Topological structure additive manufacturing local deposition defect online monitoring and control method

Abstract

The invention discloses a topological structure additive manufacturing local deposition defect online monitoring and control method which comprises the following steps: acquiring a fused image and a powder-laid image in a manufacturing process layer by layer, and performing perspective transformation correction on the images; the tomography machine collects a CT sectional view and a CT slice image of the printed part, and internal structure information is obtained; acquiring point cloud data, and reconstructing an STL model of the printed part to obtain a geometric deviation result; process parameters of all printing parts are determined, and typical parts are defined; forming geometric characteristics of the typical parts and severity of deposition defects are obtained; obtaining surface features of the typical part in the deposition process, and establishing a mapping relation between the surface features and deposition defects; obtaining a spatio-temporal evolution process of the typical part deposition defect; the part with the minimum average deviation absolute value is determined as the optimal part, and the optimal process window is obtained; and obtaining the surface characteristics of the optimal part in the deposition process, and obtaining the control method for the deposition defect of the topological structure.

Description

Technical Field

[0001] The present invention belongs to the technical field of laser powder bed fusion, and relates to an online monitoring and control method for local deposition defects in topological structure additive manufacturing. Background Art

[0002] Laser powder bed fusion (LPBF) is an advanced additive manufacturing technology capable of fabricating complex-structured parts with lightweight and high surface freedom. However, when LPBF is used to manufacture topological structures with overhang features, it faces the challenge of unsupported printing. In particular, warping deformation and deposition defects are prone to occur in the overhang area, resulting in insufficient geometric accuracy of the part or even printing failure. The traditional solution is to avoid these problems by adding support structures. However, the support structures not only increase the subsequent removal process but also may cause material waste, especially in parts with enclosed internal features where the support structures are difficult to remove.

[0003] Under unsupported conditions, warping deformation and deposition defects in the overhang area are the main technical problems. Warping deformation is usually caused by thermal stress, which may lead to collision between the overhang edge and the scraper, thereby interrupting the printing process. Deposition defects are manifested as material collapse or insufficient fusion at the lower part of the overhang area, resulting in geometric defects. Existing research shows that low energy density can reduce warping deformation but may cause insufficient fusion; while high energy density can improve the fusion quality but may exacerbate the deformation caused by thermal stress. Therefore, how to control warping deformation while reducing deposition defects is a key issue in unsupported LPBF technology.

[0004] The traditional LPBF manufacturing process relies on offline detection technology. Although offline detection can provide information on internal defects, it is costly, time-consuming, and unable to monitor the formation and evolution of defects in real time. In addition, existing online monitoring technologies mainly focus on defect detection of simple overhang structures. For complex topological structures, especially multi-branch closed features, it is difficult for existing technologies to effectively identify and quantify defects. At the same time, existing methods of online monitoring cannot capture the spatio-temporal evolution process of defects, resulting in defects not being discovered and controlled in a timely manner. Summary of the Invention

[0005] The purpose of the present invention is to provide an online monitoring and control method for local deposition defects in topological structure additive manufacturing, reduce deposition defects during unsupported printing, and improve the geometric accuracy of parts.

[0006] The technical solution adopted by the present invention is: an online monitoring and control method for local deposition defects in topological structure additive manufacturing, comprising the following steps:

[0007] S1: Use an industrial camera to collect post-melting images and post-powder spreading images during the manufacturing process layer by layer, and perform perspective transformation correction on the images;

[0008] S2: Use an X-ray micro-computed tomography machine to collect the CT cross-sectional images and CT slice images of the printed parts, and obtain the internal structure information;

[0009] S3: Collect point cloud data through an optical scanner, reconstruct the STL model of the printed parts, and obtain the geometric deviation results, where the geometric deviation results include the three-dimensional slice map of the printed parts, the two-dimensional slice map of the printed parts, the geometric deviation visualization map, and the deviation distribution map;

[0010] S4: Identify the process parameters of all printed parts and define typical parts;

[0011] S5: Based on the front view of the theoretical model STL, the front view of the typical part photo, the CT cross-sectional image, and the deviation distribution map, obtain the forming geometric features of the typical part and the severity of the deposition defects;

[0012] S6: Combine the three-dimensional slice map of the theoretical model STL, the three-dimensional slice map of the typical part, the two-dimensional slice map of the theoretical model STL, the image of the typical part after melting, the image of the typical part after powder spreading, and the CT slice image to obtain the surface features of the typical part during the deposition process, and establish the mapping relationship between the surface features and the deposition defects;

[0013] S7: Through the evolution of the defect surface features, obtain the spatio-temporal evolution process of the deposition defects of the typical part;

[0014] S8: Determine the one with the smallest absolute average deviation as the best part through the geometric deviation visualization map and deviation distribution map of different process parameters, and obtain the best process window;

[0015] S9: Combine the three-dimensional slice map of the theoretical model STL, the two-dimensional slice map of the theoretical model STL, the two-dimensional slice map of the best part, the image of the best part after melting, the image of the best part after powder spreading, and obtain the surface features of the best part during the deposition process, and obtain the control method for the topological structure deposition defects.

[0016] Further, step S1 is specifically as follows:

[0017] S11: Set up an industrial camera outside the powder bed fusion forming chamber to collect the images after melting and after powder spreading during the printing process;

[0018] S12: Use the perspective transformation method to correct the collected images after melting and after powder spreading, and the image border after correction completely coincides with the edge of the substrate.

[0019] Further, step S2 is specifically as follows:

[0020] S21: Use an X-ray micro-computed tomography machine to perform offline scanning on the printed parts to obtain voxel data;

[0021] S22: Use the VGStudioMAX software to reconstruct the voxel data to obtain cross-sectional images and CT slice images in the CT.

[0022] Further, step S3 is specifically as follows:

[0023] S31: Use an optical scanner to perform an offline scan on the printed part to obtain point cloud data, and after reconstructing the point cloud data using the FreeScan software, output the STL model of the printed part;

[0024] S32: Import the STL model of the printed part into the software Geomagic Control X, match and calibrate it with the theoretical model STL to obtain the geometric deviation result;

[0025] The geometric deviation result includes the three-dimensional slice map of the printed part, the two-dimensional slice map of the printed part, the geometric deviation visualization map, and the deviation distribution map.

[0026] Further, step S4 is specifically as follows:

[0027] S41: The process parameters of all printed parts include 120W, 1000mm / s, 200W, 1000mm / s, 350W, 1000mm / s, 350W, 800mm / s;

[0028] S42: Define the typical part as the part printed at 120W, 1000mm / s.

[0029] Further, step S5 is specifically as follows:

[0030] S51: Based on the front view of the theoretical model STL, the front view of the typical part photo, and the cross-sectional image in the CT, obtain the forming geometric features of the typical part and determine that there are deposition defects in the topological structure;

[0031] S52: Obtain the severity of the deposition defect according to the deviation distribution map of the typical sample.

[0032] Further, step S6 is specifically as follows:

[0033] S61: According to the geometric features of the typical part, divide the deposition process into three stages: before closure, at closure, and after closure;

[0034] S62: In the stage before closure, by comparing the three-dimensional slice map of the theoretical model STL, the three-dimensional slice map of the typical part, the two-dimensional slice map of the theoretical model STL, the image of the typical part after melting, the image of the typical part after powder spreading, and the CT slice image, obtain the surface feature of warping deformation, and at the same time obtain the mapping relationship corresponding to the position and range between the warping deformation and the deposition defect;

[0035] S63: During the closure stage and after the closure stage, by comparing the three-dimensional slice images of the theoretical model STL, the three-dimensional slice images of typical parts, the two-dimensional slice images of the theoretical model STL, the images of typical parts after melting, the images of typical parts after powder spreading, and the CT slice images, surface features of bulges are obtained, and at the same time, the mapping relationship corresponding to the position and range between the bulges and deposition defects is obtained.

[0036] Further, step S7 is specifically as follows:

[0037] S71: Based on the mapping relationship, it is obtained that warping deformation and bulges are defect features during the deposition process;

[0038] S72: Before the closure, the warping deformation gradually intensifies with the increase of the deposition layer number. During the closure, the surface feature changes from warping to bulges, and the height of the bulges continuously rises with the increase of the layer number. After the closure, the height of the bulges tends to be stable and is gradually covered by the subsequent powder layer by layer;

[0039] S73: Through the evolution of the warping deformation and bulge features, the spatio-temporal evolution process of the deposition defects of typical parts is obtained.

[0040] Further, step S8 is specifically as follows:

[0041] S81: Through the geometric deviation visualization diagrams of four different process parameters, the influence of process parameters on the structural integrity is obtained;

[0042] S82: By analyzing the deviation distribution diagrams of the overhanging areas, the one with the smallest absolute value of the average deviation is determined as the best part, and the best process window is obtained.

[0043] Further, the step S9 is specifically as follows:

[0044] S91: Combining the three-dimensional slice images of the theoretical model STL, the two-dimensional slice images of the theoretical model STL, the two-dimensional slice images of the best part, the images of the best part after melting, and the images of the best part after powder spreading, surface features of drum edges are obtained;

[0045] S92: By comparing the deposition processes of the best part and the typical part during the closure, the conclusion that the best part has no defect features is obtained;

[0046] S93: The control method for topological structure deposition defects is to print with the best process window.

[0047] The beneficial effects of the present invention are as follows: The present invention conducts research on the topological structure, combines on-line monitoring technology and off-line detection technology, explores the problem of deposition defects, establishes a mapping relationship between two-dimensional surface features and three-dimensional deposition defects based on the surface features at different stages of the deposition process, obtains the spatio-temporal evolution process of deposition defects based on the mapping relationship, and proposes a defect control strategy based on process parameter optimization, providing a theoretical basis for unsupported printing of topological structures.

[0048] In addition to the purposes, features, and advantages described above, the present invention has other purposes, features, and advantages. The following will refer to the drawings to further elaborate on the present invention in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of the evolution mechanism and control method of local deposition defects of the topological structure of the present invention.

[0050] Figure 2 It is a characteristic diagram of deposition defects of a typical part.

[0051] Figure 3 It is a deposition process diagram of a typical part before closure.

[0052] Figure 4 It is a deposition process diagram of a typical part when closing.

[0053] Figure 5 It is a deposition process diagram of a typical part after closure.

[0054] Figure 6 It is a schematic diagram of the evolution process of the bulge height of a typical part.

[0055] Figure 7 It is a visualization diagram of the geometric deviation of the topological structure under different process parameters.

[0056] Figure 8 It is a deviation distribution diagram of the overhanging area of the topological structure under different process parameters.

[0057] Figure 9 It is a deposition process diagram of the best part when closing. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The present invention will be described in detail below in conjunction with the drawings and specific embodiments.

[0059] A method for on-line monitoring and control of local deposition defects in additive manufacturing of topological structures. The hardware system includes a laser, a galvanometer scanner, a doctor blade, a substrate, a powder bed, an industrial camera, and a computer. The industrial camera is mounted beside the axis outside the powder bed melting and forming chamber for collecting powder bed images during the printing process. The computer is mounted outside the powder bed melting and forming chamber and uses a GigE gigabit Ethernet interface to connect to the industrial camera to receive the powder bed images collected by the industrial camera. CombiningFigure 1 , the method includes the following steps:

[0060] S1: Use an industrial camera to collect post-melting images and post-spreading images during the manufacturing process layer by layer, and perform perspective transformation correction on the images;

[0061] S11: Set up an industrial camera outside the powder bed fusion forming chamber to collect post-melting images and post-spreading images during the printing process;

[0062] S12: Adopt the perspective transformation method to correct the collected post-melting images and post-spreading images, so that the image border after correction completely coincides with the substrate edge;

[0063] S2: Use an X-ray micro-computed tomography machine to collect the CT mid-section images and CT slice images of the printed part, and obtain the internal structure information;

[0064] S21: Use an X-ray micro-computed tomography machine to perform offline scanning on the printed part to obtain voxel data;

[0065] S22: Use VGStudioMAX software to perform reconstruction processing on the voxel data to obtain the CT mid-section images and CT slice images;

[0066] S3: Collect point cloud data through an optical scanner, reconstruct the STL model of the printed part, and obtain the geometric deviation results. The geometric deviation results include the three-dimensional slice map of the printed part, the two-dimensional slice map of the printed part, the geometric deviation visualization map, and the deviation distribution map;

[0067] S31: Use an optical scanner to perform offline scanning on the printed part to obtain point cloud data, and the point cloud data is output as the STL model of the printed part after being reconstructed by FreeScan software;

[0068] S32: Import the STL model of the printed part into the software Geomagic Control X, match and calibrate it with the theoretical model STL to obtain the geometric deviation results;

[0069] S33: The geometric deviation results include the three-dimensional slice map of the printed part, the two-dimensional slice map of the printed part, the geometric deviation visualization map, and the deviation distribution map;

[0070] S4: Identify the process parameters of all printed parts and define typical parts;

[0071] S41: The process parameters of all printed parts include 120W, 1000mm / s, 200W, 1000mm / s, 350W, 1000mm / s, 350W, 800mm / s;

[0072] S42: Define the typical part as the part printed at 120W and 1000mm / s;

[0073] S5: Based on the front view of the theoretical model STL, the front view of the typical part photo, the CT middle cross-section view and the deviation distribution map, obtain the forming geometric features of the typical part and the severity of deposition defects;

[0074] S51: Based on the front view of the theoretical model STL, the front view of the typical part photo, and the CT middle cross-section view, obtain the forming geometric features of the typical part and determine that there are deposition defects in the topological structure, such as Figure 2 shown;

[0075] S52: Obtain the severity of deposition defects according to the deviation distribution map of the typical sample, such as Figure 2 shown;

[0076] S6: Combine the three-dimensional slice map of the theoretical model STL, the three-dimensional slice map of the typical part, the two-dimensional slice map of the theoretical model STL, the image of the typical part after melting, the image of the typical part after powder spreading, and the CT slice image to obtain the surface features of the typical part during the deposition process and establish the mapping relationship between the surface features and deposition defects;

[0077] S61: According to the geometric features of the typical part, divide the deposition process into three stages: before closing, during closing, and after closing. Define the stage before closing as the 1st layer to the 293rd layer, the stage during closing as the 294th layer to the 324th layer, and the stage after closing as the 325th layer to the 400th layer;

[0078] S62: In the stage before closing, by comparing the three-dimensional slice map of the theoretical model STL, the three-dimensional slice map of the typical part, the two-dimensional slice map of the theoretical model STL, the image of the typical part after melting, the image of the typical part after powder spreading, and the CT slice image, obtain the surface feature of warping deformation. At the same time, obtain the mapping relationship corresponding to the position and range between the warping deformation and deposition defects, such as Figure 3 shown;

[0079] Figure 3 This is the deposition process before closing of the typical part, showing the deposition conditions of the 120th layer, the 244th layer, and the 287th layer respectively. At the 120th layer, the overhanging area is printed normally. When printing reaches the 244th layer, defects begin to appear in the overhanging area. At the same time, warping deformation appears at position 1 in the image after melting. The warping deformation is consistent with the missing situation observed in the CT slice image. When printing reaches the 287th layer, the warping deformation at position 1 intensifies, and the missing area in the CT slice image further expands;

[0080] S63: During the closure stage and after the closure stage, by comparing the three-dimensional slice images of the theoretical model STL, the three-dimensional slice images of typical parts, the two-dimensional slice images of the theoretical model STL, the images of the typical parts after melting, the images of the typical parts after powder spreading, and the CT slice images, a surface feature of bulge is obtained. At the same time, the mapping relationship corresponding to the position and range between the bulge and the deposition defect is obtained, such as Figure 4 , Figure 5 shown;

[0081] Figure 4 is the deposition process during the closure of the typical part, showing the deposition conditions of the 294th layer, the 309th layer, and the 324th layer respectively. Among them, the 294th layer is the theoretical closure layer. At the 294th layer, warping deformation occurs at position 2. The warping deformation is consistent with the missing situation observed in the CT slice image. When printing to the 309th layer, the surface feature at position 2 is a small bulge, corresponding to the missing area in the CT slice image. When printing to the 324th layer, the range of the bulge at position 2 expands, and the height of the bulge is significantly higher than the powder layer. During this process, the warping deformation gradually transitions to the bulge, the height of the bulge increases significantly, and the bulge corresponds to the defect;

[0082] Figure 5 is the deposition process during the closure of the typical part, showing the deposition conditions of the 335th layer, the 360th layer, and the 385th layer respectively. At the 335th layer, the height of the bulge tends to be stable, and its edge is gradually covered by newly deposited materials. By the 360th layer, the range of the bulge at position 3 continues to shrink. At the 385th layer, the top surface of the deposition layer at position 3 gradually becomes flat, and the bulge is almost completely covered by powder;

[0083] S7: Through the evolution of the surface features of the defect, the spatio-temporal evolution process of the deposition defect of the typical part is obtained;

[0084] S71: Based on the mapping relationship, it is concluded that warping deformation and bulge are defect features during the deposition process;

[0085] S72: Before the closure, the warping deformation gradually intensifies with the increase of the deposition layer number. During the closure, the surface feature evolves from warping to bulge, and the height of the bulge continues to rise with the increase of the layer number. After the closure, the height of the bulge tends to be stable and is gradually covered by the subsequent powder layer by layer;

[0086] S73: Through the evolution of the warping deformation and bulge features, the spatio-temporal evolution process of the deposition defect of the typical part is obtained, such as Figure 6 shown;

[0087] Figure 6Shows a schematic diagram of the vertical evolution of the bulge. The absolute bulge height H is defined as the vertical distance from the highest point of the bulge to the reference layer (the 284th layer), while the relative bulge height ΔH is measured relative to the upper surface of the deposited layer, indicating the height of the bulge relative to the current layer. Both H and ΔH are used as indicators of bulge evolution. According to the image after powder spreading, the bulge evolution can be divided into two stages: the bulge growth stage and the bulge stabilization stage. In the growth stage, severe warping deformation of the 284th layer exceeds the powder layer, resulting in non-zero initial values for both H and ΔH. At the 294th layer, which is the theoretical closing layer, the warping deformations collide and connect to form a bulge. As the number of layers increases, both H and ΔH gradually increase. Due to the difference between the current deposited layer and the reference layer, H is greater than ΔH. The mapping relationship verifies that there is a cavity inside the bulge. In the bulge stabilization stage, H tends to be stable, while as new material is deposited, ΔH gradually decreases. By the 404th layer, the surface of the layer after powder spreading becomes flat, ΔH becomes stable and drops to zero, and the bulge evolution ends;

[0088] S8: Through the geometric deviation visualization diagrams and deviation distribution diagrams of different process parameters, determine that the one with the smallest absolute value of the average deviation is the best part, and obtain the best process window;

[0089] S81: Through the geometric deviation visualization diagrams of four different process parameters, obtain the influence of process parameters on structural integrity, such as Figure 7 shown;

[0090] Figure 7 are the geometric deviation visualization diagrams of different process parameters. The results show that there are serious deposition defects at 120W and 1000mm / s, slight defects at 200W and 1000mm / s, and the best fit with the theoretical model and meeting the geometric requirements at 350W and 1000mm / s. At 350W and 800mm / s, slag appears on the lower surface of the overhanging structure, resulting in geometric tolerance exceedance;

[0091] S82: By analyzing the deviation distribution diagram of the overhanging area, determine that the one with the smallest absolute value of the average deviation is the best part, and obtain the best process window as 350W and 1000mm / s, as Figure 8 shown;

[0092] Figure 8 shows the deviation distribution of the overhanging area. The average deviations of 120W and 1000mm / s, 200W and 1000mm / s, 350W and 1000mm / s, and 350W and 800mm / s are -1.13mm, -0.36mm, 0.11mm, and 0.39mm respectively. The absolute value of the average deviation of 350W and 1000mm / s is the smallest, and this part is the best part;

[0093] S9: Combine the three-dimensional slice map of the theoretical model STL, the two-dimensional slice map of the theoretical model STL, the two-dimensional slice map of the optimal part, the image of the optimal part after melting, and the image of the optimal part after powder spreading to obtain the surface characteristics of the optimal part during the deposition process, and obtain the control method for topological structure deposition defects;

[0094] S91: Combine the three-dimensional slice map of the theoretical model STL, the two-dimensional slice map of the theoretical model STL, the two-dimensional slice map of the optimal part, the image of the optimal part after melting, and the image of the optimal part after powder spreading to obtain the surface characteristic of a bulging edge, as Figure 9 shown;

[0095] Figure 9 This is the deposition process when the optimal prototype is closed, showing the situations of the 294th layer, the 309th layer, and the 324th layer respectively. Position 4 in the image after powder spreading shows a bulging edge. Although the bulge and the bulging edge are similar in appearance, there are significant differences in the geometric integrity of the printed part. During the upward printing process, the movement of these abnormalities is significantly different. When printing the 294 - 324th layers, the bulge is fixed at one position. In contrast, the bulging edge moves with the two-dimensional slice, gradually fuses, and then disappears when the two branches close. In addition, the warping deformation of the overhanging part is also easily confused with the bulging edge, but the surface glossiness is different.

[0096] S92: Compare the deposition processes of the optimal part and the typical part when they are closed, and draw the conclusion that the optimal part has no defect characteristics;

[0097] S93: The control method for topological structure deposition defects is to print with the optimal process window.

[0098] In this invention, through on-line monitoring technology and off-line detection technology, the formation and evolution mechanism of local deposition defects in the overhanging area of the topological structure during the LPBF process are explored. Based on geometric features, the deposition process is divided into three stages: the pre-closure stage, the closure stage, and the post-closure stage. In the pre-closure stage, the surface characteristic is warping deformation, and the warping deformation intensifies layer by layer. During the closure stage, the warping deformation evolves into a bulge, and the height of the bulge increases layer by layer. In the post-closure stage, the height of the bulge tends to be covered by powder layer by layer. Based on the mapping relationship between the surface characteristics and the deposition defects, the evolution process of the defects is obtained. Through the geometric deviation visualization map and the deviation distribution map, the optimal part is obtained, and the influence of process parameters on the structural integrity is clarified. According to the deposition process of the optimal part, the surface characteristic of a bulging edge is determined, and the control method with the optimal process window as the printing parameter can reduce the deposition defects during unsupported printing and improve the geometric accuracy of the part.

[0099] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for online monitoring and control of local deposition defects in topological structure additive manufacturing, characterized in that: The following steps are involved: S1: Use an industrial camera to collect images after melting and powder spreading during the manufacturing process layer by layer, and perform perspective transformation correction on the images; S2: Use an X-ray micro-computed tomography machine to collect CT cross-sectional images and CT slice images of printed parts to obtain internal structure information; S3: collecting point cloud data through an optical scanner, reconstructing the STL model of the printed part, and obtaining geometric deviation results, wherein the geometric deviation results include a three-dimensional slice diagram of the printed part, a two-dimensional slice diagram of the printed part, a geometric deviation visualization diagram, and a deviation distribution diagram; S4: Clarify the process parameters of all printed parts and define typical parts; S5: Based on the STL front view of the theoretical model, the front view of the typical part photo, the cross-sectional view in CT and the deviation distribution diagram, the forming geometric characteristics of the typical part and the severity of the deposition defect are obtained; S6: Combine the 3D slice image of the theoretical model STL, the 3D slice image of the typical part, the 2D slice image of the theoretical model STL, the image of the typical part after melting, the image of the typical part after powder coating and the CT slice image to obtain the surface features of the typical part during the deposition process and establish the mapping relationship between the surface features and the deposition defects; S7: Through the evolution of defect surface characteristics, the spatiotemporal evolution of deposition defects of typical parts is obtained; S8: Through the geometric deviation visualization diagram and deviation distribution diagram of different process parameters, determine that the part with the smallest absolute value of average deviation is the best part and obtain the best process window; S9: By combining the three-dimensional slice image of the theoretical model STL, the two-dimensional slice image of the theoretical model STL, the two-dimensional slice image of the best part, the image of the best part after melting, and the image of the best part after powder coating, the surface characteristics of the best part during the deposition process are obtained, and the control method for topological structure deposition defects is obtained.

2. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 1, characterized in that: Step S1 is specifically as follows: S11: An industrial camera is set up outside the powder bed fusion forming chamber to collect images after fusion and after powder spreading during the printing process; S12: The collected images after melting and after powder spreading are corrected by using a perspective transformation method, and the image frame after correction completely coincides with the edge of the substrate.

3. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 2, characterized in that: Step S2 is specifically as follows: S21: Use an X-ray micro-computed tomography machine to scan the printed parts offline to obtain voxel data; S22: Use VGStudioMAX software to reconstruct the voxel data and obtain CT cross-sectional images and CT slice images.

4. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 3, characterized in that: Step S3 is specifically as follows: S31: Use an optical scanner to scan the printed part offline to obtain point cloud data, and then use FreeScan software to reconstruct the point cloud data and output the STL model of the printed part; S32: Import the STL model of the printed part into the software Geomagic Control X, match and calibrate it with the theoretical model STL, and obtain the geometric deviation result; S33: The geometric deviation results include a three-dimensional slice diagram of the printed part, a two-dimensional slice diagram of the printed part, a geometric deviation visualization diagram, and a deviation distribution diagram.

5. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 4, characterized in that: Step S4 is specifically as follows: S41: The process parameters of all printed parts include 120W, 1000mm / s, 200W, 1000mm / s, 350W, 1000mm / s, 350W, 800mm / s; S42: defines a typical part as a part printed at 120W and 1000mm / s.

6. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 5, characterized in that: Step S5 is specifically as follows: S51: Based on the STL front view of the theoretical model, the front view of the typical part photo, and the cross-sectional view in CT, the forming geometric characteristics of the typical part are obtained to determine the presence of deposition defects in the topological structure; S52: Obtain the severity of the deposition defect based on the deviation distribution diagram of the typical sample.

7. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 6, characterized in that: Step S6 is specifically as follows: S61: According to the geometric characteristics of typical parts, the deposition process is divided into three stages: before closure, during closure, and after closure; S62: In the pre-joint stage, by comparing the 3D slices of the theoretical model STL, the 3D slices of the typical parts, the 2D slices of the theoretical model STL, the images of the typical parts after melting, the images of the typical parts after powder coating and the CT slices, the surface feature is obtained as warpage, and the mapping relationship between the position and range of the warpage and the deposition defect is obtained; S63: During and after the closure stage, by comparing the three-dimensional slices of the theoretical model STL, the three-dimensional slices of the typical parts, the two-dimensional slices of the theoretical model STL, the images of the typical parts after melting, the images of the typical parts after powder coating and the CT slices, the surface feature is obtained as a bulge, and the mapping relationship between the position and range of the bulge and the deposition defect is obtained.

8. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 7, characterized in that: Step S7 is specifically as follows: S71: Based on the mapping relationship, it is concluded that warping and bulging are defect features during the deposition process; S72: Before closure, the warping deformation gradually increases with the number of deposited layers. During closure, the surface feature changes from warping to bulging, and the bulging height continues to increase with the number of layers. After closure, the bulging height tends to be stable and is covered layer by layer by subsequent powder. S73: Through the evolution of warpage and bulging characteristics, the spatiotemporal evolution of deposition defects of typical parts is obtained.

9. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 8, characterized in that: Step S8 is specifically as follows: S81: The influence of process parameters on structural integrity is obtained through the visualization of geometric deviations of four different process parameters; S82: By analyzing the deviation distribution diagram of the overhang area, it is determined that the part with the smallest absolute value of the average deviation is the best part, and the best process window is obtained.

10. The method for online monitoring and control of local deposition defects in topological structure additive manufacturing according to claim 9, characterized in that: The step S9 is specifically as follows: S91: Combining the three-dimensional slice image of the theoretical model STL, the two-dimensional slice image of the theoretical model STL, the two-dimensional slice image of the best part, the image of the best part after melting, and the image of the best part after powdering, the surface feature is obtained as a drum edge; S92: Compare the deposition process of the best part and the typical part during closure, and conclude that the best part has no defect features; S93: The method for controlling topological structure deposition defects is to print using the optimal process window.

Images