918 results about "Material structure" patented technology

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 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.

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.

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 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 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 provides a particle reinforced composite material processing damage quantitative evaluation method based on local measurement and parameter correction. The method comprises the following steps: firstly, scanning damage feature images at different positions of the surface of a machined workpiece, and calculating the area ratio of each damage size to a scanned local area through binarization processing to obtain the measured crack density, the particle breakage rate and the particle extraction rate; then, establishing a regression model of the damage characteristics, the processing material structure parameters and the processing technology parameters, and correcting the damage characteristic data by means of the regression model to enable the damage characteristic data to better conform to the actual damage conditions under different processing conditions; then, through weighted average and correction coefficients, the damage characteristics of the local area are expanded to the whole machining area, and a mathematical model among machining material structure parameters, machining process parameters and machining damage is established; and finally, comprehensively evaluating the crack density, the particle breakage rate and the particle pull-out rate to obtain a comprehensive damage evaluation value of the whole processing area. The method can effectively evaluate the quality of the machined surface.

The invention relates to the technical field of material thermal performance testing, and discloses a material heat conductivity coefficient automatic testing method based on a heat flow gradient method. The method comprises the following steps: firstly, constructing a test environment topology network, collecting three-dimensional structure parameters and environment thermodynamic parameters of a tested material, and inputting the parameters into a thermal field simulation system to generate an internal heat flow distribution model; and analyzing internal cavity distribution characteristics according to the three-dimensional structure parameters, matching the internal cavity distribution characteristics with a cavity characteristic-heat source layout mapping table to determine a steady-state heat source layout scheme, and activating a heat flow gradient analysis device when multiple heat sources are needed. A heat conduction parameter database is called, heat source power parameters and temperature acquisition parameters are extracted based on environmental thermodynamic parameters, the heat source power parameters comprise spacing, axial angles and the like, and the temperature acquisition parameters comprise thermocouple types and acquisition frequencies; and finally, simulating temperature field evolution through a heat flow gradient analysis device, outputting and analyzing related data, and generating a parameter optimization instruction to adjust heat source configuration. According to the method, multiple links are coordinated, material structures and environmental factors are integrated, and automation of the testing process is achieved.

The invention relates to the technical field of reliability evaluation of a composite material structure, and particularly discloses an evaluation method for vibration damage mechanical properties of a composite material. The method comprises the following steps: firstly, clamping a composite material test piece on a special test clamp which can be stable and durable in a vibration environment, and carrying out parameter-controllable vibration excitation on a vibration table to simulate damage; then carrying out mechanical property test on the damaged test piece; and finally, based on the vibration data and the mechanical property data, establishing a vibration parameter-performance degradation correlation model by calculating the performance degradation rate, and performing quantitative evaluation on the vibration damage state of the composite material in combination with microscopic damage analysis. According to the invention, the problem that the vibration test and the performance evaluation process are separated and cannot be quantitatively associated in the prior art is solved, the integrated and quantitative analysis from damage simulation to state evaluation is realized, and the environment temperature influence factor is introduced; accurate data support can be provided for safety design and life prediction of the composite material structure in a specific vibration environment.

The invention relates to a composite material structure damage probability assessment method and device and electronic equipment, and relates to the technical field of aircraft maintenance, and the method comprises the steps: obtaining the structure influence factor data of a to-be-assessed composite material structure; inputting the structure influence factor data into the trained random forest model, and performing damage probability evaluation processing on the to-be-evaluated composite material structure through all decision trees in the random forest model to generate an evaluation result; the evaluation results are subjected to voting processing, the evaluation result with the maximum voting number serves as a predicted structure damage evaluation result of the composite material structure to be evaluated, and the predicted structure damage evaluation result at least comprises slight damage, moderate damage and serious damage. According to the scheme, the limitation of a traditional method in the absence of sensor data is effectively overcome, and the damage prediction requirement under the condition of no sensor data or incomplete data is met.

The invention discloses a manufacturing defect-oriented multi-scale composite material mechanical property prediction method, and belongs to the technical field of composite material mechanical property analysis. The method comprises the following steps: based on a multi-scale representative volume element (RVE) theory, respectively constructing parameterized models containing pores and fiber dislocations in a microscopic scale and a mesoscale; the equivalent elastic parameters under each scale are calculated through a finite element numerical homogenization technology, and an agent model is constructed in combination with an artificial neural network, so that the calculation cost is remarkably reduced, and the high-efficiency cross-scale prediction of the rigidity and strength performance of the composite material is realized. The method overcomes the defect of defect modeling of a traditional model, has high precision and high efficiency, and is suitable for performance evaluation and optimization design of composite material structures in the fields of aerospace, rail transit and the like.

The invention discloses a tunneling parameter optimization and hob abnormal wear avoidance method based on shield cutterhead vibration, and relates to the technical field of tunnel and underground engineering, and the method comprises the steps: collecting three-way vibration signals during tunneling of a shield cutterhead; driving the shield cutter head to execute an in-situ tunneling experiment, and synchronously calculating a vibration signal characteristic value by using the three-way vibration signal; carrying out bin opening detection and statistics on the abrasion type of each hob, and judging the abnormal damage level of the shield cutter head in combination with the material structure information of the hobs; extracting vibration intensity ranges when the shield cutter head is at different abnormal damage grades, and taking the corresponding vibration intensity range when the shield cutter head is at the lowest abnormal damage grade as a target tunneling range; and analyzing whether the vibration intensity of the shield cutter head is in the target tunneling range or not in real time during shield tunneling. According to the invention, the limitation of hysteresis of traditional geological exploration is broken through; hob breakage or eccentric wear accidents caused by geological sudden changes are avoided, and construction safety in complex stratums is remarkably improved.

The invention discloses a fragile material crack detection and damage evaluation method based on box dimension. Change information of a material surface displacement field is calculated by adopting a DIC technology (a digital image correlation technology), a main strain graph is obtained, then the box dimension of the material surface main strain field is calculated by combining a fractal theory, and the damage degree of the material is quantified based on change development of the box dimension. According to the invention, the development process of the cracks of the material test piece from no to penetrating through the test piece can be observed, the crack growth path can be obtained, the crack development can be quantified through the specific box dimension, the damage degree of the structure can be evaluated, and the early warning of the large crack propagation behavior is realized. The method has important practical engineering significance for health detection and risk assessment of fragile material structures such as steel fiber reinforced concrete.

The invention provides an anti-cracking performance evaluation method and system based on a concrete shrinkage rate. The method comprises the following steps: firstly, collecting shrinkage rate real-time sequence data and an acoustic emission signal flow of a concrete sample in a curing process, separating a high-frequency elastic wave energy attenuation characteristic value from the acoustic emission signal flow, and carrying out space-time coupling on the high-frequency elastic wave energy attenuation characteristic value and a shrinkage rate jump point in the shrinkage rate real-time sequence data to generate a crack initiation tendency index; then obtaining a calibrated crack initiation tendency index based on an ambient temperature hysteresis effect and a temperature drift error of the calibrated crack initiation tendency index, and then outputting an anti-crack performance grade according to a grading deviation degree between a pre-designed grading parameter and a fluctuation range of the calibrated crack initiation tendency index; according to the technical scheme provided by the invention, the essential defect that a traditional method cannot correlate material characteristics with environmental response is overcome, direct quantitative mapping of material structure performance and damage evolution is also realized, and a traceable material-level basis is provided for engineering anti-crack design.