A method, device, and storage medium for predicting mechanical properties of a metal material

Abstract

The application discloses a metal material mechanical property prediction method, device and equipment and a storage medium. The method comprises the following steps: obtaining the mechanical property and the first hardness value of a reference material under a first processing process; measuring the second hardness value of a to-be-predicted material under a second processing process; predicting the yield strength value and the second stress-strain curve of the to-be-predicted material under the second processing process based on the mechanical property, the first hardness value and the second hardness value; predicting the mechanical bearing strength value and the total elongation at break of the to-be-predicted material under the second processing process based on the second hardness value, the preset relationship between the mechanical bearing strength and the hardness value, and the second stress-strain curve under the second processing process; and taking the second stress-strain curve, the yield strength value, the mechanical bearing strength value and the total elongation at break under the second processing process as the predicted mechanical property of the to-be-predicted material under the second processing process. The method can accurately predict the mechanical property of the metal material.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of mechanical property prediction technology for metallic materials, and particularly to a method, apparatus, device and storage medium for predicting the mechanical properties of metallic materials. Background Technology

[0002] The mechanical properties of metallic materials are crucial for component selection, engineering design, safety assessment, and quality control. Different material types, different manufacturing processes even with the same material, and different material states (such as different heat treatment states) all affect the mechanical properties of metallic materials. In mechanical engineering, the most commonly used methods for testing the mechanical properties of metallic materials include uniaxial static tensile testing (tensile strength, yield strength, elongation) and hardness. Compared to uniaxial static tensile testing, hardness testing is widely used due to its advantages such as requiring smaller sample sizes, direct testing on the part surface without damaging the part, simpler sample preparation, faster testing, and lower cost. How to quickly and relatively accurately predict and evaluate the mechanical properties of metallic materials is a common goal pursued by the materials industry.

[0003] Currently, the industry's methods for predicting the mechanical properties of metallic materials can be mainly summarized into the following three approaches: ① Based on a large amount of measured data, the relationships between variables are constructed using methods such as the least squares method, and then the measured results are substituted into the relational formula to predict the remaining variables. For example, the mechanical property prediction method for LF2 aluminum tubes given in patent document CN118010493A; ② Based on machine learning or large-scale model algorithms, a model is trained, and then mechanical property prediction is carried out based on the model. For example, a method for predicting the yield strength of titanium alloys based on thermal expansion curves and machine learning is disclosed in patent document CN121215129A; ③ Through regression analysis of measured data, combined with casting mold flow analysis software, casting performance prediction is carried out. This performance prediction method is similar to the aforementioned ① and ② processes, the difference being that it also requires the use of casting mold flow analysis software to calculate variables such as internal defects, microstructure, and solidification characteristics of the casting.

[0004] However, the above methods require a large amount of test data for fitting or the use of algorithms such as machine learning, making them relatively complex to operate. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for predicting the mechanical properties of metallic materials, in order to solve the problem that existing technologies cannot simply, conveniently, and accurately predict the mechanical properties of metallic materials.

[0006] According to one aspect of the present invention, a method for predicting the mechanical properties of metallic materials is provided, the method comprising: The mechanical properties and first hardness value of the reference material under the first processing technology are obtained, wherein the mechanical properties include at least the first stress-strain curve and the elastic modulus. The second hardness value of the material to be predicted is measured under the second processing technology, wherein the material to be predicted and the reference material are the same type of metallic material. Based on the mechanical properties, the first hardness value and the second hardness value, the yield strength value and the second stress-strain curve of the material to be predicted under the second processing process are predicted. Based on the second hardness value, the preset relationship between mechanical bearing strength and hardness value, and the second stress-strain curve under the second processing technology, the mechanical bearing strength value and total elongation at break of the material to be predicted under the second processing technology are predicted. The second stress-strain curve, yield strength value, mechanical bearing strength value, and total elongation at break under the second processing technology are used as the predicted mechanical properties of the material to be predicted under the second processing technology.

[0007] According to another aspect of the present invention, a device for predicting the mechanical properties of metallic materials is provided, the device comprising: The acquisition module is used to acquire the mechanical properties and first hardness value of the reference material under the first processing technology, wherein the mechanical properties include at least the first stress-strain curve and elastic modulus. The measurement module is used to measure the second hardness value of the material to be predicted under the second processing technology, wherein the material to be predicted and the reference material are the same type of metal material. The first prediction module is used to predict the yield strength value and the second stress-strain curve of the material to be predicted under the second processing process based on the mechanical properties, the first hardness value and the second hardness value. The second prediction module is used to predict the mechanical bearing strength and total elongation at break of the material to be predicted under the second processing process based on the second hardness value, the preset relationship between the mechanical bearing strength and the hardness value, and the second stress-strain curve under the second processing process. The determination module is used to take the second stress-strain curve, yield strength value, mechanical bearing strength value and total elongation at break under the second processing technology as the predicted mechanical properties of the material to be predicted under the second processing technology.

[0008] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for predicting the mechanical properties of metallic materials according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for predicting the mechanical properties of metallic materials according to any embodiment of the present invention.

[0010] This invention discloses a method, apparatus, device, and storage medium for predicting the mechanical properties of metallic materials. The method includes: acquiring the mechanical properties and a first hardness value of a reference material under a first processing step, wherein the mechanical properties include at least a first stress-strain curve and an elastic modulus; measuring the second hardness value of a material to be predicted under a second processing step, wherein the material to be predicted is a metallic material of the same class as the reference material; predicting the yield strength and a second stress-strain curve of the material to be predicted under the second processing step based on the mechanical properties, the first hardness value, and the second hardness value; predicting the mechanical bearing capacity and total elongation at break of the material to be predicted under the second processing step based on the second hardness value, a preset relationship between the mechanical bearing capacity and the hardness value, and the second stress-strain curve under the second processing step; and using the second stress-strain curve, yield strength, mechanical bearing capacity, and total elongation at break under the second processing step as the predicted mechanical properties of the material to be predicted under the second processing step. This method predicts the mechanical properties of the material to be predicted under other processing steps by using the mechanical properties and hardness values ​​of a similar metallic material under a certain processing step, thus accurately predicting the mechanical properties of the material to be predicted and solving the problem in the prior art that it is impossible to predict the mechanical properties of metallic materials simply, quickly, and accurately.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a method for predicting the mechanical properties of metallic materials according to Embodiment 1 of the present invention. Figure 2 A flowchart illustrating a method for predicting the mechanical properties of metallic materials provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the stress-strain curve of a reference material provided in an embodiment of the present invention; Figure 4 A schematic diagram of a predicted stress-strain curve provided for an embodiment of the present invention; Figure 5 Another schematic diagram of a predicted stress-strain curve provided for an embodiment of the present invention; Figure 6 A flowchart illustrating a method for verifying the predicted mechanical properties of metallic materials according to an embodiment of the present invention; Figure 7 This is a schematic diagram of a measured stress-strain curve provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a device for predicting the mechanical properties of metallic materials provided in Embodiment 2 of the present invention; Figure 9 This is a schematic diagram of the electronic device used in the method for predicting the mechanical properties of metallic materials according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention. It should be understood that the various steps described in the method embodiments of the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0015] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, any variations of the terms "comprising" and "having," etc., are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0019] Example 1 Figure 1 This is a flowchart illustrating a method for predicting the mechanical properties of metallic materials according to Embodiment 1 of the present invention. This method is applicable to predicting the mechanical properties of metallic materials. The method can be executed by a device for predicting the mechanical properties of metallic materials. This device can be implemented by software and / or hardware and is generally integrated into an electronic device. In this embodiment, the electronic device includes, but is not limited to, devices such as computers.

[0020] like Figure 1 As shown in Embodiment 1 of the present invention, a method for predicting the mechanical properties of metallic materials includes the following steps: S110. Obtain the mechanical properties and first hardness value of the reference material under the first processing technology, wherein the mechanical properties include at least the first stress-strain curve and the elastic modulus.

[0021] The reference material can be a material whose mechanical properties have been actually measured. The processing technology can be a post-processing technology for the material, such as heat treatment or cold plastic deformation. In this embodiment, "first" and "second" are only used to distinguish different objects. The first hardness value can refer to the hardness value of the reference material, which refers to the material's ability to resist indentation, scratching, or frictional wear by a hard object. Mechanical properties can refer to the general physical characteristics exhibited by a material under external force. Here, mechanical properties can be measured under tests such as uniaxial static tension, multiaxial static tension, and uniaxial static compression. For example, the mechanical properties described in this invention include at least stress-strain curves, elastic modulus, mechanical bearing capacity, yield strength, and total elongation at break. The stress-strain curve can refer to a curve in a coordinate system where the horizontal axis is strain and the vertical axis is stress. The elastic modulus can refer to the ratio of stress to strain during the elastic deformation stage of a material. The mechanical bearing capacity can refer to the maximum stress that a metallic material can withstand before fracture. Depending on the testing method, the mechanical bearing capacity can be tensile strength or compressive strength, etc. Yield strength refers to the critical stress value at which a material undergoes significant plastic deformation. Total elongation at fracture refers to the ratio of the total elongation of the material at the moment of fracture to the extensometer gauge length.

[0022] In this embodiment, the mechanical properties and first hardness value of the reference material under the first processing technology can be obtained.

[0023] S120. Measure the second hardness value of the material to be predicted under the second processing technology, wherein the material to be predicted and the reference material are the same type of metal material.

[0024] The material to be predicted can be any material whose mechanical properties need to be predicted. The material to be predicted and the reference material are of the same type of metallic material. For example, both the material to be predicted and the reference material can be aluminum alloys. The heat treatment or cold plastic deformation processes of the material to be predicted are different from those of the reference material, but the processing processes other than heat treatment or cold plastic deformation are the same as those of the reference material.

[0025] In this embodiment, the second hardness value of the material to be predicted under the second processing technology can be measured.

[0026] S130. Based on the mechanical properties, the first hardness value, and the second hardness value, predict the yield strength value and the second stress-strain curve of the material to be predicted under the second processing process.

[0027] In this embodiment, the yield strength and second stress-strain curve of the material to be predicted under the second processing process can be predicted based on the mechanical properties of the reference material, the first hardness value and the second hardness value.

[0028] In one embodiment, predicting the yield strength and second stress-strain curve of the material to be predicted under a second processing process based on the mechanical properties, the first hardness value, and the second hardness value includes: determining the hardness value variation ratio of similar metallic materials under different processing processes based on the first hardness value and the second hardness value; determining the second yield strength point of the material to be predicted under the second processing process based on the mechanical properties, the hardness value variation ratio, and the stress corresponding to a specified plastic elongation; using the stress value corresponding to the second yield strength point as the yield strength value of the material to be predicted under the second processing process; and predicting the second stress-strain curve of the material to be predicted under the second processing process based on the first stress-strain curve, the second yield strength point, and the curve generation conditions.

[0029] The specified plastic elongation rate is 0.2%.

[0030] In this embodiment, the hardness value variation ratio of the same type of metal material under different processing processes can be determined based on the first hardness value and the second hardness value. Based on the mechanical properties, the hardness value variation ratio, and the stress corresponding to the specified plastic elongation, the second yield strength point of the material to be predicted under the second processing process is determined. Then, the stress value corresponding to the second yield strength point is used as the yield strength value of the material to be predicted under the second processing process. According to the first stress-strain curve, the second yield strength point, and the curve generation conditions, the second stress-strain curve of the material to be predicted under the second processing process is predicted.

[0031] In one embodiment, the curve generation condition is: the second stress-strain curve and the first stress-strain curve have the characteristics of the same family of curves, where the same family of curves means that the slope of the elastic deformation segment between the curves is the same, and the plastic deformation segment can be fitted by translation and / or amplification.

[0032] The stress-strain curve can be divided into an elastic deformation segment and a plastic deformation segment. The elastic deformation segment refers to the section of the stress-strain curve where the material undergoes only reversible deformation after the load is applied and can completely recover its original shape after unloading. The plastic deformation segment refers to the section of the stress-strain curve where the material undergoes irreversible permanent deformation after the stress exceeds the elastic limit.

[0033] In this embodiment, the curve generation condition can be that the second stress-strain curve and the first stress-strain curve have the characteristics of the same family of curves, that is, the curves have similar shapes and change rules, the slopes of the elastic deformation segments of the curves are the same, and the plastic deformation segments can be fitted by translation and / or amplification. Translation in the coordinate system can refer to translation up and down along the vertical axis.

[0034] In one embodiment, determining the second yield strength point of the material to be predicted under the second processing step based on the mechanical properties, the hardness value variation ratio, and the stress corresponding to the specified plastic elongation includes: generating a ray with the same slope as the elastic modulus, starting from the specified plastic elongation ratio on the horizontal axis of the coordinate system where the first stress-strain curve is located; taking the point on the first stress-strain curve corresponding to the stress corresponding to the specified plastic elongation ratio as the first yield strength point in the first stress-strain curve; and finding a point on the ray that meets a preset condition based on the first yield strength point and the hardness value variation ratio as the second yield strength point of the material to be predicted under the second processing step; wherein the preset condition is that the ratio between the vertical coordinate of the first yield strength point and the vertical coordinate of the second yield strength point is the same as the hardness value variation ratio.

[0035] The preset condition can refer to the ratio between the ordinate of the first yield strength point and the ordinate of the second yield strength point, which is the same as the change ratio of the hardness value.

[0036] In this embodiment, a ray with the same slope as the elastic modulus can be generated, starting from the plastic elongation rate on the horizontal axis of the coordinate system where the first stress-strain curve is located. The intersection of this ray and the first stress-strain curve can be used as the first yield strength point in the first stress-strain curve. Based on the first yield strength point and the change ratio of the hardness value, a point that meets the preset conditions can be found on the ray, and this point can be used as the second yield strength point of the material to be predicted under the second processing process.

[0037] S140. Based on the second hardness value, the preset relationship between mechanical bearing strength and hardness value, and the second stress-strain curve under the second processing technology, predict the mechanical bearing strength value and total elongation at break of the material to be predicted under the second processing technology.

[0038] The preset relationship between mechanical bearing strength and hardness value can be determined based on the mechanical bearing strength and hardness value of the reference material.

[0039] In this embodiment, the mechanical bearing strength and total elongation at break of the material to be predicted under the second processing process can be predicted based on the second hardness value, the preset relationship between the mechanical bearing strength and the hardness value, and the second stress-strain curve under the second processing process.

[0040] In one embodiment, predicting the mechanical bearing strength and total elongation at break of the material to be predicted under the second processing process based on the second hardness value, the preset relationship between the mechanical bearing strength and the hardness value, and the second stress-strain curve under the second processing process includes: predicting the mechanical bearing strength of the material to be predicted under the second processing process based on the second hardness value and the preset relationship between the mechanical bearing strength and the hardness value; and determining the total elongation at break of the material to be predicted under the second processing process based on the mechanical bearing strength value and the second stress-strain curve.

[0041] In this embodiment, since the relationship between mechanical bearing strength and hardness value of similar materials remains within a certain range under different processing techniques, the mechanical bearing strength value of the material to be predicted under the second processing technique can be predicted based on the second hardness value and the preset relationship between mechanical bearing strength and hardness value. Based on the mechanical bearing strength value and the second stress-strain curve, the total elongation at break of the material to be predicted under the second processing technique can be determined.

[0042] In one embodiment, determining the total elongation at break of the material to be predicted under the second processing step based on the mechanical bearing strength value and the second stress-strain curve includes: taking the point on the second stress-strain curve where the stress value is equal to the mechanical bearing strength value as the fracture point of the material to be predicted under the second processing step; and taking the strain value corresponding to the fracture point as the total elongation at break of the material to be predicted under the second processing step.

[0043] The fracture point can refer to the characteristic point in the stress-strain curve where the material fails due to fracture.

[0044] In this embodiment, the point on the second stress-strain curve where the stress value is equal to the mechanical bearing strength value can be taken as the fracture point of the material to be predicted under the second processing process, and the strain value corresponding to the fracture point can be taken as the total elongation at fracture of the material to be predicted under the second processing process.

[0045] S150. The second stress-strain curve, yield strength value, mechanical bearing strength value and total elongation at break under the second processing technology are used as the predicted mechanical properties of the material to be predicted under the second processing technology.

[0046] In this embodiment, the predicted second stress-strain curve, yield strength value, mechanical bearing capacity value, and total elongation at break under the second processing technology can be used as the predicted mechanical properties of the material to be predicted under the second processing technology.

[0047] The method of this invention can be based on the following principles: (1) For the same type of material, since the elastic modulus of the material is related to the interatomic forces, and the interatomic forces depend on the nature of the metal atoms and the crystal lattice type, heat treatment and cold plastic deformation have little effect on the elastic modulus of the material. When predicting and evaluating the mechanical properties of metallic materials, it can be assumed that the elastic modulus of the same type of material is basically the same. That is to say, when this type of material undergoes uniaxial static tensile or compression tests, the slope of the elastic segment of its stress-strain curve is the same.

[0048] (2) It is believed that when the same material and the same preparation process are used, but the heat treatment conditions are different, the uniaxial static tensile or static compressive stress-strain curves under different heat treatment conditions have the same family characteristics. Taking uniaxial static tensile stress as an example, the concept of family is defined as follows: multiple uniaxial static tensile stress-strain curves have similar shapes and variation patterns. The slope of the elastic deformation segment of each stress-strain curve is the same, and the plastic deformation segment can be fitted by simple translation, magnification and other operations.

[0049] (3) For materials of the same type and preparation process but different heat treatment states, when the internal and surface defects affecting mechanical properties such as microstructure and structure are negligible, the ratio of yield strength to mechanical bearing capacity, the ratio of mechanical bearing capacity to hardness, and the ratio of yield strength to hardness should be within a specific range. When conducting prediction and calibration of the mechanical properties of metallic materials, the above three ratios are assumed to be constant or within a certain range.

[0050] This invention provides a method for predicting the mechanical properties of metallic materials, comprising: acquiring the mechanical properties and a first hardness value of a reference material under a first processing step, wherein the mechanical properties include at least a first stress-strain curve and an elastic modulus; measuring the second hardness value of a material to be predicted under a second processing step, wherein the material to be predicted is a metallic material of the same type as the reference material; predicting the yield strength and a second stress-strain curve of the material to be predicted under the second processing step based on the mechanical properties, the first hardness value, and the second hardness value; predicting the mechanical bearing capacity and total elongation at break of the material to be predicted under the second processing step based on the second hardness value, a preset relationship between the mechanical bearing capacity and the hardness value, and the second stress-strain curve under the second processing step; and using the second stress-strain curve, yield strength, mechanical bearing capacity, and total elongation at break under the second processing step as the predicted mechanical properties of the material to be predicted under the second processing step. This method predicts the mechanical properties of the material to be predicted under other processing steps by using the mechanical properties and hardness values ​​of a similar metallic material under a certain processing step, thus accurately predicting the mechanical properties of the material to be predicted and solving the problem in the prior art that it is impossible to simply, conveniently, and accurately predict the mechanical properties of metallic materials.

[0051] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0052] In one embodiment, after obtaining the predicted mechanical properties of the material to be predicted under the second processing process, the method further includes: determining a first ratio based on the yield strength value and the mechanical bearing strength value under the second processing process; determining a second ratio based on the yield strength value and the second hardness value under the second processing process; and when the difference between the first ratio and the first preset ratio exceeds the first difference, and / or the difference between the second ratio and the second preset ratio exceeds the second difference, regenerating the second stress-strain curve and determining the predicted mechanical properties.

[0053] The first preset ratio can refer to the ratio of the yield strength to the mechanical bearing capacity of the reference material, and the second preset ratio can refer to the ratio of the yield strength to the hardness of the reference material. The first and second differences can be set according to actual conditions, and this embodiment does not limit them.

[0054] In this embodiment, the ratio of the yield strength value to the mechanical bearing strength value under the second processing process can be used as the first ratio, and the ratio of the yield strength value to the hardness value under the second processing process can be used as the second ratio. The first ratio is compared with the first preset ratio, and the second ratio is compared with the second preset ratio. When the difference between the first ratio and the first preset ratio exceeds the first difference, and / or the difference between the second ratio and the second preset ratio exceeds the second difference, the second stress-strain curve is regenerated and the predicted mechanical properties are determined.

[0055] Based on the technical solutions of the above embodiments, this invention provides several specific implementation methods.

[0056] As one specific implementation method of this embodiment. Figure 2 This is a flowchart illustrating a method for predicting the mechanical properties of metallic materials according to an embodiment of the present invention, as shown below. Figure 2 As shown, taking the uniaxial static tensile test as an example: First, the uniaxial static tensile properties of the material to be predicted are measured under heat treatment process A (i.e., the stress-strain curve S under this heat treatment process is obtained). A Elastic modulus E A Tensile strength value σ A Yield strength value σs A Total elongation at break δ A ) and hardness value θ A .

[0057] The hardness θ of the material was measured under heat treatment processes B, C, D, E... B θC θ D θ E ...Then θ was calculated separately. B θ C θ D θ E ...relative to θ A The percentage changes are B%, C%, D%, E%, etc.

[0058] Figure 3 A schematic diagram of the stress-strain curve of a reference material provided in an embodiment of the present invention, as shown below. Figure 3 As shown, in the stress-strain curve S A In the figure, the horizontal axis, with a plastic elongation of 0.2%, is taken as the starting point, parallel to the stress-strain curve S. A Draw ray I to the upper right of the elastic segment. A Ray I A With curve S A The intersection occurs at point A′. The value of the ordinate corresponding to point A′ is the stress of the material when the specified plastic elongation is 0.2% under heat treatment process A.

[0059] Draw a perpendicular line from A′ to the x-coordinate, intersecting the x-coordinate at point A′′. Measure the length L of line segment A′. A Then in ray I A Take points B′, C′, D′, E′… and draw perpendicular lines from B′, C′, D′, E′… to the x-coordinate, intersecting the x-coordinate at points B′′, C′′, D′′, E′′… respectively. Make the lengths of line segments B′B′′, C′C′′, D′D′′, E′E′′… relative to the length L of line segment A′A′′. A The percentage change is equal to B%, C%, D%, E%, etc.

[0060] Points B′, C′, D′, E′… correspond to the x-coordinate values ​​B′′′, C′′′, D′′′, E′′′… which represent the stress σs at a specified plastic elongation of 0.2% under heat treatment processes B, C, D, E… B σs C σs D σs E ...

[0061] Figure 4 A schematic diagram of a predicted stress-strain curve provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the stress-strain curve S A Using B′, C′, D′, E′, ... as the reference, plot the stress-strain curve S. B S C S D S E...This causes the stress-strain curve S... A S B S C S D S E ...are curves of the same family. Curve S B S C S D S E ...These are the predicted stress-strain curves of the material under heat treatment processes B, C, D, E...

[0062] Based on the hardness value θ of this material under heat treatment processes B, C, D, E... B θ C θ D θ E ...by using the relationship between the tensile strength and hardness of the material, the tensile strength value σ under heat treatment processes B, C, D, E... is estimated. B σ C σ D σ E ...

[0063] Figure 5 Another schematic diagram of a predicted stress-strain curve provided in an embodiment of the present invention is shown below. Figure 5 As shown, based on the estimated tensile strength value σ B σ C σ D σ E ..., respectively in the predicted stress-strain curve S B S C S D S E ...take point S B ′、S C ′、S D ′、S E '……。 Point S B ′、S C ′、S D ′、S E The value corresponding to the vertical axis is the estimated tensile strength σ. B σ C σ D σ E ... S B ′、S C ′、S D ′、S E The point '...' is the predicted fracture point of the material under heat treatment processes B, C, D, E...

[0064] In the predicted stress-strain curve S B S C SD S E ...on, through each fracture point S B ′、S C ′、S D ′、S E The total elongation at fracture δ of the material under heat treatment processes B, C, D, E, etc., is estimated based on the corresponding horizontal axis values. B、 δ C、 δ D、 δ E ...

[0065] Figure 6 This is a flowchart illustrating a method for verifying the predicted mechanical properties of metallic materials according to an embodiment of the present invention. Figure 6 As shown, the stress σs is based on the predicted plastic elongation of 0.2%. B σs C σs D σs E ...and the predicted tensile strength value σ B σ C σ D σ E ...Calculate the ratio of yield strength to tensile strength under heat treatment processes B, C, D, E... respectively. Determine the accuracy of the prediction results and whether it is necessary to refit the stress-strain curve and recalculate based on the calculated ratio of each yield strength to tensile strength.

[0066] Based on the predicted stress σs at a specified plastic elongation of 0.2%. B σs C σs D σs E ...and the measured hardness value θ B θ C θ D θ E ...Calculate the yield strength to hardness ratio under heat treatment processes B, C, D, E... respectively. Determine the accuracy of the prediction results based on the calculated yield strength to hardness ratios, and whether it is necessary to refit the stress-strain curve and recalculate.

[0067] The method of this invention provides a way to predict the uniaxial static tensile mechanical properties of metallic materials without obvious yielding phenomena. This method is based on the elastic modulus characteristics of metallic materials, the family characteristics of constructed tensile stress-strain curves of metallic materials, the ratio of yield strength to tensile strength, the ratio of tensile strength to hardness, and the ratio of yield strength to hardness. It exhibits high accuracy in predicting the uniaxial static tensile mechanical properties of similar materials, using the same preparation process, but under different heat treatment conditions. This mechanical property prediction and calibration method can also be extended to predict the mechanical properties of different materials and under different preparation processes.

[0068] As a specific implementation of this embodiment, Table 1 shows the measured mechanical properties and Brinell hardness of the extrusion-cast AlSi8Cu1Mg material under different T6 heat treatment process parameters A, B, C, D, and E.

[0069] Based on the measured tensile strength and hardness values ​​under the T6-A heat treatment state and the measured Brinell hardness under the T6-B, C, D, and E heat treatment states in Table 1, the tensile strength, yield strength, and total elongation at break of the material under the T6-B, C, D, and E heat treatment states are predicted using the method of this embodiment of the invention. The prediction results are shown in Table 1. When performing performance prediction, the tensile strength to hardness ratio of the material under different heat treatment states is assumed to be a constant value of 3.1.

[0070] Table 1. Predicted and measured values ​​of extruded AlSi8Cu1Mg material under different heat treatment parameters ; Figure 7 This is a schematic diagram of a measured stress-strain curve provided in an embodiment of the present invention, as shown below. Figure 7 As shown, the stress-strain curves of materials under different processing techniques can be measured. The predicted tensile strength and yield strength under the T6-B, C, D, and E heat treatment states in Table 1 are calibrated using the measured results. The calculation results are shown in Table 2.

[0071] Table 2 Prediction Result Calibration ; Example 2 Figure 8 This is a schematic diagram of a device for predicting the mechanical properties of metallic materials according to Embodiment 2 of the present invention. The device is applicable to predicting the mechanical properties of metallic materials. The device can be implemented by software and / or hardware and is generally integrated into an electronic device.

[0072] like Figure 8 As shown, the device includes: The acquisition module 210 is used to acquire the mechanical properties and first hardness value of the reference material under the first processing technology, wherein the mechanical properties include at least the first stress-strain curve and the elastic modulus; Measurement module 220 is used to measure the second hardness value of the material to be predicted under the second processing technology, wherein the material to be predicted and the reference material are the same type of metal material; The first prediction module 230 is used to predict the yield strength value and the second stress-strain curve of the material to be predicted under the second processing process based on the mechanical properties, the first hardness value and the second hardness value. The second prediction module 240 is used to predict the mechanical bearing strength and total elongation at break of the material to be predicted under the second processing process based on the second hardness value, the preset relationship between the mechanical bearing strength and the hardness value, and the second stress-strain curve under the second processing process. The determination module 250 is used to take the second stress-strain curve, yield strength value, mechanical bearing strength value and total elongation at break under the second processing technology as the predicted mechanical properties of the material to be predicted under the second processing technology.

[0073] This embodiment provides a device for predicting the mechanical properties of metallic materials. The device includes: an acquisition module for acquiring the mechanical properties and a first hardness value of a reference material under a first processing process, wherein the mechanical properties include at least a first stress-strain curve and an elastic modulus; a measurement module for measuring the second hardness value of a material to be predicted under a second processing process, wherein the material to be predicted is a metallic material of the same type as the reference material; a first prediction module for predicting the yield strength value and a second stress-strain curve of the material to be predicted under the second processing process based on the mechanical properties, the first hardness value, and the second hardness value; a second prediction module for predicting the mechanical bearing capacity value and total elongation at break of the material to be predicted under the second processing process based on the second hardness value, a preset relationship between the mechanical bearing capacity and the hardness value, and the second stress-strain curve under the second processing process; and a determination module for using the second stress-strain curve, yield strength value, mechanical bearing capacity value, and total elongation at break under the second processing process as the predicted mechanical properties of the material to be predicted under the second processing process. This device predicts the mechanical properties of the material under other processing processes by using the mechanical properties and hardness values ​​of similar metallic materials under a certain processing process. It can accurately predict the mechanical properties of the material under test, solving the problem that existing technologies cannot easily, conveniently and accurately predict the mechanical properties of metallic materials.

[0074] Furthermore, the first prediction module 230 includes: Based on the first hardness value and the second hardness value, determine the proportion of hardness value variation of the same type of metal material under different processing processes; Based on the mechanical properties, the percentage change in hardness value, and the stress corresponding to the specified plastic elongation, the second yield strength point of the material to be predicted under the second processing technology is determined. The stress value corresponding to the second yield strength point is taken as the yield strength value of the material to be predicted under the second processing technology. Based on the first stress-strain curve, the second yield strength point, and the curve generation conditions, the second stress-strain curve of the material to be predicted under the second processing technology is predicted.

[0075] Furthermore, the curve generation condition is as follows: the second stress-strain curve and the first stress-strain curve have the characteristics of the same family of curves. The same family of curves means that the slope of the elastic deformation segment between the curves is the same, and the plastic deformation segment can be fitted by translation and / or amplification.

[0076] Furthermore, determining the second yield strength point of the material to be predicted under the second processing step based on the mechanical properties, the percentage change in hardness value, and the stress corresponding to the specified plastic elongation includes: Starting from the plastic elongation rate on the horizontal axis of the coordinate system where the first stress-strain curve is located, a ray with the same slope as the elastic modulus is generated. The point on the first stress-strain curve corresponding to the stress corresponding to the specified plastic elongation is taken as the first yield strength point in the first stress-strain curve. Based on the first yield strength point and the hardness value variation ratio, a point that meets the preset conditions is found on the ray and used as the second yield strength point of the material to be predicted under the second processing process. The preset condition is that the ratio between the ordinate of the first yield strength point and the ordinate of the second yield strength point is the same as the change ratio of the hardness value.

[0077] Furthermore, the second prediction module 240 includes: Based on the second hardness value and the preset relationship between mechanical bearing strength and hardness value, the mechanical bearing strength value of the material to be predicted under the second processing technology is predicted. Based on the mechanical bearing strength value and the second stress-strain curve, the total elongation at break of the material to be predicted under the second processing technology is determined.

[0078] Furthermore, determining the total elongation at break of the material to be predicted under the second processing step based on the mechanical bearing strength value and the second stress-strain curve includes: The point on the second stress-strain curve where the stress value is equal to the mechanical bearing strength value is taken as the fracture point of the material to be predicted under the second processing technology. The strain value corresponding to the fracture point is taken as the total elongation at fracture of the material to be predicted under the second processing technology.

[0079] Furthermore, after obtaining the predicted mechanical properties of the material to be predicted under the second processing technology, the determining module 250 also includes: The first ratio is determined based on the yield strength and mechanical bearing strength values ​​under the second processing method; The second ratio is determined based on the yield strength value and the second hardness value under the second processing technology; When the difference between the first ratio and the first preset ratio exceeds the first difference, and / or the difference between the second ratio and the second preset ratio exceeds the second difference, the second stress-strain curve is regenerated and the predicted mechanical properties are determined.

[0080] The aforementioned device for predicting the mechanical properties of metallic materials can execute the method for predicting the mechanical properties of metallic materials provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0081] Example 3 Figure 9 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0082] like Figure 9As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0083] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0084] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for predicting the mechanical properties of metallic materials.

[0085] In some embodiments, the method for predicting the mechanical properties of metallic materials can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for predicting the mechanical properties of metallic materials described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for predicting the mechanical properties of metallic materials by any other suitable means (e.g., by means of firmware).

[0086] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0090] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0091] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0092] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0093] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting the mechanical properties of metallic materials, characterized in that, The method includes: The mechanical properties and first hardness value of the reference material under the first processing technology are obtained, wherein the mechanical properties include at least the first stress-strain curve and the elastic modulus. The second hardness value of the material to be predicted is measured under the second processing technology, wherein the material to be predicted and the reference material are the same type of metallic material. Based on the mechanical properties, the first hardness value and the second hardness value, the yield strength value and the second stress-strain curve of the material to be predicted under the second processing process are predicted. Based on the second hardness value, the preset relationship between mechanical bearing strength and hardness value, and the second stress-strain curve under the second processing technology, the mechanical bearing strength value and total elongation at break of the material to be predicted under the second processing technology are predicted. The second stress-strain curve, yield strength value, mechanical bearing strength value, and total elongation at break under the second processing technology are used as the predicted mechanical properties of the material to be predicted under the second processing technology.

2. The method according to claim 1, characterized in that, The method of predicting the yield strength and second stress-strain curve of the material under the second processing step based on the mechanical properties, the first hardness value, and the second hardness value includes: Based on the first hardness value and the second hardness value, determine the proportion of hardness value variation of the same type of metal material under different processing processes; Based on the mechanical properties, the percentage change in hardness value, and the stress corresponding to the specified plastic elongation, the second yield strength point of the material to be predicted under the second processing technology is determined. The stress value corresponding to the second yield strength point is taken as the yield strength value of the material to be predicted under the second processing technology. Based on the first stress-strain curve, the second yield strength point, and the curve generation conditions, the second stress-strain curve of the material to be predicted under the second processing technology is predicted.

3. The method according to claim 2, characterized in that, The curve generation conditions are as follows: the second stress-strain curve and the first stress-strain curve have the characteristics of the same family of curves. The same family of curves means that the slope of the elastic deformation segment between the curves is the same, and the plastic deformation segment can be fitted by translation and / or amplification.

4. The method according to claim 2, characterized in that, The determination of the second yield strength point of the material to be predicted under the second processing step, based on the mechanical properties, the percentage change in hardness value, and the stress corresponding to the specified plastic elongation, includes: Starting from the plastic elongation rate on the horizontal axis of the coordinate system where the first stress-strain curve is located, a ray with the same slope as the elastic modulus is generated. The point on the first stress-strain curve corresponding to the stress corresponding to the specified plastic elongation is taken as the first yield strength point in the first stress-strain curve. Based on the first yield strength point and the hardness value variation ratio, a point that meets the preset conditions is found on the ray and used as the second yield strength point of the material to be predicted under the second processing process. The preset condition is that the ratio between the ordinate of the first yield strength point and the ordinate of the second yield strength point is the same as the change ratio of the hardness value.

5. The method according to claim 1, characterized in that, The method of predicting the mechanical load-bearing strength and total elongation at break of the material under the second processing process based on the second hardness value, the preset relationship between the mechanical load-bearing strength and the hardness value, and the second stress-strain curve under the second processing process includes: Based on the second hardness value and the preset relationship between mechanical bearing strength and hardness value, the mechanical bearing strength value of the material to be predicted under the second processing technology is predicted. Based on the mechanical bearing strength value and the second stress-strain curve, the total elongation at break of the material to be predicted under the second processing technology is determined.

6. The method according to claim 5, characterized in that, The determination of the total elongation at break of the material to be predicted under the second processing step, based on the mechanical bearing strength value and the second stress-strain curve, includes: The point on the second stress-strain curve where the stress value is equal to the mechanical bearing strength value is taken as the fracture point of the material to be predicted under the second processing technology. The strain value corresponding to the fracture point is taken as the total elongation at fracture of the material to be predicted under the second processing technology.

7. The method according to claim 1, characterized in that, After obtaining the predicted mechanical properties of the material to be predicted under the second processing technology, the method further includes: The first ratio is determined based on the yield strength and mechanical bearing strength values ​​under the second processing method; The second ratio is determined based on the yield strength value and the second hardness value under the second processing technology; When the difference between the first ratio and the first preset ratio exceeds the first difference, and / or the difference between the second ratio and the second preset ratio exceeds the second difference, the second stress-strain curve is regenerated and the predicted mechanical properties are determined.

8. A device for predicting the mechanical properties of metallic materials, characterized in that, The device includes: The acquisition module is used to acquire the mechanical properties and first hardness value of the reference material under the first processing technology, wherein the mechanical properties include at least the first stress-strain curve and elastic modulus. The measurement module is used to measure the second hardness value of the material to be predicted under the second processing technology, wherein the material to be predicted and the reference material are the same type of metal material. The first prediction module is used to predict the yield strength value and the second stress-strain curve of the material to be predicted under the second processing process based on the mechanical properties, the first hardness value and the second hardness value. The second prediction module is used to predict the mechanical bearing strength and total elongation at break of the material to be predicted under the second processing process based on the second hardness value, the preset relationship between the mechanical bearing strength and the hardness value, and the second stress-strain curve under the second processing process. The determination module is used to take the second stress-strain curve, yield strength value, mechanical bearing strength value and total elongation at break under the second processing technology as the predicted mechanical properties of the material to be predicted under the second processing technology.

9. An electronic device, characterized in that, The device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for predicting the mechanical properties of metallic materials according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for predicting the mechanical properties of metallic materials according to any one of claims 1-7.

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