Process window determination method for reducing defects of selective laser melting formed iron-gallium alloy and iron-gallium alloy sample

Abstract

The invention provides a process window determination method for reducing defects of selective laser melting formed iron-gallium alloy and an iron-gallium alloy sample. The process window determination method comprises the following steps: selecting a primary process window of main process parameters; generating specific experimental parameters according to the main process parameters and other process parameters, and generating an iron-gallium alloy sample according to the specific experimental parameters; counting the porosity and the crack density of the iron-gallium alloy sample; and under the condition that other process parameters are the same, establishing an interaction relationship between the main process parameters and the defects by adopting a response surface method, establishing a defect prediction model, and determining a final process window of the main process parameters according to a prediction result. According to the method, the efficiency of exploring the process window in the selective laser melting process is effectively improved, the manufacturing cost is greatly reduced, and reference is provided for selection of part processes.

Description

Technical Field

[0001] The present invention relates to the field of metal additive manufacturing, and in particular to a process window determination method for reducing defects in iron-gallium alloy formed by selective laser melting, and an iron-gallium alloy sample. Background Art

[0002] Compared with magnetostrictive materials with rare earth elements added, iron-gallium alloy has a higher Curie temperature (750°C). <100> The magnetostriction coefficient is the largest in the direction, and a higher magnetostriction (400ppm) can be obtained without adding other elements. However, the traditional processing method has high processing costs and processing limitations, which restricts the application of magnetostrictive materials of Fe-Ga alloys. Due to its high temperature gradient and rapid solidification characteristics, SLM can retain the metastable phase precipitated at high temperature after rapid solidification, and the magnetostrictive properties of Fe-Ga alloys are mainly determined by the disordered A2 phase precipitated at high temperature and the M-D03 nanoprecipitate phase precipitated on the A2 phase. At the same time, SLM technology has the advantage of strong anisotropic molding, so there is great potential for forming Fe-Ga alloys through SLM. However, during the printing process, inappropriate process parameters will cause the molded parts to have a higher porosity and crack density, which will affect the performance of the material, and the printing process windows corresponding to different materials are different, so it is necessary to explore the appropriate range of process parameters to reduce the defects generated during the printing process. This helps to broaden the application range of Fe-Ga alloys, and clarifying the process window has great scientific research value. Summary of the invention

[0003] The purpose of the present invention is to provide a method for determining a process window for reducing defects in Fe-Gallium alloy formed by selective laser melting and an Fe-Gallium alloy sample. Based on the response surface method, the interaction relationship between laser power, scanning speed and scanning spacing and defects can be understood through a minimum number of experiments, and then a sample with the least crack density and porosity can be obtained through a prediction model, which effectively improves the efficiency of exploring the process window in the process of selective laser melting, greatly reduces the manufacturing cost, and provides a reference for the selection of part forming process.

[0004] To achieve the above object, the present invention provides a method for determining a process window for reducing defects in iron-gallium alloy formed by selective laser melting, comprising the following steps:

[0005] Step 1: Select the preliminary process window of main process parameters, including laser power, scanning speed, and scanning spacing;

[0006] Step 2: Generate specific experimental parameters according to the main process parameters and other process parameters, and generate an iron-gallium alloy sample according to the specific experimental parameters;

[0007] Step 3, counting the porosity and crack density of the Fe-Ga alloy sample;

[0008] Step 4: When other process parameters are the same, the response surface method is used to establish the interaction relationship between the main process parameters and defects, and a defect prediction model is established to predict the porosity and crack density of the material. The final process window of the main process parameters is determined according to the prediction results. The final window is a laser power of 100-110 W, a scanning speed of 1100-1300 mm / s, and a scanning spacing of 60-70 μm.

[0009] Preferably, other process parameters include

[0010] The forming process adopts zigzag scanning with an interlayer rotation angle of 90°, a powder thickness of 30-40μm, a spot diameter of 50-70μm, and a substrate preheating temperature of 100-200℃.

[0011] Preferably, the iron-gallium alloy composition is Fe 1-x Ga x , where x represents the atomic percentage of Ga, x = 15-30at.%.

[0012] Preferably, the generated iron-gallium alloy sample needs to be annealed at 100-300° C. for 5 hours after printing to complete stress relief annealing.

[0013] Preferably, the porosity and crack density of the Fe-Ga alloy sample are statistically analyzed, including

[0014] The sample was ground and polished, and five locations were randomly selected to take OM pictures. Then, ImageJ was used to measure the porosity and crack density of the sample along the construction direction to obtain the sample samples required for the response surface methodology.

[0015] Preferably, establishing a defect prediction model includes

[0016] Crack prediction model:

[0017] Cracks=13.287-0.216094*P-0.00830129*S+107.759*H-6.9e-06*P*S-0.3 56*P*H-0.0152357*S*H+0.00114436*P^2+3.96161e-06*S^2-332.357*H^2;

[0018] Porosity prediction model:

[0019] Pores=36.8782-0.195187*P-0.0292048*S-305.905*H-9.25e-09*P*S-0.8 58*P*H+0.166809*S*H+0.00119187*P^2+7.99512e-06*S^2+1621.86*H^2;

[0020] Where P represents laser power, S represents scanning speed, and H represents scanning distance.

[0021] A printed iron-gallium alloy sample is prepared by using a process window obtained by a process window determination method for reducing defects in selective laser melting of an iron-gallium alloy.

[0022] Therefore, the present invention adopts the above-mentioned method for determining the process window for reducing defects in selective laser melting of Fe-Gallium alloy and the Fe-Gallium alloy sample, determines the Fe-Gallium alloy process window through the response surface method, and obtains the sample with the least defects. A small number of experiments greatly improves the efficiency of process exploration and greatly reduces the manufacturing cost, laying a reliable foundation for the performance research of additively manufactured Fe-Gallium alloy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a method for optimizing process parameters of iron-gallium alloy selective laser melting provided by an embodiment of the present invention;

[0024] Figure 2 It is the normal distribution diagram of crack density;

[0025] Figure 3 It is the normal distribution diagram of porosity;

[0026] Figure 4 It is the corresponding relationship diagram between the residual of crack density and the predicted value of the equation;

[0027] Figure 5 It is the corresponding relationship diagram of the residual of porosity and the predicted value of the equation;

[0028] Figure 6 This is the corresponding relationship diagram between the predicted value of crack density and the actual test value;

[0029] Figure 7 This is the corresponding relationship diagram between the predicted value of porosity and the actual value of the test;

[0030] Figure 8 OM diagram of the optimal process parameters predicted by the model;

[0031] Fig. 9This is a defect comparison chart between those within the set range and those outside the set range, where No. 1 to 5 are the first five solutions of the model, and No. 6 to 10 are five samples selected in the running sequence that are not within the set range. DETAILED DESCRIPTION

[0032] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0033] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0034] Embodiment 1

[0035] Step 1, selecting a preliminary process window of main process parameters; the preliminary process window of main process parameters includes a laser power of 100-120 W, a scanning speed of 1000-1400 mm / s, and a scanning spacing of 60-80 μm.

[0036] Select the three-factor experimental table in the response surface design table and design three center points. The design type is selected as BBD response surface design. The three factors are laser power, scanning speed and scanning spacing. The three factors are set at three levels (as shown in Table 1). The three factors are the main process parameters;

[0037] Table 1 Process parameter level coding table

[0038]

[0039] Step 2: Generate specific experimental parameters according to the main process parameters and other process parameters, and generate an iron-gallium alloy sample according to the specific experimental parameters;

[0040] Table 2 Specific parameter implementation table

[0041]

[0042]

[0043] First, a 10mm*10mm*15mm 3D model is created in the 3D modeling software and the position is arranged, and then the slicing software is imported for slicing. The slice thickness is the layer thickness (30μm). Then the process parameters are set for each sliced ​​3D model through the equipment's own software (Note: except for laser power, scanning speed and scanning spacing, other strategies uniformly adopt zigzag scanning, the interlayer rotation angle is 90°, and the substrate preheating temperature is 200°C). After printing, anneal at 200°C for 5h and then perform wire cutting. When cutting, the sample block should expose the inner surface along the construction direction.

[0044] Step 3: Statistical analysis of porosity and crack density of Fe-Ga alloy samples

[0045] The inner surface of the sample was ground and polished, and 5 OM images were randomly taken for crack density and porosity statistics. The statistical results are shown in Figure 8 , Fig. 9 And as shown in Table 3.

[0046] Table 3 Statistical results of sample defects

[0047]

[0048]

[0049] Step 4: When other process parameters are the same, the response surface method is used to establish the interaction relationship between the main process parameters and defects, and a defect prediction model is established to predict the porosity and crack density of the material. The final process window of the main process parameters is determined according to the prediction results. The final window is a laser power of 100-110 W, a scanning speed of 1100-1300 mm / s, and a scanning spacing of 60-70 μm (such as Fig. 9 , 1-5 are the defect prediction values ​​of the first five solutions of the model within the final process window, and 6-10 are the five samples selected in the running sequence that are not within this range).

[0050] Crack prediction model:

[0051] Cracks=13.287-0.216094*P-0.00830129*S+107.759*H-6.9e-06*P*S-0.3 56*P*H-0.0152357*S*H+0.00114436*P^2+3.96161e-06*S^2-332.357*H^2;

[0052] Porosity prediction model:

[0053] Pores=36.8782-0.195187*P-0.0292048*S-305.905*H-9.25e-09*P*S-0.8 58*P*H+0.166809*S*H+0.00119187*P^2+7.99512e-06*S^2+1621.86*H^2;

[0054] Where P represents laser power, S represents scanning speed, and H represents scanning distance.

[0055] The prediction model established by the above method needs to test the accuracy of the model: the normal distribution of crack density and porosity is distributed along a straight line ( Figure 2 , Figure 3); The residuals of crack density and porosity are randomly distributed with the equation prediction values ​​( Figure 4 , Figure 5 ); The predicted and actual values ​​of crack density and porosity are distributed around a straight line ( Figure 6 , Figure 7 ); Finally, any two sets of parameters are selected to compare the actual and predicted values. The specific results are shown in Table 4, where the defects of sample No. 2 in Table 4 are as follows Figure 8 As shown, all errors are within 5%, so the model is accurate.

[0056] Table 4 Comparison between model prediction and actual values

[0057]

[0058] Therefore, the present invention adopts the above-mentioned process window determination method for reducing defects in selective laser melting of Fe-Gallium alloy and Fe-Gallium alloy sample, determines the Fe-Gallium alloy process window through response surface method, and obtains the sample with the least defects. A small number of experiments greatly improves the efficiency of process exploration and greatly reduces the manufacturing cost, laying a reliable foundation for the performance research of additively manufactured Fe-Gallium alloy.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for determining a process window for reducing defects in iron-gallium alloy formed by selective laser melting, characterized in that: The following steps are involved: Step 1: Select the preliminary process window of main process parameters, including laser power, scanning speed, and scanning spacing; Step 2: Generate specific experimental parameters according to the main process parameters and other process parameters, and print the iron-gallium alloy sample according to the specific experimental parameters; Step 3, counting the porosity and crack density of the Fe-Ga alloy sample; Step 4: When other process parameters are the same, the response surface method is used to establish the interaction relationship between the main process parameters and defects, and a defect prediction model is established to predict the porosity and crack density of the material. The final process window of the main process parameters is determined according to the prediction results. The final window is a laser power of 100-110 W, a scanning speed of 1100-1300 mm / s, and a scanning spacing of 60-70 μm.

2. The method for determining a process window for reducing defects in selective laser melting of Fe-Ga alloy according to claim 1, characterized in that: Other process parameters include The forming process adopts zigzag scanning with an interlayer rotation angle of 90°, a powder thickness of 30-40μm, a spot diameter of 50-70μm, and a substrate preheating temperature of 100-200℃.

3. The method for determining a process window for reducing defects in selective laser melting of Fe-Ga alloy according to claim 1, characterized in that: The composition of iron gallium alloy is Fe 1-x Ga x , where x represents the atomic percentage of Ga, x = 15-30at.%.

4. The method for determining a process window for reducing defects in selective laser melting of Fe-Ga alloy according to claim 1, characterized in that: The generated iron-gallium alloy sample needs to be annealed at 100-300℃ for 5h to complete stress relief annealing after printing.

5. The method for determining a process window for reducing defects in iron-gallium alloy formed by selective laser melting according to claim 1, characterized in that: Statistics of porosity and crack density of Fe-Ga alloy specimens, including The sample was ground and polished, and five locations were randomly selected to take OM pictures. Then, ImageJ was used to measure the porosity and crack density of the sample along the construction direction to obtain the sample samples required for the response surface methodology.

6. The method for determining a process window for reducing defects in iron-gallium alloy formed by selective laser melting according to claim 1, characterized in that: Building a defect prediction model includes Crack prediction model: Cracks=13.287-0.216094*P-0.00830129*S+107.759*H-6.9e-06*P*S-0.3 56*P*H-0.0152357*S*H+0.00114436*P^2+3.96161e-06*S^2-332.357*H^2; Porosity prediction model: Pores=36.8782-0.195187*P-0.0292048*S-305.905*H-9.25e-09*P*S-0.8 58*P*H+0.166809*S*H+0.00119187*P^2+7.99512e-06*S^2+1621.86*H^2; Where P represents laser power, S represents scanning speed, and H represents scanning distance.

7. A printed iron-gallium alloy sample, characterized in that: The process window is prepared by using the process window determination method for reducing defects in iron-gallium alloy formed by selective laser melting as described in any one of claims 1 to 6.

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