225 results about "Thermomechanical coupling" patented technology

The invention provides a thermal deformation intelligent compensation and control method and system in a metal part welding process, and relates to the technical field of metal welding, and the thermal deformation intelligent compensation and control method comprises the following steps: obtaining a three-dimensional model and process parameters, constructing a thermal-mechanical coupling analysis model, and collecting real-time temperature and strain data; thermal deformation is predicted through a dynamic hierarchical graph structure and a bidirectional propagation neural network, optimal welding process parameters are calculated through a recursive optimizer integrating a gradient strategy and an evolution strategy based on thermal deformation deviation, accurate prediction and intelligent compensation of thermal deformation in the welding process can be achieved, and the welding quality and precision are improved.

Disclosed in the present invention is a physics-informed neural network-based thermo-mechanical coupling analysis method for an inertial microsystem, the method comprising: S1, configuring material parameters and boundary conditions of an inertial microsystem, and establishing a thermo-mechanical coupling analysis model; S2, performing electro-thermal coupling analysis to obtain a temperature distribution of the inertial microsystem; S3, performing thermo-mechanical coupling simulation analysis to obtain a thermal stress distribution of the microsystem; S4, predicting temperature fields of the microsystem by means of a physics-informed neural network; S5, using the temperature fields as boundary conditions for mechanical simulation of the microsystem, obtaining mechanical properties such as stress and strain of the microsystem; and S6, performing electromechanical coupling simulation analysis to analyze the impact of structural deformation on various parameters of electrical performance. The present invention improves the solution accuracy of the neural network by means of an improved adaptive weighting strategy, combines a complete polynomial basis function with the neural network, and introduces an expanded basis function to reduce the state dimensionality, thus reducing computational costs and time, achieving accurate prediction of temperature fields of microsystems at multiple moments, and allowing for computation of the performance of microsystems under electro-thermal-mechanical multi-physics coupling.

The invention provides a residual stress prediction method considering ultrasonic peening process parameters of a welded joint, and the method specifically comprises the following steps: carrying out thermal-mechanical coupling simulation of a welded structure based on a finite element method, obtaining a residual stress field of the welded structure, taking the residual stress field as initial stress, and building an ultrasonic peening processing finite element model; on the basis of a hypercube Latin sampling method, a parameter combination sample is extracted, corresponding residual stress data after ultrasonic peening are obtained through simulation, and a residual stress prediction function model containing an undetermined coefficient is established with ultrasonic peening parameters as independent variables. And based on a particle swarm optimization algorithm, training to obtain an undetermined coefficient value which enables a prediction effect to be optimal, and completing establishment of a prediction function model. Therefore, the optimal process parameter combination of ultrasonic peening can be obtained so as to improve the mechanical property of a welded joint. According to the residual stress prediction method considering the ultrasonic impact process parameters of the welding joint, a basis is provided for improving the service performance of a large-scale thin-wall part welding structure.

The invention relates to the technical field of industrial CT, discloses an automatic light beam centering method and system based on industrial CT, and aims at solving the technical defects that in the welding process, due to thermal deformation, the centering precision of a light beam and a welding seam is low, and the reliability is poor. The method comprises the steps that before welding, three-dimensional CT volume data of a workpiece to be welded are obtained to generate a nominal welding seam trajectory; establishing a thermal-mechanical coupling finite element analysis model, and simulating a welding process to generate a predicted thermal teratogenic field map; superimposing the predicted thermal teratogenesis to a nominal trajectory to generate a dynamic welding trajectory; in the welding process, a laser contour measurement sensor is driven to collect three-dimensional contour data of a welding seam area in real time and extract the actual welding seam center position; and establishing a Kalman filtering state observer. By the adoption of the technical scheme, continuous and high-precision tracking of the center position of the welding seam can be achieved, the influence of thermal teratogenesis is overcome, and the welding quality and the yield of complex structural parts are remarkably improved.

The invention relates to a machining path planning system, in particular to the technical field of intelligent manufacturing and numerical control machining, and overcomes the defect that a thermal coupling effect is ignored in traditional geometric planning by constructing an evolution model fused with a physical law. A dynamic decoupling algorithm is utilized to extract a real cutting load from multi-source data and reconstruct a rigidity field changing along with material removal in real time, the problem that a physical boundary is difficult to perceive under a complex working condition is solved, and the system effectively eliminates size out-of-tolerance caused by heat accumulation through transient deformation advanced prediction. According to the method, the non-linear error is actively counteracted in cooperation with a reverse compensation strategy based on sensitivity analysis, the machining precision and surface quality of the thin-wall weak-rigidity part are remarkably improved, the rejection rate and trial cutting cost are reduced, and intelligent closed-loop control over the manufacturing process is achieved.

The invention relates to the technical field of topological optimization, and discloses a thermal coupling multi-scale parallel topological optimization method for additive manufacturing, and the method comprises the steps: dividing a macroscopic design domain into a plurality of macroscopic sub-design domains through a fuzzy C clustering method, and mapping the information of the macroscopic sub-design domains on the microcosmic level as the constraint condition of microcosmic optimization; performing equivalent static conversion on the transient temperature load, and introducing an equivalent thermal load into the multi-scale topological optimization model; macroscopic and microscopic thermal coupling finite element analysis is carried out according to the thermal coupling control equation, and a displacement vector matrix is solved by using an HHT-alpha method; sensitivity analysis is carried out on macro and micro objective functions and constraint conditions, and a moving asymptote MMA method is used as a solver for macro and micro design variable updating; and filling the microstructure into the macroscopic configuration to obtain an overall configuration, judging a convergence condition, and if the condition is met, outputting an optimal material distribution structure and an optimal microscopic configuration. The method can better reflect the real working state of the workpiece.

The invention relates to the technical field of production control, in particular to a control system and method for an insulating part production line, and the system is characterized in that a sensing module collects a temperature signal, a deformation monitoring signal and a viscosity state signal in real time; the main control module fuses the three paths of signals through time sequence alignment and space registration to generate a fusion process state matrix, and constructs and dynamically updates a first current shrinkage dynamic prediction model based on the fusion process state matrix in combination with material attributes and environmental parameters; predicting a spatial non-uniformity shrinkage distribution map of a subsequent forming stage by using the model; according to the atlas, a cooperative adjustment instruction set is calculated and generated through a reverse thermal coupling algorithm, and the instruction set comprises a temperature compensation instruction and an extrusion back pressure and transmission rate cooperative instruction; and the execution module regulates and controls the corresponding actuator in a linkage manner, and dynamically counteracts non-uniform contraction power caused by space temperature gradient difference.

The invention belongs to the technical field of high-performance cutter coatings, and relates to a cross-scale bionic gradient coating design method based on a multi-principal-element high-entropy system, which comprises the following steps: sequentially forming a high-entropy alloy plastic layer, a high-entropy alloy / high-entropy ceramic tough layer and a nanocrystalline high-entropy ceramic superhard layer on the surface of a cutter matrix, and constructing a gradient transition structure; the method comprises the following steps: matching a high-entropy alloy plastic layer with a tool matrix binding phase, optimizing a high-entropy alloy / high-entropy ceramic tough layer material, improving interface binding force, optimizing gradient transition design by adopting thermal-mechanical coupling calculation, constructing an atomic model, simulating and optimizing a microstructure based on molecular dynamics, and establishing a finite element model to simulate thermal cycle and mechanical load. The interface performance is optimized, a mathematical model is constructed through a BP neural network and a particle swarm optimization algorithm, and gradual transition design of the tool base body and the coating is achieved. The problems that the tool coating interface is high in brittleness, insufficient in adhesiveness, limited in thermal stability and the like are solved, and the bonding performance of the coating interface and the stability and durability of the coated tool are improved.

The invention relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which comprises the following steps of: acquiring dynamic characteristic data in a forging process in real time by acquiring multi-dimensional material performance parameters, and constructing a multi-target collaborative optimization model; and intelligent collaborative configuration of the reduction rate parameters is realized by adopting a self-adaptive weight distribution strategy. And a thermal coupling factor normalization equation and a phase field free energy functional normalization model are established, and the technical problems that in a traditional forging process, due to empirical configuration of reduction rate parameters, the structure is uneven, and residual stress exceeds the standard are solved. A lightweight model is deployed through edge computing nodes, millisecond-level dynamic correction of an optimization scheme is realized in combination with a 5G industrial private network, and the grain size qualification rate and the size precision of the titanium alloy forgings are improved. The method improves the yield of the grain size of the forge piece, ensures that the residual stress of the forge piece is reduced, stably controls the size precision, and improves the batch stability of high-end titanium alloy parts.

The invention discloses a die-casting pot production line cooperative control method and system based on data driving, and relates to the field of industrial automation and control, and the method comprises the steps: constructing a digital twinborn body integrated with fluid dynamics and thermal-mechanical coupling constraint, and carrying out the high-precision state prediction; establishing an injection, temperature control and ejection multi-intelligent sub-model collaborative framework based on an MADDPG algorithm, and synchronously optimizing quality, efficiency and energy consumption indexes by defining a comprehensive reward function; learning a collaborative strategy by adopting a centralized training distributed execution mechanism and carrying out online deployment; and through closed-loop feedback of a quality tracing result, continuous self-optimization of the model and the strategy is realized. The system effectively solves the problem of quality defects caused by isolated decision making of all control units in die-casting production, global collaborative optimization and autonomous evolution of a production line are achieved, and the product percent of pass and the equipment comprehensive efficiency are remarkably improved.

The present application relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which acquires dynamic characteristic data of a forging process in real time through multi-dimensional material performance parameter collection, constructs a multi-objective collaborative optimization model, and realizes intelligent collaborative configuration of the reduction rate parameter by using an adaptive weight distribution strategy. A thermal coupling factor normalization equation and a phase field free energy functional normalization model are established to solve the technical problems of uneven structure and excessive residual stress caused by the empirical configuration of the reduction rate parameter in the traditional forging process. A lightweight model is deployed through an edge computing node, and millisecond-level dynamic correction of the optimization scheme is realized in combination with a 5G industrial private network, thereby improving the grain size qualification rate and size accuracy of titanium alloy forgings. The present application improves the grain size qualification rate of forgings, ensures stable control of the size accuracy while reducing the residual stress of forgings, and improves the batch stability of high-end titanium alloy components.

The invention discloses a cutterhead structure design method combining topological optimization and finite element analysis, and belongs to the technical field of aerospace high-performance cutting tool design and manufacturing. The technical problems that a traditional cutter head depends on experience design, and rigidity, chip removal and heat stability are difficult to balance are solved. According to the method, a parameterized model is established, multi-working-condition loads including cutting loads, dynamic excitation and thermal-mechanical coupling are defined, a topological optimization technology with multi-working-condition weighted flexibility minimization as a target and a material volume fraction as a constraint is adopted, and a material optimal distribution cloud picture is generated based on a variable density method; geometric reconstruction and engineering semantic interpretation are carried out on an optimization result, and a material enrichment region is converted into engineering characteristics such as an internal reinforcing rib and a variable spiral transition structure, so that a lightweight high-performance conceptual model is formed; finally, through multi-working-condition finite element verification and manufacturing manufacturability analysis, a final design scheme meeting the requirements for strength, rigidity, dynamic characteristics and thermal stability is obtained through iteration.

The invention relates to the field of computer and auxiliary equipment repair, in particular to a mold structure design method and system based on the digital twin technology, and the method comprises the following steps: obtaining a three-dimensional geometric model of a target mold; generating an initial digital twin simulation model based on the three-dimensional geometric model; according to region division of simulation sub-models in the initial digital twin simulation model, sensing modules are arranged in corresponding cavity regions in the entity mold; correcting the corresponding simulation sub-models based on the real-time state data corresponding to the simulation sub-models, and generating a target digital twin simulation model; utilizing the target digital twin simulation model to simulate the thermal-mechanical coupling condition of the cavity in the corresponding area based on the opening time sequence data of the cooling cavity flow valve in the corresponding area of each simulation sub-model in the current injection period; and respectively generating opening time sequence data of the flow valve in the corresponding area of each simulation sub-model in the next injection period according to the thermal-mechanical coupling condition of the cavity in each area.

The invention provides a nickel-based high-temperature alloy cutting microstructure prediction method based on a dislocation gradient model, and aims to realize optimization of process parameters through multi-scale modeling. The method comprises the following steps: firstly, calculating dislocation mobility under different temperature and pressure conditions by utilizing molecular dynamics simulation; and a dislocation generation and annihilation rate model is established in combination with a dislocation dynamics theory, so that a dynamic relationship between cutting strain and dislocation evolution is disclosed. And further through data fitting, a constitutive equation between the dislocation density and the material hardening behavior is constructed, and the evolution law of the dislocation cell structure is described emphatically. On an ABAQUS platform, a two-dimensional cutting model containing a thermal coupling effect is established, a dislocation density constitutive equation is embedded into a user-defined material module, and closed-loop correlation of macroscopic cutting parameters and microstructure evolution is achieved. According to the method, the dislocation density is creatively used as a cross-scale bridge, the understanding of a dynamic recrystallization and dislocation motion coupling mechanism in the cutting process is deepened, a prediction model is provided for technological parameter optimization, and the method has important engineering guiding significance for improving the integrity of the machined surface of the high-temperature alloy part.

The invention discloses a rapid design method and system for a turbine blade, electronic equipment and a storage medium, and the rapid design method comprises the steps: firstly, obtaining boundary conditions of a gas side and a cold air side of the turbine blade in an actual working environment based on flow heat exchange test data, and then constructing a heat exchange boundary model based on the boundary conditions; according to the method, temperature field calculation becomes a pure solid heat conduction problem, and only depends on known boundary conditions and material attributes, so that a heat exchange boundary model can be directly applied to a blade three-dimensional model for transient temperature field calculation, and CFD simulation calculation needing iterative solution is replaced; by means of the method, the most time-consuming flow field solving and flow thermal coupling iteration link in the whole analysis process is thoroughly eliminated, the calculation efficiency is remarkably improved while the calculation precision is guaranteed, a thermal-force coupling chain is cut off through decoupling calculation of the transient temperature field and the transient stress field, and the calculation efficiency is further improved.

The invention belongs to the technical field of petroleum pipe engineering structure safety, and discloses a method and system for predicting the strength of a casing under the temperature-pressure alternating working condition, and the method comprises the steps: building a casing physical parameter calculation model, calculating coupling alternating parameters, carrying out a thermal-mechanical coupling alternating test, and obtaining casing yield strength test data. Obtaining microstructure characteristic experimental data influencing the strength of the casing, and obtaining a training sample; constructing a casing yield strength prediction model fusing physical constraint and data driving, and training the prediction model through the training sample; and predicting the yield strength of the casing through the prediction model. According to the method, the coupling alternating parameter load is used as the load condition of the thermal coupling alternating test to obtain the microstructure characteristic experimental data, the microstructure characteristic experimental data is used as the sample data, and the sample data has load, temperature and microstructure evolution characteristics at the same time, so that the prediction model obtained through training can accurately predict the yield strength of the casing.

The invention provides a heat supply digital twin modeling method and system based on a U3D engine and a medium. The method comprises the steps that a historical static data set and a historical dynamic data set are obtained for preprocessing, a historical static optimized data set and a historical dynamic optimized data set are obtained, a U3D engine is combined with a preset parameterized component library to conduct heat supply digital twinning model construction, initialization is conducted based on the historical static data set, and a heat supply digital twinning model is obtained. Obtaining an initial heat supply digital twinborn model, generating an LOD model set with different precisions through an LOD rendering method, inputting the historical dynamic optimization data set into the initial heat supply digital twinborn model for processing, obtaining a simulation data set and equipment operation state evaluation data, and evaluating the qualified state of the model through threshold comparison; according to the method, data security fusion is realized through the block chain, the hydraulic and thermal coupling model is constructed by using the U3D engine, and predictive maintenance and multi-objective optimization are realized through an intelligent algorithm, so that intelligent construction of the heat supply word twinborn model is realized.

The invention relates to the technical field of steam turbines, in particular to a lengthened blade structure of a steam turbine, which comprises an impeller and a plurality of blade bodies circumferentially fixed on the surface of the impeller, and a multi-layer nested cavity structure is arranged in each blade body along the spanwise direction and comprises an inner layer, a middle layer and an outer layer which are sequentially distributed from inside to outside; a cooling channel is formed in a cavity in the inner layer, a cooling medium flows in the cooling channel, and the cooling channel is a snake-shaped cooling flow channel layer and extends to the blade tip from the blade root of the blade body. A heat insulation layer is arranged between the inner layer and the middle layer and is filled with a carbon fiber reinforced aerogel material; and a bearing layer is arranged between the middle layer and the outer layer. According to the technical scheme, through the multi-layer nested cavity structure, namely the S-shaped cooling flow channel, the aerogel heat insulation layer and the honeycomb bearing layer, graded heat management is achieved, the problem of heat-force coupling failure of the blade in the high-temperature environment is solved, and the occurrence probability of creep fracture of the blade body is reduced.

The invention relates to the technical field of high-strength steel hot roll bending forming processes, and provides a method, device and equipment for predicting hot bending forming springback based on simulation software. The method comprises the steps that a three-dimensional assembly model is constructed based on the geometric dimension of a roll forming machine; importing the three-dimensional assembly model into finite element software to simplify the three-dimensional assembly model; performing different types of grid division on different areas, and setting fixed boundary conditions and process parameters; pre-defining a temperature field for the bending area, and constructing a thermal-mechanical coupling constitutive model to obtain a temperature field simulation result; and establishing a simulation model by using a dynamic and static combined algorithm, and predicting the springback value. According to the method, finite element simulation is carried out on the bending process, the springback value is directly output through the node displacement difference, springback deformation is visualized through a post-processing tool, and high-precision prediction is achieved.

This application belongs to the field of aircraft overall design and functional structure design, and particularly relates to a structural-functional integrated thermal design method. It includes: clarifying the functional structure design boundary through requirements analysis; comprehensively considering the basic characteristics and technical feasibility of the material database, active cooling structure configuration database, and manufacturing process database, and conducting technical option demonstration under the structural geometric constraint conditions; identifying the structural flow channel design parameters and active cooling system design parameters, combining with the experiments of key parameter components, analyzing the influence laws on the structural heat transfer and heat exchange characteristics, summarizing the key parameter groups of the structural-functional integrated thermal design, and selecting the structural flow channel design parameters and active cooling system design parameters according to the action effects to complete the scheme design; hierarchically decomposing the functional structure comprehensive evaluation index system from multiple dimensions, and completing the scheme evaluation and parameter optimization design according to the evaluation requirements; completing the thermo-mechanical coupling test of typical functional structure test pieces under simulated thermal conditions.

The invention provides a concrete crack prevention and control method and system based on digital twinning simulation, and relates to the technical field of digital twinning, and the method comprises the steps: building a digital twinning model of mass concrete, and simulating the temperature and stress development process of the mass concrete based on a thermal-mechanical coupling relation to predict the crack risk; generating an anti-crack strategy based on the predicted crack risk; and controlling intelligent maintenance equipment to execute the anti-cracking strategy so as to prevent and control concrete cracks. According to the method, the digital twin model of the mass concrete is constructed, dynamic evolution of temperature and stress is accurately simulated in combination with the thermal-mechanical coupling relation, the crack risk is effectively predicted, and prevention and control scientificity and accuracy are remarkably improved. The generated anti-crack strategy can guide intelligent maintenance equipment to realize automatic and precise maintenance operation, the manual intervention cost is reduced, and the crack occurrence probability is reduced, so that the durability and safety of a concrete structure are improved, and reliable technical support is provided for a large-scale engineering project.

The application belongs to the technical field of high-performance tool coating, and relates to a design method of a cross-scale biomimetic gradient coating based on a multi-principal-element high-entropy system, which sequentially forms a high-entropy alloy plastic layer, a high-entropy alloy / high-entropy ceramic strong and tough layer and a nanocrystalline high-entropy ceramic superhard layer on the surface of a tool base body, constructs a gradient transition structure, matches the high-entropy alloy plastic layer with a bonding phase of the tool base body, optimizes the high-entropy alloy / high-entropy ceramic strong and tough layer material, improves the interface bonding force, adopts thermal-mechanical coupling calculation to optimize the gradient transition design, constructs an atomic model and optimizes the microstructure based on molecular dynamics simulation, establishes a finite element model to simulate thermal cycles and mechanical loads, optimizes the interface performance, constructs a mathematical model by using a BP neural network and a particle swarm optimization algorithm, and realizes the gradual transition design of the tool base body and the coating. The application solves the problems of large brittleness, insufficient adhesion and limited thermal stability of the tool coating interface, and improves the interface bonding performance of the coating, the stability and durability of the coating tool.

The invention discloses an asymmetric aluminum profile extrusion process simulation method, and particularly relates to the technical field of metal forming simulation. Constructing an asymmetric profile geometric model and extracting key structure parameters; establishing a multi-cavity welding die heat-force coupling simulation model; constructing a dynamic recrystallization structure evolution model; setting initial conditions of an extrusion process and performing finite element simulation; difference comparison is carried out on the simulation result and historical working conditions, an abnormal area is identified, and a flow track feature vector is extracted; a microstructure evolution path is inversed in combination with the microstructure parameters, and a defect prediction matrix is generated; solving the optimal process parameters through a numerical optimization algorithm, and re-simulating and verifying whether the distribution of the tissue and the stress field reaches the standard or not; according to the method, prediction of tissue defects and intelligent optimization of extrusion parameters can be achieved, and the quality control capacity of the asymmetric profile extrusion process is improved.

The present invention discloses a thermomechanical coupling analysis method for an inertial microsystem based on a physical information neural network, comprising: S1, setting the material parameters and boundary conditions of the inertial microsystem and establishing a thermomechanical coupling analysis model; S2, obtaining the temperature distribution of the inertial microsystem through electrothermal coupling analysis; S3, obtaining the thermal stress distribution of the microsystem through thermomechanical coupling simulation analysis; S4, predicting the temperature field of the microsystem through a physical information neural network; S5, using the temperature field as the boundary condition for the mechanical simulation of the microsystem to obtain the mechanical properties such as stress and strain of the microsystem; S6, performing electromechanical coupling simulation analysis to analyze the influence of structural deformation on various parameters in the electrical properties. The present invention improves the solving accuracy of the neural network through an improved adaptive weight strategy, combines the complete polynomial basis function with the neural network, introduces the expanded basis function to reduce the state dimension, reduces the computational cost and time, realizes the accurate prediction of the temperature field of the microsystem at multiple moments, and calculates the performance of the microsystem under the coupling of electrothermal and mechanical multi-physical fields.

The invention relates to the technical field of offset analysis, in particular to a steel box girder bridge support offset analysis system and method considering the temperature effect, and the method comprises the following steps: building a three-dimensional temperature field model of a bridge structure based on the collection of temperature data of an area where a bridge is located; the real mechanical property of the material under the temperature field change condition is obtained, and the mechanical parameters of the support structure are calibrated, including the calibration of the steel elasticity modulus, the yield strength and the temperature expansion coefficient; in combination with the three-dimensional temperature field model and the calibrated mechanical parameters, analyzing strain data of the support structure through a thermal-mechanical coupling model; and obtaining a displacement field of the support position based on the strain data of the support structure output by the thermal-mechanical coupling model. According to the method, the analysis deviation caused by neglecting the temperature effect or assuming that the material performance is constant in the prior art is made up.

The invention discloses an MEMS structure stress distribution analysis and optimization method in a micro-nano processing technology, and relates to the technical field of optimization design. The method comprises the following steps: step 1, determining geometric parameters and material parameters of each layer of structure of the MEMS multi-layer composite structure, and constructing a stress field of the MEMS multi-layer composite structure; based on a piezoelectric elastic coupling effect, according to the stress field, establishing a piezoelectric equation; according to the strain tensor, based on a thermal coupling effect, solving a thermal coupling equation to obtain temperature distribution; 2, correcting the stress field according to the temperature distribution to obtain a corrected stress field; performing fatigue evaluation by using a preset fatigue accumulation evaluation model to obtain a fatigue damage accumulation coefficient; and step 3, setting constraint conditions according to the step 1 and the step 2, constructing an objective function according to the fatigue damage accumulation coefficient, and solving the objective function through iteration. The stability, the reliability and the service life of the MEMS device in a complex working environment can be effectively improved.

The invention provides a topological optimization-based negative chamfering tool microstructure design method, which comprises the following steps of: performing three-dimensional orthogonal cutting thermal-mechanical coupling simulation on a negative chamfering tool to obtain positive stress distribution of a target area of the negative chamfering tool in a cutting state; wherein the target area is a V-shaped area or an inclined plane area formed at the junction of the rake face and the chamfered edge; and based on the normal stress distribution of the target area, adjusting the material density distribution of the target area by using a variable density method, and carrying out topological optimization on the material density distribution of the target area by taking the tool nose stress minimization as a target function to obtain the microstructure of the target area. Compared with a traditional micro-texture tool with a simple structure, the method can perform structural design on a stress concentration area to improve the stress condition of the tool, so that the cutting performance of the tool is improved, the service life of the tool is prolonged, and the quality of a machined surface is improved.

The invention discloses a flip chip FC packaging technology method, and relates to the technical field of chip packaging, and the method comprises the steps: collecting the physical parameters of a flip chip packaging material, building a thermal-mechanical coupling model, carrying out the thermal-mechanical coupling simulation and mechanical stress analysis of a packaging structure through the built thermal-mechanical coupling model, and carrying out the analysis of the mechanical stress of the packaging structure. And a stress concentration area and a part with large deformation are identified, and optimization design of the packaging structure is carried out based on a thermal-mechanical coupling analysis result. The reliability of the packaging structure is remarkably improved through optimization design and a strict testing process, in the design stage, a stress concentration area and a potential deformation problem are analyzed and recognized through thermal-mechanical coupling, so that the geometrical shape, material selection and solder ball layout are optimized in advance, and the reliability of the packaging structure is improved through optimization of a thermal management strategy. The heat management performance of the packaging structure is effectively improved, the heat dissipation efficiency of the packaging structure is remarkably improved, and the packaging structure can better adapt to application scenes with extremely high requirements for heat management.

The invention belongs to the field of failure analysis of bearings, and discloses a fatigue life analysis method of a wind power main bearing based on thermal-mechanical coupling. In order to consider the influence of pre-tightening on the service life of the bearing, the method takes the pre-tightening force of the bearing as a starting point, and gradually establishes a relational expression between pre-tightening and a series of static characteristics such as contact deformation, contact load, fatigue life and the like by utilizing an analytical method; meanwhile, the friction torque is introduced, the finite element analysis method is used for solving the influence of thermal deformation on the static characteristics of the bearing, the influence is substituted into the static characteristic relational expression, and finally the service life analysis method of the bearing based on thermal-mechanical coupling is achieved. Compared with the conventional service life analysis method, the fatigue service life analysis method disclosed by the invention has the advantages that the thermal deformation of the bearing caused by friction is additionally considered, so that the analysis is more comprehensive, and the precision of the service life analysis of the bearing is further improved.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.