1249 results about "Finite element simulation" patented technology

The invention discloses a 3D printing path planning method based on electric arc additive anisotropy and a stress field, and the method comprises the steps: constructing a CAD three-dimensional model of a part, and obtaining the stress field of the part through finite element simulation; determining a slice plane; mapping the stress field to a slice plane to form a force flow line; according to the obtained force flow line, rotation transformation regeneration is carried out according to the anisotropy of the used electric arc additive, and a reference trajectory considering the anisotropy of the material is obtained; under the principle of alternate arc starting and extinguishing, the corresponding printing sequence in the layers and between the layers is further planned, and the reference trajectory lines are connected end to end to be planned into a continuous path; and generating a code file, and printing according to the printing sequence. The comprehensive mechanical property of a printed piece is improved.

The invention belongs to the technical field of simulation analysis, and particularly relates to a medium-high voltage switch cabinet internal humidity inversion calculation method based on simulation analysis, which comprises the following steps: establishing a geometric model of a switch cabinet, importing the geometric model into finite element simulation software to obtain a simulation analysis model, adding each physical field and computational domain and boundary conditions thereof, and calculating the internal humidity of a medium-high voltage switch cabinet. All the physical fields are coupled, and mesh generation is carried out; performing numerical solution on the simulation analysis model by using a transient solver to obtain a temperature and humidity distribution condition in the switch cabinet; the simulation analysis model is optimized, a simulation data set is constructed, and a PINN is trained; based on the environment temperature and humidity and the operation current collected in the actual operation of the switch cabinet, the trained PINN is used for prediction, and the steady-state relative humidity of the current transformer is obtained. According to the invention, based on the limited boundary monitoring data, efficient and accurate reconstruction of the humidity field of the key area in the switch cabinet is realized, and a feasible path is provided for equipment state sensing and condensation risk early warning.

The invention provides a special-shaped air film hole thermal mechanical fatigue simulation part design method, which comprises the following steps of: 1, analyzing the strength and the service life of a turbine blade, and identifying a key part of the service life of a special-shaped air film hole; 2, determining that the thermal mechanical fatigue simulation piece is of a hollow structure; 3, parameters of a clamping part of the hollow simulation piece are stipulated; 4, determining the mechanical stress of the key part through three-dimensional finite element simulation of the turbine blade; 5, verifying the included angle between the principal stress direction and the growth direction of the key part of the simulation part; step 6, measuring stress by using a strain gauge under a normal-temperature initial mechanical load, and verifying whether the load is compliant or not; 7, performing three-dimensional finite element simulation analysis on the temperature field of the test piece and calculating thermal stress; 8, controlling the thermal stress distribution of the section of the simulation piece by controlling the temperature distribution; step 9, when cooling gas is introduced into the inner cavity of the special-shaped gas film hole simulation piece, carrying out a thermal mechanical fatigue test by adopting an in-phase mechanical-temperature load; and step 10, optimizing the structure of the special-shaped gas film hole and analyzing the strength.

The invention relates to a building structure health monitoring method and system based on machine learning, and the method specifically comprises the following steps: firstly, building a target building three-dimensional numerical model through finite element simulation, generating a simulation signal, injecting Gaussian white noise, and adjusting model parameters to form a data set with health category labels; performing data enhancement by combining adaptive wavelet denoising with dynamic normalization, and extracting and enhancing high-resolution time-frequency features through adaptive window short-time Fourier transform and adaptive frequency band enhancement; then, a neural network model fusing structure physical prior guidance and multi-scale space-time interaction is constructed, a feature matrix is modulated, fused and coded to obtain a refined feature vector, and damage state probability distribution output is achieved; and training is carried out by using a feature consistency and prediction smoothness regularization term constraint model, and finally, the trained model is deployed, so that building structure health state evaluation and safety early warning are realized, the monitoring accuracy and reliability are improved, and effective technical support is provided for building safety guarantee.

The invention discloses a movable formwork construction monitoring method and system based on finite element simulation, and relates to the field of bridge construction monitoring and structure health monitoring. The problems that in traditional movable formwork construction monitoring, a model is fixed, early warning lags behind, and it is difficult to dynamically reflect the real state of a structure are solved. According to the method, a digital structure module containing adjustable parameters is established, and working condition simulation is carried out to identify a theoretical high-risk area and optimize sensor layout; performing pre-simulation before each process to output a predicted value, synchronously acquiring actual measurement data during construction, and calculating a standardized deviation sequence; dynamic safety evaluation and early warning are carried out based on time domain feature extraction and multi-index fusion; reverse calibration and online updating are carried out on model parameters through Bayesian variational inference by utilizing historical deviation data, so that closed loop of simulation, monitoring, early warning and model optimization is realized, and the safety monitoring precision and real-time performance in the construction process of the movable formwork are remarkably improved.

The invention belongs to the technical field of engineering simulation data processing, and particularly discloses a finite element grid graph structure construction method and system, a terminal and a medium. Comprising the following steps: analyzing original full-amount grid data exported by finite element simulation, segmenting unstructured grid data into a grid vertex coordinate set and a grid unit mark number set, and constructing a graph edge topological structure corresponding to a grid based on a unit mark number relationship; on the basis, loading physical field data and adopting a tolerance-based coordinate matching algorithm to realize accurate mapping of physical field labels and material attributes with grid nodes; and generating standardized finite element grid graph data which can be directly used for graph neural network processing. By means of the method, high-consistency and high-physical-reliability conversion from the finite element simulation data to the graph structure data is achieved, and the physical field modeling and simulation acceleration capacity based on graph learning is improved.

The invention provides a manufacturing method of an ultra-low dielectric loss high-speed copper-clad plate, which comprises the following steps of: constructing a physical coupling model of a multi-zone independent temperature control module and an electro-thermal conductivity adjusting layer based on the thickness of the copper-clad plate and a thermal response parameter of a resin system; space and time dynamic distribution of heating area power and heat conductivity is achieved; a dynamic thermal boundary regulation and control algorithm, finite element simulation and self-adaptive PID feedback are fused, real-time monitoring and deviation compensation of a temperature field are carried out through infrared thermal imaging and edge calculation, heating and heat dissipation strategies are cooperatively adjusted, a heat flow path can be actively guided, the uniformity of thermal stress distribution and the structural stability are achieved, and the method is suitable for large-scale popularization and application. And the thermal management efficiency of the copper-clad plate in a complex manufacturing environment is improved.

The invention discloses a finite element simulation method for evaluating the reliability of a battery structure, a product and a storage medium. The method comprises the following steps: constructing a battery finite element model comprising a shell and a sealing structure; defining elastoplastic material properties for the shell; applying a pressure load for simulating internal gas production and / or a mechanical load for simulating external impact; after solving, extracting a plastic strain result of the sealing structure area; and evaluating the integrity of the sealing function based on the result. According to the method, the elastic-plastic model is adopted, the limitation that a calculation result is distorted after a material is yielded in traditional linear simulation is overcome, and the plastic failure risk can be accurately predicted; meanwhile, a sealing function failure criterion based on plastic strain is innovatively established, and the problem that sealing performance evaluation of key connection structures such as riveting cannot be achieved is solved. According to the method, a systematic and quantitative safety boundary defining tool is provided for the battery structure design, and the reliability and efficiency of the design are remarkably improved.

The invention discloses a face gear planetary transmission system dynamic uniform load test platform and test method, and belongs to the field of gear transmission, and the test platform comprises a rack, a drive motor, a step-up gear box, a face gear planetary transmission system, a magnetic powder brake, a torque sensor, a slip ring, a stress strain test system and the like. The face gear planetary transmission system comprises an input face gear, a planet wheel, a planet carrier, a fixed face gear and the like. According to the dynamic uniform load test, stress values at the tooth root of the face gear and the tooth root of the planetary gear are measured mainly through strain gauges, and the corresponding relation between the tooth root stress and the meshing force at the gear meshing point is obtained through finite element simulation. And calculating the dynamic uniform load coefficient according to the dynamic meshing force root-mean-square values and the average values obtained at the tooth roots of the face gear and the planetary gear. By testing and analyzing the dynamic uniform load coefficient, the load distribution characteristic of the face gear planetary transmission system can be accurately revealed, a scientific basis is provided for structural parameter optimization, meshing performance improvement and reliability design of the face gear planetary transmission system, and then accurate and high-performance design of the transmission system is achieved.

The invention discloses an offshore wind turbine structure health monitoring method and system, and the method comprises the steps: an offline stage: constructing a strain snapshot matrix through finite element simulation, extracting a low-dimensional feature subspace basis matrix through intrinsic orthogonal decomposition, and mapping a structural grid into a graph; and meanwhile, Fourier feature mapping of node coordinates is carried out, and after splicing with reconstructed strain features, a spectrogram neural network based on Chebyshev graph convolution is trained. In the online monitoring stage, according to unilateral sparse strain data collected in real time, a modal coefficient is rapidly solved through a mask matrix and a pre-stored basis matrix, full-field strain is preliminarily reconstructed, then the trained network model is input, and a high-fidelity full-field displacement field and a high-fidelity full-field stress field are deduced in real time. The method can effectively adapt to any change of sensor layout, real-time and accurate reconstruction of full-field physical response of a complex structure is achieved under the harsh condition of single-side sparse measurement, and the problem that a traditional technology depends on fixed point distribution and double-side measurement is solved.

The invention relates to an iron tower foundation safety analysis method considering the dry-wet cycle effect. The method comprises the following steps that 1, a soil body strength degradation constitutive model is established; 2, determining iron tower foundation structure parameters and soil body parameters; 3, establishing an iron tower foundation-soil body three-dimensional geometric model by utilizing three-dimensional modeling software and rock-soil finite element simulation software; 4, applying a load transmitted to the iron tower foundation by the power transmission iron tower in the iron tower foundation-soil body three-dimensional geometric model, defining initial parameters, and establishing a rainwater seepage and dry-wet cycle coupling effect analysis model; 5, analyzing and predicting the cycle index when the safety coefficient of the iron tower foundation is reduced to a threshold value from the two aspects of tower foundation ultimate bearing performance and iron tower foundation-soil body interaction on the basis of a rainwater seepage and dry-wet cycle coupling effect analysis model; according to the method, an efficient and accurate numerical analysis means can be provided for safety assessment of the power transmission tower foundation in the collapsible loess area.

The invention belongs to the technical field of aircraft strength design, and particularly relates to an aircraft structure strain field reconstruction method and device based on a deep learning neural network, and the method comprises the steps: S1, carrying out the dimension reduction mapping of irregularly distributed three-dimensional grid nodes of a finite element model, and obtaining regular two-dimensional grid nodes; s2, obtaining calculation results of each two-dimensional grid node under a plurality of working conditions based on finite element simulation, and constructing a training set; s3, training the U-NET deep convolutional network through the training set to obtain a strain field proxy model; and S4, based on the strain field agent model, processing an input image with a small number of measurement point position measurement results to obtain an output image containing an aircraft structure strain field. According to the invention, the strain field of the whole aircraft can be reconstructed through displacement data of a small number of points measured by the static test of the whole aircraft.

The invention discloses a method for predicting the mechanical property of a dissimilar metal friction stir lap joint welded joint by fusing physical information, and belongs to the technical field of crossing of material processing and artificial intelligence. The method comprises the following steps that firstly, welding samples under different technological parameters are prepared through experiments, and the mechanical properties of the welding samples are tested; meanwhile, establishing a finite element simulation model, and extracting physical field data such as a temperature field and a stress-strain field; fusing the process parameters and the physical field data to serve as input features, taking the measured mechanical property data as output features, and forming a machine learning training data set; a prediction model is trained by adopting algorithms such as a gradient lifting decision tree and limit gradient lifting; and finally, new process parameters and physical field data obtained through finite element simulation are input, and the mechanical property of the joint can be efficiently and precisely predicted through the model. According to the method, the defects that a traditional trial and error method is high in cost and long in period are overcome, and key technical support is provided for optimization and intelligent design of the dissimilar metal friction stir lap welding process.

The invention discloses a GIS equipment shell vibration fault identification method and system based on a physical information neural network, and relates to the technical field of GIS fault diagnosis, and the method comprises the steps: collecting a vibration acceleration signal of the surface of a GIS shell; establishing a simplified physical model of the vibration system, and determining a control equation, initial conditions and boundary conditions of the vibration system; based on the HFT-MPINN or the RL-PINN, constructing a parameter inversion model; fusing physical constraint loss, experimental data loss and multi-parameter coupling loss of the vibration system to construct a composite loss function; training a parameter inversion model by using the vibration acceleration signal to obtain key physical parameters of the vibration system; time-frequency domain analysis and finite element simulation verification are combined, and the mechanical fault type and degree of the GIS equipment shell are recognized. According to the method, the modeling precision, the noise robustness and the multi-working-condition adaptability of the high-frequency vibration signals are improved, and reliable technical support is provided for intelligent fault diagnosis of GIS equipment.

The invention discloses a synchronous reluctance motor magnetothermal coupling rapid calculation method based on a dynamic thermal network, and belongs to the technical field of motors. Comprising the following steps: calculating motor loss by adopting a finite element electromagnetic field model, and calculating a convective heat transfer coefficient by adopting a finite element fluid field model; constructing a three-dimensional thermal network comprising conduction, contact, convection thermal resistance, thermal capacity and a heat source, and solving to obtain an average temperature and a boundary temperature of component nodes; constructing a sub-domain temperature field calculation model of the heat source component, and calculating temperature distribution in combination with boundary conditions; introducing average temperature compensation thermal resistance and carrying out dynamic correction; carrying out magnetothermal coupling iteration, and updating the loss and the temperature until the precision is met; and introducing maximum temperature compensation thermal resistance into the thermal network, and solving to obtain an average temperature and a maximum temperature transient temperature rise curve of each component. Based on the technical means of combining electromagnetic field and fluid field finite element simulation with thermal network and subdomain temperature field calculation, efficient calculation of magnetothermal coupling of the synchronous reluctance motor is achieved.

The invention belongs to the technical field of GIL temperature rise response, and relates to a conductor temperature rise prediction model based on a GIL temperature rise characteristic rapid calculation method, and the model is a three-dimensional finite element simulation model constructed according to the actual size and shape of a GIL; a two-dimensional finite element simulation model is established according to the actual size and shape of the cross section of the GIL, and the unit area loss density of a conductor and a shell is calculated according to Joule heat loss and eddy-current loss in the unit area of the GIL; solving node electromagnetic loss according to the loss density, applying the node electromagnetic loss as a heat source to the three-dimensional finite element simulation model, and carrying out repeated iteration by using a coupling control equation established according to a change relation of material physical parameters and thermodynamic parameters along with temperature until a convergence condition is met so as to calculate a discrete temperature rise characteristic of the GIL under a preset working condition; and establishing a discrete image function according to the discrete temperature rise characteristic data, and establishing a temperature rise prediction model by fitting the function through the characteristic points.

The invention provides a part lightweight design method based on a random forest model, and the method comprises the steps: defining a design variable and a response target, and generating a sample point through a test design sampling method; geometric modeling and updating, finite element preprocessing, solving calculation and result extraction are automatically completed through parameterized scripts, and a sample data set is formed; independently training a random forest regression model for each performance response by using the sample data set, and constructing a quality rapid calculation model; verifying the random forest agent model until all agent models meet preset precision requirements; on the basis of the proxy model, constructing an optimization problem, carrying out optimization solution by adopting a multi-objective optimization algorithm, and outputting a Pareto optimal solution set; and selecting a candidate design scheme from the Pareto optimal solution set, executing high-precision finite element simulation, comparing a prediction result and a simulation result of the proxy model, and if an error is within an acceptable range and all performances meet design requirements, determining a final lightweight design scheme.

The invention relates to the technical field of bearing life calculation, in particular to a transmission bearing installation pre-tightening amount calculation method and system, and the method comprises the steps: S1, building a finite element mesh model of a transmission housing and a shaft through finite element simulation software based on predefined material attributes; the material attributes comprise elastic modulus, Poisson's ratio and thermal expansion coefficient of the transmission shell and the shaft; s2, based on the actual working condition of transmission assembly life simulation analysis, importing the finite element mesh model of the transmission shell and the shaft into a pre-constructed multi-body dynamics simulation model of the transmission shell, and establishing node connection between the finite element mesh model of the transmission shell and the shaft and a transmission part in the multi-body dynamics simulation model; s3, setting a detection object and a design variable, and adopting a preset strategy to obtain a target mapping curve of the detection object and the design variable; compared with the prior art, the optimal relation between the pre-tightening amount and the service life of the bearing can be quickly determined, so that the service life of the bearing is maximized.

The invention discloses a finite element-based carbon steel corrosion modeling method, which relates to the technical field of computational mechanics and finite element analysis, and comprises the following steps: S1, acquiring real-time data from an environment sensor, acquiring an initial parameter set containing temperature, salinity and material attributes, and determining initial corrosion rate distribution; s3, if the deviation value exceeds a preset threshold value, a parameter coupling relation is processed through a genetic algorithm, and an optimized parameter set is obtained and used for correcting model input; according to the finite element-based carbon steel corrosion modeling method, the precision and reliability of corrosion prediction in a complex environment are remarkably improved, and a scientific basis is provided for carbon steel structure maintenance.

The invention relates to the technical field of finite element simulation analysis, and particularly discloses an end-to-end finite element analysis system based on multi-agent collaboration, which is characterized in that a hierarchical multi-agent collaboration architecture taking a central coordinator agent as a core is constructed, a whole-system general standard decoupling interface system is matched, a knowledge base capable of being dynamically updated is built, and the end-to-end finite element analysis system based on multi-agent collaboration is established. Precise intention analysis and task scheduling and execution of the whole simulation process are achieved in combination with a large language model, the inherent limitation of a simple series architecture of level agents in the prior art is broken through, and the problems that in an existing scheme, the coupling degree between the agents is high, interfaces are not uniform, expandability is poor, and the system performance is poor are solved. The core defect that complex industrial simulation scenes such as multi-working-condition parallelism and multi-physics field coupling cannot be flexibly adapted is overcome, complete decoupling and flexible expansion of all agents are achieved, and compared with an existing level series architecture, the system has remarkable architecture advancement.

The invention discloses a transformer abnormity identification method based on voiceprint feature analysis, and belongs to the field of power equipment state monitoring and intelligent diagnosis. The method comprises the following steps: firstly, analyzing an iron core acoustic mechanism based on a magnetostrictive effect, and establishing a three-dimensional model through finite element simulation to obtain vibration and sound field characteristics; in a complex substation environment, a hybrid noise reduction method combining density peak clustering and a CEEMDAN-wavelet threshold is provided, and the signal-to-noise ratio is effectively improved. Then extracting Mel-frequency cepstrum coefficients (MFCC) and spectrum features, and performing local linear embedding (LLE) dimension reduction to form a compact feature set; in the recognition stage, a convolutional neural network framework is designed, specifically, a spectrogram and an energy spectrum are modeled through a two-dimensional CNN, an MFCC tensor obtained after dimensionality reduction is modeled through a three-dimensional CNN, and accurate diagnosis of mechanical faults such as core looseness is achieved. The method has the advantages of being non-contact, anti-noise and high in recognition precision, and real-time diagnosis and early warning of mechanical abnormity of the transformer can be achieved under complex working conditions.

The embodiment of the invention discloses a transformer winding overheating fault diagnosis method, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: building a temperature field model of a target transformer under a normal working condition and a plurality of preset winding overheating fault working conditions through a finite element simulation method, and obtaining a temperature field model of the target transformer based on a simulation result; calculating to obtain a group of fault temperature rise critical values corresponding to the positions of the winding sections; when the transformer runs on line, actual measurement temperature rise data of a plurality of positions on the surface of a box body of the transformer are obtained, and the actual measurement temperature rise data are compared with a fault temperature rise critical value so as to locate a winding section where an overheating fault occurs; for the fault section, solving an actual overheating temperature rise value of the fault section through an inversion calculation method by utilizing a preset transformer equivalent thermal circuit model and combining the actually measured top layer oil temperature and bottom layer oil temperature data; high-precision simulation is arranged offline, an efficient algorithm is used online, the diagnosis precision and efficiency are both considered, the accuracy is high, and the practicability is high.

The invention relates to a lithium ion battery multilayer metal foil and dissimilar metal connection optimization method in the technical field of new energy, and the method comprises the following steps: constructing a geometric model of multilayer metal foil and dissimilar metal connection by adopting a finite element simulation method according to initial interface bonding parameters, and applying a preset load condition in a simulation environment, determining an atomic-scale combined stress distribution diagram; for a heat balance state formed by metallurgical bonding, quantitative indexes, including shear strength and tensile strength, of connection point strength are extracted from a finite element simulation result, and a preliminary optimization data set of high-energy-density battery connection is obtained; and outputting a final metallurgical bonding formation parameter set according to a judgment result of a connection point strength stability threshold value, and applying the final metallurgical bonding formation parameter set to simulation verification of multi-layer metal foil connection so as to obtain an interface optimization scheme for improving the battery performance.

The invention discloses a wind turbine fairing optimization design method, which relates to the technical field of fluid machinery and engineering, and comprises the following steps: a basic parameter acquisition and working condition defining step: collecting wind turbine model parameters, and measuring the wind condition of an operation area; the flow guide cover is divided into a windward section, a middle section and a wake flow section; a multi-objective optimization system construction step: determining aerodynamic resistance, structural strength, weight and wake flow influence optimization objectives; an optimization algorithm iteration solving step, wherein an improved non-dominated sorting genetic algorithm is adopted; a simulation verification and parameter correction step: selecting a scheme to perform CFD and finite element simulation; and a physical prototype manufacturing and performance testing step: manufacturing a prototype, and carrying out wind tunnel and field testing. The design efficiency of the air guide sleeve is improved, systematicness is optimized, pneumatic, structure and weight multi-target balance is achieved, cross-model universality is improved, and the problems that traditional design is dependent on experience and unbalanced in performance are solved.

The invention relates to the technical field of bridge structure engineering, in particular to a new and old bridge joint vibration disturbance control method, which comprises the following steps: S1, arranging a sensor at a new and old bridge joint, and collecting vibration data of the part; according to the method, a multi-source information identification model is constructed by fusing a physical mechanism and deep learning, robust identification of a vibration source and dynamic reconstruction of a transmission path are realized in combination with vibration transmission entropy and Granger causal test, an accurate basis is provided for subsequent vibration reduction scheme design, and an adaptive scheme is screened based on vibration characteristics, so that the accuracy of vibration reduction is improved. A four-dimensional coupling model is constructed through finite element simulation, an improved genetic algorithm is adopted to solve a Pareto optimal solution under the constraint of cooperative work performance of new and old bridges, parameters are selected according to engineering priorities, and cooperative optimization of the vibration reduction effect, structural safety, cost control and durability is achieved. Therefore, the accuracy and engineering applicability of vibration disturbance control at the joint of the new bridge and the old bridge are remarkably improved.

The invention discloses an air inlet casing full-process simulation closed-loop optimization method, which relates to the technical field of mechanical manufacturing and processing, and comprises the following steps: simplifying an air inlet casing model; selecting a plurality of key procedures from the whole procedures; according to process parameters and boundary conditions of the key process, grid division is carried out on the simplified model, and a corresponding finite element simulation model is constructed; sequentially simulating the finite element simulation model according to a key process sequence, correcting a simulation result according to deformation data of actual processing, transmitting the corrected simulation result to a next key process for model simulation until simulation and correction of all key processes are completed, and constructing a full-process simulation prediction model; performing iterative optimization on multiple parameters in the whole-process simulation prediction model to obtain an optimal parameter combination; and correcting the simulation model according to the deviation between the actual processing data and the simulation result under the control of the optimal parameter combination. According to the method, high-precision prediction of various complex manufacturing processes can be realized, and the manufacturing precision is improved.

The invention discloses a twelve-beam six-dimensional force sensor, and relates to the technical field of force sensing. The sensor effectively solves the problem of coupling interference in six-dimensional force measurement by optimizing the structural design of the elastic body, innovating the bridging mode of the strain gauges and accurately positioning the patches, and improves the measurement precision and stability. The sensor is of a twelve-beam elastomer structure, stress distribution under different loads is analyzed through COMSOL finite element simulation software, and the position of a strain sensitive area is determined; a group bridge circuit is designed through tension and compression strain characteristics of the symmetrical strain beams, and decoupling is achieved from the hardware level; mEMS silicon strain gauges are selected and accurately assembled, so that the measurement error is further reduced. Experimental data show that the crosstalk rate of the sensor under loads of all dimensions is as low as 2%, net output signals are stable, and the sensor can be widely applied to the fields of robots, precision manufacturing, aerospace and the like with high requirements on force measurement precision.

The invention provides an underwater vehicle measuring point layout optimization method and system, and belongs to the technical field of experimental measuring point layout optimization. Comprising the following steps: obtaining simulation data of an underwater vehicle structure through finite element simulation, and extracting strain data of a unit capable of arranging a strain gauge area according to actual engineering constraints; centroid coordinates of the units, sensitivity values of strain response values relative to working conditions and strain gradients are calculated by taking centroids of the units in the areas where the strain gauges can be arranged as nodes; mapping the strain response values of all the nodes to a unified interval to obtain a normalized strain response value; obtaining sensitivity coverage and strain gradient coverage, and calculating space coverage of all nodes; integrating the normalized strain response value, sensitivity coverage, strain gradient coverage and space coverage as optimization indexes, and performing optimization by adopting an improved discrete artificial bee colony algorithm; and a final layout scheme is decided by outputting the relationship between the number of the measuring points and the information amount.

The invention discloses a method for testing heat treatment residual stress of a bearing ring based on nanoindentation, and belongs to the technical field of stress testing. The method comprises the following steps: establishing a calibration curve, preparing and testing a sample, and calculating residual stress. According to the method, the indentation work-residual stress quantitative calibration curve for the bearing steel is established through finite element simulation, the specific constitutive relation of materials is fully considered in the model, and system errors caused by mismatching of material parameters of a general model are eliminated. The change trend of the residual stress value calculated by the method is consistent with that of a prediction result of finite element analysis, and it is proved that the method can accurately, reliably and quantitatively characterize the residual stress distributed in the microscale after heat treatment.

The invention provides an axial flux motor multi-objective optimization method based on an improved homing religious algorithm. Firstly, key structure parameters are screened through sensitivity analysis, and the optimization range of the key structure parameters is determined; then, sample data is generated by adopting improved Latin hypercube sampling, and a data set is constructed through finite element simulation; then, performing automatic optimization on smooth factors of the generalized regression neural network based on a He-horse optimization algorithm, and establishing a high-precision agent model; improving a horizon algorithm by adopting Tent chaotic mapping, non-dominated sorting and congestion degree distance, and combining the horizon algorithm with the proxy model to carry out multi-objective optimization to generate a Pareto solution set; and finally, selecting an optimal motor parameter according to the target weight. According to the method, parameterized finite element analysis, an optimization agent model and an improved multi-target algorithm are deeply fused, so that the global optimization capability is ensured, the optimization efficiency and precision are remarkably improved, and an effective solution is provided for rapid and accurate design of the axial flux motor.