59 results about "Material Design" patented technology

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 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 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 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).

The application provides a kind of anti-hydrogen embrittlement material design method and system based on deep generative model, belongs to material design field.The method first collects the material composition of anti-hydrogen embrittlement material, hydrogen charging process parameters and corresponding hydrogen embrittlement sensitivity index obtained by experiment, constructs experimental data set, and is preprocessed to obtain training data set;Again, based on deep generative model, a component and hydrogen charging process-performance bidirectional mapping model is constructed, including a component and hydrogen charging process to performance bidirectional mapping network and a generation network acting on performance space, and a loss function is constructed, and the component and hydrogen charging process-performance bidirectional mapping model is trained and verified;Preset target performance, based on the component and hydrogen charging process-performance bidirectional mapping model after training, the composition and hydrogen charging process parameters of anti-hydrogen embrittlement material are designed reversely.The application realizes the composition reverse design of anti-hydrogen embrittlement material from target performance, shortens the material development cycle, and improves the anti-hydrogen embrittlement performance of material.

The invention discloses a fiber reinforced composite material variable stiffness reliability multi-scale optimization design method, and belongs to the technical field of composite material structure reliability optimization design. The method comprises the following steps: constructing a composite material multi-scale reliability topological optimization model considering material and load uncertainty; an improved single-cycle single-vector chaos control (SLSV-MCC) method is provided, and probability constraints are efficiently and accurately processed by adopting a current design point gradient and a stable factor; and in combination with a normal distribution fiber optimization (NDFO) model, parameterizing a discrete fiber angle to realize synchronous optimization of a macroscopic topology and a microcosmic fiber path. According to the method, the problems of low calculation efficiency and unstable convergence when the existing method is faced with multi-scale and strong-nonlinearity problems are effectively solved, and a lightweight composite material design scheme with high reliability in an uncertain environment can be obtained.

The invention relates to the technical field of cement-based material design, and discloses a machine learning-based cement-based material multi-scale intelligent design method, which comprises the following steps of: constructing a system element database of a cement-based material under nano-scale, micro-scale, meso-scale and macro-scale; based on a machine learning algorithm, training the system element database to construct a machine learning model so as to establish mapping and association among different scales; designing parameters under any scale are input into the machine learning model, forward prediction is executed, and performance and structural features of the target object under the scale are output and obtained; and inputting the performance or structural characteristics of the target object under any scale into the machine learning model, executing reverse derivation, and outputting design elements required for realizing the performance or the structure. The method has the characteristics of intelligence, automation and high efficiency, the labor cost is remarkably reduced, the limitation that a traditional fitting method is long in period and low in efficiency is overcome, and the technical blank of multi-scale information transmission and performance mapping is filled.

The application provides a multi-target optimization method for material performance, and belongs to the technical field of material science and artificial intelligence. The method process provided by the application takes a small sample learning model as a core, combines an active learning optimization strategy, and realizes quantitative optimization of multi-target performance indexes in material research and development in an extreme service environment. The method fully utilizes limited test data for learning, significantly reduces the number of physical tests, improves the efficiency and success rate of searching for a material design scheme meeting multi-target performance requirements through intelligent optimization search, and has important significance for accelerating development of new materials in harsh environments.

The invention relates to the field of material design, and discloses an aluminum-based material intelligent design method and equipment based on 5G communication and machine learning, and the method comprises the steps: receiving collected data, such as locally preprocessed components and technological parameters, in real time through an edge computing system and a 5G mobile network base station of an aluminum-based material production line; components and process parameters are obtained through a 5G mobile network base station, phase diagram and thermodynamic property calculation is conducted through a thermodynamic database by means of a phase diagram calculation method, and a comprehensive database is established; inputting the components and the process parameters into a plurality of performance index independent prediction models based on a comprehensive database and machine learning; when the predicted value is abnormal, a comprehensive performance prediction model and a non-dominated sorting genetic algorithm are adopted to carry out collaborative optimization on the multiple performance indexes and the comprehensive performance to obtain a Pareto optimal design scheme, and the Pareto optimal design scheme is issued to a production line for trial production and is fed back to a comprehensive database to enable the model to learn and update online. 5G, phase diagram calculation and machine learning are fused, and the design efficiency of the aluminum-based material is improved.

The invention relates to the technical field of material informatics and artificial intelligence, in particular to a method for training and calculating a high-entropy material structure descriptor based on a large language model, and the method is used for a machine learning task of high-entropy material design and performance prediction. According to the 3D chemical structure model of the high-entropy material or the element information in the chemical structural formula, the corresponding information is extracted through the large language model, and the structure descriptor for the high-entropy material machine learning task is automatically calculated according to the element information with the highest utilization rate in the literature and the knowledge base, so that the method has the characteristics of simple operation, reliable data, high speed and the like; and the structure descriptor with the highest literature recognition degree can be obtained through calculation.

The invention relates to the technical field of material design, in particular to an automatic material optimization method, device and equipment based on an integration algorithm, and the method comprises the steps: carrying out the weighted summation of prediction results of a plurality of machine learning fitting models through the integration algorithm on the basis of an automatic material optimization mode, and obtaining a final prediction result. And an optimization result is obtained through an optimization algorithm. Therefore, the problem that the optimization result is inaccurate due to the fact that only one algorithm is used for material optimization at present is solved. The method has an automatic optimization workflow, cleaning, data division, integrated calculation model training and component and process multi-objective automatic optimization of a material design data set can be fully automatically carried out, an optimization scheme report is generated, programming and personnel operation are not needed in the process, the material design efficiency can be greatly improved, and the method is suitable for large-scale popularization and application. And an efficient intelligent tool is provided for material designers.

The embodiment of the invention provides an optimization design method for a graded porous structure of a composite phase change material and related equipment, and belongs to the technical field of material design. The method comprises the following steps: acquiring material parameters of the composite phase change material; constructing a physical equation according to the material parameters to obtain a first physical equation based on the flow process; with flow irreversible process minimization as an optimization target, optimization function construction is carried out according to the first physical equation, and a first optimization function is obtained; and carrying out aperture analysis on the first optimization function to obtain a first aperture distribution relationship between the mother pore channel and the son pore channel in the hierarchical porous structure. The performance and reliability of the composite phase change material can be stably improved.

The invention provides a material generation method, electronic equipment and a computer program product. The material generation method comprises the following steps: in response to a received material generation instruction input by a user, obtaining material design knowledge and material design style characteristics related to the material generation instruction; performing cross-modal semantic alignment on the material generation indication, the material design knowledge and the material design style features to obtain material alignment features; and generating a material corresponding to the material generation indication based on the material alignment feature.

The invention discloses a high-toughness nodular cast iron design method fused with a physical information neural network, and belongs to the technical field of material design and intelligent manufacturing. The method comprises the following steps: firstly, systematically constructing a nodular cast iron data set covering chemical components, microstructures and mechanical properties, and carrying out preprocessing and feature dimension reduction, so as to construct a deep neural network regression model, and taking a physical law of nodular cast iron as a constraint condition; forming a physical information neural network through a composite loss function formed by weighting data loss and physical loss and embedding the composite loss function into a training process; and the trained physical information neural network is used as a fitness predictor of a genetic algorithm, multi-target optimization is carried out on the chemical components, and an optimal parameter combination is reversely deduced. According to the method, deep fusion of data driving and a physical mechanism is achieved, the problems that a traditional trial and error method is long in period and a pure data model is poor in extrapolation are solved, and an innovative solution is provided for rapid and accurate design of high-performance nodular cast iron.

The application relates to the field of material design, and in particular to a tin-based material composition performance inference method based on a Bayesian network. The method comprises the following steps: taking material performance variables and composition proportion variables as nodes in a DAG, learning a posterior distribution of the DAG based on a reward function based on GFlowNet; based on a tin-based Bayesian network, performing a maximum likelihood estimation algorithm to learn CPT parameters of each node in the tin-based Bayesian network, and obtaining a target tin-based Bayesian network; performing a Monte Carlo sampling algorithm in the target tin-based Bayesian network to obtain a data set, and based on preset composition proportion variables or preset material performance variables, combining a preset candidate cause set to perform KL divergence calculation, and determining the causal strength and importance between the nodes of each target tin-based Bayesian network. The purpose of inferring the remaining composition proportion from the given expected material performance and part of the composition proportion, and then obtaining the composition proportion meeting the material performance requirement is achieved.

The invention relates to the technical field of material design, and discloses a design method and system for a capsule liquid crystal dimming film, and the method comprises the steps: obtaining a basic performance index of a design material corresponding to the capsule liquid crystal dimming film, constructing a preliminary design architecture of the design material, carrying out the spatial distribution optimization of the preliminary design architecture, and obtaining a preliminary optimization architecture; determining material matching parameters of the design material, and performing optical simulation on the design material to determine the optical performance of the design material; constructing an electrode layout and a circuit topological structure of the design material, and constructing a design scheme of the design material based on the electrode layout and the circuit topological structure; and performing packaging simulation on the design material to obtain simulated material performance, and performing material packaging on the design material when an evaluation value of performance index evaluation meets a preset value. The quality of the capsule liquid crystal dimming film can be improved.

The invention relates to the technical field of material informatics, and discloses a method for predicting a microstructure of reinforced polypropylene based on data analysis, which is used for deducing the microstructure of a fiber reinforced polypropylene composite material in real time in the processing process. Comprising the following steps: synchronously collecting a dynamic process sequence reflecting overall shear energy and an online visual sequence reflecting local enhanced phase distribution; mapping the dynamic process sequence and the online visual sequence to a potential feature space to generate a fusion feature vector; and inputting the fusion feature vector into a pre-trained deep neural network model deployed on special computing hardware, decoupling in a forward reasoning mode, and outputting fiber length distribution and average orientation tensor to realize real-time quantitative inference. By means of the method, online, lossless and quantitative inference on the fiber microstructure is achieved, and key technical support is provided for material design and process optimization.

The invention discloses a real recycled aggregate accumulation simulation and concrete modeling method, and belongs to the field of building material digitization and computer simulation, and the method comprises the following steps: constructing a real recycled aggregate digitization model library with multiphase component labels; according to the target mix proportion, parameterizing and screening aggregate and carrying out grading scaling; establishing a father-child assembly relationship of the multiphase model; by setting an adjustable collision edge distance parameter in physical simulation, the aggregate spacing is controlled to digitally design the mix proportion; performing free falling simulation to generate a loose accumulation state, performing compaction simulation to realize compaction, and performing strike-off simulation to obtain a final flat surface in sequence; and extracting each final dense stacking static model, counting the volume, and quantitatively verifying the design target. The method solves the problem that the traditional method cannot truly reflect the form, multiphase composition and designable mix proportion of the recycled aggregate, and provides an efficient and accurate digital tool for material design and performance simulation of the recycled concrete.

The invention discloses a method for determining microcosmic RVE of an acupuncture ceramic matrix composite material, and belongs to the field of composite material design. According to the method, a CT projection image of the microstructure of the material is obtained by cutting a sample and performing CT scanning. Secondly, recognizing the geometric dimension (side length and height) of the RVE in the image, and counting the volume distribution of the RVE; then, fitting an exponential distribution function through a volume distribution histogram, and defining an integral range of a characteristic length, which is a core index for representing the statistical uniformity of the material, on the basis of the function; and finally, calculating the average height of the RVE in combination with the integral range, and determining the side length of the RVE as a volume constraint, thereby establishing a mesoscopic RVE model with statistical representativeness. According to the method, the size of the RVE is determined from the aspect of probability by quantifying the volume distribution and the feature length.

This invention relates to a method for determining the strength of amorphous carbon interfacial layers based on molecular dynamics, belonging to the field of aerospace lightweight composite material design technology. This invention utilizes molecular dynamics to construct a method that satisfies the interlayer spacing and interlayer crosslinking segments of actual amorphous carbon interfacial layers, and calculates the tensile, compressive, and shear strengths at room temperature (300K) to high temperature (2000K). Furthermore, the experimental and simulation results show extremely high agreement, providing a strong theoretical basis for the development of interfacial layer preparation processes and the calculation of service strength in high-performance composite materials.

This invention discloses a design method and apparatus for refractory high-entropy alloys based on a large model, relating to the field of alloy material design technology. The method includes: First, by generating and collecting data, pre-trained surrogate models for thermodynamics (covering phase diagrams, solidification processes, etc.) and mechanical properties are constructed, and calculation models for physical parameters such as valence electron concentration and density are established. Then, these models are integrated into a single ensemble model, and a large language model is introduced as the core of intelligent scheduling. The large model is responsible for parsing the design task, intelligently planning and calling the interfaces of each model based on a knowledge base and rule base. Finally, based on the set design objectives, a multi-objective genetic algorithm is initiated for iterative optimization and adaptive adjustment, efficiently outputting a final alloy design scheme that meets multiple performance requirements. This invention enables intelligent multi-objective design of refractory high-entropy alloys, solving the problem of high dependence on manual intervention.

The invention provides a reverse design method and device for a material structure, equipment and a storage medium, and relates to the field of material design. Constructing a three-dimensional feature vector matrix of the sample material; the three-dimensional feature vectors in the matrix are used for representing the three-dimensional microstructure features of the sample material. And extracting macroscopic performance parameters of the sample material, and taking the macroscopic performance parameters as model condition information. And forming a training data pair by using the three-dimensional feature vector and the model condition information. And carrying out iterative training on the conditional diffusion model by utilizing the training data pair until a training ending condition is reached, thereby obtaining a target conditional diffusion model. And inputting the target macroscopic performance parameters into the target condition diffusion model to obtain a target three-dimensional microstructure meeting target macroscopic performance parameter conditions. The three-dimensional microstructure of the material can be reversely designed according to the required performance, the material development efficiency is improved, and the development cost is reduced; and the designed structure can more accurately meet the required performance requirements, and is more suitable for the real process.

The application discloses a kind of based on big model's graphic propaganda material intelligent design method, it is related to graphic-text relationship modeling technical field, including the following steps: S1, constructs graphic propaganda material design input description set;S2, using design input description set carries out graphic-text expression relationship analysis;S3, using graphic-text expression relationship structure carries out graphic-text layout structure deduction;S4, using graphic-text layout structure description carries out design constraint uniformity;S5, using graphic propaganda material design constraint set carries out design consistency verification.The application is analyzed by setting based on design input description set and carried out graphic-text expression relationship, to graphic-text expression relationship structure, structured determination mechanism of corresponding relationship, attachment relationship and emphasis relationship is introduced between text expression unit and image expression unit, so that the semantic association between graphic and text is classified under information hierarchy constraint and output as stable graphic-text expression relationship structure.

The invention relates to the field of composite materials, in particular to an efficient operation method for a composite material database based on Visual FoxPro. According to the method, common types of composite material products are classified and sorted, then data structure design is carried out according to attributes of each product, and Windows desktop application of a composite material database is developed based on a Visual FoxPro platform. According to application characteristics and requirements, the database comprises five main modules including a material module, a process module, a knowledge management module, an auxiliary module and a user authority management module, each main module comprises a plurality of sub-modules, and some sub-modules further comprise some sub-modules. According to the method, related functions of material classification and performance description, material design optimization, structural simulation material data support and training learning are provided for the composite material product, and a powerful tool is provided for data management, standardization, quality management and the like in the development process of the composite material product.

The invention discloses a metamaterial with an evanescent shear modulus and a topological optimization design method of the metamaterial, and belongs to the technical field of aerospace structures and advanced material design. The method comprises the following steps: establishing a design domain of a periodic unit cell and discretizing; constructing a topological optimization mathematical model taking a relaxation function as an optimization target, and converting the problem of maximizing the ratio of the bulk modulus to the shear modulus, which is difficult to process directly, into a weighted optimization problem which can be solved steadily; predicting equivalent performance in combination with an energy-based homogenization method, and performing iterative solution by adopting an improved moving asymptote method and a novel floating projection function; and through error verification and parameterization reconstruction, outputting a geometric model with a clear boundary. According to the method, novel metamaterials such as two-dimensional quasi-single-mode / dual-mode and three-dimensional quasi-three-mode metamaterials with extremely high B / G ratio can be designed, seamless connection from optimization design to additive manufacturing is guaranteed, and an innovative material design solution is provided for high-performance vibration isolation and acoustic regulation and control components in the aerospace field.

The invention discloses a composite cemented filling material design method, and relates to the technical field of mine filling, and the method comprises the following steps: determining optimization components and optimization domains thereof, randomly generating a plurality of design combinations, preparing a filling material, and filling a to-be-filled sample to form a composite filling body test piece; maintaining the sample test body in a simulated environment, and dynamically adjusting a sampling interval and determining a plurality of sampling moments by taking a hydration heat reaction degree as an index; carrying out a coupling monitoring test by adopting a detection method combining a pulse speed and a low-frequency dielectric spectrum, extracting an interface coupling degree parameter, calculating a cementation coupling degree, and determining a termination moment based on a change rate of the cementation coupling degree; according to the method, the structural strength at the moment is obtained, the preset strength is taken as a constraint, the cementing coupling degree maximization and the experiment time minimization are taken as optimization objectives, a dual-objective optimization function is set, and the optimal design combination is determined through an optimization algorithm, so that the engineering risk can be remarkably reduced, and the service life of the structure is prolonged.