A method for predicting porosity defects in additively manufactured parts considering melt pool offset

Abstract

The application is suitable for the field of metal additive manufacturing, and provides a porosity defect prediction method for additive manufacturing of a formed part considering molten pool offset. The method can comprehensively consider the influence of laser power, scanning speed, scanning spacing, powder laying thickness and interlayer rotation angle on porosity defects, can quickly, intuitively and accurately predict the possible porosity defects in a sample under given process parameters, and can quickly predict the forming process scheme for preparing a high-density sample. The key technology of the application is that the influence of molten pool offset on porosity defects in the forming process is considered, the generation of porosity defects caused by insufficient molten pool overlap can be effectively predicted, the accuracy of density prediction is improved, and the repeatability and stability of the additive manufacturing sample are enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of metal additive manufacturing, and particularly relates to a method for predicting porosity defects of an additive manufactured part considering melt pool offset. BACKGROUND

[0002] As a common metal additive manufacturing process, laser powder bed fusion (LPBF) can manufacture products with complex geometries; however, the complex layer-by-layer manufacturing process has many uncertainties, resulting in poor quality stability of LPBF manufactured parts, which poses a challenge to the repeated manufacturing of high-quality products in large-scale production and limits the wide application of the technology in industry; among them, porosity defects are the main reason for poor repeatability and stability of the sample.

[0003] Currently, researchers mainly use the size of the molten pool to judge the possible defects in the material, such as using the ratio of molten pool depth to powder laying thickness to judge the unmelted defects caused by spheroidization and discontinuity, and using the ratio of molten pool depth to molten pool width to judge the porosity defects caused by keyhole effect.

[0004] The above method predicts possible porosity defects based on the size of the molten pool, which has certain practical value; however, in the LPBF forming process, the sample is stacked by the molten pool to form a dense part, which is not only related to the size of the molten pool, but also closely related to the position of the molten pool; in fact, the melt pool will shift left and right along the laser scanning direction in the LPBF forming process, as shown in FIG. 1, which will lead to poor metallurgical bonding between the melt pools and will inevitably introduce additional porosity defects in the formed sample. Figure 1

[0005] It should be noted that the melt pool refers to the area formed after laser scanning, and the cross section of the melt pool is called the molten pool; such offset of the melt pool is affected by many factors, such as laser power, scanning speed and powder distribution; by increasing the heat input, the melt pool offset can be alleviated to some extent, but due to the random distribution of powder particles, the melt pool offset cannot be completely eliminated, which will inevitably affect the accuracy of the existing porosity prediction model; how to establish a fast and accurate porosity defect prediction model considering the melt pool offset is a key problem that needs to be solved to improve the stability of the LPBF prepared sample. SUMMARY

[0006] In view of the above shortcomings of the prior art, the purpose of the present application is to provide a method for predicting porosity defects of an additive manufactured part considering melt pool offset, which establishes a prediction model considering the influence of melt pool offset on porosity defects, and improves the prediction accuracy.

[0007] To achieve the above purpose, the present application adopts the following technical route:

[0008] ​Step 1: Calculation of the size of the molten pool; analytical calculation is used to quickly predict the width and depth of the molten pool under different laser power and scanning speed, where the laser value ranges from 100 to 400 W, the scanning speed value ranges from 200 to 1200 mm / s, and the laser line energy density is between 0.2 and 2 J / mm; under this process condition, it is ensured that there are no spheroidization and discontinuity in the molten channel, and no keyhole porosity defects, at this time the molten pool is a typical semi-elliptical shape, and the width and depth of the molten pool are calculated by the following formula:

[0009]

[0010] In the formula: a is the width of the molten channel, a is the laser absorption rate; P is the laser power, p s is the material density, c s and c l are the specific heat capacity of the material in solid and liquid states, T0 is the ambient temperature, T m is the metal melting temperature, v is the scanning speed, u is the laser diameter, K is the thermal conductivity, K is the temperature gradient on the solid-liquid interface, h is the convective heat transfer coefficient, e is the material emissivity, s b is the Boltzmann constant, and L is the latent heat of fusion.

[0011] Step 2: Calculation of the size of the molten channel cross section; during LPBF forming, the laser inputs energy into the molten pool, causing the powder particles to melt and solidify, forming a new forming area; at the same time, this process will also cause the remelting of the previously solidified material. During the solidification stage, the newly deposited material will form a "small hat" shape on the previously deposited material under the action of surface tension. Based on the observation of the morphology characteristics of the molten channel, it is assumed that the remelted area and the forming area during the forming process both present a semi-elliptical shape, where the horizontal major axis length of the remelted area ellipse is the width of the molten channel a, and the vertical major axis length d is the depth of the molten pool. The vertical major axis length of the forming area ellipse is the same as the remelted area, which is a, and the vertical major axis length is the molten channel residual height d t ; considering the influence of molten channel overlap on the molten channel residual height when multiple channels are formed, it is assumed that after the powder is melted, it is only in this area that the powder is deposited within a single scanning pitch; based on this assumption, and under the condition of ignoring the loss of material evaporation, the molten channel residual height d t is obtained by the volume invariance condition before and after forming, which is described by the following formula:

[0012]

[0013] Where, p p is the bulk density of the powder, p s is the material density, t is the powder thickness, h1 is the scanning pitch, and a is the width of the molten channel.

[0014] Step 3: Prediction model of porosity defects; a calculation model for simulating the stack of the melt pool in the LPBF forming process, i.e. the porosity defect prediction model, is developed based on the Python language; by inputting the melt pool width a, height d and excess height d t , and the scanning interval h, powder laying thickness t and interlayer rotation angle parameters, the model can realize the stack of the melt pool along the building direction layer by layer, and can predict the possible porosity defects in the forming process; in the simulation prediction, the size of the stacking area is set to 1000*800 mu m, and the stacking process is carried out in the order from left to right and from bottom to top; in order to consider the influence of melt pool offset on porosity defects, based on the theoretical position of each melt pool, random offset is carried out in the width direction; the offset is randomly determined according to the Gaussian distribution, wherein the central position of x value is randomly generated in the range of 0-3, and the concentration degree is randomly determined in the interval of 4-6;

[0015] Step 4: Prediction of density; the porosity of the sample δ can be obtained from the ratio of the number of white pixel points to the total pixels in the model prediction result; considering the influence of possible slag and impurities in the material, it is assumed that the density of the sample without porosity defects is 99%, and the density of the porosity area filled with powder in the sample with porosity defects is the ratio of the bulk density of the powder to the theoretical density of the material; based on these assumptions, the model predicts the density of the sample ρ 预测 , which is calculated by the following formula:

[0016]

[0017] , wherein: ρ p is the bulk density of the powder, ρ s is the material density, and δ is the porosity.

[0018] The technical effects of the present application relative to the prior art are:

[0019] The prediction model established in the present application considers the influence of melt pool offset on porosity defects, and improves the prediction accuracy; at the same time, the model can also consider the influence of laser power, scanning speed, scanning interval, powder laying thickness and interlayer rotation angle on porosity defects; therefore, the present application can effectively predict the unpredictable porosity defects caused by melt pool offset under the condition of high-quality melt pool forming, improve the prediction accuracy of sample density, improve the repeatability and stability of the additive manufacturing sample, and promote the popularization and application of additive manufacturing technology. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a melt pool offset diagram;

[0021] Figure 2 is a schematic diagram of the cross-sectional size of the melt pool;

[0022] Figure 3is the prediction result of the model for the porosity defects of the example 10 hole;

[0023] Figure 3 (a) without considering the offset of the melt pool, (b) with random offset of the melt pool, and (c) actual test result;

[0024] Figure 4 is the prediction result of the model for the density of each example. DETAILED DESCRIPTION

[0025] In order to make the content described in the present application easier to understand, the technical solutions described in the present application are further described below in conjunction with specific embodiments, but the scope of the present application is not limited.

[0026] One scheme of the present application is:

[0027] A prediction method for porosity defects of a formed part by additive manufacturing considering offset of a melt pool comprises the following steps:

[0028] Step 1: Calculation of the cross-sectional size of the melt pool; the width a and the depth d of the melt pool are calculated by using an analytical method, the remaining height dt of the melt pool is calculated according to the condition that the volume before and after forming is unchanged, and the cross-sectional size information of the formed melt pool under different laser powers and scanning speeds is obtained;

[0029] Step 2: Based on the stacking principle of the melt pool, a porosity defect prediction model is established considering the offset law of the melt pool; by inputting the laser power, scanning speed, scanning spacing, powder laying thickness and interlayer rotation angle into the prediction model, the possible porosity defects in the test sample are predicted by the model, and the density is calculated;

[0030] In the step 1, by matching the laser power and the scanning speed, the laser line energy density is between 0.2 J / mm-2 J / mm, so as to ensure that the formed melt pool does not have spheroidization and discontinuity, spoon hole porosity defects;

[0031] In the step 1, the width a and the depth d of the melt pool are calculated according to the following formulas respectively:

[0032]

[0033] In the formula, a is the width of the melt pool, a is the laser absorption rate; P is the laser power, p s is the material density, c s and c l are the specific heat capacities of the material in solid and liquid states, T0 is the ambient temperature, T m is the metal melting temperature, v is the scanning speed, u is the laser diameter, K is the thermal conductivity, K is the temperature gradient on the solid-liquid interface, h is the convective heat transfer coefficient, e is the material emissivity, s b is the Boltzmann constant, and L is the latent heat of melting.

[0034] The overhang of the melt pool dt is calculated as follows:

[0035]

[0036] Wherein, ρ p is the bulk density of the powder, ρ s is the material density, t is the powder thickness, h1 is the scanning interval, and a is the melt pool width;

[0037] When stacking, the melt pool is randomly offset in the width direction at its theoretical position, and the offset is randomly determined according to a Gaussian distribution, wherein the central position of the value is randomly generated in the range of 0-3, and the concentration is randomly determined in the interval of 4-6;

[0038] When calculating the density, it is assumed that the density of a sample without porosity defects is 99%, and the density of a sample with porosity defects is the ratio of the bulk density of the powder to the theoretical density of the material. The model calculates the density of the test sample ρ 预测 According to the following formula:

[0039]

[0040] Wherein: ρ p is the bulk density of the powder, ρ s is the material density, and δ is the porosity.

[0041] A more specific scheme of the present application is:

[0042] A method for predicting porosity defects of an additive manufacturing part considering melt pool offset,

[0043] Step 1: Calculation of the size of the melt pool; analytical calculation is used to quickly predict the width and depth of the melt pool under different laser power and scanning speed, wherein the laser value range is 100-400W, the scanning speed value range is 200-1200mm / s, and the laser line energy density is between 0.2-2J / mm; under this process condition, it is ensured that there is no spheroidization and discontinuity in the melt pool, and the keyhole porosity defect, at this time the melt pool is a typical semi-elliptical appearance, and the width and depth of the melt pool are calculated by the following formula:

[0044]

[0045] In the formula: a is the melt pool width, α is the laser absorption rate; P is the laser power, ρ s is the material density, c s and c l are the specific heat capacity of the material in solid and liquid states, T0 is the ambient temperature, and T mTm is the metal melting temperature, v is the scanning speed, u is the laser diameter, k is the thermal conductivity, K is the temperature gradient on the solid-liquid interface, h is the convective heat transfer coefficient, e is the material emissivity, s b is the Boltzmann constant, and L is the latent heat of fusion.

[0046] Step 2: Calculation of the cross-sectional size of the melt pool; during LPBF forming, the laser inputs energy into the melt pool, causing the powder particles to melt and solidify, forming a new forming area; at the same time, this process will also cause the remelting of the solidified material of the previous layer; during the solidification stage, the newly deposited material will form a "small hat" shape on the previously deposited material under the action of surface tension; based on the observation of the melt pool morphology, it is assumed that the remelted area and the forming area during the forming process are both in the shape of a semi-ellipse, where the horizontal major axis length of the remelted area ellipse is the melt pool width a, and the vertical major axis length d is the melt pool depth; the horizontal major axis length of the forming area ellipse is the same as that of the remelted area, which is a, and the vertical major axis length is the melt pool excess height d t ; considering the influence of melt pool overlap on the melt pool excess height during multi-track forming, it is assumed that after the powder is melted, it is only deposited in this area within a single scanning pitch; based on this assumption and under the condition of ignoring the loss of material evaporation, the melt pool excess height d t is obtained by the volume invariance condition before and after forming, which is described by the following formula:

[0047]

[0048] where p p is the loose bulk density of the powder, p s is the material density, t is the powder laying thickness, h1 is the scanning pitch, and a is the melt pool width.

[0049] Step 3: Prediction model of porosity defects; a calculation model for simulating the stacking of melt pools during LPBF (laser powder bed fusion technology) forming, i.e., the porosity defect prediction model, was developed based on Python language; by inputting the melt pool width a, height d, and excess height d t , scanning pitch h, powder laying thickness t, and layer rotation angle parameters, the model can realize the stacking of melt pools layer by layer along the building direction, and can predict the possible porosity defects during the forming process; during simulation and prediction, the stacking area size is set to 1000x800 pm, and the stacking process is carried out in the order from left to right and from bottom to top; in order to consider the influence of melt pool deviation on porosity defects, a random deviation is made in the width direction based on the theoretical position of each melt pool; the deviation is randomly determined according to the Gaussian distribution, where the central position of x value is randomly generated in the range of 0-3, and the concentration degree is randomly determined in the interval of 4-6.

[0050] Step 4: Prediction of the density; the porosity δ of the sample can be obtained from the ratio of the number of white pixels in the model prediction result to the total pixels; considering the influence of possible slag and impurities in the material, it is assumed that the density of the sample without porosity defects is 99%, and the density of the porosity area filled with powder in the sample with porosity defects is the ratio of the bulk density of the powder to the theoretical density of the material; based on these assumptions, the model predicts the density ρ of the sample 预测 is calculated by the following formula:

[0051]

[0052] wherein: ρ p is the bulk density of the powder, ρ s is the density of the material, and δ is the porosity.

[0053] That is, the present application considers the prediction method of the porosity defects of the additive manufacturing part formed by the melt pool offset, which is carried out according to the following steps:

[0054] Step 1: Calculation of the cross-sectional size of the melt pool; the width and depth of the melt pool cross section are calculated according to formulas (1) and (2); the melt pool excess height d t is calculated according to formula (3), and the cross-sectional size of the melt pool under different laser powers and scanning speeds is completely determined.

[0055] Step 2: Prediction of the porosity defects and calculation of the density; the established porosity defect prediction model is used to predict the morphology of the porosity defects under different laser powers and scanning speeds, and the density is calculated according to formula (4); in order to improve the accuracy of the model prediction, 10 melt pool stacking pictures are randomly generated to calculate the density and replace the average value during prediction.

[0056] Example: Taking 2205 duplex stainless steel as the material, the prediction ability of the model for the porosity defects and the density of the sample under different laser energy densities and scanning speeds is explored, the process parameters for prediction are shown in Table 1, and the powder thickness is 30 μm during forming, and the scanning strategy is 67° rotation per layer; as shown in Table 1, the laser energy density covers a wide range, which is 83.33-312.50 J / mm 3 .

[0057] Table 1 Test process parameters

[0058]

[0059] The prediction results of the model for the porosity defects of Example 10 are as follows Figure 3As shown in the figure, the prediction results of regular arrangement of the sprue under the same process parameters and the real test measurement results are compared; it is shown in the figure that no obvious porosity defects are observed in the prediction results when the sprue is regularly arranged; in contrast, under the condition of considering the random offset of the sprue, the prediction results show that additional porosity defects are generated in the sample, and these defects are mainly concentrated in the overlapping area of the molten pool, which is consistent with the distribution position of the porosity defects in the actual forming sample, indicating that the model can better predict the unfused defects caused by the insufficient overlap of the sprue.

[0060] The prediction results of the model on the density of each embodiment are as shown in the figure Figure 4 As shown in the figure, compared with the model prediction results under the condition of regular arrangement of the sprue, the model prediction results are closer to the test measurement results after introducing the offset of the sprue, which further proves that the established porosity defect prediction model has high prediction accuracy.

[0061] The above only describes the preferred embodiments of the present application, and any changes and modifications made within the scope of the patent application of the present application shall be included in the scope of the present application.

Claims

1. A method for predicting porosity defects in additively manufactured parts considering melt channel offset, characterized in that, It includes the following steps: Step 1: Calculation of the cross-sectional dimensions of the melt channel; the width a and depth d of the melt channel are calculated by an analytical method, and the remaining height dt of the melt channel is calculated based on the condition of constant volume before and after forming, so as to obtain the cross-sectional dimension information of the formed melt channel under different laser powers and scanning speeds; Step 2: Based on the melt channel stacking principle and considering the melt channel offset law, a pore defect prediction model is established; by inputting the laser power, scanning speed, scanning spacing, powder spreading thickness and interlayer rotation angle into the prediction model, the possible pore defects in the specimen are predicted by the prediction model, and its density is calculated; In the said Step 1, the width a and depth d of the melt channel are calculated respectively according to the following formulas: In the formula: a is the weld channel width, α is the laser absorptivity; P is the laser power, ρ s For the material density, c s , is a solid material, c l Specific heat capacity in liquid state, T0 is ambient temperature, T m σ is the metal melting temperature, v is the scanning speed, u is the laser diameter, κ is the thermal conductivity, K is the temperature gradient at the solid-liquid interface, h is the convective heat transfer coefficient, ε is the material emissivity, and σ is the thermal conductivity. b Where is Boltzmann's constant, and L is the latent heat of fusion; The remaining height dt of the melt channel is calculated according to the following formula: Where, ρ p ρ is the loose packing density of the powder. s t is the material density, h1 is the powder thickness, a is the scanning interval, and a is the melt channel width.

2. The method for predicting porosity defects in additively manufactured parts considering melt channel offset as described in claim 1, characterized in that, In the said Step 1, by matching the laser power and scanning speed, the laser line energy density is between 0.2 J / mm and 2 J / mm, ensuring that there are no balling, discontinuity and keyhole porosity defects in the formed melt channel.

3. A method for predicting porosity defects in additively manufactured parts considering melt channel offset, as described in claim 1 or 2, characterized in that... During stacking, the melt channel randomly offsets in the width direction at its theoretical position, and the offset amount is randomly determined according to the Gaussian distribution, where the central position of the value is randomly generated within the range of 0 - 3, and the concentration degree is randomly determined within the interval of 4 - 6.

4. The method for predicting porosity defects in additively manufactured parts considering melt channel offset as described in claim 3, characterized in that, In density calculations, it is assumed that the density of a sample without pore defects is 99%. However, in samples with pore defects, the density of the powder-filled pore regions is the ratio of the powder's loose bulk density to the material's theoretical density. The model predicts the sample's density ρ. 预测 Calculated by the following formula: Where: ρ p ρ is the loose packing density of the powder. s δ represents the material density, and δ represents the porosity.

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