1140 results about "Material structure" patented technology

A valve seat for a pumping assembly. The valve seat includes a body including a gradient material structure that defines a strike face for the valve seat. The gradient material structure has a first material, a second material spaced from the first material, and a smooth compositional transition between the first material and the second material. In addition, the valve seat includes a bore extending through the body.

The invention provides a digital twin construction method for metal stamping forming and a computer system, and the method comprises the steps: obtaining a multi-source monitoring data set in a metal stamping forming process, carrying out the collaborative feature extraction processing, and generating a material structure feature set and a process dynamic feature set; a mixed feature space is constructed based on the material structure feature set and the process dynamic feature set, multi-dimensional feature matching processing is executed in the mixed feature space, a feature mapping relation network is generated, a dynamic twin model is constructed according to the feature mapping relation network, and real-time state deduction is conducted on the stamping forming process based on the dynamic twin model. And a forming quality prediction result and a process defect positioning result are generated, and a stamping process parameter adjustment scheme is generated and fed back to a stamping control system to trigger parameter optimization operation. According to the method, the early warning capability of process defects and the precision of process parameter adjustment under complex working conditions are improved, and meanwhile, the real-time performance and engineering feasibility of digital twin construction in the large-scale stamping forming process are guaranteed.

The invention discloses a macroscopic progressive damage intelligent evaluation method driven by multi-scale microscopic damage observation, and the method comprises the steps: selecting a plurality of representative materials to carry out a microscopic mechanical test, and observing the damage evolution process under different loading conditions through MicroCT to obtain a microscopic mechanical test result; simulating a crack state based on a micro-mechanical model to reproduce a micro-mechanical test result, and performing material parameter correction of simulation calculation by taking test data as a constraint to obtain a micro-numerical modeling calculation result; and determining a representative volume element (RVE) scale based on a microscopic numerical modeling calculation result, and describing a macroscopic damage behavior by utilizing a continuous damage mechanical model so as to obtain evolution relation data of a macroscopic damage variable, stress and strain. According to the method, the damage behaviors of metal and composite material structures under complex working conditions can be accurately predicted, and the safety and reliability of aircraft structures can be improved.

The invention discloses a thermal barrier coating multi-dimensional terahertz nondestructive testing method and system based on an adaptive optimization mechanism, and the method comprises the steps: constructing a matrix sample library, and preparing thermal barrier coating samples with different thicknesses, porosities and uniformity; obtaining terahertz detection data of three dimensions of geometric dimension stability, microstructure integrity and material structure uniformity of the thermal barrier coating; establishing a multi-parameter correction model based on surface roughness, and performing feature extraction, fusion analysis and error compensation on the detection data by adopting a multi-modal integration algorithm; an adaptive optimization model is established through a deep learning algorithm, and continuous iterative optimization of the detection system is realized in combination with a feedback correction mechanism; the method is applied to an engine blade thermal barrier coating, an actual blade coating is detected, detection data are collected for system optimization, system parameters and models are updated, and system applicability and robustness are improved. The method can be widely applied to the field of nondestructive testing of coating structures such as aero-engine blade thermal barrier coatings.

The invention belongs to the technical field of material structure optimization, and discloses an enhanced structure design method and system for a three-period minimal curved surface, and the method comprises the steps: modeling a geometric model of the three-period minimal curved surface, and exporting the model; expanding a design domain of the geometric model to obtain a corresponding expanded design space of the three-period minimal curved surface; a design space file is converted into an analysis data structure, a general expression mode is constructed for the three-period minimum curved surface, an equal geometric element stiffness matrix is solved by using a Gaussian integral method and the elastic performance of each point in a design domain and assembled into a global stiffness matrix, and the structural response of the model is solved; constructing a local density distribution function in the Bayesian unit, and assembling all LDDFs to obtain a global density distribution function for presenting structural topology; and constructing a stiffness maximization topological optimization mathematical model for the TPMS, calculating an objective function and analyzing sensitivity, and carrying out iterative updating on design variables in the optimization model based on an optimal criterion method to obtain a reinforced structure configuration.

The invention discloses a method for testing mechanical properties of a carbon fiber reinforced composite material based on image recognition, and relates to the technical field of material tests.The method comprises the steps that a carbon fiber sample of a fiber laying layer is prepared, the width tolerance is controlled through laser cutting, and speckle patterns are sprayed on the surface; training a U-Net + + neural network based on the collected macro / micro image, and analyzing the dynamic change of the fiber angle in real time; fusing the acquired macro / micro image with the dynamic change of the fiber angle, and constructing a constitutive equation of dynamic fiber orientation distribution; and importing the constitutive equation into a finite element model, iteratively optimizing the fiber-matrix interface strength parameters through inversion calculation, performing blind test verification on the sample by using the optimized finite element model, and outputting a cross-scale correlation test report. According to the method, an interface strength parameter is subjected to inversion iteration optimization through a finite element model, a macro-micro strain field, a fiber slip angle and an interface damage parameter are integrated through a cross-scale correlation test report, and full-chain data support is provided for a composite material structure.

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 provides a structural material corrosion damage cross-scale evaluation method and system, and belongs to the field of service performance cross-scale evaluation. The method comprises the following steps: firstly, collecting corrosion data of related structural materials, establishing a structural material service safety evaluation database based on the corrosion data, and then respectively extracting material scale characteristics and structural scale characteristics of corrosion damage of the structural materials; predicting the service life of the structural material according to the scale characteristics of the material; setting a service safety evaluation index of a material scale and measuring the service safety evaluation index; predicting the service life of the structural material according to the structural scale characteristics; setting a service safety evaluation index of a structure scale and measuring the service safety evaluation index; and carrying out cross-scale correlation on the material scale characteristics and the structure scale characteristics, constructing a transmission model for representing the service performance through material-structure cross-scale correlation, fusing the evaluation indexes of the material scale and the structure scale, generating the evaluation index of the structure material, and evaluating the service performance of the structure material. The accuracy and precision of corrosion damage evaluation are improved.

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.

A valve seat for a pumping assembly. The valve seat includes a body including a gradient material structure that defines a strike face for the valve seat. The gradient material structure has a first material, a second material spaced from the first material, and a smooth compositional transition between the first material and the second material. In addition, the valve seat includes a bore extending through the body.

The invention provides an intelligent optimization method for inverting new material structure parameters. The method relates to the technical field of material science and engineering structure analysis, and comprises the following steps: S1, acquiring time sequence data and image data of a material under different loading conditions, and preprocessing; s2, constructing a deep diagnosis model: constructing a time sequence feature extractor and an image feature extractor, and respectively extracting time sequence features and image topological structure features; time sequence features and image topological structure features are fused through a stepped mutual attention mechanism; s3, training a neural network based on the fusion feature vector, outputting a final predicted value, and updating network parameters by adopting a residual error-based alternative hyperplane Kaczmarz optimization method and an oblique projection optimization method; and S4, inputting the preprocessed measured data into the trained model, and outputting a material structure parameter inversion result. In this way, rapid inversion of complex material structure parameters is achieved, and the current technical problem is solved.

A silicon carbide source material structure for use in a sublimation system for growing single crystal silicon carbide is provided. The silicon carbide source material structure may comprise a first layer and a second layer, the first layer being different from the second layer in at least one property.

The invention relates to the related technical field of zipper anti-blocking, in particular to a light and thin fabric jacket zipper anti-blocking process optimization method, which comprises the following steps: integrating material structure parameters and mechanical behavior characteristics, performing dynamic stress simulation on a light and thin fabric jacket zipper mounting area, analyzing stress distribution and evolution rules under different motion states and opening and closing speeds, and determining the anti-blocking process of a light and thin fabric jacket zipper. And a zipper anti-clamping groove structure is arranged, and a process optimization parameter combination is obtained through parameter optimization. The technical problems that comprehensive analysis of material structure parameters such as yarn density and friction coefficient and mechanical behavior characteristics is lacked, stress distribution of a zipper installation area cannot be accurately determined, and jamming and fabric wrinkling and damage are prone to occurring are solved, stress distribution of the zipper installation area in different motion states is determined, and the application range of the zipper installation area is widened. The elastic buffer layer and the guide sliding rail are arranged through stress distribution, the elastic buffer layer is differentially configured according to a high-stress area, the guide sliding rail is sectionally arranged according to pressure distribution of a contact surface, stress is effectively absorbed and dispersed, and the zipper is guided to slide smoothly.

The invention belongs to the technical field of metal matrix composite materials, and relates to a tungsten-copper three-dimensional orthogonal woven composite material and an intelligent design method thereof.The composite material comprises a space interwoven network structure formed by tungsten fiber skeletons arranged in the X orthogonal direction, the Y orthogonal direction and the Z orthogonal direction, and the tungsten fiber skeletons are wrapped by a copper matrix to form a three-dimensional heterogeneous composite system; the method comprises the following steps: generating a composite material structure based on parametric modeling, acquiring mechanical and electrical performance data by utilizing finite element and multi-physics field simulation, and constructing a structure-performance data set; performing performance prediction by adopting three types of machine learning models, namely ANN, SVR and LightGBM, and determining an optimal model in combination with Bayesian optimization; performing parameter reverse screening based on the optimal agent model, screening a structural combination meeting performance constraints, and constructing a closed-loop optimization process through simulation verification and model iteration; according to the method, efficient design and accurate performance prediction of the composite material structure can be realized, and the method is suitable for high-performance electromagnetic structural materials.

The invention discloses a carbon fiber fuselage structure layering optimization design method and system, and relates to the technical field of material structure intelligent design, and the method comprises the steps: importing a three-dimensional CAD model of a fuselage structure, collecting physical field input parameters, integrating and generating a digital engineering package, and carrying out the temperature field calculation through a thermal coupling solver based on the digital engineering package, the method comprises the following steps: acquiring temperature distribution of a fuselage, mapping the temperature distribution of the fuselage into a thermal stress field through a thermoelastic constitutive equation, performing vector superposition on the thermal stress field and a mechanical load to generate a comprehensive stress cloud picture, starting asymmetric layering design for a high-stress region based on the comprehensive stress cloud picture, and verifying mechanical balance of layering angle combination through a balance optimization algorithm. And obtaining a layering optimization scheme. The thermal deformation sensitivity is accurately evaluated by using the thermal-mechanical coupling solver and the finite element disturbance analysis method, and thermal deformation can be effectively predicted and controlled, so that the dimensional stability and the surface quality of a final product are improved.

A valve seat for a pumping assembly. The valve seat includes a body including a gradient material structure that defines a strike face for the valve seat. The gradient material structure has a first material, a second material spaced from the first material, and a smooth compositional transition between the first material and the second material. In addition, the valve seat includes a bore extending through the body.

The invention belongs to the field of lattice structure design, and particularly discloses a continuous fiber reinforced composite material strength Poisson's ratio adjustable design method and system.The design method comprises the steps that the Poisson's ratio structure type needed by a continuous fiber reinforced composite material component to be prepared is determined, the Poisson's ratio structure type comprises a zero Poisson's ratio structure, a positive Poisson's ratio structure or a negative Poisson's ratio structure; according to the method, regular hexagonal unit cell models and hourglass-shaped hexagonal unit cell models are constructed, and according to the Poisson's ratio structure type, a plurality of regular hexagonal unit cell models and / or a plurality of hourglass-shaped hexagonal unit cells are utilized to construct a corresponding honeycomb porous structure model, so that the Poisson's ratio adjustable design of the continuous fiber reinforced composite material is realized. By means of the method, standardized design of material structures of high-strength, light-weight, vibration-reducing and energy-absorbing parts needed by various parts in different application fields can be achieved.

A three-dimensional transformable structure containing a set of individual elementary components, so-called IECs, forming a single-piece assembly. An IEC includes an active or inert material and at least one portion of the set of IECs includes at least three connecting means. Each connecting means of a considered IEC is arranged to cooperate with at least one connecting means of an IEC adjacent to the considered IEC to connect the considered IEC to the adjacent IEC. For the at least one portion of the set of IECs, at least one of the connecting means of a given IEC is located on a side opposite to at least another one of the connecting means of the considered IEC. At least one portion of the set of IECs includes so-called “active” IECs spatially arranged in the transformable structure so that at least one property of the transformable structure is modified in response to the application of at least one stimulus.

The invention provides a plastic packaging barrel online defect detection method and system based on machine vision and deep learning. According to the method, visible light and thermal image data, aligned in time, of the surface of the plastic packaging barrel are collected, the dynamic range of a thermal image is expanded to enhance the identification degree, then highlight interference is compensated through the mapping relation between a thermal image stable area and a visible light reflection area, and surface reflection suppression is achieved; superposing the processed dual-mode data to generate a multi-mode fusion image, dynamically adjusting the temperature reference value of the fusion image based on the thermal conductivity of the material and the environment temperature of the production line, and positioning an abnormal temperature region; and finally, extracting topological contours and temperature diffusion characteristics of the regions, and inputting a deep learning model to judge material structure attributes and spatial positions of the defects so as to finish online detection. According to the method, thermal and visual data are fused to enhance the perception capability on fine structure defects, and the precision and robustness of online detection of the plastic packaging barrel in a complex illumination environment are remarkably improved.

The invention relates to the technical field of infrared light machine system thermal deformation prediction, in particular to a multi-material structure thermally induced stress deformation prediction method based on a graph neural network. The method comprises the steps of data set establishment, graph structure establishment, graph neural network model establishment and training and model and parameter optimization. Finite element nodes correspond to graph nodes, finite element edges correspond to graph edges, an encoder-message passing-decoder architecture model is established, and node states are updated through a three-layer physical symmetry message passing mechanism. Physical constraint loss including minimum displacement smoothness constraint and stress continuity constraint is innovatively added into a loss function. Compared with traditional finite element calculation, the method has the advantages that the speed is increased by more than 100 times, high hardware adaptability is achieved, the black box limitation of a data-driven neural network model is broken through, thermally induced stress deformation analysis caused by different material coefficients can be processed, the adaptability to geometric changes is high, and good engineering application value is achieved.

Methods involving physics-informed deep learning to help solve inverse problems of solid materials / structures related to unknown geometry include (1) identifying and characterizing unknown materials / structures and defects with accuracy and predictive capability and limited non-destructive measurements, and / or (2) designing geometrical features and parameters of solid materials and structures to achieve optimized and / or improved performance.

A silicon carbide source material structure for use in a sublimation system for growing crystalline silicon carbide is provided. The silicon carbide source material structure includes a composite shaped solid. The composite shaped solid may be made by casting, extrusion, 3D printing, graphite conversion, or other suitable process.

The invention provides a multi-material microstructure and macrostructure concurrent isogeometric topology optimization method, device and equipment and a medium, and relates to the technical field of isogeometric topology optimization. A global stress constraint function constructed based on equivalent yield strength of a microscopic unit corresponding to a to-be-optimized multi-material structure is used as a constraint condition, structural mass minimization is used as an optimization target, a preset isogeometric topology optimization model is constructed, sensitivity is calculated, a macroscopic design variable and a microscopic design variable are updated by using an MMA optimizer based on the sensitivity, and a multi-material structure is optimized. Obtaining a target macro design variable and a target micro design variable until a preset convergence condition is met; and carrying out isogeometric topological optimization design on the to-be-optimized multi-material structure on the basis of the target macroscopic design variable and the target microscopic design variable, and realizing a structure lightweight optimization design target while ensuring that the maximum von Mises stress of the structure meets a stress constraint condition.

The invention relates to a preparation method of a spiral dislocation stacked imine covalent organic framework material. The preparation method comprises the steps of preparing an imine covalent oligomer and preparing the spiral dislocation stacked imine covalent organic framework material. The invention also provides the spiral dislocation accumulation imine covalent organic framework material obtained by the preparation method and application of the spiral dislocation accumulation imine covalent organic framework material in light absorption semiconductor materials. The preparation method provided by the invention is simple, convenient, universal and easy to implement, and the imine covalent organic framework material with the spiral dislocation stacking structure is obtained, is clear in material structure and good in crystallinity, is a novel topological structure, and has a good potential application prospect in the photoelectric field.

The invention relates to a damage assessment method and device for a composite material structure and electronic equipment, and relates to the technical field of aircraft maintenance, and the method comprises the steps: obtaining an aircraft typical structure operation and maintenance database and an actual service environment database; extracting structure damage characteristics from the aircraft typical structure operation and maintenance database, and extracting service environment factor data of the aircraft from the actual service environment database; performing clustering analysis on the extracted service environment factor data to obtain a clustering analysis result, and performing correlation analysis on the extracted structure damage features and the clustering analysis result to obtain a correlation analysis result; and based on the correlation analysis result, constructing an evaluation index system, and performing damage evaluation on the composite material structure of the aircraft by using the evaluation index system. According to the scheme, various environmental factors and structural damage characteristics can be comprehensively considered, the service reliability and safety of the structure are evaluated more accurately, a more reliable basis is provided for structure maintenance decision, and potential risks for flight safety are reduced.

The invention relates to a method for simulating the operation impact load of a wind generating set. The method comprises the steps that aerodynamic load distribution data, elastic structure geometric material parameters, control logic dynamic parameters and environment parameters of the wind generating set are acquired, and initial multi-physical coupling model data are obtained; generating an initial impact load time sequence based on the initial multi-physical coupling model data; according to the initial impact load time sequence, the elastic modulus and the stiffness matrix of the composite material structure of the wind generating set are dynamically adjusted, and updated composite material structure parameters are generated; obtaining a smooth impact spectrum based on the updated composite material structure parameters and the initial impact load time sequence; and according to the smooth impact spectrum, generating impact load simulation data. By adopting the method, potential problems and risks can be found in advance, the design and operation strategy of the wind generating set is optimized, the reliability and economical efficiency of the wind generating set are improved, and the maintenance cost and downtime are reduced.

The invention belongs to the technical field of material science, and relates to a generation method of a material formula, a target material, an automobile structural part, a vehicle, a device, a medium and electronic equipment. The method comprises the following steps: acquiring target performance data of a target material to be prepared; and inputting the target performance data into a pre-trained material large model, so that the material large model outputs formula data for preparing the target material and corresponding process data. The formula data and the process data of the preparation method are reversely deduced according to the target performance data required by the target material by utilizing the large material model, the large model is introduced into the field of material science, and the generation method of the reverse material formula realizing'target performance driving 'is provided without relying on a large number of manual experiment trials and errors; and the advantages of strong generalization ability and reasoning ability of the large material model are exerted, so that the research and development efficiency and success rate of new materials are improved, and the application scenarios of the large material model are enriched.

The invention discloses a density matrix energy and atom stress calculation method, a local density matrix DM and an energy density matrix EDM of a target material structure are predicted through a machine learning model such as a HamGNN-DM model, the atom stress of the material structure is calculated, and the calculation amount of the whole process is in direct proportion to the number of atoms contained in the material structure. The atom stress of the obtained material structure is used for molecular dynamics simulation of the material structure, and meanwhile, the electronic structure property of the material in the molecular dynamics simulation process can be efficiently obtained. The HamGNN-DM model is based on a graph neural network, but can be based on any machine learning model, and is not limited to the graph neural network.

The invention discloses a solid rocket engine bearing structure lightweight determination method based on a carbon fiber composite material, and relates to the technical field of composite material structure design, and the method comprises the steps: obtaining the structure size and geometric shape of a fixed body; replacing a titanium alloy material with a carbon fiber composite material, and determining a carbon fiber composite material structure response model based on a finite element modeling method; determining an optimal layering scheme based on structural response sensitivity analysis and a multi-objective optimization method; then determining a thickness optimization scheme based on the structural size and geometric shape of the fixed body and load distribution; determining a reinforcement optimization scheme based on a parametric modeling method; determining a metal reinforcement configuration by adopting a structural rigidity-quality collaborative evaluation method; and finally, determining a lightweight structure of the fixed body based on a composite material layering process design method. The mass of the fixing body structure can be reduced, the mechanical property of the fixing body structure is improved, and the high-performance design requirement of a solid rocket engine is met.

The invention discloses a meshless EFGM and style migration-based multi-material structure topological optimization method, which comprises the following steps of: (1) inputting material attributes such as Young modulus, shear modulus and Poisson's ratio of a structure, and discretizing a design domain by using meshless nodes; (2) transforming the designed meshless density field to obtain a physical density field, and performing length scale filtering; (3) an alternating active phase algorithm is executed, and the displacement field, the system flexibility and the related sensitivity of the structure are solved based on the meshless EFGM; (4) executing a neural style migration program to calculate the difference between the current topological structure and the reference style image, and calculating the derivative of the physical density field of the topological structure to the network parameter; and (5) updating a design variable by using a moving asymptote algorithm, and judging whether an optimization loop is converged or not. According to the method, based on meshless EFGM and style migration multi-material structure topological optimization, topological structures with different artistic styles can be designed, and the method has engineering application value.