81 results about "Material properties" patented technology

The embodiment of the invention discloses an external wall thermal insulation falling risk prediction method and system based on a deep learning model, and the method comprises the steps: firstly collecting the multi-dimensional state data of a target building external wall, covering the thermal insulation layer material attribute, environment exposure history, structure connection strength, and surface deformation monitoring and construction technology record data; extracting a material aging feature vector from the thermal insulation layer material attribute data, and extracting a space-time distribution feature from the environmental exposure historical data to generate an environmental action feature tensor; fusing structural connection strength and surface deformation monitoring data into a structural deformation association map, converting construction process record data into process defect probability distribution, inputting the data into a pre-trained multi-modal risk prediction network, outputting an outer wall thermal insulation layer falling risk level and risk area thermodynamic diagram, and predicting the falling risk level of the outer wall thermal insulation layer. And finally, generating a dynamic monitoring strategy containing a sensor deployment optimization scheme and a detection period adjustment parameter according to a result, and realizing accurate risk prediction and efficient monitoring.

The invention discloses a brittle material laser cutting thermal stress control method and system, and the method comprises the steps: obtaining real-time temperature distribution through infrared thermal imaging, combining finite element analysis and material characteristic dynamic data, constructing a thermal stress and fracture relation model, carrying out the iterative calculation of a thermal stress out-of-control threshold through a lambda algorithm, and determining a regulation boundary condition. According to the boundary condition, laser parameters are optimized, synchronous cooling airflow is introduced, the temperature gradient is dynamically controlled through an airflow pressure adjusting algorithm, historical data are processed through a convolutional neural network, the optimal laser parameter combination is predicted, in the cutting process, the microcrack diffusion state is monitored in real time, and auxiliary technological parameters are finely adjusted when necessary. The problem of fracture caused by out-of-control thermal stress in laser cutting of the brittle material is effectively solved, the cutting quality and efficiency are improved, and a new technical scheme is provided for precision machining of the brittle material.

The invention discloses a control method and system of a servo press, and relates to the field of press control, a force displacement curve is obtained in real time from a current punching period, a key feature vector of the force displacement curve is extracted, the key feature vector is compared with an ideal golden feature vector, and a multi-dimensional deviation vector is obtained. Particularly, a deviation decoupling method based on morphological analysis is introduced, and the total deviation can be accurately decomposed into a thermal drift compensation amount caused by the thermal effect of equipment and a material characteristic compensation coefficient caused by plate performance fluctuation by analyzing translation and zooming differences of a current curve and a gold curve in form. Therefore, independent and accurate compensation can be carried out for a physical source, the problems of information aliasing and compensation failure caused by the fact that disturbance sources cannot be distinguished in the prior art are effectively solved, intelligent and feed-forward type cooperative compensation for the next stamping period is achieved, and long-term stability and high precision of the production process are ensured.

The invention discloses a material performance prediction method and device based on a multi-machine learning regression model and a high-throughput experiment, a medium, a product and application thereof, and relates to the technical field of material design and optimization, and the method comprises the steps: obtaining preset material parameters; any performance is determined as the current performance; based on material components in the preset material parameters, determining target key features of the current performance under the preset material parameters; respectively inputting the process in the preset material parameters and the target key features of the current performance under the preset material parameters into a plurality of optimal performance prediction regression models corresponding to the current performance to obtain a plurality of sub-prediction values of the current performance under the preset material parameters; and based on the plurality of sub-predicted values of the current performance under the preset material parameters, determining a total predicted value of the current performance under the preset material parameters. The material performance prediction precision and the research and development efficiency are improved, and the research and development cost is reduced.

The invention provides a thermally driven micro-nano compliant mechanism topological optimization method considering a scale effect. The method comprises the following steps: defining design conditions of a thermally driven micro-nano compliant mechanism, and setting material attribute indexes; establishing a finite element analysis model of the thermally driven micro-nano compliant mechanism; obtaining a structure displacement response; establishing a topological optimization model of the thermally driven micro-nano compliant mechanism; calculating and optimizing an objective function and constrained sensitivity information; performing smoothing processing on the sensitivity information; and solving the optimization problem of the thermally driven micro-nano compliant mechanism by adopting a moving progressive optimization algorithm, judging whether the convergence condition of the moving progressive algorithm is met or not, and if so, outputting the optimal topological configuration of the thermally driven micro-nano compliant mechanism considering the scale effect. The thermally-driven micro-nano compliant mechanism obtained through topological optimization has an obvious scale effect, the topological configuration of the thermally-driven micro-nano compliant mechanism is changed along with increasing of scale parameters related to the feature length, and it is indicated that the scale effect becomes more obvious.

The invention provides a low-temperature-resistant performance evaluation method for a composite material, and belongs to the technical field of material performance determination.The method comprises the steps that firstly, in a low-temperature environment box, a temperature sensor is used for accurately controlling the temperature of a sample, and meanwhile, a high-frequency strain sensor is used for collecting data; then, wavelet transform is adopted to carry out denoising and decomposition on the strain signals, and key low-temperature characteristic parameters are extracted; and constructing a performance evaluation model based on deep learning, inputting the temperature field, the strain characteristics and the material basic parameters into the model, and predicting the low-temperature strength. A prediction result is verified through a standard test, a prediction error is calculated, an error distribution matrix is established, and an improved back propagation algorithm is adopted to optimize the model. Finally, a fuzzy comprehensive evaluation method is applied, indexes such as strength retention rate and strain stability are comprehensively considered, and the low temperature resistance grade of the composite material is scientifically evaluated. The method solves the problem that the actual mechanical properties of the composite material in the low-temperature environment are difficult to comprehensively reflect in the prior art.

The invention relates to the technical field of machining simulation, in particular to a composite material ring piece low-stress machining method based on finite element analysis. The method comprises the following steps: constructing a two-dimensional finite element model of the composite material ring piece; grid division is carried out on the geometric model, and the structure is decomposed into units; setting boundary conditions and loads of the simulation model; defining material attributes including density, thermal conductivity, specific hot melting, elastic modulus, Poisson's ratio, plasticity and creep; setting processing and heat treatment parameters, including processing procedures, processing amount, heat treatment temperature and time; the simulation model is operated, and the response of the geometric model under the given boundary and loading condition is obtained; and analyzing an operation result, summarizing an influence rule of machining parameters and heat treatment setting on machining stress, and optimizing a machining scheme. According to the method, a stable component low-stress machining scheme can be rapidly obtained through an analogue simulation technology for the machining process of the composite material ring piece, and high-precision machining of the large-size fiber reinforced composite material ring piece is achieved.

The invention provides a surface protection composite material formula optimization method, which belongs to the technical field of electric digital data processing, and comprises the following steps of: establishing a performance equation set comprising thermal stress, interface mechanics and chemical reaction kinetics by collecting initial formula data and interface chemical characteristics; and performing a precision test in a low-temperature saline water environment of-40 to-60 DEG C. And establishing a performance prediction model by adopting a least square method, and constructing a multi-dimensional formula space. A genetic algorithm and a grey wolf hunting algorithm are innovatively combined, multi-objective optimization of a formula is achieved, and minimization of interface stress and maximization of bonding strength are taken as core objectives. Through an orthogonal test and orthogonal analysis, an influence mechanism of component matching on material performance is deeply analyzed, and a failure mechanism criterion is established. Through multiple rounds of limit tests and directional optimization, the optimal surface protection composite material formula adapting to the extremely low temperature environment is finally obtained, and the problem that a global optimal formula is difficult to obtain in the prior art is solved.

The invention relates to the technical field of dynamics, and discloses a dynamic modeling method for a bolt boundary cone-column combined shell under a non-uniform temperature field, and the method comprises the steps: integrating the temperature dependence of material performance and a thermally induced stress field, developing a nonlinear bolt connection interface model, and considering the dynamic evolution characteristic of a friction coefficient along with the temperature. Accurate characterization of structural dynamic behaviors in a non-uniform thermal environment is realized, a non-uniform temperature field is efficiently reproduced based on a built-in heat source experimental platform, the reliability of a model is verified by combining modal and response tests, and the influence of thermally induced material degradation and an interface stick-slip mechanism on structural rigidity and damping is disclosed. The accuracy of vibration characteristic prediction in a high-temperature environment is improved, algorithm type refined matching is carried out based on the combination information of the static and dynamic waveforms of the track in combination with the artificial intelligence bionic algorithm and the standard waveform information stored by big data, the combination data of the static and dynamic waveform parameters of the track is constructed, and reliable support is provided for algorithm matching.

The invention relates to the technical field of interface optimization of composite materials, and discloses an interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material, which is applied to a material compounding system comprising a dispersity sensor and an interface regulation and control system in a hot pressing mold. The method comprises the following steps: acquiring spherocrystal dispersion data and interface stress data through a sensor to generate a data set, constructing a dispersion matrix by a regulation and control system, determining material interface combination characteristics, namely processing the matrix, performing thermal field distribution modeling, calculating thermal field distribution gradient and combination prediction information, constructing a coupling regulation and control model, outputting topological distribution characteristics and updating the data set. And finally dynamically regulating and controlling the composite parameters according to the combined characteristics. The method realizes real-time monitoring and accurate regulation and control of the composite material interface, improves the interface bonding quality and the material performance, and is suitable for interface optimization in the composite material preparation process.

The invention discloses a three-dimensional thermal stress coupling simulation method for borosilicate glass / Cu / dielectric layers. The method comprises the following steps: collecting and sorting effective information of material attributes and physical fields involved in a simulation process; preliminarily screening material characteristic parameters, and determining interfacial compatibility among different materials; a preliminary three-dimensional model of the borosilicate glass, Cu and dielectric layer composite material is constructed, stability analysis is carried out for different material combinations, and an optimal material combination is screened; establishing a three-dimensional thermal stress coupling simulation model by combining the preliminary three-dimensional model, simulating thermal stress distribution under different working conditions, analyzing a stress transfer mechanism between different material layers, and optimizing thermal adaptability of a material interface; optimizing the structure of the composite material by using a finite element analysis technology, and adjusting the thicknesses and proportions of different material layers; through a repeated optimization process, an optimal three-dimensional thermal stress distribution design scheme is finally determined, and the effectiveness and reliability of the three-dimensional thermal stress coupling simulation method are verified through experiments.

The invention provides a laser dissimilar metal self-adaption welding method and device based on blue light surrounding swing, and the method comprises the steps that welding material attributes and a laser welding technology needed in the composite laser welding process are obtained, and the welding material attributes and the laser welding technology are obtained based on laser welding power, a heat source acting position and the material attributes; the energy input quantity of red-blue composite laser absorbed by different metals in the welding process is calculated, the total melting quantity of dissimilar metal materials in the welding process is obtained based on the attribute characteristics of welding materials, the width of a composite laser welding pool is solved based on the laser welding swing amplitude, and the depth of the pool formed when a welded plate is infinite in thickness is calculated based on the width. And comparing the thickness with the actual thickness of the welded plate, determining the actual shape in the laser welding process, and predicting the appearance of the composite laser welding seam in combination with related shape parameters. The method solves the problems of poor welding joint forming quality, low mechanical property and the like caused by laser absorptivity difference of dissimilar metals and overlarge difference of thermophysical parameters.

The present application belongs to the technical field of thermal load deformation and force load deformation measurement, and relates to a thermal and force deformation field decoupling method and system based on two-dimensional visual measurement. According to the temperature field at discrete temperatures, the thermal deformation field at discrete temperatures and the corresponding temperature, and by using a binary function model, the influence coefficient of fitting temperature on thermal strain is obtained, the jumps between discrete data are eliminated, and the continuity and prediction ability of thermal strain are improved. According to the influence coefficient of fitting temperature on thermal strain and the binary function model, a fitted binary function model is obtained, which is more in line with the actual material properties. According to the temperature field under the thermal and force coupling condition, the deformation field under the thermal and force coupling condition and the fitted binary function model, an equivalent thermal deformation field is obtained, the thermal and force coupling deformation is decoupled, the dynamic correlation of thermal strain and deformation field is realized, and the decoupling precision is improved.

This invention belongs to the field of additive remanufacturing technology, specifically relating to an optimization method and system for the additive remanufacturing process of brake discs for rail vehicles. The method analyzes the material properties of the brake disc substrate to obtain material property data, monitors the solidification front morphology of the molten pool in real time during the additive remanufacturing process, analyzes and infers the differences between the solidification morphology of the substrate and the molten pool based on these two sets of data, and optimizes and adjusts the additive remanufacturing process parameters according to the inference results. By accurately identifying the material properties of the substrate and combining real-time monitoring of the molten pool solidification front, this application can deeply analyze the underlying causes of local thermal anomalies in the molten pool, identify and correct specific problems such as insufficient or excessive laser energy, uneven laser spot energy distribution, or abnormal scanning paths, thereby achieving precise optimization and adjustment of the additive remanufacturing process parameters, providing a reliable and efficient solution for the additive remanufacturing of rail vehicle brake discs.

The present invention belongs to the technical field of the interaction between laser and matter, and relates to a simulation method and system for the temperature field and stress field of a material irradiated by short-pulse laser. The simulation method includes: determining simulation parameters, including short-pulse laser irradiation parameters and material property parameters; constructing a thermo-mechanical coupling transient model of the material, and determining the boundary conditions of the heat transfer physical field and the solid mechanics physical field of the model; performing grid division on the thermo-mechanical coupling transient model, and configuring solver parameters; specifically: inputting the short-pulse laser pulse width and the short-pulse laser frequency into a pre-trained linear neural network model, and calculating to obtain the solver parameters; applying the solver to solve the thermo-mechanical coupling transient model to obtain the temperature field and stress field distribution of the material at different irradiation times. The simulation method of the present invention can comprehensively and accurately capture the complex transient physical process of the material under the action of short-pulse laser.

The invention belongs to the technical field of material performance testing, and relates to a metal material large-range equivalent stress-strain curve testing method which comprises the following steps: 1, preparing a test piece; preparing the test piece model into a round bar test piece, wherein the middle section of the round bar test piece is provided with an axisymmetric annular notch; 2, testing; a loading device is adopted to load the round bar test piece under displacement control, meanwhile, a high-speed camera is adopted to directly face the round bar test piece to shoot, and the applied load and the change of the notch size are monitored; stopping when the round bar test piece is broken; 3, processing data after the test; sequentially calculating the size of the notch, the strain in the transverse direction of the notch, the real stress in the test process and the engineering stress; 4, correcting real stress; converting the real stress obtained by the notched sample into the equivalent stress of the material; the equivalent stress-strain curve of the material can be obtained by measuring the initial fracture size and the hardening index, and the test cost is lower.

The invention discloses an on-line testing method for simultaneously measuring characteristic parameters of multiple MEMS materials. The on-line testing method comprises a design and use process of an MEMS on-line testing structure. A required testing structure consists of a V-shaped thermal actuator and two comb-tooth-shaped electrostatic actuators at the front end and the rear end of the V-shaped thermal actuator, and Young modulus, residual stress and thermal expansion coefficient of the silicon material layer can be calculated by analyzing behaviors of the actuators. Therefore, firstly, geometric parameters of two comb-shaped electrostatic actuators with different geometric parameters and a V-shaped thermal actuator are designed by establishing a physical model so as to ensure that the actuators are sufficiently sensitive to the properties of a material layer and are relatively easy to drive; the measurement process is completed by applying electric excitation, analyzing electric signal feedback and performing subsequent data processing, the method does not depend on other complex test equipment or harsh test environment, and the method can be suitable for different MEMS processes without adding extra process steps. The method has practical significance for improving the MEMS test efficiency and the automation degree of the test process.

This invention discloses a method and system for predicting fine-grained volumetric material properties based on a physical world model. The method includes: acquiring visual observation data and three-dimensional geometric data of a target object; extracting visual features from the visual observation data and geometric features from the three-dimensional geometric data, while simultaneously acquiring semantic description information characterizing the material properties of the target object; generating physical parameter reference constraint information for the corresponding material based on the semantic description information; fusing the visual features, geometric features, and physical parameter reference constraint information to obtain a multimodal global fusion feature; and using a hierarchical volume prediction network based on physical constraint distillation based on the multimodal global fusion feature to predict voxel-level volumetric material property data of the target object. The volumetric material property data includes at least the material category probability distribution and intrinsic physical parameters of the material corresponding to each voxel inside the target object, i.e., a fine-grained volumetric material property field.

This technology enables the numerical analysis of the effect that anisotropic materials have on the overall rigidity of a structure when the material is anisotropic. [Solution] A structural analysis method for an anisotropic material according to one aspect of the present invention comprises the steps of: assigning calculation points to structural data (S11); assigning material properties including anisotropy to structural data (S12); assigning load points, support points, and arbitrary points to the calculation points (S13); and determining an index (A) based on the ratio of a first work required for the displacement of the load point when the material axis vector at the arbitrary point is set according to the anisotropy of the material properties, and a second work required for the displacement of the load point when the direction of the material axis vector is rotated to coincide with the displacement direction of the load point. * The steps involve calculating the index (A) while changing an arbitrary point. * ) is calculated, and the index (A) in the structural data is calculated. * The system comprises the steps of calculating the distribution of (S14-S16) and

The present invention provides a numerical simulation method for the Brazilian splitting test of PBX considering non-uniform material properties, aiming to establish a complete heterogeneous model of PBX explosive Brazilian splitting to accurately reproduce the crack propagation process and the final morphology during splitting, and provide practical theoretical guidance for the analysis of the actual cracking mechanism; the heterogeneous method is realized by modeling through the finite element software ABAQUS combined with self-written Python scripts: First, establish a two-dimensional Brazilian splitting complete matrix model of irregularly shaped explosive particles, then assign random linear elastic material parameters within a certain range to each explosive particle item by item, assign Cohesive bilinear constitutive parameters to the zero-thickness interface between the explosive particles, and finally set boundary conditions, mesh division and analysis steps to complete the establishment of the heterogeneous overall model of PBX Brazilian splitting; the beneficial effect of the present invention is that on the basis of the existing technology, the model further considers the material property differences of the explosive particles, fully reflects the material heterogeneity of PBX, and can reproduce the complete fracture process and the final morphology of PBX Brazilian splitting.

The invention discloses a system and a method for testing the high-temperature performance of a heat-proof material, which are characterized in that the coupling effect of dynamic heat flow and force load on a heat-proof material test piece is realized by establishing variable-temperature force-drive composite equipment, and a variable-temperature execution device is designed to ensure that the single-side thermal examination of the heat-proof material test piece is realized; a first control system is used for controlling the temperature change of the variable-temperature heat source assembly by referring to a flight temperature curve, and the temperature change of the actual working environment of the product is simulated, and a second control system is used for controlling the temperature change of the variable-temperature heat source assembly before the single-side heat assessment test is finished; controlling the tensile testing machine to apply an axial tensile load to the heat release material test piece according to set test time and a set test rate, and simulating the load change of an actual working environment of a product, so as to accurately test the attenuation rule of the performance of the heat protection material test piece along with the temperature and the heating time under the action of single-side radiation heat flow and the load; and the material performance analysis capability is improved.

The invention provides an anti-blocking control method and device, and the method comprises the steps: carrying out the position division and control grade division of temperature control regions of equipment, and carrying out the clustering of all temperature control regions according to structure parameters, and obtaining different control regions; collecting material properties and monitoring data of an equipment wall surface, calculating a material change coefficient according to the material properties and the monitoring data, and if the material change coefficient meets a trigger condition, updating the control level of each temperature control area according to the material change coefficient; switching the working mode of each control area according to the equipment state, and if the working mode is switched into an adjusting mode, outputting a vibration control adjusting matrix and a temperature control adjusting matrix of each control area according to the anti-blocking level and the control level of each temperature control area; controlling the vibration module according to the vibration control adjusting matrix, and controlling the heating module according to the temperature control adjusting matrix. Dual-mode collaborative anti-blocking control is realized through zoning temperature control and internal vibration, and the maintenance cost and the overall energy consumption are reduced.

The invention discloses an uncertain quantization and propagation method for constitutive parameters of a viscoelastic composite material, which comprises the following steps of: developing a multi-scale micro-mechanical model on the basis of a fractional order differential and average field homogenization theory so as to reveal a nonlinear relation among component material performance, microstructure parameters and macroscopic viscoelasticity. And secondly, the Levenberg-Marquardt optimization algorithm and the Physics-Informed Neural Network proxy model are used for realizing the self-adaptive optimization of the sampling initial conjecture and the improvement of the calculation efficiency. Therefore, a Bayesian probability model is established, and model parameter posterior distribution is calculated by adopting a Markov chain Monte Carlo method so as to evaluate uncertainty, correlation and model errors of parameters. And finally, calculating posterior prediction distribution of the overall response, the constitutive response and the noise response. According to the method, the provided multi-scale Bayesian framework provides an effective way for optimizing component material performance and microstructure parameters, and the method has an important guiding effect on design of advanced viscoelastic composite materials with higher bearing capacity.

This application discloses a method for obtaining the detonation velocity and detonation pressure of energetic materials based on atomic-scale dynamics, relating to the field of materials science and technology. The method includes: acquiring an initial configuration file of the energetic material to be tested, and performing dynamic processing on the initial configuration file using a materials simulation model to generate a first training dataset; iteratively training the potential function using the first training dataset to obtain a structure file corresponding to each generation of training; sampling and screening the structure files, and calculating the accuracy adjustment parameters corresponding to the sampled and screened structure files using first-principles calculation software; adjusting the parameters of the materials simulation model using the accuracy adjustment parameters, and using the adjusted materials simulation model to generate a second training dataset from the structure files obtained from each generation of training and the first training dataset; and predicting the performance of the energetic material to be tested based on the potential function trained on the second training dataset. A general method for predicting material properties that balances accuracy and efficiency is proposed.

The invention relates to the technical field of gas insulation, in particular to an epoxy composite material internal crack defect detection method, equipment and a medium, structural parameters of a 110kV epoxy basin-type insulator are measured, and three-dimensional modeling software is adopted to construct a three-dimensional structural model equal to a real object in proportion; applying a thermal boundary condition to the three-dimensional structure model body, respectively setting the depth and the deflection angle of the crack defect as h and theta, and setting the material attribute of the bubble defect area as air; the method comprises the following steps: acquiring surface thermal imaging characteristics of the defect of the epoxy basin-type insulator based on a solid heat transfer theory, outputting the surface thermal imaging characteristics of the defect of the epoxy basin-type insulator, and acquiring surface thermal imaging characteristics of the crack defect at different deflection angles under different defect depths by changing the depth and the deflection angle of the crack defect for many times based on the solid heat transfer theory. Equipment temperature resolution is used as a judgment basis, and feasibility exploration of crack defect detection at different deflection angles under different defect depths is realized.

The application discloses a kind of ceramic matrix composite temperature-related mechanical properties inversion method, specifically related to the field of composite material performance characterization.It includes: the ceramic matrix composite to be measured is divided into multiple test pieces;Each test piece is heated to target temperature, and the measured thermal strain of multiple points on the surface of each test piece is obtained;For each test piece, at its target temperature, the test piece is uniaxially stretched by loading end, and the measured tensile strain of multiple points on the surface of the test piece during uniaxial stretching, the measured stress-strain curve is obtained;A parameterized function of the mechanical property parameter vector of the ceramic matrix composite to be measured changes with temperature is constructed, to minimize the error function as the goal, the hyperparameter vector is inverted and identified, to obtain the optimal hyperparameter vector, and the mechanical property parameter vector of the ceramic matrix composite to be measured is obtained in combination with the parameterized function.Based on the above method, the mechanical property parameters of microscale component materials can be obtained.

An accuracy of prediction of a characteristic value of a material is improved. A method according to one aspect of the present invention includes acquiring an image of a material, performing a topological data analysis on the image of the material to extract features of the material, and predicting a characteristic value of the material from the features of the material.

The application discloses a method for determining the Debye temperature of a crystal material, and comprises the following steps: collecting the isobaric heat capacity data C P (T) of a crystal material in a temperature range; P (T) of the crystal material, and finding the temperature T0 corresponding to the extreme point of C P (T) by derivation; P (T) of the crystal material, and calculating the isochoric heat capacity C P (T) of the crystal material according to a thermodynamic formula; V (T) of the crystal material according to a Debye isochoric heat capacity evaluation model; V (T) of the crystal material according to the extreme temperature T0; P (T0) determined according to the extreme temperature T0 and the isochoric Debye temperature θ V (T0) of T0, and the temperature closest to the isochoric Debye temperature θ (T0) is the Debye temperature of the material. The method for determining the Debye temperature of the crystal material is simple and easy to implement, and can provide strong support for rapid evaluation and accurate design of material performance in the material research and development process.

The present invention provides a topology optimization method for a thermally actuated micro-nano compliant mechanism that considers scale effects. The method comprises defining design conditions for the thermally actuated micro-nano compliant mechanism and setting material property indicators; establishing a finite element analysis model of the thermally actuated micro-nano compliant mechanism; obtaining a structural displacement response; establishing a topology optimization model for the thermally actuated micro-nano compliant mechanism; calculating an optimization objective function and constraint sensitivity information; smoothing the sensitivity information; solving the optimization problem of the thermally actuated micro-nano compliant mechanism using a moving progressive optimization algorithm, and determining whether convergence conditions of the moving progressive algorithm are met. If so, outputting an optimal topology configuration of the thermally actuated micro-nano compliant mechanism that considers scale effects. The thermally actuated micro-nano compliant mechanism obtained by topology optimization in the present invention exhibits a significant scale effect. As a scale parameter related to the characteristic length increases, the topology configuration of the thermally actuated micro-nano compliant mechanism changes, indicating that the scale effect becomes more significant.