A method for simulating mesoscopic fracture failure of a material

Abstract

The embodiment of the specification provides a material mesoscopic fracture failure simulation method, sample material pairs are sheared according to a preset shearing strategy, a stress evolution process of the sample material is measured, a temperature evolution process and a mesoscopic organization deformation evolution process of the sample material are synchronously measured, evolution process data of the mesoscopic organization is collected, a mesoscopic simulation model is built, attribute data of the sample material is acquired, model parameters of the mesoscopic simulation model are trained in combination with the attribute data of the sample material, the shearing strategy and the evolution process data, after training, future period evolution processes of stress, temperature and mesoscopic organization deformation generated by the shearing strategy implemented on the current material are predicted by using the mesoscopic simulation model, whether the sheared material produces fracture failure is judged based on the future period evolution processes of the stress, the temperature and the mesoscopic organization deformation, and the advance and effectiveness of the material fracture failure simulation are improved.

Description

Technical Field

[0001] This application relates to the field of computer science, and in particular to a method for simulating microscopic fracture failure of materials. Background Technology

[0002] Computer technology can obtain the response behavior of materials under load through numerical simulation. Although there are various computational models for studying material responses, these methods are mostly calculated from a macroscopic perspective and have low effectiveness. It is necessary to provide a more effective and accurate failure simulation method. Summary of the Invention

[0003] This specification provides an embodiment of a material microstructure fracture failure simulation method to improve the predictability and effectiveness of material fracture failure simulation.

[0004] This specification provides an embodiment of a method for simulating microscopic fracture failure of materials, including:

[0005] The sample material was sheared according to the preset shearing strategy. The stress evolution process of the sample material was measured, the evolution process of the sample material and the evolution process of microstructure deformation were measured simultaneously, and the evolution process data of microstructure were collected.

[0006] A microscopic simulation model is constructed, and the property data of the sample material is obtained. The model parameters of the microscopic simulation model are trained by combining the property data of the sample material, the shearing strategy, and the evolution process data. After training is completed, the microscopic simulation model is used to predict the future evolution process of stress, temperature, and microstructure deformation caused by the shearing strategy applied to the current material. Based on the future evolution process of stress, temperature, and microstructure deformation, it is determined whether the sheared material will experience fracture failure.

[0007] Optionally, it also includes:

[0008] The laboratory measures the defect distribution of the sample material and collects the chemical composition of the sample material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment.

[0009] The training objective is set using the defect distribution state, and the chemical composition of the sample material, the measured process data of the production process, the temperature and humidity data sequence of the production process, and the temperature and humidity data sequence of the storage environment are used as training sample inputs to train the defect learning model.

[0010] The prediction of the future evolution of stress, temperature, and microstructural deformation resulting from the shearing strategy applied to the current material using the microscopic simulation model includes:

[0011] The chemical composition of the current material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment are acquired. The defect learning model is used to process the chemical composition of the current material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment to output the predicted defect distribution state. The predicted defect distribution state, the shape of the current material, the shear rate change sequence in the shearing strategy, and the shearing orientation are input into the microscopic simulation model to simulate and predict the future evolution of stress, temperature, and microstructure deformation after applying the shearing strategy to the current material.

[0012] Optionally, the defects include shrinkage cavities, porosity, segregation, internal cracks, bubbles, white spots, and localized corrosion.

[0013] Optionally, it also includes: acquiring fiber images of the sample material using a scanning electron microscope, and extracting data on its microstructural deformation process through feature recognition.

[0014] Optionally, the material is armor steel.

[0015] Optionally, determining whether the sheared material will experience fracture failure based on the future evolution of the stress, temperature, and microstructure deformation includes:

[0016] The microstructure deformation degree is simulated by combining the initial stress and temperature with the preset stress-strain conversion rules. The stress and temperature change process is corrected according to the deviation between the microstructure deformation degree and the preset deformation degree. The process is iteratively continued until the stress and temperature meet the preset steady-state conditions. The ultimate deformation of the microstructure when the stress and temperature reach the steady-state conditions is determined. The ultimate deformation predicted at future moments is used to determine whether the sheared material will fracture and fail in the future.

[0017] Optionally, it also includes:

[0018] A search space is constructed, with dimensions including a time axis, dynamic shearing speed, shearing angle, and cumulative working time of the sheared component. An environment space is also constructed, incorporating material temperature, the predicted defect distribution of the current material, and the strength of the sheared component. A reward function is constructed, with indices including shearing time, whether the microstructure fails due to ultimate deformation, and the influence factor of the cumulative working time of the sheared component on its strength. A shearing strategy is generated by searching the search space and applied to the environment space. The reward function value is calculated, and the largest reward function value is selected. The process is iterated to obtain the globally optimal shearing strategy, which is used to force the shearing of the current material.

[0019] Optionally, the simultaneous measurement of the evolution process of the sample material includes:

[0020] The surface temperature of the sample material is detected using an infrared temperature detector.

[0021] Optionally, it also includes:

[0022] Based on the shape and thermal conductivity properties of the sample material, the evolution of the internal temperature distribution of the sample material is calculated using the surface temperature of the sample material.

[0023] The various technical solutions provided in the embodiments of this specification shear sample materials according to a preset shearing strategy, measure the stress evolution process of the sample materials, simultaneously measure the evolution process of the sample materials and the evolution process of microstructure deformation, collect microstructure evolution process data, build a microstructure simulation model, obtain the property data of the sample materials, and train the model parameters of the microstructure simulation model by combining the property data of the sample materials, the shearing strategy and the evolution process data. After training, the microstructure simulation model is used to predict the future evolution process of stress, temperature and microstructure deformation caused by the shearing strategy implemented on the current material. Based on the future evolution process of stress, temperature and microstructure deformation, it is determined whether the sheared material will fracture failure, thus improving the predictability and effectiveness of material fracture failure simulation. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1 This is a schematic diagram illustrating the principle of a material microstructure fracture failure simulation method provided in the embodiments of this specification. Detailed Implementation

[0026] Exemplary embodiments of the invention will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limiting the invention to the embodiments set forth herein. Rather, these exemplary embodiments are provided to make the invention more comprehensive and complete, and to facilitate a full communication of the inventive concept to those skilled in the art. The same reference numerals in the drawings denote the same or similar elements, components, or parts, and therefore repeated descriptions of them will be omitted.

[0027] Subject to the technical concept of this invention, the features, structures, characteristics or other details described in a particular embodiment may be combined in one or more other embodiments in a suitable manner.

[0028] In the description of specific embodiments, the features, structures, characteristics, or other details described in this invention are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that those skilled in the art can practice the technical solutions of this invention without one or more of the specific features, structures, characteristics, or other details.

[0029] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily need to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The terms “and / or” or “and / or” include all combinations of any one or more of the listed items.

[0032] Figure 1 This is a schematic diagram illustrating the principle of a material microstructure fracture failure simulation method provided in the embodiments of this specification. The method may include:

[0033] S101: Shear the sample material according to the preset shearing strategy, measure the stress evolution process of the sample material, simultaneously measure the temperature evolution process of the sample material and the evolution process of microstructure deformation, and collect the evolution process data of microstructure.

[0034] S102: Build a microscopic simulation model, obtain the property data of the sample material, and train the model parameters of the microscopic simulation model by combining the property data of the sample material, the shearing strategy, and the evolution process data. After training, use the microscopic simulation model to predict the future evolution process of stress, temperature, and microstructure deformation caused by the shearing strategy applied to the current material. Based on the future evolution process of stress, temperature, and microstructure deformation, determine whether the sheared material will experience fracture failure.

[0035] This method involves shearing a sample material according to a preset shearing strategy, measuring the stress evolution, temperature evolution, and microstructural deformation of the sample material, and collecting microstructural evolution data. A microstructural simulation model is then built, and the material's property data is obtained. The model parameters are trained using the material's property data, shearing strategy, and evolution data. After training, the microstructural simulation model is used to predict the future evolution of stress, temperature, and microstructural deformation resulting from the shearing strategy applied to the current material. Based on the future evolution of stress, temperature, and microstructural deformation, it is determined whether the sheared material will experience fracture failure, thus improving the predictability and effectiveness of material fracture failure simulation.

[0036] The variables of the shearing strategy include: time axis, dynamic shearing speed, and shearing angle.

[0037] Dynamic shear rate refers to the curve of change in shear rate during the shearing process.

[0038] For example, it could be an accelerated shearing process in the first half and a decelerated shearing process in the second half, or it could be an accelerated shearing process.

[0039] The sample material is steel, which is the object of the experimental measurement.

[0040] Shearing refers to the various compressive actions that cause steel to be sheared, and no specific restrictions are made here.

[0041] The detailed simulation model can be a three-dimensional finite element model, with position coordinates, temperature, stress data, and attribute data set.

[0042] Attribute data may include thermal conductivity and yield strength, etc.

[0043] The evolution of microstructure is the result of the combined effects of stress and temperature generated after applying a shear strategy. Therefore, by simulating the evolution, the final crack state of the microstructure can be determined, thereby determining whether failure has occurred.

[0044] Considering that simulations often use standard attribute values, but in reality, the composition and distribution of each material are uneven, and storage environment and storage time can also lead to differences in material properties, in order to more accurately predict the material's microstructure and deformation, we can combine the above information and use a training model to predict the defect distribution of the sample material.

[0045] Therefore, in the embodiments of this specification, it also includes:

[0046] The laboratory measures the defect distribution of the sample material and collects the chemical composition of the sample material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment.

[0047] The training objective is set using the defect distribution state, and the chemical composition of the sample material, the measured process data of the production process, the temperature and humidity data sequence of the production process, and the temperature and humidity data sequence of the storage environment are used as training sample inputs to train the defect learning model.

[0048] The prediction of the future evolution of stress, temperature, and microstructural deformation resulting from the shearing strategy applied to the current material using the microscopic simulation model includes:

[0049] The chemical composition of the current material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment are acquired. The defect learning model is used to process the chemical composition of the current material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment to output the predicted defect distribution state. The predicted defect distribution state, the shape of the current material, the shear rate change sequence in the shearing strategy, and the shearing orientation are input into the microscopic simulation model to simulate and predict the future evolution of stress, temperature, and microstructure deformation after applying the shearing strategy to the current material.

[0050] Among them, temperature and humidity data sequences and temperature and humidity data sequences of storage environments refer to time series that record the environmental conditions of the production and storage processes.

[0051] In the embodiments described in this specification, the defects include shrinkage cavities, porosity, segregation, internal cracks, bubbles, white spots, and localized corrosion.

[0052] In the embodiments of this specification, the method further includes: acquiring fiber images of the sample material using a scanning electron microscope, and extracting data on its microstructural deformation process through feature recognition.

[0053] In the embodiments described in this specification, the material is armor steel.

[0054] In the embodiments of this specification, determining whether the sheared material will experience fracture failure based on the future evolution of stress, temperature, and microstructure deformation includes:

[0055] The microstructure deformation degree is simulated by combining the initial stress and temperature with the preset stress-strain conversion rules. The stress and temperature change process is corrected according to the deviation between the microstructure deformation degree and the preset deformation degree. The process is iteratively continued until the stress and temperature meet the preset steady-state conditions. The ultimate deformation of the microstructure when the stress and temperature reach the steady-state conditions is determined. The ultimate deformation predicted at future moments is used to determine whether the sheared material will fracture and fail in the future.

[0056] The evolution of the state over time is simulated by iterative time progression.

[0057] The embodiments in this specification also include:

[0058] A search space is constructed, with dimensions including a time axis, dynamic shearing speed, shearing angle, and cumulative working time of the sheared component. An environment space is also constructed, incorporating material temperature, the predicted defect distribution of the current material, and the strength of the sheared component. A reward function is constructed, with indices including shearing time, whether the microstructure fails due to ultimate deformation, and the influence factor of the cumulative working time of the sheared component on its strength. A shearing strategy is generated by searching the search space and applied to the environment space. The reward function value is calculated, and the largest reward function value is selected. The process is iterated to obtain the globally optimal shearing strategy, which is used to force the shearing of the current material.

[0059] The longer the shearing time, the lower the production efficiency. Therefore, the longer the shearing time, the smaller the reward function value. The smaller the limiting deformation of the microstructure, the smaller the reward function value. The influence factor of the cumulative working time of the sheared part on the strength of the sheared part can be a function that decreases with time. It can take into account the impact of each continuous production on the sheared part, thus affecting the shearing performance.

[0060] By using reinforcement learning, a shearing strategy that balances high production efficiency with preventing crack propagation in microstructure can be learned.

[0061] Weights can be configured and normalized for each of the above data points to unify the units, thus facilitating the calculation of the reward function value.

[0062] In the embodiments of this specification, the synchronous measurement of the evolution process of the sample material includes:

[0063] The surface temperature of the sample material is detected using an infrared temperature detector.

[0064] The embodiments in this specification also include:

[0065] Based on the shape and thermal conductivity properties of the sample material, the evolution of the internal temperature distribution of the sample material is calculated using the surface temperature of the sample material.

[0066] The sample material can be decomposed into finite element methods using calculus, and then its thermal conductivity properties can be used to simulate the temperature conduction and heat dissipation process over time periods, thereby obtaining the evolution process of the internal temperature distribution.

[0067] In this way, the predicted temperature evolution of the current material can be the evolution of its internal temperature distribution.

[0068] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0069] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for simulating microscopic fracture failure of materials, characterized in that, include: The sample material was sheared according to the preset shearing strategy. The stress evolution process of the sample material was measured, the temperature evolution process of the sample material and the evolution process of microstructure deformation were measured simultaneously, and the evolution process data of microstructure were collected. The laboratory measures the defect distribution of the sample material and collects the chemical composition of the sample material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment. The training objective is set using the defect distribution state, and the chemical composition of the sample material, the measured process data of the production process, the temperature and humidity data sequence of the production process, and the temperature and humidity data sequence of the storage environment are used as training sample inputs to train the defect learning model. The prediction of the future evolution of stress, temperature, and microstructural deformation resulting from the shearing strategy applied to the current material using the microscopic simulation model includes: The chemical composition of the current material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment are acquired. The defect learning model is used to process the chemical composition of the current material, measured process data of the production process, temperature and humidity data sequence of the production process, and temperature and humidity data sequence of the storage environment to output the predicted defect distribution state. The predicted defect distribution state, the shape of the current material, the shear rate change sequence in the shear strategy, and the shear orientation are input into the microscopic simulation model to simulate and predict the future evolution of stress, temperature, and microstructure deformation after applying the shear strategy to the current material. A microscopic simulation model is constructed, and the property data of the sample material is obtained. The model parameters of the microscopic simulation model are trained by combining the property data of the sample material, the shearing strategy, and the evolution process data. After training is completed, the microscopic simulation model is used to predict the future evolution process of stress, temperature, and microstructure deformation caused by the shearing strategy applied to the current material. Based on the future evolution process of stress, temperature, and microstructure deformation, it is determined whether the sheared material will experience fracture failure.

2. The method according to claim 1, characterized in that, The defects include shrinkage cavities, porosity, segregation, internal cracks, bubbles, white spots, and localized corrosion.

3. The method according to claim 1, characterized in that, Also includes: The fiber images of the sample material were acquired using a scanning electron microscope, and the data on its microstructural deformation process were extracted through feature recognition.

4. The method according to claim 1, characterized in that, The material is armor steel.

5. The method according to claim 1, characterized in that, The determination of whether the sheared material will experience fracture failure based on the future evolution of stress, temperature, and microstructure deformation includes: The microstructure deformation degree is simulated by combining the initial stress and temperature with the preset stress-strain conversion rules. The stress and temperature change process is corrected according to the deviation between the microstructure deformation degree and the preset deformation degree. The process is iteratively continued until the stress and temperature meet the preset steady-state conditions. The ultimate deformation of the microstructure when the stress and temperature reach the steady-state conditions is determined. The ultimate deformation predicted at future moments is used to determine whether the sheared material will fracture and fail in the future.

6. The method according to claim 1, characterized in that, Also includes: A search space is constructed, with dimensions including a time axis, dynamic shearing speed, shearing angle, and cumulative working time of the sheared component. An environment space is also constructed, incorporating material temperature, the predicted defect distribution of the current material, and the strength of the sheared component. A reward function is constructed, with indices including shearing time, whether the microstructure fails due to ultimate deformation, and the influence factor of the cumulative working time of the sheared component on its strength. A shearing strategy is generated by searching the search space and applied to the environment space. The reward function value is calculated, and the largest reward function value is selected. The process is iterated to obtain the globally optimal shearing strategy, which is used to force the shearing of the current material.

7. The method according to claim 1, characterized in that, The evolution process of the synchronously measured sample material includes: The surface temperature of the sample material is detected using an infrared temperature detector.

8. The method according to claim 7, characterized in that, Also includes: Based on the shape and thermal conductivity properties of the sample material, the evolution of the internal temperature distribution of the sample material is calculated using the surface temperature of the sample material.

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