sequence convolution network; a cross-space-time attention module is constructed, and bidirectional feedback fusion of space feature screening and time sequence feature optimization is realized; and finally, carrying out dimensionality reduction and nonlinear transformation on the fused features through a full-connection layer, and outputting performance prediction values such as yield strength and Young modulus. According to the method, the precision and reliability of performance prediction of the copper-based composite material can be improved, and efficient and accurate theoretical support is provided for material design and performance optimization.

This material designing device is provided with: a search unit (121) that sets a generation condition (21) for generating a three-dimensional structure (23) of a material, the generation condition (21) including information indicating at least one of the characteristic and structure of the material; a cross-sectional image generation unit (123) that generates a cross-sectional image (22) of the material on the basis of the generation condition (21) set by the search unit (121); and a three-dimensional structure generation unit (124) that generates the three-dimensional structure (23) of the material on the basis of the cross-sectional image (22) generated by the cross-sectional image generation unit (123).

1. The name of the design product: the graphic user interface of the interface design software for electronic equipment. 2. The use of the design product: an electronic equipment. 3. The design points of the design product: the interface content of the graphic user interface in the screen. 4. The picture or photo that best indicates the design points: the front view. 5. The use of the graphic user interface: for measurement and observation after data import in the intelligent material design system. The front view is the initial interface of the software.

This invention relates to the field of printer nozzle technology, and more particularly to a high-precision FDM 4D printing nozzle for metal-plastic functional graded structures. The nozzle includes a first feeding device, two second feeding devices, a feeding connector, a water cooling device, a melting and stirring device, a feeding conduit shell, and multiple filament guides. This invention solves the problems of poor material mixing uniformity in existing technologies. In the design and fabrication of plastic functional graded structures, the composite precision and segregation of the plastic directly affect the performance and interface quality of the functional graded structure. Current plastic functional graded structure designs mainly focus on different material content ratios, requiring material design to be completed before molding. This results in a small range of material variability and poor flexibility. Furthermore, uneven plastic mixing directly causes inconsistencies between the functional graded structure performance and the design, severely leading to interface cracking and molding failure.

The invention provides a material microstructure digital design method based on a non-local model. The material microstructure digital design method can be used for controlling the microstructure and performance of a material. The method comprises the following steps: establishing a fractional order Cahn-Hilliard equation model for describing diffusion and phase separation processes of components in a material; selecting non-local parameters to control the phase distribution characteristics of the material; the fractional order equation is solved through numerical values, and material component space distribution under different non-local parameter values is obtained; constructing a material atom model based on the obtained component distribution; and analyzing the performance of the constructed model in combination with molecular dynamics simulation. By regulating and controlling non-local parameters, materials with different microstructures can be obtained under the same component proportion, accurate control of the microstructures and collaborative optimization of multiple properties such as strength, toughness and conductivity are achieved, and the method is suitable for material design in the fields of electronic packaging, high-performance conductors, aerospace and the like.