Electric arc additive carbon steel material model correction and prediction evaluation method

Abstract

The invention relates to an electric arc additive material carbon steel material model correction and prediction evaluation method, which comprises the following steps of: obtaining a plurality of groups of different electric arc additive material carbon steel constitutive models, and carrying out correction prediction; predicting parameters of the arc additive carbon steel; and testing and evaluating the parameters of the arc additive carbon steel. The method has the beneficial effects that quantitative evaluation of surface geometric fluctuation, material anisotropy and inclined yield platform characteristics of a carbon steel material test piece in constitutive model correction prediction caused by a novel electric arc additive manufacturing process is realized; reasonable correction and accurate prediction calculation of a grouping constitutive model relation curve are realized by grouping different yield strengths, sampling directions and surface conditions; and through effective evaluation of test data of key material parameters, accurate prediction and calculation of the key material parameters of the arc additive carbon steel are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing, and more specifically, to a method for correcting and predicting an arc additive carbon steel material model. Background Art

[0002] In the additive manufacturing industry, the Electric Arc Additive Manufacturing (WAAM) technology combines traditional welding technology with advanced robotics to effectively realize the layer-by-layer stacking and manufacturing of complex metal structures. Due to its low carbon and environmental protection, small size restrictions and other advantages, this technology is particularly suitable for the manufacturing of large-scale and optimized complex structures in construction projects, with fast production speed and high material utilization.

[0003] Carbon steel is the most widely used metal material in the field of construction engineering. The arc additive manufacturing process will cause surface geometry fluctuations, material anisotropy, and inclined yield platforms in carbon steel materials. The existing constitutive model design formulas for manufacturing carbon steel materials using traditional processes (forging, casting, etc.) cannot take these factors into account, and whether they are applicable is worth exploring; therefore, it is necessary to conduct relevant research and summarization, and to make model corrections and prediction improvements. In the process of studying the model correction and prediction evaluation methods for arc additive carbon steel materials, there are three main problems. First, the method of constitutive correction prediction of arc additive carbon steel; second, the method of predicting the parameters of arc additive carbon steel materials; third, the method of experimental evaluation of arc additive carbon steel parameters. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies in the prior art and to propose a method for correcting and predicting an arc-added carbon steel material model.

[0005] First, a method for arc additive carbon steel material model correction and prediction evaluation is provided, including:

[0006] S1. Obtain multiple sets of different arc additive carbon steel constitutive models and perform modified prediction; the multiple sets of different arc additive carbon steel constitutive models include an inclined yield model, a flat yield model and a two-stage RO model;

[0007] S2. Predicting arc additive carbon steel parameters; the arc additive carbon steel material parameters include a first group of parameters and a second group of parameters. When the yield strength is lower than a preset strength threshold, the first group of parameters is predicted, otherwise the second group of parameters is predicted;

[0008] S3. Experimental evaluation of arc additive manufacturing parameters for carbon steel.

[0009] Preferably, in S1, the yield strength of the inclined yield model is lower than the preset strength threshold and the direct manufacturing sampling direction is not 0°; the yield strength of the flat yield model is lower than the preset strength threshold and the direct manufacturing sampling direction is 0° or the surface is smoothly processed; the yield strength of the two-stage RO model is higher than or equal to the preset strength threshold.

[0010] Preferably, in S1, the inclined yield model is an inclined yield platform four-linear hardening model or an inclined yield platform bilinear plus nonlinear hardening model; the flat yield model is a flat yield platform four-linear hardening model or a flat yield platform bilinear plus nonlinear hardening model.

[0011] Preferably, in S2, the first group of parameters includes: strength-to-yield ratio f u / f y , ultimate strain ε u , strain hardening strain ε sh , strain hardening modulus E p and the third stage strain hardening modulus E sh ; The second set of parameters includes a first strain hardening exponent n and a second strain hardening exponent m.

[0012] Preferably, in S3, when the yield strength is lower than the preset strength threshold, corresponding scatter plots or curve plots are drawn for the first group of parameters, and the accuracy and rationality of the parameter prediction are evaluated and determined;

[0013] When the yield strength is higher than or equal to the preset strength threshold, corresponding scatter plots are drawn for the second group of parameters, and the accuracy and rationality of the parameter prediction are evaluated and determined.

[0014] Preferably, in S1 to S3, the preset strength threshold value ranges from 400 to 600 MPa.

[0015] In a second aspect, an arc additive carbon steel material model correction and prediction evaluation system is provided, which is used to execute any method described in the first aspect, including:

[0016] A correction module is used to obtain multiple sets of different arc additive carbon steel constitutive models and perform correction prediction; the multiple sets of different arc additive carbon steel constitutive models include an inclined yield model, a flat yield model and a two-stage RO model;

[0017] A prediction module, used to predict arc additive carbon steel parameters; the arc additive carbon steel material parameters include a first group of parameters and a second group of parameters, when the yield strength is lower than a preset strength threshold, the first group of parameters is predicted, otherwise the second group of parameters is predicted;

[0018] Evaluation module for experimental evaluation of arc additive manufacturing parameters for carbon steel.

[0019] According to a third aspect, a computer storage medium is provided, wherein a computer program is stored in the computer storage medium; when the computer program is executed on a computer, the computer executes any method described in the first aspect.

[0020] In a fourth aspect, an electronic device is provided, including:

[0021] Memory, used to store computer programs;

[0022] A processor is used to execute the computer program to implement any method as described in the first aspect.

[0023] The beneficial effects of the present invention are:

[0024] 1. The arc additive carbon steel material model correction and predictive evaluation method provided by the present invention realizes the quantitative evaluation of the surface geometric fluctuations, material anisotropy and inclined yield platform characteristics of the carbon steel material specimen caused by the new arc additive manufacturing process in the correction prediction of the constitutive model; by grouping different yield strengths (lower than a preset strength threshold, higher than or equal to a preset strength threshold, the preset strength threshold range is 400-600MPa), sampling directions (θ=0°, θ≠0°) and surface conditions (direct manufacturing, surface smoothing processing), the reasonable correction and accurate predictive calculation of the grouped constitutive model relationship curve are realized; through the effective evaluation of the experimental data of key material parameters, the accurate predictive calculation of the key material parameters of arc additive carbon steel is realized.

[0025] 2. The arc additive carbon steel material model correction and predictive evaluation method provided by the present invention realizes the accurate prediction of the key material parameters and stress-strain relationship curve of carbon steel manufactured by the arc additive process, provides theoretical support and design basis for the design and application of arc additive carbon steel structures, and helps engineers and researchers to have a deeper understanding of the mechanical behavior of carbon steel materials and structures manufactured by new arc additive processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of the flow chart of the arc additive manufacturing carbon steel material model correction and prediction evaluation method provided by the present invention;

[0027] Figure 2a It is a schematic diagram of the parallel deposition scheme of plates;

[0028] Figure 2b It is a schematic diagram of the plate oscillation deposition scheme;

[0029] Figure 2c It is a schematic diagram of the point-by-point deposition scheme of the specimen;

[0030] Figure 3 It is a schematic diagram of the sampling direction of the specimen;

[0031] Figure 4a It is a schematic diagram of the stress-strain curve of the inclined yield plateau quadrilinear hardening model;

[0032] Figure 4b It is a schematic diagram of the stress-strain curve of the inclined yield platform bilinear plus nonlinear hardening model;

[0033] Figure 5a It is a schematic diagram of the stress-strain curve of the flat yield plateau quadrilinear hardening model;

[0034] Figure 5b It is a schematic diagram of the stress-strain curve of the flat yield platform bilinear plus nonlinear hardening model;

[0035] Figure 6 It is a schematic diagram of the stress-strain curve of the two-stage RO model;

[0036] Figure 7 It is a schematic diagram of the strength-yield ratio-yield strength test scatter points and the predicted evaluation curve;

[0037] Figure 8a It is a schematic diagram of the ultimate strain-yield ratio test scatter points and the predicted evaluation curve of the smooth surface processed specimen;

[0038] Figure 8b It is a schematic diagram of the ultimate strain-yield ratio test scatter points and the predicted evaluation curve of the directly manufactured specimen;

[0039] Figure 9a It is a schematic diagram of the test scatter points and prediction evaluation curve of the strain-yield ratio of the strain hardening of the smooth surface processed specimen;

[0040] Figure 9b It is a schematic diagram of the scatter points and predicted evaluation curve of the strain-yield ratio test of the strain hardening of the directly manufactured specimen;

[0041] Fig.10 is a schematic diagram of the modulus hardening ratio-sampling direction test scatter points and the modulus hardening ratio average value curve (M is surface smooth processing, AB is direct manufacturing);

[0042] Fig.11 Yes (ff sh ) / (f u -f sh ) and (ε-ε sh ) / (ε u -ε sh ) is a diagram of the curve relationship;

[0043] Fig.12It is a schematic diagram of the first strain hardening index-sampling direction test scatter points and predicted evaluation curve;

[0044] Fig.13 It is a schematic diagram of the second strain hardening exponent-sampling direction test scatter points and predicted evaluation curve. DETAILED DESCRIPTION

[0045] The present invention is further described below in conjunction with embodiments. The description of the following embodiments is only used to help understand the present invention. It should be noted that for ordinary persons in the art, without departing from the principle of the present invention, the present invention can also be modified in some ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0046] Embodiment 1:

[0047] In order to solve the problems of the prior art, Example 1 of the present application provides a method for correcting and predicting the material model of arc additive carbon steel, which realizes the rapid and accurate prediction and calculation of the material parameters and constitutive model correction curve of arc additive carbon steel while considering the quantitative evaluation of the surface geometric fluctuations, material anisotropy and inclined yield platform characteristics of the carbon steel material caused by the arc additive manufacturing process.

[0048] Specifically, Figure 1 As shown, the method includes:

[0049] S1. Obtain multiple groups of different arc additive carbon steel constitutive models and perform modified predictions; the multiple groups of different arc additive carbon steel constitutive models are divided into three groups, including an inclined yield model, a flat yield model and a two-stage RO model.

[0050] In S1, the yield strength of the inclined yield model is lower than the preset strength threshold (such as 500MPa) and the direct manufacturing sampling direction is not 0°; the yield strength of the flat yield model is lower than the preset strength threshold and the direct manufacturing sampling direction is 0° or the surface is smoothed; the yield strength of the two-stage RO model is higher than or equal to the preset strength threshold.

[0051] The inclined yield model is an inclined yield platform four-linear hardening model or an inclined yield platform bilinear plus nonlinear hardening model; the flat yield model is a flat yield platform four-linear hardening model or a flat yield platform bilinear plus nonlinear hardening model.

[0052] Due to the process characteristics of arc additive manufacturing, compared with traditional carbon steel materials, the constitutive model correction of arc additive carbon steel materials has two obvious differences: 1) There are non-negligible geometric fluctuations on the surface of arc additively manufactured steel structures, which leads to a decrease in effective mechanical properties, and due to different sampling directions, there is a certain degree of material anisotropy, so the prediction calculation formula of material parameters varies according to the surface conditions (direct manufacturing or surface smoothing processing) and the sampling direction θ; 2) For conventional strength steel (yield strength less than 500MPa) manufactured by arc additive manufacturing, when the sampling direction is not 0°, experimental measurements show that the second stage of the stress-strain curve is an inclined yield platform, that is, there is a hardening process in the second stage.

[0053] The deposition strategies of arc additive manufacturing metal specimens include parallel deposition, oscillating deposition and point-by-point deposition modes (Figure 2); the sampling directions of the specimens include θ = 0°, θ = 45°, θ = 90°, etc. ( Figure 3 )

[0054] S2. Predicting arc additive carbon steel parameters; the arc additive carbon steel material parameters include a first group of parameters and a second group of parameters. When the yield strength is lower than a preset strength threshold, the first group of parameters is predicted, otherwise the second group of parameters is predicted.

[0055] In S2, the first group of parameters includes: strength-to-yield ratio f u / f y , ultimate strain ε u , strain hardening strain ε sh , strain hardening modulus E p and the third stage strain hardening modulus E sh ; The second set of parameters includes a first strain hardening exponent n and a second strain hardening exponent m.

[0056] S3. Experimental evaluation of arc additive manufacturing parameters for carbon steel.

[0057] In S3, when the yield strength is lower than the preset strength threshold, corresponding scatter plots or curve plots are drawn for the first group of parameters, and the accuracy and rationality of the parameter prediction are evaluated and determined.

[0058] Specifically, according to the test data, for the tensile strength f u , ultimate strain ε u , strain hardening strain ε sh , strain hardening exponent n and m, and draw scatter plots respectively. And use the mean value Mean and covariance COV calculation to evaluate the accuracy of the parameter prediction formula. p, draw a scatter plot, and take the conservative value of the average value to obtain the prediction formula for the two cases of sampling direction 0° and non-0°; for the third stage strain hardening modulus E sh , draw a curve relationship diagram, and obtain the prediction formula through least squares (OLS) regression analysis.

[0059] When the yield strength is higher than or equal to the preset strength threshold, corresponding scatter plots are drawn for the second group of parameters, and the accuracy and rationality of the parameter prediction are evaluated and determined.

[0060] Specifically, according to the collected literature data and experimental data, for the first strain hardening exponent n and the second strain hardening exponent m of arc additive carbon steel materials, the corresponding scatter diagrams of n and θ, and m and θ were drawn respectively, and the accuracy and rationality of the parameter prediction formula were evaluated and determined by using the mean value Mean and covariance COV calculation.

[0061] In S1 to S3, the preset strength threshold range is 400 to 600 MPa. In this embodiment, 500 MPa is taken as the threshold.

[0062] Embodiment 2:

[0063] Based on Example 1, Example 2 of the present application provides a more specific arc additive carbon steel material model correction and prediction evaluation method, including:

[0064] S1. Obtain multiple sets of different arc additive carbon steel constitutive models and perform modified prediction; the multiple sets of different arc additive carbon steel constitutive models include an inclined yield model, a flat yield model and a two-stage RO model.

[0065] Specifically, in S1:

[0066] (1) The yield strength is lower than the preset strength threshold (e.g. 500 MPa) and the direct manufacturing sampling direction is not 0°

[0067] like Figure 4a , Figure 4b As shown, the stress-strain curve of arc-added carbon steel material shows a discontinuous yield response, which is set as a four-linear hardening model or a bilinear plus nonlinear hardening model, in which the second stage has an inclined yield platform, and the corresponding modified prediction calculation formulas are shown in formulas (1) and (2) respectively;

[0068] 1) Four-linear hardening model with inclined yield plateau ( Figure 4a )

[0069]

[0070] Where E is the elastic modulus, f y is the yield strength, εy is the yield strain; E p is the strain hardening modulus, f sh is the strain hardening stress, ε sh is the strain hardening strain; E sh is the third stage strain hardening modulus, is the third stage strain hardening stress, C1ε u is the third stage strain hardening strain, C1 and C2 are the third stage strain parameters, C1=[ε sh +0.3(ε u -ε sh )] / ε u ;f u is the tensile strength, ε u is the limiting strain.

[0071] 2) Bilinear plus nonlinear hardening model of inclined yield platform ( Figure 4b )

[0072]

[0073] Where K1, K2, K3, and K4 are material hardening parameters;

[0074] (2) The yield strength is lower than the preset strength threshold (e.g. 500 MPa) and the sampling direction is directly manufactured at 0° or the surface is smooth.

[0075] like Figure 5a , Figure 5b As shown, the stress-strain curve of arc-added carbon steel material shows a discontinuous yield response, which is set as a four-linear hardening model or a bilinear plus nonlinear hardening model, in which the second stage has a flat yield platform. The corresponding modified prediction calculation formulas are shown in formulas (3) and (4) respectively;

[0076] 1) Four-linear hardening model with flat yield plateau ( Figure 5a )

[0077]

[0078] 2) Bilinear plus nonlinear hardening model of flat yield platform ( Figure 5b )

[0079]

[0080] (3) The yield strength is higher than the preset strength threshold (e.g. 500 MPa)

[0081] like Figure 6 As shown, the stress-strain curve of arc-added carbon steel material shows a continuous yield response, which is set as a two-stage RO model. The corresponding modified prediction calculation formula is shown in formula (5);

[0082]

[0083] In the formula, is the tangent modulus corresponding to 0.2% residual strain in the stress-strain curve, ε 0.2 is the corresponding yield strength f y 0.2% residual strain, n and m are the first and second strain hardening exponents respectively.

[0084] S2. Predicting arc additive carbon steel parameters; the arc additive carbon steel material parameters include a first group of parameters and a second group of parameters. When the yield strength is lower than a preset strength threshold, the first group of parameters is predicted, otherwise the second group of parameters is predicted.

[0085] Specifically, in S2:

[0086] (1) The yield strength is lower than the preset strength threshold (e.g. 500 MPa)

[0087] Parameter prediction of arc additive carbon steel materials including strength-to-yield ratio f u / f y , ultimate strain ε u , strain hardening strain ε sh , strain hardening modulus E p , the third strain hardening modulus E sh The parameter prediction calculation formula is applicable to the following cases in step S1: (1) the yield strength is lower than the preset strength threshold (e.g., 500 MPa) and the direct manufacturing sampling direction is not 0°, and (2) the yield strength is lower than the preset strength threshold (e.g., 500 MPa) and the direct manufacturing sampling direction is 0° or the surface is smoothed.

[0088] like Figure 7 As shown, the strength-yield ratio f u / f y The prediction formula is:

[0089] f u / f y =1+(150 / f y ) 1.3 (6)

[0090] like Figure 8a , Figure 8b As shown, the ultimate strain ε u The prediction formula is:

[0091]

[0092] Wherein, the unit of sampling direction θ is radian (rad);

[0093] like Figure 9a, Figure 9b As shown, the strain hardening strain ε sh The prediction formula is:

[0094]

[0095] In the formula, ε sh The value range is 0.015≤ε sh ≤0.03;

[0096] like Fig.10 As shown, the strain hardening modulus E p The prediction formula is:

[0097]

[0098] Wherein, modulus hardening ratio k = 0.007;

[0099] like Fig.11 As shown, the third strain hardening modulus E sh The prediction formula is:

[0100]

[0101] (2) The yield strength is higher than or equal to the preset strength threshold (e.g. 500 MPa)

[0102] The parameter prediction of arc-assisted carbon steel material includes the prediction calculation formula of strain hardening exponent parameters n and m, which is applicable to the case where the yield strength in step S1 (3) is higher than a preset strength threshold (eg, 500 MPa).

[0103] like Fig.12 As shown, the prediction formula for the first strain hardening exponent n is:

[0104]

[0105] In the formula, f 0.04 is the stress intensity corresponding to 0.04% residual strain;

[0106] like Fig.13 As shown, the prediction formula for the second strain hardening exponent m is:

[0107]

[0108] S3. Experimental evaluation of arc additive manufacturing parameters for carbon steel.

[0109] Specifically, in S3:

[0110] (1) The yield strength is lower than the preset strength threshold (e.g. 500 MPa)

[0111] like Figure 7 , Figure 8a-8b , Figure 9a-9b As shown in the figure, based on the collected literature data and test data, the tensile strength f of arc-added carbon steel material is u , ultimate strain ε u , strain hardening strain ε sh , respectively draw the corresponding f u / f y With f y , ε u With f y / f u , ε sh With f y / f u The scatter plot is plotted, and the accuracy and rationality of the parameter prediction formulas (6), (7a) to (7b), (8a) to (8b) are evaluated and determined by using the mean value Mean and covariance COV calculation. In this embodiment, the preset strength threshold is 500MPa, and the mean value and covariance of the ratio of the arc additive carbon steel material parameter prediction value to the test value are shown in Table 1.

[0112] Table 1

[0113]

[0114] like Fig.10 As shown in the figure, based on the collected literature data and experimental data, the strain hardening modulus E of arc-added carbon steel material is p , a scatter plot of the corresponding modulus hardening ratio k and the sampling direction θ is drawn, and for the two cases of smooth surface processing of the specimen and direct manufacturing of the specimen, a conservative value of the average modulus hardening ratio is taken for prediction calculation to obtain the prediction calculation formula (9). In this embodiment, the average values ​​of the modulus hardening ratio are 0.015 and 0.007, respectively, and the conservative value is 0.007.

[0115] like Fig.11 As shown in the figure, based on the collected literature data and experimental data, the third stage strain hardening modulus E of arc-added carbon steel material is sh , draw the corresponding (ff sh ) / (f u -f sh ) and (ε-ε sh ) / (ε u -ε sh ) and the prediction formula (10) was obtained through least squares (OLS) regression analysis.

[0116] (2) The yield strength is higher than the preset strength threshold (e.g. 500 MPa)

[0117] like Fig.12 , Fig.13As shown, according to the collected literature data and test data, for the first strain hardening index n and the second strain hardening index m of the arc-added carbon steel material, the corresponding scatter plots of n and θ, m and θ are drawn respectively, and the mean value Mean and covariance COV are calculated to evaluate and determine the accuracy and rationality of the parameter prediction calculation formulas (11) and (12). In this embodiment, the preset strength threshold is 500MPa, and the mean value and covariance of the ratio of the arc-added carbon steel material parameter prediction value to the test value are shown in Table 2 below.

[0118] Table 2

[0119]

[0120]

[0121] It should be noted that the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be described in detail in this application.

[0122] Embodiment 3:

[0123] Based on Examples 1 and 2, Example 3 of the present application provides an arc additive carbon steel material model correction and prediction evaluation system, including:

[0124] A correction module is used to obtain multiple sets of different arc additive carbon steel constitutive models and perform correction prediction; the multiple sets of different arc additive carbon steel constitutive models include an inclined yield model, a flat yield model and a two-stage RO model;

[0125] A prediction module, used to predict arc additive carbon steel parameters; the arc additive carbon steel material parameters include a first group of parameters and a second group of parameters, when the yield strength is lower than a preset strength threshold, the first group of parameters is predicted, otherwise the second group of parameters is predicted;

[0126] Evaluation module for experimental evaluation of arc additive manufacturing parameters for carbon steel.

[0127] Specifically, the system provided in this embodiment is a system corresponding to the method provided in Embodiments 1 and 2. Therefore, the parts in this embodiment that are the same or similar to Embodiments 1 and 2 can be referenced to each other and will not be repeated in this application.

[0128] In summary, the arc additive carbon steel material model correction and prediction evaluation method provided by the present invention realizes the quantitative evaluation of the surface geometric fluctuations, material anisotropy and inclined yield platform characteristics of carbon steel material specimens caused by the new arc additive manufacturing process in the constitutive model correction prediction; by grouping different yield strengths (lower than the preset strength threshold, higher than or equal to the preset strength threshold, the preset strength threshold range is 400-600MPa), sampling directions (θ=0°, θ≠0°) and surface conditions (direct manufacturing, surface smoothing processing), the reasonable correction and accurate prediction calculation of the grouped constitutive model relationship curve are realized; by effectively evaluating the experimental data of key material parameters, the accurate prediction calculation of key material parameters of arc additive carbon steel is realized; and the material parameter value and structural design application problems of arc additive carbon steel are effectively solved. And after actual verification, the method of the present invention is effective.

Claims

1. Arc additive carbon steel material model correction and prediction evaluation method, characterized in that: include: S1. Obtain multiple sets of different arc additive carbon steel constitutive models and make correction predictions; The multiple groups of different arc-added carbon steel constitutive models include an inclined yield model, a flat yield model and a two-stage RO model; S2. Predicting arc additive carbon steel parameters; the arc additive carbon steel material parameters include a first group of parameters and a second group of parameters. When the yield strength is lower than a preset strength threshold, the first group of parameters is predicted, otherwise the second group of parameters is predicted; S3. Experimental evaluation of arc additive manufacturing parameters for carbon steel.

2. The arc additive carbon steel material model correction and prediction evaluation method according to claim 1 is characterized in that: In S1, the yield strength of the inclined yield model is lower than the preset strength threshold and the direct manufacturing sampling direction is not 0°; the yield strength of the flat yield model is lower than the preset strength threshold and the direct manufacturing sampling direction is 0° or the surface is smoothed; the yield strength of the two-stage RO model is higher than or equal to the preset strength threshold.

3. The arc additive carbon steel material model correction and prediction evaluation method according to claim 2 is characterized in that: In S1, the inclined yield model is an inclined yield platform four-linear hardening model or an inclined yield platform bilinear plus nonlinear hardening model; the flat yield model is a flat yield platform four-linear hardening model or a flat yield platform bilinear plus nonlinear hardening model.

4. The arc additive carbon steel material model correction and prediction evaluation method according to claim 3 is characterized in that: In S2, the first group of parameters includes: strength-to-yield ratio f u / f y , ultimate strain ε u , strain hardening strain ε sh , strain hardening modulus E p and the third stage strain hardening modulus E sh ; The second set of parameters includes a first strain hardening exponent n and a second strain hardening exponent m.

5. The arc additive carbon steel material model correction and prediction evaluation method according to claim 4 is characterized in that: In S3, when the yield strength is lower than the preset strength threshold, corresponding scatter plots or curve plots are drawn for the first group of parameters, and the accuracy and rationality of the parameter prediction are evaluated and determined; When the yield strength is higher than or equal to the preset strength threshold, corresponding scatter plots are drawn for the second group of parameters, and the accuracy and rationality of the parameter prediction are evaluated and determined.

6. The arc additive carbon steel material model correction and prediction evaluation method according to claim 4 is characterized in that: In S1 to S3, the preset strength threshold is 400 to 600 MPa.

7. Arc additive carbon steel material model correction and prediction evaluation system, characterized in that: Used to perform the method according to any one of claims 1 to 6, comprising: A correction module is used to obtain multiple sets of different arc additive carbon steel constitutive models and perform correction prediction; the multiple sets of different arc additive carbon steel constitutive models include an inclined yield model, a flat yield model and a two-stage RO model; A prediction module, used to predict arc additive carbon steel parameters; the arc additive carbon steel material parameters include a first group of parameters and a second group of parameters, when the yield strength is lower than a preset strength threshold, the first group of parameters is predicted, otherwise the second group of parameters is predicted; Evaluation module for experimental evaluation of arc additive manufacturing parameters for carbon steel.

8. A computer storage medium, characterized in that: The computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any one of the methods described in claims 1 to 6.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 6.

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