Electric arc additive aluminum alloy material model correction and prediction evaluation method

Abstract

The invention relates to an electric arc additive aluminum alloy material model correction and prediction evaluation method. The method comprises the following steps of electric arc additive aluminum alloy constitutive correction prediction, electric arc additive aluminum alloy material parameter prediction and electric arc additive aluminum alloy parameter test evaluation. The method has the beneficial effects that quantitative evaluation of surface geometric fluctuation, material anisotropy, mixed manufacturing, printing direction and other influence factors of an aluminum alloy material test piece caused by a novel electric arc additive manufacturing process in constitutive model correction prediction is realized; reasonable correction and accurate prediction calculation of a two-stage R-O constitutive model relation curve are realized through two-stage distinguishing and circular response transition of a linear elastic stage and a nonlinear elastic-plastic stage; and through effective evaluation of test data of key material parameters, accurate prediction and calculation of the key material parameters of the arc additive aluminum alloy are realized.

Description

Technical Field

[0001] The present invention belongs to the technical fields of structural engineering and additive manufacturing, and particularly relates to a method for arc additive manufacturing aluminum alloy material model correction and prediction evaluation. Background Art

[0002] In the additive manufacturing industry, wire arc additive manufacturing (WAAM) combines traditional welding technology with advanced robotic technology to effectively achieve layer-by-layer stacking forming of complex metal structures. Due to its advantages such as low carbon environmental protection and small size limitations, this technology is particularly suitable for the manufacturing of large-sized and topologically optimized complex structural components in construction engineering, with high production speed and material utilization rate.

[0003] Aluminum alloy materials are widely used metal materials in the field of construction engineering, such as space lattice shells, door and window frames, and curtain wall brackets. The arc additive manufacturing process can cause characteristics such as surface geometric fluctuations, anisotropy of material strength and ductility in aluminum alloy materials. Different influencing factors such as wire raw materials, deposition strategies, additive materials, hybrid manufacturing, heat treatment, and printing directions may also have a certain impact on the mechanical properties of arc additive manufacturing aluminum alloy materials. The existing constitutive model design formulas for aluminum alloy materials manufactured by traditional processes such as forging and casting cannot consider these factors, and it is worth exploring whether they are applicable; thus, it is necessary to conduct relevant research and summary, and perform model correction and prediction improvement. During the process of studying the method for arc additive manufacturing aluminum alloy material model correction and prediction evaluation, there are three main problems. First, the method for arc additive manufacturing aluminum alloy constitutive correction prediction; second, the method for arc additive manufacturing aluminum alloy material parameter prediction; third, the method for arc additive manufacturing aluminum alloy parameter experimental evaluation.

[0004] In summary, it is very necessary to study a method for arc additive manufacturing aluminum alloy material model correction and prediction evaluation to realize rapid and accurate prediction calculation of the material parameters and constitutive model correction curve of arc additive manufacturing aluminum alloy while considering the quantitative evaluation of the influence of surface geometric fluctuations, material anisotropy, and other influencing factors such as hybrid manufacturing and printing direction caused by the arc additive manufacturing process. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for arc additive manufacturing aluminum alloy material model correction and prediction evaluation.

[0006] This method for arc additive manufacturing aluminum alloy material model correction and prediction evaluation includes the following steps:

[0007] S1. Arc additive manufacturing aluminum alloy constitutive correction prediction: Modify and predict the calculation of the two-stage R-O constitutive model relationship curve through the linear elastic stage and the nonlinear elastic-plastic stage;

[0008] S2. Prediction of arc additive manufacturing aluminum alloy material parameters: Predict the range values of the elastic modulus E and the second strain hardening index m, and predict the strength ratio f u / f y , ultimate strain ε u , and the calculation formulas of the first strain hardening index n;

[0009] S3. Experimental evaluation of arc additive manufacturing aluminum alloy parameters: According to the experimental data, for the elastic modulus E, draw a bar distribution diagram of the measured values and evaluate the accuracy of the predicted range values; for the tensile strength f u , ultimate strain ε u , the first strain hardening index n, and the second strain hardening index m, draw scatter relationship diagrams respectively, and evaluate the accuracy of the parameter prediction calculation formulas obtained by regression analysis.

[0010] Preferably, in step S1, the modified prediction calculation formula of the two-stage R-O constitutive model relationship curve of the arc additive manufacturing aluminum alloy material is:

[0011]

[0012] In the formula, f and ε are stress and strain respectively, E is the elastic modulus, f y is the yield strength; is the tangent modulus corresponding to 0.2% residual strain in the stress-strain curve, ε 0.2 is the 0.2% residual strain corresponding to the yield strength f y ; n is the first strain hardening index, m is the second strain hardening index; ε u is the ultimate strain, f u is the tensile strength corresponding to the ultimate strain.

[0013] Preferably, in step S2, the prediction calculation formula of the strength ratio f u / f y is a function related to the yield strength f y , and the prediction calculation formula of the ultimate strain ε u is a function related to f u / f y ; in step S3, collect literature data and experimental data, and draw the scatter relationship diagrams of f u / f y versus f y , ε u versus f y / f u , ε sh versus f y / f u respectively, calculate the average value and covariance, and perform the strength ratio f u / f y and the prediction calculation formula of the ultimate strain ε u for accuracy evaluation.

[0014] Preferably, in step S2, samples are taken in different directions on the arc additive manufactured aluminum alloy plate to evaluate the yield strength f y , tensile strength f u and the anisotropy of the ultimate strain ε u . The sampling directions of the specimens include θ = 0°, θ = 45° and θ = 90°, where θ is the angle between the length direction of the specimen and the printing direction.

[0015] Preferably, in step S3, specimens with θ = 0° and θ = 90° are taken to evaluate the influence of anisotropy on the yield strength f y , tensile strength f u and the ultimate strain ε u . After plotting the scatter diagrams of the test values of f y_90 / f y_0 , f u_90 / f u_0 and ε u_90 / ε u_0 , the average values of f y_90 / f y_0 , f u_90 / f u_0 and ε u_90 / ε u_0 are calculated respectively to evaluate the anisotropy of the yield strength f y , tensile strength f u and the ultimate strength ε u .

[0016] Preferably, in step S2, the calculation prediction formula of the first strain hardening index n is a function related to the stress intensity f y / f 0.04 corresponding to 0.04% residual strain; in step S3, literature data and test data are collected, and the scatter diagrams of the predicted value n pred of the first strain hardening index n and the test value m test of n, the test value m test of the second strain hardening index m and f y / f u are plotted respectively, and the average value and covariance are calculated to evaluate the prediction range value of the second strain hardening index m and the accuracy of the prediction calculation formula of the first strain hardening index n.

[0017] Preferably, in step S1, a circular response area is set between the linear elastic stage and the non-linear elastoplastic stage for the transition between the linear elastic stage and the non-linear elastoplastic stage.

[0018] The beneficial effects of the present invention are as follows:

[0019] 1) The method for correcting and predicting the evaluation of the arc additive manufacturing aluminum alloy material model provided by the present invention realizes the quantitative evaluation of the surface geometric fluctuations of aluminum alloy material specimens, material anisotropy, and other influencing factors such as hybrid manufacturing and printing direction in the constitutive model correction prediction caused by the new arc additive manufacturing process; through the two-stage distinction of the linear elastic stage and the non-linear elastoplastic stage and the circular response transition, the reasonable correction and accurate prediction calculation of the two-stage R-O constitutive model relationship curve are realized; through the effective evaluation of the test data of key material parameters, the accurate prediction calculation of the key material parameters of arc additive aluminum alloy is realized.

[0020] 2) The method for correcting and predicting the evaluation of the arc additive manufacturing aluminum alloy material model provided by the present invention realizes the accurate prediction of the key material parameters of aluminum alloy manufactured by the arc additive process and the stress-strain relationship curve through the effective evaluation of the test data of key material parameters, provides a theoretical support and design basis for the design and application of arc additive aluminum alloy structures, helps engineers and researchers to more deeply understand the mechanical behavior of aluminum alloy materials and structures manufactured by the new arc additive process, and effectively solves the problems of material parameter values and structural design applications of arc additive aluminum alloy. Description of the Drawings

[0021] Figure 1 is a schematic flow chart of the method for correcting and predicting the evaluation of the arc additive manufacturing aluminum alloy material model of the present invention;

[0022] Figure 2 is a schematic diagram of the stress-strain curve for correcting the two-stage R-O model;

[0023] Figure 3a is a schematic diagram of the parallel deposition scheme of the plate;

[0024] Figure 3b is a schematic diagram of the oscillating deposition scheme of the plate;

[0025] Figure 3c is a schematic diagram of the rotating deposition scheme of the specimen;

[0026] Figure 4 is a schematic diagram of the sampling direction of the specimen;

[0027] Figure 5 is a schematic diagram of the bar distribution of the measured values of the elastic modulus E;

[0028] Figure 6a is a schematic diagram of the scatter points and prediction evaluation curve of the yield strength ratio-yield strength test;

[0029] Figure 6b is a schematic diagram of the scatter points of the yield strength ratio-yield strength test under additional operations;

[0030] Figure 7a It is a schematic diagram of the scatter points and predicted evaluation curve of the ultimate strain - yield strength ratio test;

[0031] Figure 7b It is a schematic diagram of the scatter points of the test of the ratio of the ultimate strain under additional operation to that without additional operation;

[0032] Figure 8a It is a schematic diagram of the scatter points and average value curve of the ratio of the yield strength of the specimens with sampling directions of 90° and 0° - database number;

[0033] Figure 8b It is a schematic diagram of the scatter points and average value curve of the ratio of the tensile strength of the specimens with sampling directions of 90° and 0° - database number;

[0034] Figure 8c It is a schematic diagram of the scatter points and average value curve of the ratio of the ultimate strain of the specimens with sampling directions of 90° and 0° - database number;

[0035] Figure 9 It is a schematic diagram of the scatter points and predicted evaluation curve of the ratio of the predicted value to the test value of the first strain hardening index;

[0036] Figure 10 It is a schematic diagram of the scatter points and predicted evaluation curve of the test of the ratio of the test value of the second strain hardening index to the yield strength ratio. Detailed implementation mode

[0037] The following further describes the present invention 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 those of ordinary skill in the art, without departing from the principle of the present invention, several modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0038] Embodiment 1

[0039] Due to the process characteristics of arc additive manufacturing, compared with traditional aluminum alloy materials, there are two obvious differences in the constitutive model correction of arc additive aluminum alloy materials:

[0040] On the one hand, there are non - negligible geometric fluctuations on the surface of arc additive aluminum alloy structural parts, resulting in a reduction in effective mechanical properties, and due to different sampling directions, there is material anisotropy, specifically manifested as the isotropy of yield strength and tensile strength parameters, as shown in Figure 8a , Figure 8b , and the anisotropic ductility of the ultimate strain parameter, as shown in Figure 8c .

[0041] On the other hand, other influencing factors of arc additive aluminum alloy materials, including wire materials, deposition strategies, additive materials, hybrid manufacturing, heat treatment, and printing directions, will also have a certain impact on their mechanical properties, such as Figures 3a to 7b as shown.

[0042] As an embodiment, as Figure 1 shown, this method for modifying and predicting the evaluation of arc additive aluminum alloy material models includes the following steps:

[0043] S1. Constitutive modification prediction of arc additive aluminum alloy: The constitutive model modification prediction is set to modify the two-stage R-O model, including the linear elastic stage and the non-linear elastic-plastic stage, with a circular response area for transition between the two stages;

[0044] S2. Prediction of arc additive aluminum alloy material parameters: The prediction of material parameters is divided into two types: predicted range values and prediction calculation formulas. The elastic modulus E and the second strain hardening index m are set as predicted range values, and the strength ratio f u / f y , ultimate strain ε u and the first strain hardening index n are set as parameter prediction calculation formulas.

[0045] S3. Experimental evaluation of arc additive aluminum alloy parameters: According to the experimental data, for the elastic modulus E, a bar distribution diagram of measured values is drawn, and the accuracy of the predicted range value is evaluated by calculating the mean value Mean; for the tensile strength f u , ultimate strain ε u , strain hardening indices n and m, scatter relationship diagrams are respectively drawn, and the accuracy of the parameter prediction calculation formula obtained by regression analysis is evaluated by calculating the mean value Mean and covariance, where m corresponds to the predicted range value; the analysis of the tensile strength f u and the ultimate strain ε u is evaluated by distinguishing the sampling directions θ = 0° and θ = 90°.

[0046] Embodiment 2

[0047] As another embodiment, this Embodiment 2 is proposed on the basis of Embodiment 1, and a more specific method for modifying and predicting the evaluation of arc additive aluminum alloy material models:

[0048] In step S1, as Figure 2 shown, the stress-strain curve of the arc additive aluminum alloy material shows a continuous yield response, which is set as the modified two-stage R-O model, and the corresponding modified prediction calculation formula is as shown in formula (1);

[0049]

[0050] In the formula, f and ε are stress and strain respectively, E is the elastic modulus, fy is the yield strength; is the tangent modulus corresponding to the 0.2% residual strain in the stress-strain curve, ε 0.2 is the 0.2% residual strain corresponding to the yield strength f y ; n and m are the first and second strain hardening exponents respectively; ε u is the ultimate strain, f u is the tensile strength corresponding to the ultimate strain.

[0051] As Figures 3a to 3c shown, the deposition strategies of the arc additive manufactured aluminum alloy material specimens include parallel deposition, oscillatory deposition, and rotational deposition modes; as Figure 4 shown, the sampling directions of the specimens are divided into θ = 0°, θ = 45°, θ = 90°, etc.; the additional operations of the specimens include adding materials and hybrid manufacturing, aiming to achieve the synergistic improvement of the strength and plasticity mechanical properties of the aluminum alloy, where the added materials are divided into adding nanoparticles, powder particles, etc., and the hybrid manufacturing is divided into friction stir, interlayer rolling, interlayer hammering, vibration assistance, magnetic field assistance, laser assistance, and ultrasonic assistance, etc.

[0052] In step S2: The parameter prediction of the arc additive manufactured aluminum alloy material includes the predicted range values of the elastic modulus E, the second strain hardening exponent m, and the strength-to-yield ratio f u / f y , the ultimate strain ε u , and the prediction calculation formula of the first strain hardening exponent parameter n.

[0053] As Figure 5 shown, the predicted range value of the elastic modulus E is:

[0054] E = (65 - 77) GPa (2)

[0055] In the formula, the standard value of 70 GPa can be taken in actual application, and there is no obvious influence trend between different aluminum alloy wires and printing direction parameters and the elastic modulus E. In this embodiment, the E value of the arc additive manufactured aluminum alloy material obtained from the original data table of 30 samples of the literature is about 77 GPa, which is relatively close to 70 GPa of the traditional aluminum alloy in the European code EN1999-1-1; among the 420 measured E value samples obtained by expanding the literature and experiments, the average value extracted from the stress-strain curve is about 42 GP; therefore, the Figure 5 sample data with an E value lower than 40 GP is excluded to minimize potential measurement errors and variability from other sources; the average value of the measured E value data of the remaining 125 samples is about 65 GPa; therefore, the predicted range value is recommended to be (65 - 77) GPa.

[0056] As shown in FIGS. 6 and 8, the prediction calculation formula of the strength-to-yield ratio f u / f y is:

[0057] f u / f y = 1 + (120 / f y ) 1.5 (3)

[0058] In this embodiment, as Figure 6b shown, there is no obvious influence trend between hybrid manufacturing and the yield ratio f u / f y ; heat treatment has a certain effect of reducing the yield ratio; the yield strength and tensile strength of the specimens with sampling directions θ = 0° and θ = 90° are not much different, that is, it shows isotropic strength, as Figure 8a , Figure 8b shown;

[0059] As shown in FIGS. 7 and 8, the prediction calculation formula for the ultimate strain ε u is:

[0060]

[0061] In the formula, the ultimate strain of the specimen with the sampling direction θ = 0° is larger than that with θ = 90°, showing anisotropic ductility, as Figure 8c shown; since applying the reduction coefficient in the prediction expression of the specimen with the sampling direction θ = 90° does not significantly improve the accuracy of the prediction calculation formula, this indicates the sensitivity of the change of the ultimate strain, so a unified prediction calculation formula for the ultimate strain is still adopted.

[0062] As Figure 9 shown, the prediction calculation formula for the first strain hardening index n is:

[0063]

[0064] In the formula, f 0.04 is the stress intensity corresponding to a 0.04% residual strain.

[0065] As Figure 10 shown, the predicted range value of the second strain hardening index m is:

[0066] m = (1.4 - 4.0) (6)

[0067] In the formula, when actually applied, the average value 2.8 can be taken, corresponding to high prediction accuracy and small covariance.

[0068] It should be noted that the parts that are the same or similar to those in Embodiment 1 in this embodiment can be referred to each other and will not be elaborated in this application.

[0069] Embodiment 3

[0070] As another embodiment, this third embodiment is proposed based on the first and second embodiments, and a more specific method for correcting and predicting the evaluation of an arc additive aluminum alloy material model. In step S3:

[0071] As Figure 5 shown, according to the collected literature data and test data, for the elastic modulus E of the arc additive aluminum alloy material, a bar chart of measured values is plotted, and the accuracy of the predicted range formula (2) is evaluated using the mean value Mean;

[0072] As Figure 6a 、 Figure 6b 、 Figure 7a and Figure 7b shown, according to the collected literature data and test data, for the tensile strength f u 、ultimate strain ε u , scatter plots of f u / f y versus f y 、ε u versus f y / f u are respectively plotted, and the accuracy and rationality of the parameter prediction calculation formulas (3) and (4) are evaluated and determined using the mean value Mean and covariance. At the same time, the ratio distribution of ε u_附 under different additional operations and ε u_无 without additional operations is detected. In this embodiment, the mean value and covariance of the ratio of the predicted value to the test value of the arc additive aluminum alloy material parameters are shown in the following table.

[0073] Predicted value / Experimental value <![CDATA[f u,pred / f u,test > <![CDATA[ε u,pred / ε u,test > Average value 0.998 0.996 Covariance 0.177 0.803

[0074] As Figures 8a to 8c shown, as for the evaluation of the anisotropy effect, for the yield strength f y 、tensile strength f u 、ultimate strength ε u , scatter plots of f y_90 / f y_0 、f u_90 / f u_0 、ε u_90 / ε u_0 versus the database number are respectively plotted, and the evaluation is determined using the mean value Mean calculation; in this embodiment, the mean values of the yield strength ratio, tensile strength ratio, and ultimate strain ratio of the arc additive aluminum alloy material specimens with sampling directions θ = 0° and θ = 90° are shown in the following table. It can be seen that f y and f u change less with the specimen angle, showing isotropic strength, while ε uIt varies greatly with the angle of the specimen, showing anisotropic ductility.

[0075]

[0076] As Figure 9 , Figure 10 shown, according to the collected literature data and experimental data, for the first strain hardening index n and the second strain hardening index m of the arc additive manufacturing aluminum alloy material, the corresponding predicted values n pred and the experimental values n test , the experimental value m test and f y / f u are plotted as scatter diagrams of the relationship, and the accuracy and rationality of the parameter prediction calculation formula (5) and the prediction range value formula (6) are evaluated and determined by using the mean value Mean and covariance calculation. In this embodiment, the first strain hardening index n is obtained by using formula (5), and the mean value and covariance of the ratio of the predicted value and the experimental value of the second strain hardening index m = 2.8 obtained by using formula (6) are shown in the following table.

[0077] Predicted value / Experimental value <![CDATA[n u,pred / n u,test > <![CDATA[m u,pred / m u,test > Average value 1.003 0.998 Covariance 0.274 0.272

[0078] It can be seen from Embodiment 1 and Embodiment 2 that the arc additive manufacturing aluminum alloy material model correction and prediction evaluation method provided by the present invention realizes the quantitative evaluation of the surface geometric fluctuation, material anisotropy, and other influencing factors such as hybrid manufacturing and printing direction of the aluminum alloy material specimen in the constitutive model correction prediction; through the two-stage distinction of the linear elastic stage and the nonlinear elastic-plastic stage and the circular response transition, the reasonable correction and accurate prediction calculation of the two-stage R-O constitutive model relationship curve are realized; through the effective evaluation of the experimental data of the key material parameters, the accurate prediction calculation of the key material parameters of the arc additive manufacturing aluminum alloy is realized; the problems of material parameter value selection and structural design application of the arc additive manufacturing aluminum alloy are effectively solved. And through actual verification, the method of the present invention is effective.

[0079] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other.

Claims

1. A method for correcting and predicting the material model of arc additive aluminum alloy, characterized in that: The following steps are involved: S1. Prediction and correction of arc additive aluminum alloy constitutive model: correction and prediction of the relationship curve of the two-stage RO constitutive model are performed through the linear elastic stage and the nonlinear elastic-plastic stage; S2. Arc additive aluminum alloy material parameter prediction: predict the range of elastic modulus E and second strain hardening exponent m, predict strength-to-yield ratio f u / f y , ultimate strain ε u and the calculation formula of the first strain hardening exponent n; S3. Arc additive aluminum alloy parameter test evaluation: Based on the test data, draw a bar graph of the measured values ​​for the elastic modulus E, and evaluate the accuracy of the predicted range value; for the tensile strength f u , ultimate strain ε u , the first strain hardening exponent n and the second strain hardening exponent m, and draw scatter plots respectively, and evaluate the accuracy of the parameter prediction formula obtained by regression analysis.

2. The arc additive aluminum alloy material model correction and prediction evaluation method according to claim 1 is characterized in that: In step S1, the modified prediction calculation formula of the two-stage RO constitutive model relationship curve of the arc additive aluminum alloy material is: Where f and ε are stress and strain respectively, E is the elastic modulus, and f y is the yield strength; 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 is the first strain hardening exponent, m is the second strain hardening exponent; ε u is the ultimate strain, f u is the tensile strength corresponding to the ultimate strain.

3. The arc additive aluminum alloy material model correction and prediction evaluation method according to claim 1 is characterized in that: In step S2, the strength-to-yield ratio f u / f y The prediction formula is the same as the yield strength f y The function of the limit strain ε u The prediction formula is as follows: u / f y In step S3, literature data and experimental data are collected and f u / f y With f y , ε u With f y / f u , ε sh With f y / f u The scatter plot of the test values ​​is plotted, and the mean and covariance are calculated to perform a strong yield ratio f u / f y and the ultimate strain ε u Evaluation of the accuracy of the prediction calculation.

4. The arc additive aluminum alloy material model correction and prediction evaluation method according to claim 1 is characterized in that: In step S2, sampling is performed in different directions on the aluminum alloy plate by arc additive to evaluate the yield strength f y , tensile strength f u and the ultimate strain ε u In the anisotropic case, the sampling directions of the specimen include θ=0°, θ=45° and θ=90°, where θ is the angle between the length direction of the specimen and the printing direction.

5. The arc additive aluminum alloy material model correction and prediction evaluation method according to claim 4 is characterized in that: In step S3, the specimens with θ = 0° and θ = 90° are taken for anisotropic analysis of the yield strength f y , tensile strength f u and the ultimate strain ε u The impact assessment is to plot f y_90 / f y_0 、f u_90 / f u_0 and ε u_90 / ε u_0 After the scatter plot of the test values, calculate f y_90 / f y_0 、f u_90 / f u_0 and ε u_90 / ε u_0 The average value of the yield strength f y , tensile strength f u and ultimate strength ε u anisotropy.

6. The arc additive aluminum alloy material model correction and prediction evaluation method according to claim 1, characterized in that: In step S2, the calculation prediction formula of the first strain hardening exponent n is the stress intensity f corresponding to the residual strain of 0.04% y / f 0.04 In step S3, literature data and experimental data are collected, and the corresponding predicted values ​​of the first strain hardening exponent n are plotted. pred The experimental value of n test , the experimental value of the second strain hardening exponent m test With f y / f u The scatter plot is drawn, and the mean value and covariance are calculated to evaluate the accuracy of the prediction range value of the second strain hardening exponent m and the prediction calculation formula of the first strain hardening exponent n.

7. The arc additive aluminum alloy material model correction and prediction evaluation method according to claim 1, characterized in that: In step S1, a circular response area is set between the linear elastic stage and the nonlinear elastic-plastic stage for transition between the linear elastic stage and the nonlinear elastic-plastic stage.

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