Data processing method and electronic device

Abstract

The present disclosure relates to a data processing method and an electronic device, and relates to the technical field of data processing. The method comprises: obtaining original mechanical response data corresponding to a target material sample under tensile testing of different test conditions, to obtain an original mechanical response data set; for the original mechanical response data of any test condition, determining plastic stress and plastic strain data of the target material sample under the test condition according to the original mechanical response data, to obtain a plastic stress and plastic strain data set corresponding to the original mechanical response data set; converting the loading speed condition in the plastic stress and plastic strain data set into a strain rate condition, to obtain an updated plastic stress and plastic strain data set; inputting the updated plastic stress and plastic strain data set and a plurality of extension test condition data into a data extension model, to obtain extended plastic stress and plastic strain data of the target material sample under each extension test condition. The method can realize plastic stress and strain data extension of a full process window.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more specifically, to a data processing method and an electronic device. Background Technology

[0002] In the simulation analysis of hot forming of metallic materials, the mechanical response data of the material has a crucial impact on the simulation accuracy. For temperature-sensitive and strain-rate-sensitive materials, their stress-strain relationship varies significantly with changes in temperature and strain rate, and the materials exhibit different work hardening behaviors under different deformation conditions. However, experimental testing of material mechanical properties is costly and time-consuming, and experimental data usually only covers a limited range of test conditions, making it difficult to comprehensively cover all deformation conditions in actual working conditions. In addition, there is often a problem in related technologies of confusing external loading rate with the intrinsic strain rate of the material, failing to accurately establish the conversion relationship between the two, which affects the accuracy of simulation results. Summary of the Invention

[0003] One objective of this disclosure is to provide a data processing technical solution to address the problems of high cost and long cycle in material mechanical property testing, and the fact that experimental data typically only covers a limited range of test conditions, making it difficult to fully cover all deformation conditions in actual working conditions.

[0004] According to a first aspect of this disclosure, a data processing method is provided, comprising: Obtain the original mechanical response data of the target material sample under tensile testing conditions under different test conditions to obtain the original mechanical response dataset; for the original mechanical response data under any test condition, determine the plastic stress and plastic strain data of the target material sample under the test conditions based on the original mechanical response data to obtain the plastic stress and plastic strain dataset corresponding to the original mechanical response dataset. The loading rate condition in the plastic stress-plastic strain dataset is converted into the strain rate condition to obtain the updated plastic stress-plastic strain dataset. The updated plastic stress-plastic strain dataset and multiple extended test conditions are input into the data extension model to obtain and output the extended plastic stress-plastic strain data of the target material sample under each extended test condition.

[0005] Optionally, the target material sample is a temperature-sensitive material and / or a strain rate-sensitive material.

[0006] Optionally, obtaining the original mechanical response data of the target material sample under tensile testing conditions under different test conditions includes: Multiple different test conditions are obtained; wherein, the test conditions include temperature conditions and loading speed conditions; For each of the aforementioned test conditions, the material mechanical property testing device is controlled to perform tensile tests on the target material sample according to the test conditions, and the loading force data and tensile displacement data during the tensile test are recorded as the original mechanical response data of the target material sample under the test conditions.

[0007] Optionally, the raw mechanical response data includes loading force data and tensile displacement data. The step of determining the plastic stress and plastic strain data of the target material sample under the test conditions based on the raw mechanical response data includes: Obtain the dimensional information of the target material sample; the dimensional information includes the gauge length, gauge width, and sample thickness. Based on the tensile displacement data, the applied force data, and the dimensional information, the original mechanical response data is converted into true stress and true strain data; The data in the descending region starting from the peak stress point is deleted from the true stress and true strain data to obtain the deleted true stress and true strain data. The deleted true stress and true strain data is then monotonically extended from the peak stress point to the target true strain to obtain the extended true stress and true strain data. The elastic strain portion is removed from the extended true stress and true strain data to obtain the plastic stress and plastic strain data.

[0008] Optionally, converting the loading rate condition in the plastic stress-plastic strain dataset into a strain rate condition to obtain an updated plastic stress-plastic strain dataset includes: Obtain the loading speed condition in each of the test conditions, as well as the gauge length and total tensile deformation of the target material sample; Based on the gauge length and the target total tensile deformation, determine the reference true strain value corresponding to the target total tensile deformation; Based on the target total tensile deformation and the loading rates in the plastic stress and plastic strain dataset, determine the total duration of the tensile test corresponding to each loading rate; The strain rate corresponding to each loading speed is determined based on the ratio between the reference true strain value and the total duration of the tensile test corresponding to each loading speed. The updated plastic stress-plastic strain dataset is obtained by replacing each loading rate condition in the plastic stress-plastic strain dataset with the corresponding strain rate condition.

[0009] Optionally, the data extension model is constructed based on the thermal deformation constitutive baseline equation, which is used to describe the quantitative relationship between the flow stress and temperature, strain rate and true strain of the target material sample, and the thermal deformation constitutive baseline equation contains constitutive parameters to be fitted.

[0010] Optionally, the step of inputting the updated plastic stress-plastic strain dataset and multiple extended test conditions into the data extension model to obtain and output the extended plastic stress-plastic strain data of the target material sample under each extended test condition includes: The updated plastic stress and plastic strain dataset is used as a fitting sample and input into the data extension model to obtain the fitted thermal deformation constitutive baseline equation; wherein, the data extension model uses the fitted sample as the fitting baseline and uses a numerical fitting method to determine the constitutive parameters to be fitted in the thermal deformation constitutive baseline equation to obtain the fitted thermal deformation constitutive baseline equation. Substituting the temperature and strain rate values ​​in the extended test conditions, as well as the plastic strain sequence in the updated plastic stress-plastic strain dataset, into the fitted thermal deformation constitutive baseline equation, yields the extended plastic stress-plastic strain data of the target material sample under each extended test condition.

[0011] Optionally, the data expansion model is a machine learning interpolation model based on the constraints of the thermal deformation constitutive baseline equation. The machine learning interpolation model uses temperature, strain rate, and plastic strain as input features and flow stress as the output target. The machine learning interpolation model learns the sample patterns in the updated plastic stress and plastic strain dataset and generates expanded plastic stress and plastic strain data of the target material sample under the expanded test conditions under the constraints of the thermal deformation constitutive baseline equation.

[0012] Optionally, the method further includes: If there are numerical outliers in the expanded plastic stress and plastic strain data, the numerical outliers are removed from the expanded plastic stress and plastic strain data, and the adjacent data points are interpolated at the positions of the removed data points. The interpolation results are then filled into the positions of the removed data points to obtain the verified expanded plastic stress and plastic strain data.

[0013] According to a second aspect of the present disclosure, an electronic device is also provided, including a memory and a processor, the memory being used to store a computer program; the processor being used to execute the computer program to implement the method according to the first aspect of the present disclosure.

[0014] This embodiment of the disclosure acquires the original mechanical response data of the target material under limited test conditions (a few combinations of temperature and loading rate), first converts it into plastic stress-strain data, and then converts the loading rate conditions into the physical parameters of strain rate, obtaining an updated plastic stress-strain dataset. Finally, this dataset, along with extended test conditions (more combinations of temperature and strain rate), is input into a data extension model to obtain plastic stress-strain data under each extended condition. This allows for wide coverage of the process window based on a small amount of experimental data, enabling the low-cost and high-efficiency construction of a complete material parameter database suitable for hot stamping simulation. This avoids the high cost and time consumption of numerous high-temperature, high-strain-rate experiments, thereby effectively improving the accuracy and applicability of hot forming simulation.

[0015] Other features and advantages of the embodiments of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the disclosure.

[0017] Figure 1 This is a schematic diagram of the composition structure of a data processing system that can apply the data processing method of the embodiments of this disclosure.

[0018] Figure 2 This is a schematic flowchart of a data processing method provided in an embodiment of this disclosure.

[0019] Figure 3(a) is a schematic diagram of the true stress-true strain curve of an example provided in an embodiment of this disclosure.

[0020] Figure 3(b) is a schematic diagram of the true stress-true strain curve after removing the necking stage according to an embodiment of this disclosure.

[0021] Figure 3(c) is a schematic diagram of a plastic stress-plastic strain curve provided in an embodiment of this disclosure.

[0022] Figure 4 This is a schematic diagram of an extended plastic stress-plastic strain curve provided in an embodiment of this disclosure. Detailed Implementation

[0023] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0024] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the scope of this disclosure or its application or use.

[0025] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0026] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0028] It should be noted that all data acquisition actions in this disclosure were carried out in compliance with the relevant data protection laws and policies of the country where the data is located, and with the authorization of the relevant equipment owner.

[0029] In hot forming simulation analysis, the stress-strain relationship of a material has a crucial impact on simulation accuracy, especially for materials such as magnesium alloys that exhibit both temperature sensitivity and strain rate sensitivity. Temperature sensitivity refers to the significant difference in flow stress under different temperature conditions; for example, the yield strength of magnesium alloys at 150℃ and 350℃ may differ by several times. Strain rate sensitivity refers to the significant difference in work hardening behavior under different strain rate conditions; for example, at 0.001 s⁻¹… - ¹Quasi-static deformation and 50 s - ¹Under rapid stamping conditions, the stress-strain curve shape may be completely different.

[0030] Figure 1 This is a schematic diagram of the composition structure of a data processing system capable of applying the data processing method of the embodiments of this disclosure. For example... Figure 1 As shown, the data processing system 100 includes a material mechanical property testing device 110 and an electronic device 120.

[0031] The material mechanical property testing device 110 can be, for example, a universal testing machine.

[0032] Electronic device 120 can be a computer, laptop, etc., and there is no limitation here.

[0033] like Figure 1As shown, the electronic device 120 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, etc. The processor 1100 may be a central processing unit (CPU), a microprocessor (MCU), etc. The memory 1200 may include, for example, ROM (Read-Only Memory), RAM (Random Access Memory), or non-volatile memory such as a hard disk. The interface device 1300 may include, for example, a USB interface, a headphone jack, etc. The communication device 1400 may be capable of wired or wireless communication. The display device 1500 may be, for example, a liquid crystal display (LCD), a touch screen, etc. The input device 1600 may include, for example, a touch screen, a keyboard, etc. Users can input / output voice information through the speaker 1700 and the microphone 1800.

[0034] exist Figure 1 In the system shown, during thermoforming simulation, the material mechanical property testing device 110 performs tensile tests on the target material sample under different temperature and loading speed conditions, acquiring raw mechanical response data (e.g., force-displacement curves). The electronic device 120 receives the raw mechanical response data and performs preprocessing. The processed data is then used for thermoforming simulation.

[0035] However, the processing flow of the aforementioned related technologies has the following obvious shortcomings: First, experimental data typically only cover a limited process window, making it difficult to meet the needs of full-condition simulation. For example, the temperature testing range of conventional universal testing machines is often below 200℃, and the strain rate testing range is usually below 0.1 s. - ¹, while in actual hot stamping processes, the temperature can reach 400℃, and the local strain rate can reach 50 s⁻¹. - ¹. The limitations of experimental conditions result in a lack of a complete material parameter database covering the actual process window in the simulation, which restricts the simulation accuracy.

[0036] Secondly, related technologies often confuse external loading rate with the intrinsic strain rate of a material, failing to establish an accurate conversion relationship between the two. Loading rate is the rate at which the equipment applies displacement to the specimen (unit: mm / min), while strain rate is a measure of the rate of deformation within the material (unit: s). - ¹), the relationship between the two depends on geometric parameters such as the gauge length of the specimen. Confusing the two will lead to inaccurate material strain rate settings in the simulation, especially for strain rate sensitive materials, which will directly affect the reliability of the predicted results such as forming force, springback and microstructure evolution.

[0037] To address this, this disclosure provides a data processing method. By acquiring the raw mechanical response data of the target material under limited test conditions (a few combinations of temperature and loading rate), the data is first converted into plastic stress-strain data. Then, the loading rate condition is converted into the physical parameter of strain rate, resulting in an updated plastic stress-strain dataset. Finally, this dataset, along with extended test conditions (more combinations of temperature and strain rate), is input into a data extension model to obtain plastic stress-strain data under each extended condition. This allows for wide coverage of the process window based on a small amount of experimental data, enabling the low-cost and high-efficiency construction of a complete material parameter database suitable for hot stamping simulation. This avoids the high cost and time consumption of numerous high-temperature, high-strain-rate experiments, thereby effectively improving the accuracy and applicability of hot forming simulation.

[0038] <First Embodiment> This embodiment provides a data processing method. Figure 2 This is a schematic flowchart of a data processing method provided in an embodiment of this disclosure. Figure 2 As shown, the method includes the following steps S210 to S240.

[0039] Step S210: Obtain the original mechanical response data of the target material sample under tensile testing conditions under different test conditions, and obtain the original mechanical response dataset.

[0040] In this embodiment, multiple tensile tests are performed on the target material sample using a material mechanical property testing device to obtain raw mechanical response data under different test conditions, which serves as the basic dataset for subsequent data processing and expansion.

[0041] The target material sample can be a metallic material specimen that requires thermoforming simulation analysis, such as magnesium alloy sheet or aluminum alloy sheet. In some examples, the target material sample is a temperature-sensitive material and / or a strain rate-sensitive material.

[0042] In this example, temperature-sensitive materials are those whose flow stress, yield strength, or elongation changes significantly with temperature. For example, some aluminum alloys have high strength at room temperature, but their yield strength can decrease by more than half when heated to several hundred degrees Celsius; some heat-resistant steels experience a significant decrease in deformation resistance at high temperatures. Strain rate-sensitive materials are those whose work hardening behavior, peak stress, or fracture strain is highly dependent on the deformation rate. For example, the flow stress of some stainless steels can increase by tens to hundreds of megapascals when the strain rate increases from quasi-static to high speed; in some high-temperature alloys, an increase in strain rate during hot deformation significantly alters their dynamic recrystallization behavior, thereby affecting the morphology of the flow curve.

[0043] In addition, many engineering materials are both temperature-sensitive and strain-rate-sensitive. For example, some low-alloy structural steels, some high-strength aluminum alloys, magnesium alloys and titanium alloys, under hot stamping or hot forming conditions, their stress-strain response depends not only on the temperature field, but is also strongly modulated by the local deformation rate.

[0044] Because the temperature and strain rate ranges of the aforementioned materials during actual forming processes often far exceed the capabilities of conventional laboratory testing (e.g., the upper temperature limit of a universal testing machine is below 200℃, and the upper strain rate limit is below 0.1 s⁻¹). - ¹, while hot stamping can reach 400℃ and 50 seconds. - ¹), therefore, it is impossible to obtain the original mechanical response data of these materials directly through experiments.

[0045] The fundamental purpose of this disclosure is to generate mechanical response data covering the entire process window based on the original mechanical response data of a limited "material sample", thereby providing reliable input for thermoforming simulation.

[0046] Test conditions include temperature conditions and loading rate conditions. Temperature conditions refer to the ambient temperature of the target material sample during the tensile test, such as 150℃ or 200℃. Loading rate conditions refer to the movement speed of the clamps in the material mechanical property testing device, such as 2 mm / min, 20 mm / min, 200 mm / min, or 1000 mm / min. By setting multiple different combinations of temperature and loading rate conditions, the mechanical response characteristics of the target material sample under different test conditions can be obtained.

[0047] Raw mechanical response data refers to the loading force and tensile displacement data directly measured by the material mechanical property testing device during tensile testing. Loading force data characterizes the axial load borne by the target material sample during tensile testing, reflecting the resistance level of the gauge length and overall cross-section of the target material sample; it is the raw response directly related to material strength. Loading force data can be acquired by a force sensor. Tensile displacement data is acquired by the clamp displacement sensor of the material mechanical property testing device, characterizing the total elongation of the target material sample in the loading direction; it reflects the degree of macroscopic deformation of the target material sample throughout the tensile process; it is the raw response related to material ductility and deformation history. Together, they constitute a force-displacement curve, which fully records the entire mechanical response process of the target material sample under specific temperature and loading rate conditions; this force-displacement curve is used as the raw mechanical response data.

[0048] The raw mechanical response dataset refers to a collection of raw mechanical response data from multiple sets of different test conditions.

[0049] For example, if two temperature conditions (150℃, 200℃) and four loading speed conditions (2mm / min, 20mm / min, 200mm / min, 1000 mm / min) are set, eight sets of test conditions can be formed, corresponding to eight sets of raw mechanical response data, which constitute the raw mechanical response dataset.

[0050] In some examples, step S210 obtains the original mechanical response data of the target material sample under tensile testing under different test conditions, and the resulting original mechanical response dataset includes the following sub-steps S2111 to S2113.

[0051] Step S2111: Obtain multiple different test conditions, including temperature conditions and loading speed conditions.

[0052] In this embodiment, multiple sets of test conditions are set according to the actual operating conditions of the thermoforming process and the capability range of the material mechanical property testing device.

[0053] For example, considering the actual hot stamping process conditions (press loading speed 2~20 mm / s, temperature range 180~350℃) and the testing capabilities of the universal testing machine, two sets of temperature conditions (T=150℃, 200℃) and four sets of tensile speeds (v=2mm / min, 20mm / min, 200mm / min, 1000 mm / min) can be selected.

[0054] Step S2112: For each test condition, control the material mechanical property testing device to perform tensile tests on the target material sample according to the test conditions, and record the loading force data and tensile displacement data during the tensile test as the original mechanical response data of the target material sample under the test conditions.

[0055] In this embodiment, tensile tests are performed one by one under each set of test conditions. The material mechanical property testing device can be a universal testing machine equipped with a high-temperature furnace, capable of applying tensile loads to the target material sample at a set temperature. During the tensile test, the force sensor records the applied force in real time, and the displacement sensor records the tensile displacement in real time, forming a force-displacement data sequence.

[0056] For example, the target material sample can be a standard dog-bone shaped magnesium alloy tensile specimen with the following dimensions: total length L0 = 100 mm, gauge length L = 30 mm, clamping section width D0 = 15 mm, gauge length width D = 10 mm, transition fillet radius r = 6.25 mm, and specimen thickness t determined according to the actual magnesium alloy sheet thickness, with a range of 0.2~0.5 mm. Under test conditions of temperature T = 150℃ and loading speed v = 2 mm / min, the material mechanical property testing device heats the specimen to 150℃ and holds it at that temperature until it is stable, then applies tension at a speed of 2 mm / min until the specimen breaks, recording the loading force and tensile displacement data throughout the process. In this way, eight sets of raw mechanical response data are obtained for eight different test conditions.

[0057] Step S220: For the original mechanical response data under any test condition, determine the plastic stress and plastic strain data of the target material sample under the test condition based on the original mechanical response data, and obtain the plastic stress and plastic strain dataset corresponding to the original mechanical response dataset.

[0058] In this embodiment, the original force-displacement data is converted into stress-strain data that better meets the requirements of the material constitutive relation description, and the influence of the necking stage and elastic stage is eliminated, and the effective data of the pure plastic deformation stage is extracted.

[0059] Plastic stress-strain data can be true stress-strain data that only includes the plastic deformation stage. This data eliminates the elastic strain component and the stress reduction component caused by necking, and can accurately reflect the work hardening behavior of the target material sample during the plastic flow stage.

[0060] A plastic stress-strain dataset refers to a collection of data obtained under various test conditions. Each set of data in this dataset corresponds to a specific temperature and loading rate condition, and includes the plastic stress-strain relationship under that condition.

[0061] In some examples, the raw mechanical response data includes loading force data and tensile displacement data. Step S220 determines the plastic stress and plastic strain data of the target material sample under the test conditions based on the raw mechanical response data, including steps S2211 to S2214.

[0062] Step S2211: Obtain the size information of the target material sample; the size information includes the gauge length, gauge width and sample thickness.

[0063] In this embodiment, the dimensional information can be obtained by measuring the target material sample using measuring tools (such as vernier calipers or micrometers) before the tensile test. For example, the gauge length L = 30 mm, the gauge width D = 10 mm, and the sample thickness t = 0.5 mm.

[0064] Step S2212: Based on the tensile displacement data, loading force data, and dimensional information, the original mechanical response data is converted into true stress and true strain data.

[0065] In this embodiment, the dimensional information of the target material sample (including gauge length L, gauge width D, and sample thickness t), as well as the loading force F and tensile displacement ΔL recorded in real time during the tensile process, are converted point by point according to the following formula: True response ); True stress = .

[0066] The true strain formula is based on the assumption of uniform deformation within the gauge length, converting the displacement into logarithmic strain. The true stress formula considers the real-time reduction of the specimen's cross-sectional area during deformation, calculating the stress based on the current actual bearing area.

[0067] By performing the above transformation on each data point in the original mechanical response data, a complete true stress-strain curve can be obtained, which serves as true stress-true strain data.

[0068] For example, at a certain moment, the tensile displacement =3 mm, loading force F=500 N, gauge length L=30 mm, gauge width D=10 mm, specimen thickness t=0.5 mm, then, True response ; True stress MPa; By performing the above transformation on each data point during the tensile test, a complete true stress-strain curve can be obtained.

[0069] As shown in Figure 3(a), it is a schematic diagram of the true stress-true strain curve of an example provided in the embodiments of this disclosure, which includes the entire deformation process from the elastic stage, the plastic stage to the necking stage.

[0070] Step S2213: Delete the descending segment data starting from the peak stress point from the true stress and true strain data to obtain the deleted true stress and true strain data. Then, perform monotonically increasing extension processing on the deleted true stress and true strain data from the peak stress point to the target true strain to obtain the extended true stress and true strain data.

[0071] In this embodiment, the stress reduction segment caused by necking is eliminated, and an extension process is performed to ensure that the curve increases monotonically.

[0072] The peak stress point refers to the point on the true stress-true strain data where the stress reaches its maximum value, corresponding to the moment when the material begins to experience necking. The descending stress range refers to the segment of data where the stress decreases with increasing strain, starting from the peak stress point. This segment of data is affected by localized deformation due to necking and cannot accurately reflect the constitutive relationship of the material.

[0073] Monotonically increasing extension processing refers to extending the deleted true stress and true strain data to the target true strain, starting from the peak stress point and following certain mathematical rules (such as keeping the peak stress constant or using polynomial fitting). The target true strain is a preset value, which can be, for example, 1.0 or larger, to cover the large deformation range that may occur in thermoforming simulation.

[0074] Figure 3(b) shows a schematic diagram of the true stress-true strain curve after removing the necking stage according to an embodiment of this disclosure. The curve exhibits a monotonically increasing trend, ensuring the reasonableness of the curve over a large strain range. This method avoids the adverse effects of necking stage data on subsequent data expansion model fitting.

[0075] Step S2214: Delete the elastic strain portion from the extended true stress and true strain data to obtain plastic stress and plastic strain data.

[0076] In this embodiment, the elastic strain portion refers to the strain corresponding to the elastic deformation stage in the initial loading phase of the target material sample, where stress and strain have a linear relationship. The elastic strain portion can recover after unloading and is not considered permanent plastic deformation. Removing the elastic strain portion from the extended true stress-true strain data allows the plastic stress-plastic strain data to reflect the plastic flow behavior of the material.

[0077] Specifically, based on the elastic modulus E of the target material, the elastic strain corresponding to each stress value in the extended true stress-true strain data is calculated. Then, the elastic strain is subtracted from the true strain corresponding to the stress value in the true stress-true strain data to obtain the plastic strain. Finally, the stress is correlated with the plastic strain to obtain the plastic stress-plastic strain data.

[0078] The data includes plastic stress and plastic strain sequences, as well as corresponding plastic flow stress sequences. Each data set also includes two condition labels: temperature and strain rate.

[0079] As shown in Figure 3(c), it is a schematic diagram of a plastic stress-plastic strain curve provided in an embodiment of this disclosure. Compared with the extended true stress-true strain data shown in Figure 3(b), the starting point of the plastic stress-plastic strain data is shifted to the vicinity of the origin, and the plastic stress-plastic strain data only reflects the stress-strain relationship during the plastic deformation stage.

[0080] In the example with 8 sets of test conditions, 8 sets (corresponding to 8 test conditions) of plastic stress and plastic strain data can be obtained, which constitute the plastic stress and plastic strain dataset.

[0081] Step S230: Convert the loading rate condition in the plastic stress-plastic strain dataset into a strain rate condition to obtain the updated plastic stress-plastic strain dataset.

[0082] In this embodiment, a conversion relationship between the external loading rate and the intrinsic strain rate of the material is established, that is, a set conversion relationship is established, and the loading rate condition in the plastic stress-plastic strain dataset is replaced with the strain rate condition, so that the data better meets the input requirements of the data expansion model.

[0083] The strain rate condition refers to the rate of change of the true strain of a target material with respect to time during deformation, and its unit is seconds (s). 1 Strain rate is an intrinsic parameter of the target material, which directly reflects the deformation rate of the target material.

[0084] The updated plastic stress-plastic strain dataset refers to the dataset after replacing the loading rate condition of each data set in the plastic stress-plastic strain dataset with the corresponding strain rate condition.

[0085] For example, the test conditions in the plastic stress-plastic strain dataset before the update were "T=150℃, v=2 mm / min", while the test conditions in the updated plastic stress-plastic strain dataset are "T=150℃, v=2 mm / min". 0.001s 1 ".

[0086] In some examples, step S230 converts the loading rate condition in the plastic stress-plastic strain dataset into a strain rate condition to obtain an updated plastic stress-plastic strain dataset, including the following steps S2311 to S2315.

[0087] Step S2311: Obtain the loading speed condition, gauge length of the target material sample, and total tensile deformation in each test condition.

[0088] In this embodiment, the target total tensile deformation refers to the target value of the total tensile displacement. The target total tensile deformation can be set according to the elongation requirements or test standards of the target material sample.

[0089] Step S2312: Determine the reference true strain value corresponding to the target total tensile deformation based on the gauge length and the target total tensile deformation.

[0090] In this embodiment, the reference true strain value ε0 can be obtained by substituting the gauge length L and the target total tensile deformation ΔL0 into the following formula.

[0091] ε0=ln(1+ΔL0 / L) For example, the gauge length L = 30 mm and the target total tensile deformation... =15 mm, thus,

[0092] Step S2313: Determine the total duration of the tensile test corresponding to each loading speed based on the target total tensile deformation and the loading speed in the plastic stress and plastic strain dataset.

[0093] In this embodiment, for any loading speed, the ratio of the target total tensile deformation to the loading speed is taken as the total duration of the tensile test corresponding to that loading speed.

[0094] Step S2314: Determine the strain rate corresponding to each loading speed based on the reference strain value and the total duration of the tensile test corresponding to each loading speed.

[0095] In this embodiment, the strain rate corresponding to each loading speed is obtained based on the ratio between the reference strain value and the total duration of the tensile test corresponding to each loading speed.

[0096] Step S2315: Replace each loading rate condition in the plastic stress-plastic strain dataset with the corresponding strain rate condition to obtain the updated plastic stress-plastic strain dataset.

[0097] For example, update the data set originally labeled "T=150℃, v=2 mm / min" to "T=150℃, 0.001 s 1 The data set "T=200℃, v=1000 mm / min" will be updated to "T=200℃, 0.5 s 1 .

[0098] In this way, the plastic stress-plastic strain dataset is transformed into an updated plastic stress-plastic strain dataset.

[0099] Step S240: Input the updated plastic stress and plastic strain dataset and multiple extended test conditions into the data extension model to obtain and output the extended plastic stress and plastic strain data of the target material sample under each extended test condition.

[0100] In this embodiment, an extended data model is used to generate extended plastic stress and plastic strain data covering a wider range of test conditions based on limited experimental data (i.e., the updated plastic stress and plastic strain dataset) and multiple extended test conditions, thereby meeting the requirements of full-condition simulation.

[0101] A data expansion model is a model used to generate more data under test conditions based on a limited sample of input data, through mathematical modeling or machine learning methods. The core function of this model is to perform data interpolation and extrapolation, thereby expanding the coverage of the data.

[0102] Extended test conditions refer to test conditions that exceed the coverage of the original experimental data, including higher or lower temperatures, faster or slower strain rates, etc.

[0103] For example, the original experimental data only covered T=150℃, 200℃ and 0.001, 0.01, 0.1, 0.5 s 1 Extended test conditions can include T=250℃, 300℃, 350℃, and 1, 10, 50 s 1 .

[0104] Extended plastic stress-strain data refers to the stress-strain data output by the data extension model that covers extended test conditions. This data has the same format as the original experimental data and also represents plastic stress-strain relationships, but it covers a wider range of temperatures and strain rates, meeting the material parameter requirements for the entire process window in thermoforming simulation.

[0105] In some examples, the data extension model is built upon the thermal deformation constitutive baseline equation. The thermal deformation constitutive baseline equation is a mathematical equation used to describe the quantitative relationship between flow stress and temperature, strain rate, and true strain of a target material during thermal deformation.

[0106] The baseline constitutive equations for hot deformation can include the Arrhenius equation and the Johnson-Cook equation. These equations contain constitutive parameters to be fitted, such as material constants and activation energies, which need to be determined through fitting experimental data.

[0107] For example, the general form of the thermal deformation constitutive equation can be expressed as follows:

[0108] in, Where is the flow stress, and T is the temperature. For strain rate, This represents plastic strain. The equation typically contains multiple constitutive parameters to be fitted. By substituting experimental data into the equation and employing numerical fitting methods such as the least squares method, the optimal values ​​of each parameter can be determined.

[0109] In these examples, step S240 inputs the updated plastic stress-plastic strain dataset and multiple extended test conditions into the data extension model to obtain and output the extended plastic stress-plastic strain data of the target material sample under each extended test condition, including the following steps S2411 to S2412.

[0110] Step S2411: The updated plastic stress and plastic strain dataset is used as a fitting sample and input into the data expansion model to obtain the fitted thermal deformation constitutive baseline equation.

[0111] In this embodiment, the updated plastic stress and plastic strain dataset is used as a fitting sample and provided to the data expansion model. The fitting sample refers to the data sample used for constitutive parameter fitting, which contains multiple sets of plastic stress and plastic strain data under different temperature and strain rate conditions.

[0112] The data extension model uses the fitted samples as the fitting benchmark and employs a numerical fitting method to determine the constitutive parameters to be fitted in the thermal deformation constitutive benchmark equation, thereby obtaining the fitted thermal deformation constitutive benchmark equation.

[0113] Numerical fitting methods can include least squares, genetic algorithms, particle swarm optimization, etc. The goal of the fitting process is to minimize the error between the stress values ​​calculated by the thermal deformation constitutive baseline equation and the stress values ​​in the updated plastic stress-plastic strain dataset.

[0114] The fitted thermal deformation constitutive baseline equation refers to the thermal deformation constitutive baseline equation with determined parameters. This equation can calculate the corresponding flow stress based on the input temperature, strain rate, and plastic strain sequence, and has the ability to predict the mechanical response of materials under different conditions.

[0115] Step S2412: Substitute the temperature and strain rate values ​​in the extended test conditions, as well as the updated plastic stress and plastic strain data set, into the fitted thermal deformation constitutive baseline equation to obtain the extended plastic stress and plastic strain data of the target material sample under each extended test condition.

[0116] In this embodiment, firstly, extended test conditions are set. For example, this includes 5 temperatures (T=150, 200, 250, 300, 350℃) and 6 strain rates ( 0.001, 0.01, 0.1, 1, 10, 50 seconds 1 This results in a total of 5 × 6 = 30 sets of extended test conditions. These extended test conditions can be user-defined.

[0117] Then, for each combination of temperature and strain rate, the corresponding temperature and strain rate values ​​are substituted into the fitted thermal deformation constitutive baseline equation. Then, the corresponding flow stress is calculated for each plastic strain in the plastic strain sequence in the updated plastic stress-plastic strain dataset, thereby obtaining the plastic stress-plastic strain data under this extended test condition.

[0118] Then, by iterating through the above 30 sets of extended test conditions, extended plastic stress and plastic strain data covering a wide range of temperature and strain rates can be obtained.

[0119] like Figure 4 As shown, it is a schematic diagram of an extended plastic stress-plastic strain curve provided in an embodiment of this disclosure, which constitutes a full-process window material parameter database that can be directly used for thermoforming simulation.

[0120] Using the above method, material data under 30 or even more conditions can be generated based on only 8 sets of experiments, significantly reducing experimental costs and time.

[0121] In other examples, the data expansion model is a machine learning interpolation model based on the constraints of the thermal deformation constitutive baseline equation.

[0122] This machine learning interpolation model is essentially a data-driven regression interpolator, using temperature, strain rate, and plastic strain as input features and flow stress as the output target. The model constructs a continuous mapping from input conditions (temperature, strain rate, plastic strain) to output results (flow stress) by learning from a finite number of discrete sample points in the updated plastic stress and plastic strain dataset. In the expansion phase, the user inputs preset expanded test conditions (e.g., interpolation target temperature points, target strain rate points, and plastic strain sequences) into the model. The model performs nonlinear interpolation between the learned discrete sample points, outputting predicted flow stress values ​​under these untested conditions, thus expanding the finite discrete experimental data points into dense, continuous data covering the entire process window.

[0123] To ensure the physical rationality of the interpolation results, a thermal deformation constitutive baseline equation is introduced as a constraint during model training. For example, a physical loss term based on this equation is added to the loss function, so that the model's interpolation prediction in sparse regions of experimental data not only depends on data fitting but is also guided by physical laws.

[0124] In this way, the machine learning interpolation model utilizes artificial intelligence technology to achieve dense interpolation in the temperature and strain rate dimensions. Under the constraint of the thermal deformation constitutive baseline equation, it generates extended plastic stress and plastic strain data of the target material sample under extended test conditions. This method combines the advantages of data-driven interpolation and physical-driven constraints, making full use of limited experimental data while ensuring the reliability of the extended data.

[0125] In some examples, the method also includes step S250.

[0126] Step S250: If there are numerical outliers in the expanded plastic stress and plastic strain data, remove the numerical outliers from the expanded plastic stress and plastic strain data, perform adjacent data point interpolation on the removed data point position, and fill the interpolation result into the removed data point position to obtain the verified expanded plastic stress and plastic strain data.

[0127] In this embodiment, the expanded plastic stress and plastic strain data output by the data expansion model are subjected to quality checks and corrections to ensure the integrity and reliability of the data.

[0128] Numerical outliers refer to data points in the expanded plastic stress-strain data that significantly deviate from the normal trend, such as sudden jumps in stress values, negative values, or extreme values ​​exceeding the reasonable range. These outliers may be caused by model fitting errors, numerical calculation errors, or data processing errors.

[0129] Methods for checking whether there are numerical outliers in the expanded plastic stress-strain data can include checking whether the stress value is non-negative, checking whether the stress-strain curve is monotonically increasing or conforms to the expected trend, and using statistical methods (such as the 3σ criterion and box plot method) to identify outliers.

[0130] After identifying numerical outliers, these outliers can be removed from the expanded plastic stress-strain data. Simultaneously, to maintain data continuity and integrity, interpolation processing is required at the locations of the removed data points. Adjacent data point interpolation involves using the adjacent normal data points before and after the outlier, and employing interpolation algorithms (such as linear interpolation, spline interpolation, etc.) to calculate the appropriate value at that location.

[0131] Through the above verification and correction, the verified and expanded plastic stress and plastic strain data can be obtained, ensuring that the data quality meets the requirements of simulation applications.

[0132] As described in steps S210-S240 above, this embodiment of the invention obtains the original mechanical response data of the target material under limited test conditions (a few combinations of temperature and loading rate), first converts it into plastic stress-strain data, then converts the loading rate condition into the physical parameters of strain rate, and obtains an updated plastic stress-strain dataset. Finally, this dataset, along with extended test conditions (more combinations of temperature and strain rate), is input into a data extension model to obtain plastic stress-strain data under each extended condition. This allows for wide coverage of the process window based on a small amount of experimental data, enabling the low-cost and high-efficiency construction of a complete material parameter database suitable for hot stamping simulation. This avoids the high cost and cycle time of numerous high-temperature, high-strain-rate experiments, thereby effectively improving the accuracy and applicability of hot forming simulation.

[0133] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method in any embodiment of this disclosure.

[0134] Optionally, the computer-readable storage medium may be a non-transitory storage medium, but is not limited thereto; it may also be a temporary storage medium.

[0135] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0136] This disclosure may be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having a computer-readable program loaded thereon for causing a processor to implement any of the methods in the foregoing embodiments of this disclosure.

[0137] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media may include, for example, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), compact disc-read-only memory (CD-ROM), digital versatile disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any combination thereof. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0138] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include one or more of copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to computer-readable storage media in the respective computing / processing device.

[0139] The computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object programs written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as the "C" language or similar programming languages). The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network (e.g., a local area network or a wide area network), or it may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays, or programmable logic arrays, can execute computer-readable program instructions to implement various aspects of the embodiments of this disclosure by utilizing state information from the computer-readable program instructions.

[0140] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0141] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0142] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It should be noted that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are all equivalent.

[0144] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of this disclosure is defined by the appended claims.

Claims

1. A data processing method, characterized in that, include: The original mechanical response data of the target material sample under tensile testing under different test conditions are obtained to obtain the original mechanical response dataset; wherein, the test conditions include temperature conditions and loading speed conditions; For any original mechanical response data under any test condition, the plastic stress and plastic strain data of the target material sample under the test condition are determined based on the original mechanical response data, and the plastic stress and plastic strain dataset corresponding to the original mechanical response dataset is obtained. The loading rate condition in the plastic stress-plastic strain dataset is converted into a strain rate condition to obtain an updated plastic stress-plastic strain dataset. The updated plastic stress and plastic strain dataset and multiple extended test conditions are input into the data extension model to obtain and output the extended plastic stress and plastic strain data of the target material sample under each extended test condition. The step of converting the loading rate condition in the plastic stress-plastic strain dataset into a strain rate condition to obtain an updated plastic stress-plastic strain dataset includes: Obtain the loading speed condition in each of the test conditions, as well as the gauge length and total tensile deformation of the target material sample; Based on the gauge length and the target total tensile deformation, determine the reference true strain value corresponding to the target total tensile deformation; Based on the target total tensile deformation and the loading rates in the plastic stress and plastic strain dataset, determine the total duration of the tensile test corresponding to each loading rate; The strain rate corresponding to each loading speed is determined based on the ratio between the reference true strain value and the total duration of the tensile test corresponding to each loading speed. The updated plastic stress-plastic strain dataset is obtained by replacing each loading rate condition in the plastic stress-plastic strain dataset with the corresponding strain rate condition.

2. The method according to claim 1, characterized in that, The target material sample is a temperature-sensitive material and / or a strain rate-sensitive material.

3. The method according to claim 1, characterized in that, The acquisition of the original mechanical response data of the target material sample under tensile testing conditions under different test conditions includes: Multiple different test conditions are obtained; wherein, the test conditions include temperature conditions and loading speed conditions; For each of the aforementioned test conditions, the material mechanical property testing device is controlled to perform tensile tests on the target material sample according to the test conditions, and the loading force data and tensile displacement data during the tensile test are recorded as the original mechanical response data of the target material sample under the test conditions.

4. The method according to claim 1, characterized in that, The original mechanical response data includes applied force data and tensile displacement data. Determining the plastic stress and plastic strain data of the target material sample under the test conditions based on the original mechanical response data includes: Obtain the dimensional information of the target material sample; the dimensional information includes the gauge length, gauge width, and sample thickness. Based on the tensile displacement data, the applied force data, and the dimensional information, the original mechanical response data is converted into true stress and true strain data; The data in the descending region starting from the peak stress point is deleted from the true stress and true strain data to obtain the deleted true stress and true strain data. The deleted true stress and true strain data is then monotonically extended from the peak stress point to the target true strain to obtain the extended true stress and true strain data. The elastic strain portion is removed from the extended true stress and true strain data to obtain the plastic stress and plastic strain data.

5. The method according to claim 1, characterized in that, The data extension model is constructed based on the thermal deformation constitutive baseline equation, which is used to describe the quantitative relationship between the flow stress and temperature, strain rate and true strain of the target material sample, and the thermal deformation constitutive baseline equation contains constitutive parameters to be fitted.

6. The method according to claim 5, characterized in that, The step of inputting the updated plastic stress-plastic strain dataset and multiple extended test conditions into the data extension model to obtain and output the extended plastic stress-plastic strain data of the target material sample under each extended test condition includes: The updated plastic stress and plastic strain dataset is used as a fitting sample and input into the data extension model to obtain the fitted thermal deformation constitutive baseline equation; wherein, the data extension model uses the fitted sample as the fitting baseline and uses a numerical fitting method to determine the constitutive parameters to be fitted in the thermal deformation constitutive baseline equation to obtain the fitted thermal deformation constitutive baseline equation. Substituting the temperature and strain rate values ​​in the extended test conditions, as well as the plastic strain sequence in the updated plastic stress-plastic strain dataset, into the fitted thermal deformation constitutive baseline equation, yields the extended plastic stress-plastic strain data of the target material sample under each extended test condition.

7. The method according to claim 5, characterized in that, The data extension model is a machine learning interpolation model based on the constraints of the thermal deformation constitutive baseline equation. The machine learning interpolation model takes temperature, strain rate, and plastic strain as input features and flow stress as the output target. The machine learning interpolation model learns the sample patterns in the updated plastic stress and plastic strain dataset and generates extended plastic stress and plastic strain data of the target material sample under the extended test conditions under the constraints of the thermal deformation constitutive baseline equation.

8. The method according to claim 1, characterized in that, The method further includes: If there are numerical outliers in the expanded plastic stress and plastic strain data, the numerical outliers are removed from the expanded plastic stress and plastic strain data, and the adjacent data points are interpolated at the positions of the removed data points. The interpolation results are then filled into the positions of the removed data points to obtain the verified expanded plastic stress and plastic strain data.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory being used to store a computer program; the processor being used to execute the computer program to implement the method according to any one of claims 1-8.

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