A 3D printing quality detection and repair method

Abstract

The present invention provides a 3D print quality inspection and repair method, comprising the following steps: Step 1: Acquire a reflected light image of a 3D print layer, mark a reference area and a defective area, and simultaneously determine the fuzzy outer boundary of the defective area; the reference area is an area where the reflected light data is consistent and meets a preset physical height of the 3D print layer; the defective area is an area where the reflected light data is inconsistent and does not meet the preset physical height of the 3D print layer; Step 2: Acquire point cloud data of the reference area and the defective area, reconstruct a solid point cloud set of the defective area, and obtain a precise outer boundary; Step 3: Repair the defective area of ​​the 3D print layer based on the precise outer boundary. The method of the present invention achieves precise repair of internal defects in 3D printed parts.

Description

Technical Field

[0001] The present invention belongs to the field of metal additive manufacturing detection, and specifically relates to a 3D printing quality detection and repair method. Background Art

[0002] 3D printing technology holds great promise, but the surface quality of its printed parts, such as surface roughness and defects, remains a concern. Surface roughness and defects in 3D printing not only affect common properties, such as the fluid dynamics of the printed part, but can also lead to early fatigue failures, such as cracks. Compared to machined parts, 3D-printed parts often exhibit lower fatigue strength due to surface quality.

[0003] The external surface quality problem of 3D printed parts can be easily improved by additional milling machining or sandblasting, so it is not a big problem. However, it is difficult to improve the internal surface quality problem of components with complex and small-sized internal groove structures through milling or sandblasting. The existing technology also proposes to solve the internal surface quality problem of 3D printing through electropolishing or abrasive flow polishing. However, the etching speed of electropolishing is greatly affected by the complex electric field defined by the shape of the printed part, not to mention the possible degradation effects of the etching process. Similarly, the grinding speed of abrasive flow polishing is also greatly affected by the fluid dynamics of the complex internal grooves of the printed part, so it is difficult to control the accuracy without affecting the shape of the internal structure.

[0004] In view of this, it is urgent to propose a 3D printing quality detection and repair method to accurately repair the internal defects of 3D printed parts. Summary of the Invention

[0005] To this end, the 3D printing quality inspection and repair method of the present invention combines reflective imaging and point cloud to reconstruct the defective area in the metal additive manufacturing process to perform repair.

[0006] The 3D printing quality detection and repair method of the present invention includes:

[0007] Step 1: Acquire a reflected light image of the 3D printed layer, mark a reference area and a defect area, and determine the fuzzy outer boundary of the defect area; the reference area is an area where the reflected light data is consistent and meets the preset physical height of the 3D printed layer; the defect area is an area where the reflected light data is inconsistent and does not meet the preset physical height of the 3D printed layer;

[0008] Step 2: Obtain point cloud data of the reference area and the defect area, reconstruct the entity point cloud set of the defect area, and obtain the precise outer boundary;

[0009] Step 3: Repair the defective area of ​​the 3D printing layer according to the precise outer boundary.

[0010] Furthermore, after obtaining the point cloud data of the reference area and the defect area, the following steps are included:

[0011] Step 101: Slice the reference area and the defect area into slices with predetermined widths to obtain a plurality of slices;

[0012] Step 102: Perform a coarse filter on the points in the reference area slice. The filtering formula is:

[0013] He<HPC<H+e

[0014] Among them, HPC is the elevation value of the point;

[0015] H is the fitted height of the physical top surface of the reference area slice;

[0016] e is a correction value, which is determined by the metal powder particle size obtained during the 3D printing process and the predetermined roughness of the physical top surface of the reference area.

[0017] e=Ra-kf

[0018] Wherein, Ra is the predetermined roughness;

[0019] f is the particle size of metal powder;

[0020] k is a proportional coefficient, ranging from 0 to 1. According to the mathematical relationship between f and Ra, when the metal powder particle size f = Ra, K is 0; when the metal powder particle size f < 0.5 Ra, k is 1.

[0021] Then, all points that meet the filtering formula constitute the initial seed point set;

[0022] Step 103: Growing the seed points using the initial seed point set to obtain a reconstructed point cloud region.

[0023] Furthermore, in step 103, growth is performed using the following growth algorithm:

[0024] Step 103a: randomly select a point from the initial seed point set, and make the point grow downward along the height direction of the point cloud slice;

[0025] Step 103b: After obtaining the nearest neighbor of the seed point, delete the seed point, and form a second seed point set with the nearest neighbor points, and continue to grow until the distance between the nearest neighbor of the N+1th seed point set and the physical top surface of the defect area is less than the particle size of the metal powder;

[0026] Where N is the number of growth times;

[0027] The nearest point is the point whose elevation value satisfies the filtering formula;

[0028] Step 103c, repeating steps 103a to 103b until the initial seed point set is empty;

[0029] Step 103d: Obtain all points including the second to Nth seed point sets and combine them into reconstructed point cloud data of the slice.

[0030] Furthermore, when growing,

[0031] Slice the reference area slice and the defect area slice twice along the height direction;

[0032] The physical height slices in the reference area secondary slice and the defect area secondary slice are grown simultaneously.

[0033] Furthermore, when growing by secondary slicing, the points on the physical top surface of the secondary slice of each reference region are used as initial seed points.

[0034] Furthermore, repairing the defective area of ​​the 3D printing layer according to the precise outer boundary includes the following steps:

[0035] Step 201: combining the reconstructed point cloud data of each slice;

[0036] Step 202: determining the top outer boundary of the point cloud from the top surface of the reconstructed point cloud data, and fitting the top outer boundary to the fuzzy outer boundary;

[0037] Step 203: If the fitting distance is smaller than the metal powder particle size, the boundary is determined to be a precise outer boundary;

[0038] If the fitting distance is greater than the metal powder particle size, the boundary point is used as a seed point to execute a growth algorithm until the fitting distance is less than the metal powder particle size.

[0039] The present invention also provides a computer storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the 3D printing quality detection and repair method.

[0040] The present invention also provides an electronic device, comprising a processor and a memory for storing instructions executable by the processor, wherein the processor is used to execute the 3D printing quality detection and repair method.

[0041] The above technical solution of the present invention has the following advantages over the prior art:

[0042] The present invention uses reflectometric images and point cloud images to jointly collect defects in the 3D printed layer, uses a point cloud growth algorithm to grow reconstructed point cloud data accurate to the size of metal particles, and performs repairs on the 3D printed layer based on the reconstructed point cloud data, greatly improving the accuracy of the repair. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the method provided by an embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of a partial structure of an electronic device provided by an embodiment of the present invention;

[0045] Figure 3 is a schematic diagram of a growth algorithm provided by an embodiment of the present invention;

[0046] Figure 4 Schematic diagram of the secondary slicing growth algorithm provided by an embodiment of the present invention.

[0047] Among them, 1. Slice the reference area; 2. Slice the defect area; 3. Reconstruct the point cloud set; 4. Secondary slicing of the reference area; 5. Secondary slicing of the defect area; 6. Secondary slicing to reconstruct the point cloud. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] The 3D printing quality detection method of this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0050] Step 1: Acquire a reflected light image of the 3D printed layer, mark a reference area and a defect area, and determine the fuzzy outer boundary of the defect area; the reference area is an area where the reflected light data is consistent and meets the preset physical height of the 3D printed layer; the defect area is an area where the reflected light data is inconsistent and does not meet the preset physical height of the 3D printed layer;

[0051] Step 2: Obtain point cloud data of the reference area and the defect area, reconstruct the entity point cloud set of the defect area, and obtain the precise outer boundary;

[0052] Step 3: Repair the defective area of ​​the 3D printing layer according to the precise outer boundary.

[0053] Furthermore, after obtaining the point cloud data of the reference area and the defect area, the following steps are included:

[0054] Step 101: Slice the reference area and the defect area into slices with predetermined widths to obtain a plurality of slices;

[0055] Step 102: Perform a coarse filter on the points in the reference area slice. The filtering formula is:

[0056] He<HPC<H+e

[0057] Among them, HPC is the elevation value of the point;

[0058] H is the fitted height of the physical top surface of the reference area slice;

[0059] e is a correction value, which is determined by the metal powder particle size obtained during the 3D printing process and the predetermined roughness of the physical top surface of the reference area.

[0060] e=Ra-kf

[0061] Wherein, Ra is the predetermined roughness;

[0062] f is the particle size of metal powder;

[0063] k is a proportional coefficient, ranging from 0 to 1. According to the mathematical relationship between f and Ra, when the metal powder particle size f = Ra, K is 0; when the metal powder particle size f < 0.5 Ra, k is 1.

[0064] Then, all points that meet the filtering formula constitute the initial seed point set;

[0065] Step 103: Growing the seed points using the initial seed point set to obtain a reconstructed point cloud region.

[0066] Furthermore, in step 103, growth is performed using the following growth algorithm:

[0067] Step 103a: randomly select a point from the initial seed point set, and make the point grow downward along the height direction of the point cloud slice;

[0068] Step 103b: After obtaining the nearest neighbor of the seed point, delete the seed point, and form a second seed point set with the nearest neighbor points, and continue to grow until the distance between the nearest neighbor of the N+1th seed point set and the physical top surface of the defect area is less than the particle size of the metal powder;

[0069] Where N is the number of growth times;

[0070] The nearest point is the point whose elevation value satisfies the filtering formula;

[0071] Step 103c, repeating steps 103a to 103b until the initial seed point set is empty;

[0072] Step 103d: Obtain all points including the second to N+1th seed point sets and combine them into reconstructed point cloud data of the slice.

[0073] Furthermore, when growing,

[0074] Slice the reference area slice and the defect area slice twice along the height direction;

[0075] The physical height slices in the reference area secondary slice and the defect area secondary slice are grown simultaneously. When growing through the secondary slice, the physical top surface point of each reference area secondary slice is used as the initial seed point.

[0076] In this embodiment, since there is no point in the part from the height of the physical top surface of the defective area slice to the height of the physical top surface of the reference area, the reference area slice and the physical top surface slice are overlapped and grown during the growth process. After the growth is completed, the initial seed point and the points where the reference slice area overlaps with the defective area are deleted, and all points in the defective area are also deleted. This embodiment uses a growth algorithm instead of directly deleting the points in the overlapping part because the point cloud can only obtain the collectible part. Since the reference area is an entity, the points within the entity cannot be obtained, and only the points on each surface can be obtained. Therefore, an accurate point cloud dataset can be constructed through this growth algorithm to meet the repair requirements.

[0077] During the secondary slicing process, except for the points on the physical top surface which have entities, the physical top surface of each slice does not have any points. The points on the physical top surface are translated to the slicing surface of the secondary slice according to the slice height as a new top surface slicing point set (new initial seed point set), and then grown. Since the defect heights of the defect areas are different, the secondary slices of the reference area are grown one by one with the secondary slices of the defect area at the corresponding slice heights. A reconstructed point cloud from the secondary slice surface of the reference area to the top surface of the corresponding secondary slice of the defect area can be obtained. Then, based on the secondary slice reconstructed point cloud formed by multiple secondary slice surfaces, a new point cloud set with the top surface boundary of the defect area as the bottom surface boundary is obtained. Then, the point cloud set is filled to obtain the reconstructed point cloud to meet the repair requirements while improving the growth efficiency.

[0078] Furthermore, repairing the defective area of ​​the 3D printing layer according to the precise outer boundary includes the following steps:

[0079] Step 201: combining the reconstructed point cloud data of each slice;

[0080] Step 202: determining the top outer boundary of the point cloud from the top surface of the reconstructed point cloud data, and fitting the top outer boundary to the fuzzy outer boundary;

[0081] Step 203: If the fitting distance is smaller than the metal powder particle size, the boundary is determined to be a precise outer boundary;

[0082] If the fitting distance is greater than the metal powder particle size, the boundary point is used as a seed point to execute a growth algorithm until the fitting distance is less than the metal powder particle size.

[0083] This embodiment uses reflectometric images and point cloud images to jointly capture defects in the 3D printed layer, uses a point cloud growth algorithm to grow reconstructed point cloud data accurate to the size of metal particles, and performs repairs on the 3D printed layer based on the reconstructed point cloud data, greatly improving the accuracy of the repair.

[0084] Below, reference Figure 2 The electronic device according to the embodiment of the present application is described. The electronic device may be either or both of the first device and the second device, or a standalone device independent of them, and the standalone device may communicate with the first device and the second device to receive collected input signals from them.

[0085] Figure 2 A block diagram of an electronic device according to an embodiment of the present application is shown.

[0086] like Figure 2 As shown, the electronic device 10 includes one or more processors 11 and a memory 12 .

[0087] The processor 11 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0088] The memory 12 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the method for preparing gradient functional materials by 3D printing in this embodiment described above and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0089] In one example, the electronic device 10 may further include an input device 13 and an output device 14 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0090] When the electronic device is a stand-alone device, the input device 13 may be a communication network connector, configured to receive collected input signals from the first device and the second device.

[0091] In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.

[0092] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and its connected remote output device, etc.

[0093] Of course, to simplify, Figure 2 Only some of the components related to the present application in the electronic device 10 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device 10 may further include any other appropriate components according to specific application scenarios.

[0094] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method for preparing gradient functional materials by 3D printing according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0095] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0096] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, causes the processor to execute the steps of the method for preparing gradient functional materials by 3D printing according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0097] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0098] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A 3D printing quality detection and repair method, characterized in that: The steps include: Step 1: Obtain a reflected light image of the 3D printed layer, mark a reference area and a defect area, and determine the fuzzy outer boundary of the defect area; the reference area is an area where the reflected light data is consistent and meets the preset physical height of the 3D printed layer; The defective area is an area where the reflected light data is inconsistent and does not meet the preset physical height of the 3D printing layer; Step 2: Obtain point cloud data of the reference area and the defect area, reconstruct the entity point cloud set of the defect area, and obtain the precise outer boundary; After obtaining the point cloud data of the reference area and the defect area, the following steps are included: Step 101: Slice the reference area and the defect area into slices with predetermined widths to obtain a plurality of slices; Step 102: Perform a coarse filter on the points in the reference area slice. The filtering formula is: He<HPC<H+e Among them, HPC is the elevation value of the point; H is the fitted height of the physical top surface of the reference area slice; e is a correction value, which is determined by the metal powder particle size obtained during the 3D printing process and the predetermined roughness of the physical top surface of the reference area. e=Ra-kf Wherein, Ra is the predetermined roughness; f is the particle size of metal powder; k is a proportional coefficient, ranging from 0 to 1. According to the mathematical relationship between f and Ra, when the metal powder particle size f = Ra, K is 0; when the metal powder particle size f < 0.5 Ra, k is 1. Then, all points that meet the filtering formula constitute the initial seed point set; Step 103: Growing the seed points using the initial seed point set to obtain a reconstructed point cloud region; Step 2 includes: Step 201: combining the reconstructed point cloud data of each slice; Step 202: determining the top outer boundary of the point cloud from the top surface of the reconstructed point cloud data, and fitting the top outer boundary to the fuzzy outer boundary; Step 203: If the fitting distance is smaller than the metal powder particle size, the boundary is determined to be a precise outer boundary; If the fitting distance is greater than the metal powder particle size, the boundary point is used as a seed point to execute the growth algorithm until the fitting distance is less than the metal powder particle size; Step 3: Repair the defective area of ​​the 3D printing layer according to the precise outer boundary.

2. The 3D printing quality detection and repair method according to claim 1, characterized in that: In step 103, growth is performed using the following growth algorithm: Step 103a: randomly select a point from the initial seed point set, and make the point grow downward along the height direction of the point cloud slice; Step 103b: After obtaining the nearest neighbor of the seed point, delete the seed point, and form a second seed point set with the nearest neighbor points, and continue to grow until the distance between the nearest neighbor of the N+1th seed point set and the physical top surface of the defect area is less than the particle size of the metal powder; Where N is the number of growth times; The nearest point is the point whose elevation difference with the seed point is less than e; Step 103c, repeating steps 103a to 103b until the initial seed point set is empty; Step 103d: Obtain all points including the second to N+1th seed point sets and combine them into reconstructed point cloud data of the slice.

3. The 3D printing quality detection and repair method according to claim 2, characterized in that: When growing, it also includes, Slice the reference area slice and the defect area slice twice along the height direction; The physical height slices in the reference area secondary slice and the defect area secondary slice are grown simultaneously.

4. The 3D printing quality detection and repair method according to claim 3, characterized in that: When growing by secondary slicing, the points on the physical top surface of the secondary slice of each reference area constitute the initial seed points.

5. A computer storage medium storing a computer program, wherein the computer program is used to execute the 3D printing quality detection and repair method according to any one of claims 1 to 4.

6. An electronic device comprising a processor and a memory for storing instructions executable by the processor, wherein the processor is configured to execute the 3D printing quality detection and repair method according to any one of claims 1 to 4.

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