Method for predicting material stress and server

Abstract

The application provides a material stress prediction method and a server. The prediction method comprises the following steps: a first correction model is established according to a strain hardening effect model and a thermal softening effect model. A strain rate strengthening term in a JC model is replaced by the first correction model to establish a corrected JC model. The JC model is a product of a strain hardening term, a strain rate strengthening term and a temperature softening term. The stress of the material is predicted according to the corrected JC model. According to the corrected JC model, the complex strain rate effect of the material under high strain rate can be accurately characterized, the prediction accuracy is improved, and a theoretical basis is provided for production and processing. For users, when the condition parameters are changed, the target of the metal sheet component to be prepared can also be obtained according to the corrected JC model, the trial-and-error method of the users to obtain the target of the metal sheet component to be prepared is reduced, the cost is saved, and the time is saved.

Description

Technical Field

[0001] This application belongs to the field of materials fracture technology, specifically relating to methods and servers for predicting material stress. Background Technology

[0002] In new energy vehicles, lithium batteries can generate a large amount of gas under extreme conditions such as overcharging, over-discharging, short circuits, or collision damage, leading to a sudden increase in internal pressure and potential accidents. Currently, the mainstream pressure relief measure is to install aluminum alloy explosion-proof plates in the battery structure. When the pressure reaches a threshold, the explosion-proof plate ruptures promptly, releasing the internal gas and reducing the battery pressure. These thin aluminum alloy plates must not only ensure reliability and durability under normal pressure conditions but also guarantee timely rupture and pressure release under instantaneous overpressure conditions.

[0003] At present, the production process parameters of such thin metal sheet components working under different strain rate environments are mostly obtained by trial and error methods combining experiments and simulations. This method is time-consuming and costly. Furthermore, a large number of trial and error experiments need to be conducted separately for the same material with different heat treatment parameters. There is no theoretical guidance for its specific production process parameters. Summary of the Invention

[0004] In view of this, the first aspect of this application provides a method for predicting material stress, the prediction method comprising: A first modified model is established based on the strain hardening effect model and the thermal softening effect model; The strain rate hardening term in the JC model is replaced with the first modified model to establish the modified JC model; wherein, the JC model is the product of the strain hardening term, the strain rate hardening term, and the temperature softening term; The stress of the material is predicted based on the modified JC model.

[0005] The first modified model is: ;in, This represents the first modified model. This represents a strain hardening effect model. This represents a model of thermal softening effect. Indicates the strain rate during the experiment. Indicates the reference strain rate. This represents the critical strain rate at which the sample thermally softens. C4, C5, C6, and n4 represent the size factor, while C4, C5, C6, and n4 represent the correlation coefficients in the strain rate hardening term.

[0006] Wherein, the critical strain rate for thermal softening of the sample is: ;in, This represents the critical strain rate at which the sample thermally softens. C3 represents the size factor, and C3 and n3 represent the correlation coefficients of the thermal softening correction term.

[0007] Prior to predicting the stress of the material based on the modified JC model, the method further includes: A classic surface layer model is established based on the size factor model and the Holpage model; Replace the strain hardening term in the JC model with the classical surface layer model.

[0008] The process, following the establishment of the classical surface layer model based on the size factor model and the Holpage model, also includes: Based on the classical surface layer model and the grain uniformity influence model, a second modified model is proposed. The step of replacing the strain hardening term in the JC model with the classical surface layer model includes: Replace the strain hardening term in the JC model with the second modified model.

[0009] The grain uniformity influence model is as follows: Where P represents the influence of grain uniformity on the model. Indicates the size factor. C1 and C2 represent the critical size coefficients, and C1 and C2 represent the correlation coefficients in the model of the influence of grain uniformity.

[0010] The step of predicting the stress of the material based on the modified JC model includes: Based on the relevant parameters of the material, the quasi-static stress-strain curve, the dynamic stress-strain curve, and the isothermal stress-strain curve, the undetermined coefficients in the modified JC model can be obtained, and the prediction model can be obtained. The stress-strain relationship curve is obtained based on the prediction model. Based on the stress-strain relationship curve, the stress-load relationship curve is obtained; The load at which the material fractures is obtained from the stress-load relationship curve.

[0011] The process further includes, after replacing the strain rate hardening term in the JC model with the first modified model and establishing the modified JC model, the following steps: Damage prediction of the material is performed based on the modified JC model and the uncoupled fracture criterion.

[0012] A second aspect of this application provides a server including units for performing the prediction method as provided in the first aspect of this application.

[0013] A third aspect of this application provides a server, including a memory and a processor, wherein the memory is used to store program code, and the processor is used to call the program code to execute the prediction method provided in the first aspect of this application.

[0014] This application provides a method and server for predicting material stress. In the traditional JC model, the strain rate hardening term describes the dynamic hardening behavior of materials in a log-linear form, which simply stipulates that the forming stress of materials increases monotonically with increasing strain rate. However, this application finds that the strain rate effect of materials exhibits complex nonlinear characteristics as mentioned above. When the strain rate crosses a specific critical value, due to the influence of thermal softening effect and dynamic recrystallization mechanism, the strain rate effect of materials at high strain rates no longer exhibits monotonically increasing behavior. The linear superposition method used in existing constitutive models cannot describe this phenomenon of strain rate effect reversal. Therefore, to achieve accurate prediction of material deformation behavior at high strain rates, this application proposes a modified strain rate hardening term that is compatible with multiple competing mechanisms.

[0015] In the traditional JC model, the strain rate sensitivity coefficient C in the strain rate intensification term is a constant, thus making the strain rate intensification term a monotonic function. This application finds that the thermal softening effect gradually increases with increasing strain rate. At low strain rates, the strain hardening effect plays a dominant role, while at high strain rates, the thermal softening effect plays a dominant role. Currently, no competitive mechanism between the thermal softening effect and the strain hardening effect has been proposed to influence the flow stress of microscale plastic deformation at high strain rates. Therefore, this application establishes a first modified model based on the strain hardening effect model and the thermal softening effect model, combining the two influencing factors so that the forming stress of the material no longer increases monotonically with increasing strain rate, proposing a multi-competitive mechanism effect.

[0016] Subsequently, the strain rate hardening term in the JC model is replaced by the first modified model. At this point, the strain hardening and temperature softening terms remain unchanged, resulting in the modified JC model. Finally, the stress in the material is predicted based on the modified JC model, accurately characterizing the complex, non-monotonic strain rate effect of the material at high strain rates, improving prediction accuracy, and providing a theoretical basis for production and processing. For users, even when condition parameters change, the modified JC model can still determine the target material for the thin metal sheet component, reducing the need for trial and error in obtaining the target material, thus saving costs and time. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments of this application will be described below.

[0018] Figure 1The strength limit of a 0.5 mm thick specimen at different strain rates.

[0019] Figure 2 This is a flowchart of a method for predicting material stress in one embodiment of this application.

[0020] Figure 3 This is a flowchart included before S300 in one embodiment of this application.

[0021] Figure 4 This is a flowchart included after S210 in one embodiment of this application.

[0022] Figure 5 This is a flowchart of S220 included in one embodiment of this application.

[0023] Figure 6 This is the basic technical solution of this application.

[0024] Figure 7 This is a process flow diagram of S300 in one embodiment of this application.

[0025] Figure 8 This is a flowchart included after S200 in one embodiment of this application.

[0026] Figure 9 This is a comparison chart of the revised JC model, the original JC model, and experimental data.

[0027] Figure 10 This is a burst pressure curve representing the stress state at the rupture point of the explosion-proof disc.

[0028] Figure 11 The simulation results are compared to those of the stress state at the rupture point of the explosion-proof sheet.

[0029] Figure 12 A server provided for this application. Detailed Implementation

[0030] The following are preferred embodiments of this application. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

[0031] Before introducing the technical solution of this application, let's go over the technical issues in related technologies in detail.

[0032] The failure criteria for most sheet metal components are relatively fixed: the component fails when the ultimate stress it experiences under normal operating conditions exceeds the allowable stress of the material and causes plastic deformation. However, for some sheet metal components used in special environments, it is necessary to prevent plastic deformation under normal loads, while under certain extreme conditions, it is necessary to precisely damage and fracture under specified stress conditions.

[0033] For example, in new energy vehicles, lithium batteries can generate a large amount of gas under extreme conditions such as overcharging, over-discharging, short circuits, or collision damage, leading to a sudden increase in internal battery pressure and potential accidents. Currently, the mainstream pressure relief protection measure is to install aluminum alloy explosion-proof plates in the battery structure. When the pressure reaches a threshold, the explosion-proof plate ruptures promptly, releasing the gas inside the battery and reducing the internal pressure. These thin aluminum alloy plates must not only ensure their reliability and durability under normal pressure conditions but also ensure that they can rupture and release pressure promptly under instantaneous overpressure conditions.

[0034] At present, the production process parameters of such thin metal sheet components working under different strain rate environments are mostly obtained by trial and error methods combining experiments and simulations. This method is time-consuming and costly. Furthermore, a large number of trial and error experiments need to be conducted separately for the same material with different heat treatment parameters. There is no theoretical guidance for its specific production process parameters.

[0035] Specifically, this application used four groups of 0.5mm thick 6061 aluminum alloy sheet component samples. The first group was the original sample, without heat treatment. The second group was annealed at 350℃ for 30 minutes and then cooled in the furnace, with an average grain size of 15μm. The third group was annealed at 480℃ for 30 minutes and then cooled in the furnace, with an average grain size of 50μm. The fourth group was annealed at 580℃ for 60 minutes and then cooled in the furnace, with an average grain size of 120μm. Stress-strain curves of the samples at different strain rates were then obtained, and the ultimate tensile strength of the samples at different strain rates was also obtained based on these stress-strain curves. Please refer to [reference needed]. Figure 1 , Figure 1 The strength limit of a 0.5 mm thick specimen at different strain rates.

[0036] Since the first set of original samples did not undergo heat treatment, the grain size within the samples was uneven, inevitably leading to unstable test results; therefore, they were not analyzed. Thus, after discarding the original samples, [the analysis proceeded according to...]. Figure 1 It can be seen that, under the same grain size, the variation law of the ultimate tensile strength of the specimen with increasing strain rate is not the same. For example, when the grain size is 15 μm, the ultimate tensile strength of the specimen first increases with increasing strain rate and then increases further at a strain rate of 2000 s⁻¹. -1The strength was then slightly lower. When the grain size was 50 μm, the ultimate tensile strength of the sample initially increased with increasing strain rate and then decreased to a lower value at a strain rate of 1000 s⁻¹. -1 The strength gradually decreases thereafter. However, when the grain size is 120 μm, the ultimate tensile strength of the sample initially increases with increasing strain rate and then decreases further at a strain rate of 1000 s⁻¹. -1 The strain rate was then reduced, and then increased again at a strain rate of 2000 s⁻¹. -1 This was added later. It is evident that the variation patterns of ultimate tensile strength and strain rate differ with varying grain sizes, and users cannot discern the underlying principles. Therefore, users cannot determine the specific process parameters required to achieve the changed conditions for the thin metal sheet component.

[0037] Therefore, the industry urgently needs a transstrain rate damage model that quantifies the effects of strain rate and size in microscale plastic deformation as theoretical guidance. This model would accurately predict the stress at any point on a thin metal sheet component under different strain rate environments and heat treatment temperatures, and pinpoint the location of cracks under ultimate load conditions. Traditional JC constitutive models are not ideal for predicting stress or cracks in micro-formed thin sheet components at high strain rates. Figure 1 As shown, its strain hardening term does not take into account the strain inhomogeneity in microscale plastic deformation, while the strain rate strengthening term does not reflect the effect of thermal softening under high strain rates.

[0038] In view of this, to solve the above problems, this application provides a method and server for predicting material stress. Please refer to... Figure 2 , Figure 2 This is a flowchart of a method for predicting material stress according to one embodiment of this application. This embodiment provides a method for predicting material stress, which includes steps S100, S200, and S300. Detailed descriptions of S100, S200, and S300 are as follows.

[0039] S100, the first modified model is established based on the strain hardening effect model and the thermal softening effect model.

[0040] S200, replace the strain rate hardening term in the JC model with the first modified model to establish the modified JC model; wherein, the JC model is the product of the strain hardening term, the strain rate hardening term, and the temperature softening term.

[0041] S300, the stress of the material is predicted based on the modified JC model.

[0042] The materials referred to in this embodiment include, but are not limited to, thin metal sheet components, wherein the metal materials include, but are not limited to, aluminum alloys, titanium alloys, tantalum alloys, etc. Thin sheet refers to a metal sheet with a thickness of less than 1 mm, reaching the micrometer scale, also known as microscale. The high strain rate mentioned in this embodiment refers to a strain rate of not less than 1000 s⁻¹. -1 No more than 4000s -1 The situation at that time.

[0043] As known from relevant technologies, the ultimate tensile strength of thin metal sheet components with different grain sizes obtained through different heat treatments exhibits different patterns with increasing strain rate. More specifically, for thin metal sheet components with different grain sizes, the ultimate tensile strength decreases at low strain rates (e.g., strain rates less than 1000 s⁻¹). -1 The variation law is the same at all strain rates, with the ultimate tensile strength increasing with increasing strain rate. However, the variation law of the ultimate tensile strength at high strain rates is different, as detailed above. Therefore, it is urgent to know the relationship between the ultimate tensile strength and the strain rate at high strain rates for thin metal sheet components under microscale conditions.

[0044] However, there is limited research on microscale plastic deformation in existing technologies because obtaining microscale data using the split Hopkinson struts necessary for studying high strain rates is difficult. The traditional JC model, or Johnson-Cook flow stress model, is one of the classic constitutive models for plastic materials. The JC model is the product of strain hardening, strain rate strengthening, and temperature softening terms. Specifically, the JC model was proposed by Johnson and Cook in 1983 and includes… ,in, Represents the JC model. Represents the strain hardening term. Indicates the strain rate strengthening term. The term represents temperature softening. The strain hardening term is related to flow stress and flow strain. The strain rate hardening term is related to strain rate, while the temperature softening term is related to temperature. A represents the yield strength of the material at a reference strain rate and reference temperature, and B represents the strain hardening coefficient. The expression represents the equivalent plastic strain, where n represents the hardening exponent and C represents the strain rate sensitivity coefficient. Indicates the strain rate during the experiment. Indicates the reference strain rate. Indicates the experimental temperature. Indicates reference temperature. denoted by melting point temperature, and p represents the temperature index.

[0045] In the traditional JC model, the strain rate hardening term describes the dynamic hardening behavior of materials in a log-linear form, simply stipulating that the forming stress of the material increases monotonically with increasing strain rate. However, this application finds that the strain rate effect of materials exhibits complex nonlinear characteristics as mentioned above. When the strain rate crosses a specific critical value, due to the effects of thermal softening and dynamic recrystallization, the strain rate effect of materials at high strain rates no longer exhibits a monotonically increasing behavior. The linear superposition method used in existing JC constitutive models cannot describe this phenomenon of strain rate effect reversal. Therefore, to achieve accurate prediction of material deformation behavior at high strain rates, this application proposes a modified strain rate hardening term that is compatible with multiple competing mechanisms.

[0046] In the traditional JC model, the strain rate sensitivity coefficient C in the strain rate intensification term is a constant, thus making the strain rate intensification term a monotonic function. This application finds that the thermal softening effect gradually increases with increasing strain rate. At low strain rates, the strain hardening effect plays a dominant role, while at high strain rates, the thermal softening effect plays a dominant role. Currently, no competitive mechanism between the thermal softening effect and the strain hardening effect has been proposed to influence the flow stress of microscale plastic deformation at high strain rates. Therefore, this application establishes a first modified model based on the strain hardening effect model and the thermal softening effect model, combining the two influencing factors so that the forming stress of the material no longer increases monotonically with increasing strain rate, proposing a multi-competitive mechanism effect.

[0047] Subsequently, the strain rate hardening term in the JC model is replaced by the first modified model. At this point, the strain hardening and temperature softening terms remain unchanged, resulting in the modified JC model. Finally, the stress of the material is predicted based on the modified JC model, accurately characterizing the complex, non-monotonic strain rate effect of the material at high strain rates, improving prediction accuracy, and providing a theoretical basis for manufacturing. For users, even with changes in material, the modified JC model can still provide the required process parameters for thin metal sheet components to achieve fracture under the same load. This reduces the need for trial and error in determining the desired fabrication parameters, saving costs and time.

[0048] In this embodiment, the first correction model is: ;in, This represents the first modified model. This represents a strain hardening effect model. This represents a model of thermal softening effect. Indicates the strain rate during the experiment. Indicates the reference strain rate. This represents the critical strain rate at which the sample thermally softens. C4, C5, C6, and n4 represent the size factor, while C4, C5, C6, and n4 represent the correlation coefficients in the strain rate hardening term.

[0049] Based on the traditional logarithmic linear JC model and considering the influence of thermal softening, this application finds that the variation trend of forming stress with strain rate for materials with different grain sizes conforms to a composite model of logarithmic and negative exponential functions. Therefore, a first modified model compatible with multiple competing mechanisms is proposed to replace the strain rate strengthening term in the traditional JC model. Specifically, using... To replace ,in, This represents a strain hardening effect model. This represents a model of thermal softening effect, and therefore can also be understood as using... This replaces the strain rate sensitivity coefficient C of the strain rate hardening term in the traditional JC model. Furthermore, , ,Will and Substituting these values ​​into the above formula yields the first modified model.

[0050] In this embodiment, the critical strain rate for thermal softening of the sample is: ;in, This represents the critical strain rate at which the sample thermally softens. C3 represents the size factor, and C3 and n3 represent the correlation coefficients of the thermal softening correction term.

[0051] Based on the distinct strain rate effects exhibited by microscale specimens with different grain sizes in dynamic tensile tests, this application further proposes a power-law relationship between the critical strain rate for thermal softening and the size coefficient. Specifically, Substituting this model into the first modified model yields the final first modified model, which is: Furthermore, given a fixed macroscopic size of the sample, the critical strain rate for thermal softening can also be transformed into a form related to the grain size, such as... This is to facilitate subsequent calculations.

[0052] Please refer to Figure 3 , Figure 3 This is a flowchart of the process preceding step S300 in one embodiment of this application. In this embodiment, before S300 predicts the stress of the material based on the modified JC model, steps S210 and S220 are included. Detailed descriptions of S210 and S220 are as follows.

[0053] S210, a classic surface layer model is established based on the size factor model and the Hall Page model.

[0054] S220, replace the strain hardening term in the JC model with the classical surface layer model.

[0055] exist Figure 1 Besides the fact that the variation of the ultimate tensile strength with increasing strain rate differs under the same grain size condition, the variation of the ultimate tensile strength with increasing grain size also differs under the same strain rate condition. Specifically, disregarding the original sample, when the strain rate is 0, the ultimate tensile strength gradually decreases with increasing grain size. At a strain rate of 1000 s⁻¹, the variation increases further. -1 At this time, as the grain size increases, the tensile strength first increases and then decreases. When the strain rate is 2000 s⁻¹... -1 At this point, the tensile strength gradually decreases with increasing grain size. When the strain rate is 3000 s⁻¹... -1 At that time, as the grain size increases, the tensile strength limit first decreases and then increases.

[0056] Therefore, it is evident that when the strain rate is constant, the tensile strength limit no longer changes monotonically with increasing grain size; there is no discernible pattern, and users cannot obtain a valid theoretical basis. This is because the strain hardening term in the traditional JC model does not consider the strain inhomogeneity in microscale plastic deformation. Specifically, at the macroscale, the size effect is not significant, but at the microscale, i.e., the micrometer scale, the size effect intensifies, affecting the final result.

[0057] Therefore, this application introduces the size factor model into the extended Hall-Petch model to obtain the classic surface layer model. The size factor model is as follows: ;in, Indicates the size factor. Indicates the number of surface grains. This represents the total number of grains, hence the size factor. The ratio of surface grains to total grains is given by , w represents the width of the thin plate sample cross-section, t represents the thickness of the thin plate sample cross-section, and d represents the grain size.

[0058] The Hall-Petch model is as follows: ;in, This represents the flow stress considering the size effect. This indicates the stress contribution related to the size factor. This represents the stress contribution independent of the size factor. M represents the polycrystalline orientation factor, typically taken as 3.16, and m represents the single-crystal orientation factor, typically taken as 2. This indicates that the principal shear stress of a single grain is extended to a function related to strain. and This indicates its correlation coefficient. This indicates that the resistance to stress at grain boundaries has been extended to a function related to strain. and This represents the correlation coefficient, where d represents the grain size. Indicates the size factor.

[0059] Will By introducing the Hall-Petch model described above, we can obtain the classic surface layer model, which is as follows: .

[0060] Therefore, the classical surface layer model can be correlated with the size effect, thereby quantifying the influence of the size effect on static plastic deformation. A grain uniformity influence function is proposed to quantify the influence of the size effect on static plastic deformation. Based on this, the strain hardening term in the JC model is replaced with the classical surface layer model, so that the classical surface layer model takes into account the strain non-uniformity phenomenon in microscale plastic deformation, accurately characterizing the complex non-monotonic strain rate effect of materials at high strain rates.

[0061] It is worth noting that size effects typically include characteristic size effects and grain size effects. In this application, the grain size is altered through heat treatment to investigate the influence of size effects on stress. In other embodiments, characteristic size effects should also exhibit the same or similar influence patterns as grain size effects, which will not be elaborated upon in this embodiment.

[0062] Please refer to this as well. Figures 4-5 , Figure 4 This is a flowchart included after S210 in one embodiment of this application. Figure 5 This is a flowchart of S220 in one embodiment of this application. In this embodiment, after S210 establishes the classical surface layer model based on the size factor model and the Hall-Page model, S211 is also included. A detailed description of S211 is as follows.

[0063] S211, based on the classical surface layer model and the grain uniformity influence model, a second modified model is proposed.

[0064] S220 replaces the strain hardening term in the JC model with the classical surface layer model, including S221. A detailed description of S221 is as follows.

[0065] S221, replace the strain hardening term in the JC model with the second modified model.

[0066] After establishing the classical surface layer model, it can be found that the stress variation with increasing grain size is monotonic, which does not match the actual situation. Based on this, this embodiment can also add an adjustment term containing a critical size coefficient to the classical surface layer model to make up for the limitations of the traditional equation. In other words, a second modified model can be proposed based on the classical surface layer model and the grain uniformity influence model. The grain uniformity influence model is used to correct the influence of strain inhomogeneity in quasi-static deformation, and then the strain hardening term in the JC model is replaced with the second modified model.

[0067] Specifically, the model for the influence of grain uniformity is as follows: Where P represents the influence of grain uniformity on the model. This represents the size factor, obtained from metallographic observations. C1 and C2 represent the critical size coefficients, and C1 and C2 represent the correlation coefficients in the model of the influence of grain uniformity.

[0068] Depending on the material, the critical size factor is generally between 0.15 and 0.18. For macroscale polycrystalline materials, the size factor is mostly below 6%. At this scale, the influence of surface grains on flow stress is relatively small, and the influence of local non-uniform grains can be ignored. Once the size factor exceeds 15%, more obvious characteristic size effects and grain size effects can be observed in the experimentally obtained stress-strain curves. Under these conditions, the influence of local non-uniform grains on the size effect must be considered.

[0069] The second corrected model is now: After integration, we get: .

[0070] In this embodiment, by simultaneously replacing the strain hardening term in the JC model with the second modified model and the strain rate hardening term in the JC model with the first modified model, a microscale high strain rate stress model compatible with multi-mechanism competition can be obtained, i.e., the final modified JC model. Specifically, this application modifies the first term of the JC model equation based on the surface layer model and the Hall-Petch equation and couples the size coefficient, proposing a grain uniformity influence function to quantify the influence of size effect on static plastic deformation, thereby quantifying the influence of size effect on static plastic deformation. A critical strain rate related to grain size is proposed for the influence of thermal softening effect. For the strain hardening effect, the grain size coupling coefficient is combined with the second term of the JC model; the two are coupled to form a modified second term of the JC model, thus obtaining a JC modified model compatible with multi-mechanism competition.

[0071] Finally, the revised JC model is as follows:

[0072] .

[0073] This application proposes for the first time a grain uniformity influence function in microscale strain hardening at the same strain rate, and also proposes for the first time a critical strain rate affected by thermal softening in high-strain-rate plastic deformation, incorporating these into the JC model. Furthermore, this application can accurately obtain high-strain-rate stress-strain curves and accurately predict the fracture point pressure and location of a microscale metal sheet component—an explosion-proof sheet—under different stresses for verification. This application provides a research foundation for microscale high-strain-rate plastic deformation and its damage, and provides necessary theoretical guidance for rapidly determining the production process parameters of microscale thin-plate components suitable for high-speed impact.

[0074] This application also provides specific technical solutions, please refer to them. Figure 6 , Figure 6 This is the basic technical solution of this application. This application went through three stages: basic experimental stage, modeling stage, and verification stage, and three types of experiments were conducted: static experiments, characterization experiments, and dynamic experiments. In the basic experimental stage, the static experiments first involved microscale quasi-static uniaxial tensile tests; the characterization experiments first involved metallographic experiments; and the dynamic experiments first involved microscale Hopkinson bar experiments. In the modeling stage, microscale quasi-static stress-strain curves were obtained in the static experiments, and then the local stress inhomogeneity correction coefficient was calculated, coupled with a surface layer model containing a grain uniformity function. In the characterization experiments, grain size and the proportion of surface grains were measured, and the size factor was calculated. Subsequently, the size factor was coupled with the surface layer model to replace the strain hardening term in the JC model. In the dynamic experiments, microscale dynamic stress-strain curves were obtained, and then the critical strain rate for thermal softening was calculated. Subsequently, the size factor was coupled with the critical strain rate for thermal softening to obtain a composite hardening function of thermal softening and size effect, which was then used to replace the strain rate hardening term in the JC model, ultimately resulting in a multi-competitive mechanism microscale high strain rate constitutive model. Finally, the modified JC model can be used to predict high strain rate stress in microscale template components. Furthermore, the modified JC model combined with the uncoupled fracture criterion can be used to predict high strain rate damage in microscale thin-plate components. These will be described in detail below.

[0075] Please refer to Figure 7 , Figure 7 This is a process flow diagram including S300 in one embodiment of this application. In this embodiment, S300 predicts the stress of the material based on the modified JC model, including S310, S320, S330, and S340. Detailed descriptions of S310, S320, S330, and S340 are as follows.

[0076] S310, based on the relevant parameters of the material, the quasi-static stress-strain curve, the dynamic stress-strain curve, and the isothermal stress-strain curve, the undetermined coefficients in the modified JC model can be obtained, thus obtaining the prediction model.

[0077] S320, The stress-strain relationship curve is obtained based on the prediction model.

[0078] S330, obtain the stress-load relationship curve based on the stress-strain relationship curve.

[0079] S340, the load at which the material fractures is obtained based on the stress-load relationship curve.

[0080] This implementation method can fit all the undetermined coefficients in the modified JC model based on the relevant parameters of the material (e.g., material type, grade, thickness, etc.), quasi-static stress-strain curves, dynamic stress-strain curves, and isothermal stress-strain curves, thus obtaining a prediction model. Subsequently, the stress-strain relationship curve can be obtained based on the prediction model. Then, based on the stress-strain relationship curve, material properties (e.g., elastic and plastic parameters; elastic parameters include Young's modulus, Poisson's ratio, plastic parameters, etc.), boundary conditions, and mesh generation, the stress-load relationship curve can be obtained. Finally, the load at material fracture can be obtained from the stress-load relationship curve. Therefore, the load at material fracture under the given process parameters can be obtained. When the user needs to change the material, the required process parameters can also be calculated, providing a good theoretical basis for manufacturing.

[0081] Please refer to Figure 8 , Figure 8 This is a flowchart following S200 in one embodiment of this application. In this embodiment, after S200, where the strain rate strengthening term in the JC model is replaced with the first modified model to establish the modified JC model, S230 is also included. A detailed description of S230 is as follows.

[0082] S230, Damage prediction of the material is performed based on the modified JC model and the uncoupled fracture criterion.

[0083] To verify whether a material can completely or nearly completely fracture under high instantaneous loads, it is necessary to apply a high load instantaneously within a short period of time. However, most users are typically unable to conduct such experiments because loads usually increase gradually over time, making it impossible to apply a high load instantaneously in a short period. This prevents users from verifying whether a material can completely or nearly completely fracture under high instantaneous loads. Therefore, this implementation uses a modified JC model and a decoupled fracture criterion to predict material damage, simulating the material's fracture process in the software to evaluate the damage and determine whether the material has completely or nearly completely fractured.

[0084] Effect Example This application uses 6061 aluminum alloy to verify the effectiveness of the scheme. 6061 aluminum alloy plates with a thickness of 0.5 mm are divided into four groups of samples. One group of original samples is retained without any heat treatment, and the other three groups are annealed and then cooled to room temperature in the furnace. The specific relevant parameters have been described in detail in the relevant technology and will not be repeated here. The annealing parameters and the obtained grain size are shown in Table 1.

[0085] Table 1 Heat treatment parameters and grain size

[0086] Based on the grain size in Table 1, and combined with the quasi-static stress-strain curves and dynamic stress-strain curves of a 0.5mm thick 6061 aluminum alloy sheet obtained from experiments, the final modified JC model formula was decoupled and then the quasi-static stress-strain curves and dynamic stress-strain curves were used respectively. The isothermal stress-strain curves were obtained using the preload method proposed in the applicant's previous research. This allowed for the fitting and solution of all undetermined coefficients in the model, resulting in the following final form:

[0087] .

[0088] It is worth noting that this application uses aluminum alloy 6061 as the verification material. If other metal materials or heat treatment temperatures are used, metallographic experiments are required to obtain the grain size, and the undetermined coefficients of the constitutive model need to be refitted using the low strain rate stress-strain curve and the high strain rate stress-strain curve of the metal.

[0089] Figure 9 This is a comparison chart of the revised JC model, the original JC model, and experimental data. Figure 9 (a) has d=15µm and a strain rate of 1000s. -1 ; Figure 9 (b) In this case, d = 15 μm and strain rate is 2000 s⁻¹. -1 ; Figure 9 (c) In this case, d = 15 μm and strain rate is 3000 s⁻¹. -1 ; Figure 9 (d) has d=50µm and a strain rate of 1000s. -1 ; Figure 9 (e) has d=50µm and a strain rate of 2000s. -1 ; Figure 9 (f) has d=50µm and a strain rate of 3000s. -1 ; Figure 9 (g) has a d = 120 μm and a strain rate of 1000 s⁻¹. -1 ; Figure 9 (h) has a d=120um and a strain rate of 2000s. -1 ; Figure 9(i) where d = 120 μm and strain rate is 3000 s⁻¹ -1 .

[0090] In high-strain-rate plastic forming, the curves of the original JC model differ significantly from experimental data under different grain sizes and strain rates, with an average error of 38% under various conditions. This makes it impossible to accurately predict the dynamic forming of materials. This is because the traditional original JC model does not adequately consider the influence of local stress inhomogeneity at the same strain rate and the thermal softening effect at high strain rates on the dynamic stress-strain curve. In contrast, the modified original JC model constructed in this application, which comprehensively considers size effects, thermal softening effects, and dynamic recrystallization mechanisms, exhibits good agreement with experimental data and accurately characterizes the complex, non-monotonic strain rate effect of materials at high strain rates. Its average error compared to experimental data under different conditions is only 13%, far lower than the standard original JC model, demonstrating the necessity and effectiveness of the modification.

[0091] Automotive explosion-proof discs are typical thin-plate metal components that operate under varying strain rates. The explosion-proof threshold internal burst pressure is 0.8~1.1 MPa, and the opening pressure of the hemispherical aluminum diaphragm of the disc when overcharging increases the internal battery pressure is 2.0~2.2 MPa. When a company replaces the previously used 1020 aluminum alloy with 6061 aluminum alloy or other user-specified materials according to customer needs, the required heat treatment method or fillet correction parameters for the explosion-proof disc can only be obtained through trial and error experiments, which is time-consuming and labor-intensive. However, based on the aforementioned prediction model, the stress-burst pressure curves of the explosion-proof disc at different heat treatment temperatures—comparing the specified burst threshold pressure, hemispherical opening pressure, and the actual initial fracture point stress of the disc—can be quickly obtained. Figure 10 As shown, Figure 10 This is a burst pressure curve representing the stress state at the rupture point of the explosion-proof disc.

[0092] Depend on Figure 10 It can be seen that the initial point fracture equivalent stress-burst pressure curves of the explosion-proof sheet samples at each heat treatment temperature all have two troughs, corresponding to the compression and fracture states during the explosion-proof sheet testing process. The initial trough corresponds to a burst pressure of 2.3 MPa, which represents the appearance of a rupture point and macroscopic cracks on the hemispherical surface of the explosion-proof sheet. When the internal pressure of the battery continues to increase to 5 MPa, or when the initial pressure exceeds 5 MPa, the cracks on the hemispherical surface of the explosion-proof sheet will rapidly propagate to the linear region, corresponding to the second trough on the graph. When the internal pressure of the battery exceeds 8 MPa, the hemispherical film of the explosion-proof sheet completely breaks and detaches, ensuring the smooth depressurization of the gas inside the battery. This demonstrates that this model can accurately predict the stress on thin metal sheet components.

[0093] Furthermore, this application also conducted simulation experiments under higher loads, such as 8MPa. Figure 11 As shown, Figure 11This is a comparison of simulation results regarding the stress state at the fracture point of the explosion-proof sheet. From... Figure 11 It can be seen that under a load of 8MPa, the sample did indeed completely or nearly completely fracture, which meets the user's expected results.

[0094] This embodiment provides a server including a unit for performing the prediction method as provided in the above embodiments of this application.

[0095] The server provided in this embodiment can accurately, simply, and quickly predict the stress state of a material and determine its fracture state by utilizing the unit of the prediction method provided in the above embodiments of this application.

[0096] Specifically, the server provided in this embodiment includes: a first establishment unit, a first replacement unit, and a first calculation unit.

[0097] The first establishment unit is used to establish the first modified model based on the strain hardening effect model and the thermal softening effect model.

[0098] The first replacement unit is used to replace the strain rate hardening term in the JC model with the first modified model to establish the modified JC model; wherein, the JC model is the product of the strain hardening term, the strain rate hardening term, and the temperature softening term; The first calculation unit is used to predict the stress of the material based on the modified JC model.

[0099] Optionally, the first modified model is: ;in, This represents the first modified model. This represents a strain hardening effect model. This represents a model of thermal softening effect. Indicates the strain rate during the experiment. Indicates the reference strain rate. This represents the critical strain rate at which the sample thermally softens. C4, C5, C6, and n4 represent the size factor, while C4, C5, C6, and n4 represent the correlation coefficients in the strain rate hardening term.

[0100] Optionally, the critical strain rate for thermal softening of the sample is: ;in, This represents the critical strain rate at which the sample thermally softens. C3 represents the size factor, and C3 and n3 represent the correlation coefficients of the thermal softening correction term.

[0101] The server provided in this embodiment further includes a second establishment unit and a second replacement unit, and before predicting the stress of the material according to the modified JC model, it further includes: The second building unit is used to build a classic surface layer model based on the size factor model and the Holpage model.

[0102] The second replacement unit is used to replace the strain hardening term in the JC model with the classical surface layer model.

[0103] The server provided in this embodiment also includes a third establishment unit and a third replacement unit. After establishing the classic surface layer model based on the size factor model and the Hall-Page model, it further includes: The third establishment unit is used to suggest a second modified model based on the classical surface layer model and the grain uniformity influence model; Replacing the strain hardening term in the JC model with the classical surface layer model includes: The third replacement unit is used to replace the strain hardening term in the JC model with the second modified model.

[0104] Optionally, the grain uniformity influence model is as follows: Where P represents the influence of grain uniformity on the model. Indicates the size factor. C1 and C2 represent the critical size coefficients, and C1 and C2 represent the correlation coefficients in the model of the influence of grain uniformity.

[0105] The server provided in this embodiment further includes an acquisition unit, which predicts the stress of the material based on the modified JC model, including: The obtaining unit is used to fit the undetermined coefficients in the modified JC model based on the relevant parameters of the material, the quasi-static stress-strain curve, the dynamic stress-strain curve, and the isothermal stress-strain curve, and to obtain the prediction model.

[0106] The unit is used to obtain a stress-strain relationship curve based on the prediction model.

[0107] The unit is used to obtain the stress-load relationship curve based on the stress-strain relationship curve.

[0108] The obtaining unit is used to obtain the load at which the material fractures based on the stress-load relationship curve.

[0109] The server provided in this embodiment further includes a second computing unit, which, after replacing the strain rate hardening term in the JC model with the first modified model and establishing the modified JC model, also includes: The second calculation unit is used to predict the damage of the material based on the modified JC model and the uncoupled fracture criterion.

[0110] Please refer to Figure 12 , Figure 12This application provides a server. This embodiment provides a server including a memory and a processor, wherein the memory is used to store program code, and the processor is used to call the program code to execute the prediction method provided in the above embodiment of this application.

[0111] This embodiment provides a server including a memory 304 and a processor 301. The memory 304 stores program code, and the processor 301 calls the program code to execute the prediction method provided in this embodiment. The server provided in this embodiment, through the processor 301 calling the program code to execute the prediction method provided in this embodiment, can accurately, simply, and quickly predict the stress state of materials and determine their fracture state.

[0112] This device can be a server, such as Figure 12 The server shown includes one or more processors 301, one or more input devices 302, one or more output devices 303, and a memory 304. The processors 301, input devices 302, output devices 303, and memory 304 are connected via a bus 305. The memory 304 is used to store instructions, and the processors 301 are used to execute the instructions stored in the memory 304.

[0113] When the device is used as a server, the processor 301 is configured to: establish a first modified model based on the strain hardening effect model and the thermal softening effect model; replace the strain rate hardening term in the JC model with the first modified model to establish a modified JC model; wherein the JC model is the product of the strain hardening term, the strain rate hardening term, and the temperature softening term; and predict the stress of the material based on the modified JC model.

[0114] It should be understood that, in this embodiment, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0115] Input device 302 may include an antenna, and output device 303 may include a display (e.g., a Liquid Crystal Display, LCD), an antenna, etc.

[0116] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0117] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the following: establishing a first modified model based on a strain hardening effect model and a thermal softening effect model; replacing the strain rate hardening term in the JC model with the first modified model to establish a modified JC model; wherein the JC model is the product of the strain hardening term, the strain rate hardening term, and the temperature softening term; and predicting the stress of the material based on the modified JC model.

[0118] The aforementioned computer-readable storage medium can be an internal storage unit of the terminal described in any of the foregoing embodiments, such as a hard disk or memory of the terminal. The aforementioned computer-readable storage medium can also be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal. Furthermore, the aforementioned computer-readable storage medium may include both internal storage units and external storage devices of the terminal. The aforementioned computer-readable storage medium is used to store the aforementioned computer program and other programs and data required by the terminal. The aforementioned computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0119] This embodiment also provides a computer program including program instructions that, when executed by a processor, cause the processor to perform the prediction method provided in this embodiment. Specifically, when the computer program is executed by the processor, it performs the following: establishing a first modified model based on the strain hardening effect model and the thermal softening effect model; replacing the strain rate hardening term in the JC model with the first modified model to establish a modified JC model; wherein the JC model is the product of the strain hardening term, the strain rate hardening term, and the temperature softening term; and predicting the stress of the material based on the modified JC model.

[0120] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, servers, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed systems, servers, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0123] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the objectives of the embodiments described in this application, depending on actual needs.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0127] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise expressly specified. Moreover, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0128] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," "fixing," etc., should be interpreted broadly. For example, they can refer to a connection, a detachable connection, or an integral part. They can refer to a mechanical connection or an electrical connection. They can refer to a direct connection or an indirect connection through an intermediate medium, or the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0129] The foregoing has provided a detailed description of the embodiments of this application, elucidating and explaining the principles and implementation methods of this application. These descriptions are merely for the purpose of aiding understanding the method and core ideas of this application. However, the content of this specification should not be construed as a limitation of this application. Those skilled in the art can make various modifications and variations to this application without departing from its spirit and scope. These modifications and variations fall within the scope of the claims of this application and their equivalents.

Claims

1. A method for predicting material stress, characterized in that, The prediction method includes: A first modified model is established based on the strain hardening effect model and the thermal softening effect model; The strain rate hardening term in the JC model is replaced with the first modified model to establish the modified JC model; wherein, the JC model is the product of the strain hardening term, the strain rate hardening term, and the temperature softening term; The stress of the material is predicted based on the modified JC model.

2. The prediction method as described in claim 1, characterized in that, The first modified model is: ;in, This represents the first modified model. This represents a strain hardening effect model. This represents a model of thermal softening effect. Indicates the strain rate during the experiment. Indicates the reference strain rate. This represents the critical strain rate at which the sample thermally softens. C4, C5, C6, and n4 represent the size factor, while C4, C5, C6, and n4 represent the correlation coefficients in the strain rate hardening term.

3. The prediction method as described in claim 2, characterized in that, The critical strain rate for thermal softening of the sample is: ;in, This represents the critical strain rate at which the sample thermally softens. C3 represents the size factor, and C3 and n3 represent the correlation coefficients of the thermal softening correction term.

4. The prediction method according to any one of claims 1-3, characterized in that, Before predicting the stress of the material based on the modified JC model, the method further includes: A classic surface layer model is established based on the size factor model and the Holpage model; Replace the strain hardening term in the JC model with the classical surface layer model.

5. The prediction method as described in claim 4, characterized in that, After establishing the classical surface layer model based on the size factor model and the Holpage model, the following is also included: Based on the classical surface layer model and the grain uniformity influence model, a second modified model is proposed. The step of replacing the strain hardening term in the JC model with the classical surface layer model includes: Replace the strain hardening term in the JC model with the second modified model.

6. The prediction method as described in claim 5, characterized in that, The grain uniformity influence model is as follows: Where P represents the influence of grain uniformity on the model. Indicates the size factor. C1 and C2 represent the critical size coefficients, and C1 and C2 represent the correlation coefficients in the model of the influence of grain uniformity.

7. The prediction method as described in claim 1, characterized in that, The prediction of the stress of the material based on the modified JC model includes: Based on the relevant parameters of the material, the quasi-static stress-strain curve, the dynamic stress-strain curve, and the isothermal stress-strain curve, the undetermined coefficients in the modified JC model can be obtained, and the prediction model can be obtained. The stress-strain relationship curve is obtained based on the prediction model. Based on the stress-strain relationship curve, the stress-load relationship curve is obtained; The load at which the material fractures is obtained from the stress-load relationship curve.

8. The prediction method as described in claim 1, characterized in that, After replacing the strain rate hardening term in the JC model with the first modified model to establish the modified JC model, the method further includes: Damage prediction of the material is performed based on the modified JC model and the uncoupled fracture criterion.

9. A server, characterized in that, Includes a unit for performing the prediction method as described in any one of claims 1-8.

10. A server, characterized in that, It includes a memory and a processor, wherein the memory is used to store program code, and the processor is used to call the program code to execute the prediction method as described in any one of claims 1-8.

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