High-flux nondestructive testing method for prefabricated metal additive defective part

Abstract

The invention discloses a high-throughput nondestructive testing method for a prefabricated metal additive defective part, which takes additive manufacturing metal as a research object, and aims at realizing prefabrication of controllable defects through a selective laser melting process based on prefabricated cavity modeling. And the high-throughput forming effect evaluation of the defective part is completed by combining a micro-computer tomography (muCT) technology. The method is characterized in that the size range, the shape and the relative position relation of the prefabricated cavity are defined; a metal additive manufacturing detection defect part model is designed, and the model can meet the requirements of mu CT detection precision and high-throughput detection; the invention provides a molding effect quantitative evaluation method based on the size of the prefabricated cavity. By standardizing the prefabricated cavity and the parameter design in the prefabrication process of the prefabricated cavity, the efficient muCT high-throughput nondestructive testing method is provided, the metal additive manufacturing link can be guided to print high-quality prefabricated defective parts, and a new method and thought can be provided for the metal additive manufacturing defect muCT high-throughput testing method.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of metal additive manufacturing and non-destructive testing, and relates to but is not limited to a high-throughput non-destructive testing method for a preformed metal additive defect part. BACKGROUND

[0002] Compared with the traditional subtractive manufacturing technology, additive manufacturing gradually accumulates a solid part according to a set three-dimensional model by adding materials, and this manufacturing method can realize the processing of complex structure parts and difficult-to-process materials due to its flexible processing characteristics. With the continuous development and improvement of additive manufacturing technology, the demand for metal additive manufacturing parts in the aerospace field is expanding, and additive manufacturing aircraft parts represented by titanium alloy have been put into use. Due to the special forming principle of metal additive manufacturing process, the internal defects are quite different from conventional parts, causing the performance of additive manufacturing parts to be different from that of parts processed by traditional processes. Therefore, there are quite a number of studies on the influence of defects on mechanical properties by preforming defects in test pieces. However, it is very difficult to achieve controllable preforming of additive manufacturing defects. Scholars generally use mechanical processing and sealing or fill in spherical fillers during printing to preform defects to ensure controllable defect morphology. However, the above methods cannot accurately simulate the morphology of real defects. Although the method of establishing a three-dimensional model of defects and directly printing into shape can maximize the simulation of real defects, due to process limitations, the defect size may have a large deviation. Therefore, in order to achieve controllable preforming of defects, it is necessary to optimize the defect geometric parameters and additive manufacturing equipment parameters and perform printing quality characterization after printing. SUMMARY

[0003] To solve the above problems of the prior art, the embodiments of the present application provide a high-throughput non-destructive testing method for a preformed metal additive defect part, which aims to realize controllable preforming of defects based on a preformed defect cavity model through a laser selective melting process, and to complete high-throughput non-destructive testing of the defect part in combination with μCT to obtain feasible cavity preforming parameters of the defect part.

[0004] The technical scheme of the embodiments of the present application is implemented as follows: The embodiment of the application provides a high-throughput nondestructive testing method for a prefabricated metal additive defect piece, the method is executed by a micro-computed tomography detection system, the micro-computed tomography detection system at least comprises an X-ray tube, a rotating sample stage and an X-ray receiver, the method comprises the following steps: placing the defect piece on the rotating sample stage in a manner that the length direction of the defect piece is perpendicular to the rotating sample stage; wherein the defect piece is generated by implanting a plurality of prefabricated cavities in a metal cuboid with a preset size in a manner of Boolean operation; rotating the rotating sample stage according to a preset angle step, and at each rotation step, emitting high-energy rays to the defect piece by the X-ray tube to the X-ray receiver, the X-ray receiver records X-ray imaging to obtain a two-dimensional projection image of the defect piece, until the scanning is completed after one rotation, a two-dimensional projection image sequence of the defect piece is obtained; data reconstruction software is used to perform data reconstruction on the two-dimensional projection image sequence to obtain a three-dimensional digital model containing internal information of the defect piece; data post-processing software is used to analyze the size and geometric characteristics of the prefabricated cavity of the three-dimensional digital model, and according to the analysis result and in combination with the design size of the prefabricated cavity of the defect piece, the feasible cavity prefabrication parameters of the defect piece are determined.

[0005] In some embodiments, the data reconstruction software is used to perform data reconstruction on the two-dimensional projection image sequence to obtain a three-dimensional digital model containing internal information of the defect piece, comprising: using a three-dimensional reconstruction algorithm of the data processing reconstruction software to perform data reconstruction on the two-dimensional projection image sequence to obtain three-dimensional body data containing internal information of the defect piece, and generating the three-dimensional digital model containing internal information of the defect piece stored in an STL format by performing isosurface extraction on the three-dimensional body data; wherein the conversion precision error of isosurface extraction is 0.005 mm, and the angle error is 10°.

[0006] In some embodiments, the data post-processing software is used to analyze the size and geometric characteristics of the three-dimensional digital model, and according to the analysis result and in combination with the design size of the prefabricated cavity of the defect piece, the feasible cavity prefabrication parameters of the defect piece are determined, comprising: using the data post-processing software to analyze the size and geometric characteristics of the prefabricated cavity of the three-dimensional digital model to obtain quantitative indexes of the size, geometric characteristics and grid quality of the prefabricated cavity of the defect piece; performing numerical simulation according to the quantitative indexes of the size, geometric characteristics and grid quality of the prefabricated cavity of the defect piece to obtain simulation data of the prefabricated cavity of the defect piece and compare the simulation data with the design size of the prefabricated cavity of the defect piece to obtain effect data for characterizing subjective forming of the defect piece; performing controllability comprehensive screening of the effect data on the prefabrication cavity parameters to obtain the feasible cavity prefabrication parameters of the defect piece.

[0007] In some embodiments, the data post-processing software is used to analyze the size and geometric characteristics of the preformed cavity of the three-dimensional digital model to obtain quantitative indexes of the size, geometric characteristics and grid quality of the preformed cavity of the defective part, including: using a data segmentation submodule of the data post-processing software to perform phase segmentation on the three-dimensional digital model to obtain quantized preformed cavity data of the defective part; using a quantitative analysis submodule of the data post-processing software to perform quantitative analysis on the quantized preformed cavity data of the defective part to obtain quantitative indexes of the size, geometric characteristics and grid quality of the preformed cavity of the defective part.

[0008] In some embodiments, the preformed cavity includes: a first preformed sub-cavity in the shape of a sphere; wherein the diameter of the first preformed sub-cavity is any size in a size sequence; a second preformed sub-cavity in the shape of an ellipsoid; wherein the long axis of the second preformed sub-cavity is any size in the size sequence; the ratio of the long and short axes of the second preformed sub-cavity is 2:1 or 3:1 or 5:1; and the size sequence at least includes: 150 μm, 200 μm, 250 μm, 300 μm, 350 μm, 400 μm, 450 μm, 500 μm, 550 μm and 600 μm.

[0009] In some embodiments, the feasible cavity preforming parameters include: a feasible spacing between any two of the preformed cavities, and a feasible size of the preformed cavities.

[0010] In some embodiments, the process of obtaining the defective part includes: obtaining a preset cuboid constructed with dimensions of 12 mm x 7.3 mm x 4 mm; determining the spacing between any two of the preformed cavities; wherein the spacing includes a horizontal spacing of not less than 1 mm, and a vertical spacing of a first preset size, the first preset size being any one of 0.15 mm, 0.5 mm, 1 mm, 1.5 mm and 2.5 mm; constructing a defective part model according to the preset cuboid, the plurality of preformed cavities and the spacing between any two of the preformed cavities; and printing the defective part based on the defective part model using a powder-falling powder laser selective melting additive manufacturing device.

[0011] In some embodiments, the device parameters of the powder-falling powder laser selective melting additive manufacturing device at least include: a particle size distribution range of spherical metal powder of 15 μm to 40 μm; a single layer forming thickness of metal of 0.14 mm; laser scanning parameters of 67° rotation of each layer scanning direction relative to the previous layer; a substrate material compatible with the powder-falling powder laser selective melting additive manufacturing device; and an upper surface parallelism error of the substrate of not more than 0.04 mm.

[0012] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects: The embodiments of the present application provide a high-throughput nondestructive testing method for a prefabricated metal additive defect part. The method is executed by a micro-computed tomography detection system, which at least includes an X-ray tube, a rotating sample stage, and an X-ray receiver. During the execution of the method, first, the defect part is placed on the rotating sample stage in a manner that the length direction of the defect part is perpendicular to the rotating sample stage. The defect part is generated by implanting a plurality of prefabricated cavities in a metal cuboid of a preset size by using a Boolean operation. Second, the rotating sample stage is rotated by a preset angle step, and at each rotation step, the X-ray tube emits high-energy rays to the defect part to the X-ray receiver. The X-ray receiver records X-ray imaging to obtain a two-dimensional projection image of the defect part, until the scanning ends after one rotation to obtain a two-dimensional projection image sequence of the defect part. Third, data processing reconstruction software is used to reconstruct the two-dimensional projection image sequence to obtain a three-dimensional digital model containing internal information of the defect part. Finally, data post-processing software is used to analyze the size and geometric characteristics of the prefabricated cavities of the three-dimensional digital model, and according to the analysis result and in combination with the design size of the prefabricated cavities of the defect part, the feasible cavity prefabrication parameters of the defect part are determined. In this way, the micro-computed tomography system is used to scan, image reconstruct and analyze the defect part with a plurality of prefabricated cavities to determine the feasible cavity prefabrication parameters of the defect part. In this way, the present application realizes efficient and nondestructive detection and accurate analysis of the internal cavities of the metal additive defect part, and provides a reliable basis for optimizing the manufacturing process. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any inventive labor. Figure 1 A flowchart of a high-throughput nondestructive testing method for a prefabricated metal additive defect part provided by the embodiments of the present application; Figure 2 A schematic diagram of the spatial layout of the defect part and the embedded prefabricated cavity; Figure 3 A schematic diagram of the structure of a micro-computed tomography detection system; Figure 4 A visualization result diagram of high-throughput detection of prefabricated cavities; Figure 5 A specific flowchart of a high-throughput nondestructive testing method for a prefabricated metal additive defect part provided by the embodiments of the present application. DETAILED DESCRIPTION

[0014] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0016] It should be noted that the terms "first, second, and third" used in the embodiments of this application are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0017] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0018] Example 1 Please see Figure 1 , Figure 1 This application provides a flowchart illustrating a high-throughput non-destructive testing method for prefabricated metal additive manufacturing defective parts. The method is executed by a microcomputed tomography (MCT) system, which includes at least an X-ray tube, a rotating stage, and an X-ray receiver. The method includes: Step 101: Place the defective part on the rotating platform with its length direction perpendicular to the rotating platform.

[0019] The defective component is generated by implanting multiple prefabricated cavities into a metal cuboid of a preset size using Boolean operations.

[0020] In some embodiments, the defect piece length direction is perpendicular to the rotating stage, which ensures that the entire preformed cavity inside the defect piece is scanned at the highest possible scanning accuracy, achieving the purpose of high-throughput detection. In the detection process, different defect pieces are scanned with the highest possible μCT accuracy to ensure the accuracy of the μCT scanning results. This step places the defect piece containing the preformed cavity in a specific direction, mainly achieving two technical effects: first, by making the length direction of the defect piece perpendicular to the rotating stage, it ensures that the high-energy rays emitted by the X-ray tube can penetrate the maximum cross-sectional area of the defect piece at any projection angle of μCT scanning, thereby optimizing the projection contrast and signal-to-noise ratio of the defect piece, laying a solid foundation for subsequent high-fidelity three-dimensional reconstruction. Second, by standardizing the parameter design in the defect piece modeling and preforming process, an accurate conversion relationship from pixels to real physical size is established in the reconstructed three-dimensional volume data, which can be directly used to verify the limit resolution and size measurement accuracy of the system for defect detection, providing a key basis for realizing accurate and quantitative characterization of defects.

[0021] Step 102, rotate the rotating stage according to a preset angle step, and at each rotation step, use the X-ray tube to emit high-energy rays to the defect piece to the X-ray receiver, which records the X-ray imaging to obtain the two-dimensional projection image of the defect piece, until the scanning is completed after one rotation, obtaining a sequence of two-dimensional projection images of the defect piece.

[0022] In some embodiments, the rotating stage is rotated according to a preset angle step, and at each rotation step, the X-ray tube emits high-energy rays to the defect piece, and the two-dimensional projection image of the defect piece is presented on the X-ray receiver, obtaining the two-dimensional projection image of the defect piece, until the scanning is completed after one rotation, and the two-dimensional projection images of the defect piece corresponding to multiple rotation steps are integrated to obtain a sequence of two-dimensional projection images of the defect piece. This step realizes the non-missing scanning of the defect piece from multiple angles through the precise control of the preset angle step and the sequential acquisition of the two-dimensional projection images. This operation actually decomposes and records the internal structural information of the defect piece in the sequence of two-dimensional projection images through rotation scanning and projection image accumulation, laying a solid data foundation for subsequent image reconstruction. This method not only ensures the comprehensiveness and systematicness of the detection, effectively avoiding missed detection, but also accurately reflects the topographic features of the defect piece at different viewing angles, thereby significantly improving the accuracy and reliability of defect positioning, qualitative and quantitative analysis, and providing indispensable raw data guarantee for high-precision three-dimensional volume data reconstruction.

[0023] Step 103, using data processing reconstruction software, data reconstruction is performed on the sequence of two-dimensional projection images to obtain a three-dimensional digital model containing the internal information of the defect piece.

[0024] In some embodiments, a three-dimensional reconstruction algorithm of the data processing reconstruction software is employed to reconstruct the data of the two-dimensional projection images corresponding to each rotation step, to obtain a three-dimensional digital model containing internal information of the defective part. This step converts the two-dimensional projection image sequence into an accurate three-dimensional digital model containing internal structures through the three-dimensional reconstruction algorithm of the data processing reconstruction software. This process realizes a qualitative leap from discrete and abstract two-dimensional information to intuitive and continuous three-dimensional data, enabling the three-dimensional morphology, accurate size, spatial position and distribution relationship of the hidden preformed cavity inside the defective part to be presented losslessly and stereoscopically.

[0025] In some embodiments, the above step 103 is implemented by the following step A1: Step A1, a three-dimensional reconstruction algorithm of the data processing reconstruction software is employed to reconstruct the data of the two-dimensional projection image sequence, to obtain three-dimensional volume data containing internal information of the defective part, and an isosurface extraction is performed on the three-dimensional volume data to generate the three-dimensional digital model containing internal information of the defective part stored in STL format.

[0026] In some embodiments, the conversion accuracy error of the isosurface extraction is 0.005 mm, and the angle error is 10°.

[0027] In some embodiments, this step converts the two-dimensional projection sequence into a three-dimensional digital model (StereoLithography, STL) that can accurately reflect the internal true morphology of the defective part through the three-dimensional reconstruction algorithm of the data processing reconstruction software and high-precision isosurface extraction. The advantage of this processing is that, on the one hand, the three-dimensional volume data realizes all-around and unobstructed visualization of internal defects, enabling the spatial position, three-dimensional size and complex geometric shape of the defects to be intuitively presented and analyzed. On the other hand, by strictly controlling the conversion accuracy error of the isosurface extraction within 0.005 mm and the angle error within 10°, the fidelity of the morphology from data to model is effectively guaranteed, and distortion or omission of key features in the conversion process is effectively avoided. This provides a reliable three-dimensional data basis of metrological precision for subsequent defect accurate quantification, product quality traceability and reverse engineering applications, and significantly improves the credibility and practical value of the detection results.

[0028] It should be noted that the STL format file is used to "wrap" the surface of a three-dimensional model with countless small triangles, thereby approximately describing the shape of the model. A more intuitive description is that STL is a simple and universal file format that uses a triangular mesh to approximately describe the surface of a three-dimensional object.

[0029] Step 104, using the data post-processing software, analyzing the size and geometric characteristics of the pre-cavity of the three-dimensional digital model, and determining the feasible cavity pre-parameters of the defective part according to the analysis results and the design size of the pre-cavity of the defective part.

[0030] Here, this step realizes scientific and quantitative decision of the feasible cavity pre-parameters by accurately comparing the objective analysis results of the pre-cavity of the three-dimensional digital model with the original design size.

[0031] In some embodiments, the above step 104 is realized by the following steps B1 to B3: Step B1, using the data post-processing software, analyzing the size and geometric characteristics of the pre-cavity of the three-dimensional digital model, and obtaining the quantitative index of the size, geometric characteristics and grid quality of the pre-cavity of the defective part.

[0032] In some embodiments, the size of the pre-cavity of the defective part is the diameter of the pre-cavity when the pre-cavity is spherical, and the size of the pre-cavity of the defective part is the ratio of the major axis and the minor axis of the pre-cavity when the pre-cavity is ellipsoidal; the geometric characteristics of the pre-cavity of the defective part are the shape of the pre-cavity; and the quantitative index of the grid quality of the pre-cavity of the defective part is the quality of the triangular grid on the surface of the three-dimensional model wrapped around the pre-cavity.

[0033] In some embodiments, the above step B1 is realized by the following steps B101 to B102: Step B101, using the data segmentation submodule of the data post-processing software, phase-segmenting the three-dimensional digital model to obtain the quantized pre-cavity data of the defective part.

[0034] In some embodiments, the data segmentation submodule of the data post-processing software identifies the geometric characteristics of different regions in the three-dimensional digital model, and segments the three-dimensional digital model according to the geometric characteristics of different regions (i.e. segments the three-dimensional digital model into components with specific properties), to obtain the quantized pre-cavity data of the defective part. This step extracts the pre-cavity structure inside the defective part and converts the pre-cavity structure into quantifiable three-dimensional data, providing a basis for subsequent quality detection and process analysis.

[0035] Step B102, using the quantitative analysis submodule of the data post-processing software, quantitatively analyzing the quantized pre-cavity data of the defective part to obtain the quantitative index of the size, geometric characteristics and grid quality of the pre-cavity of the defective part.

[0036] In some embodiments, the quantitative analysis submodule of the data post-processing software is used to automatically identify and quantify the model features by algorithm, and the quantitative calculation of the preform cavity data of the defective part is performed to obtain the quantitative indexes of the size, geometric features and grid quality of the preform cavity of the defective part.

[0037] Step B2, numerical simulation is performed according to the quantitative indexes of the size, geometric features and grid quality of the preform cavity of the defective part to obtain simulation data of the preform cavity of the defective part and compare the simulation data with the design size of the preform cavity of the defective part to obtain the effect data for characterizing the subjective forming of the defective part.

[0038] In some embodiments, numerical simulation is performed according to the quantitative indexes of the size, geometric features and grid quality of the preform cavity of the defective part to obtain simulation data of the preform cavity of the defective part; and 3D length (Length3D) evaluation of the forming quality is performed on the simulation data of the preform cavity of the defective part and the design size of the preform cavity of the defective part to obtain the effect data for characterizing the subjective forming of the defective part. This step determines the effect data for characterizing the subjective forming of the defective part by performing numerical simulation on the quantitative indexes of the size, geometric features and grid quality of the preform cavity of the defective part to obtain simulation data of the preform cavity of the defective part, and performing Length3D evaluation of the forming quality on the simulation data of the preform cavity of the defective part and the design size of the preform cavity of the defective part.

[0039] Step B3, the effect data is subjected to comprehensive screening of controllable preform cavity parameters to obtain feasible preform cavity parameters of the defective part.

[0040] In some embodiments, the feasible preform cavity parameters of the defective part are obtained by comprehensively screening the qualified equipment parameters and defect size and morphology parameters of the plurality of preform cavities of the defective part according to the effect data. This step effectively eliminates unqualified or uncontrollable equipment parameters by comprehensively screening a large amount of simulation data, and accurately identifies feasible parameter combinations that can meet the forming quality requirements and are compatible with certain inherent defects, i.e., feasible preform cavity parameters.

[0041] In some embodiments, the preform cavity comprises: a first preform sub-cavity in the shape of a sphere; wherein the diameter of the first preform sub-cavity is any size in the size sequence.

[0042] a second preform sub-cavity in the shape of an ellipsoid; wherein the major axis of the second preform sub-cavity is any size in the size sequence; and the ratio of the major axis to the minor axis of the second preform sub-cavity is 2:1 or 3:1 or 5:1.

[0043] The size sequence at least includes 150 μm, 200 μm, 250 μm, 300 μm, 350 μm, 400 μm, 450 μm, 500 μm, 550 μm, and 600 μm.

[0044] In some embodiments, for example, there are 3 preformed cavities in the defective part; wherein the 3 preformed cavities include 2 first preformed sub-cavities and 1 second preformed sub-cavity. The diameters of the 2 first preformed sub-cavities are 150 μm and 200 μm, respectively; the ratio of the major axis to the minor axis of the 1 second preformed sub-cavity is 2:1, the major axis is 300 μm, and the minor axis is 150 μm.

[0045] In some embodiments, the feasible cavity preforming parameters include a feasible distance between any two of the preformed cavities and a feasible size of the preformed cavities.

[0046] In some embodiments, the feasible distance between any two of the preformed cavities includes a feasible horizontal distance and a feasible vertical distance.

[0047] Correspondingly, when the preformed cavity is a first preformed sub-cavity, the feasible size of the preformed cavity is a feasible diameter of the first preformed sub-cavity; when the preformed cavity is a second preformed sub-cavity, the feasible size of the preformed cavity is a feasible ratio of the major axis to the minor axis and a feasible major axis of the second preformed sub-cavity.

[0048] In some embodiments, the obtaining process of the defective part includes the following steps: First, a preset cuboid with a size of 12 mm x 7.3 mm x 4 mm is obtained.

[0049] Second, a distance between any two of the preformed cavities is determined.

[0050] The distance includes a horizontal distance of not less than 1 mm and a vertical distance of a first preset size, wherein the first preset size is any one of 0.15 mm, 0.5 mm, 1 mm, 1.5 mm, and 2.5 mm.

[0051] Then, a defective part model is constructed according to the preset cuboid, the plurality of preformed cavities, and the distance between any two of the preformed cavities.

[0052] Finally, based on the defective part model, a powder laying and laser selective melting additive manufacturing device is used for printing to obtain the defective part.

[0053] In some embodiments, in order to realize high-throughput detection, the influence between prefabricated cavities is considered as much as possible under the premise of implanting prefabricated cavities, so as to stipulate that the horizontal distance between any two prefabricated cavities is not less than 1 mm; the long axis direction of the ellipsoidal prefabricated cavity is arranged in parallel and perpendicular to the deposition direction, and the center of the prefabricated cavity is arranged at five different depths from the upper surface of the printed defect part (that is, the vertical distance between any two prefabricated cavities can be any one of 0.15 mm, 0.5 mm, 1 mm, 1.5 mm and 2.5 mm).

[0054] It should be noted that after the printing of the defect part is completed, the defect part can be separated from the substrate by wire electrical discharge machining for subsequent high-throughput nondestructive testing.

[0055] In some embodiments, the device parameters of the powder-falling powder-laying laser selective melting additive manufacturing device at least include: the particle size distribution range of the spherical powder of the metal is 15 μm to 40 μm; the single-layer forming thickness of the metal is 0.14 mm; the laser scanning parameters are: the scanning direction of each layer is rotated by 67° relative to the previous layer; the material of the substrate is a metal material compatible with the powder-falling powder-laying laser selective melting additive manufacturing device; and the parallelism error of the upper surface of the substrate is not greater than 0.04 mm.

[0056] In some embodiments, the device parameters of the powder-falling powder-laying laser selective melting additive manufacturing device described above guarantee the stability of the molten pool and the densification of the forming, and the prefabricated defects are realized by the high-throughput detection of the prefabricated cavities in the defect part through direct printing design, so the selected device parameters cannot introduce natural defects to affect the prefabricated defects.

[0057] The high-throughput nondestructive detection method for the prefabricated metal additive defect part provided in the embodiments of the present application is executed by a micro-computed tomography detection system, which at least comprises an X-ray tube, a rotating sample stage and an X-ray receptor. In the execution process of the method, first, the defect part is placed on the rotating sample stage in a manner that the length direction of the defect part is perpendicular to the rotating sample stage; wherein the defect part is generated by implanting a plurality of prefabricated cavities in a metal cuboid of a preset size in a manner of Boolean operation; second, the rotating sample stage is rotated in a preset angle step, and at each rotation step, high-energy rays are emitted from the X-ray tube to the defect part to the X-ray receptor, the X-ray receptor records X-ray imaging to obtain a two-dimensional projection image of the defect part, until the scanning is completed after one rotation, a two-dimensional projection image sequence of the defect part is obtained; then, data reconstruction software is used to perform data reconstruction on the two-dimensional projection image sequence to obtain a three-dimensional digital model containing internal information of the defect part; finally, data post-processing software is used to perform prefabricated cavity size and geometric feature analysis on the three-dimensional digital model, and the feasible cavity prefabrication parameters of the defect part are determined according to the analysis result and in combination with the design size of the prefabricated cavity of the defect part. In this way, the feasible cavity prefabrication parameters of the defect part are determined by scanning, image reconstruction and analysis of the defect part implanted with a plurality of prefabricated cavities by the micro-computed tomography system. In this way, the present application realizes efficient and nondestructive detection and accurate analysis of the internal cavity of the metal additive defect part, and provides a reliable basis for optimizing the manufacturing process.

[0058] The high-throughput nondestructive detection method for the prefabricated metal additive defect part provided in the embodiments of the present application will be described below in combination with a specific embodiment, however, it is worth noting that the specific embodiment is only for better illustrating the present application and does not constitute an improper limitation on the present application.

[0059] The high-throughput nondestructive detection method for the prefabricated metal additive defect part provided in the embodiments of the present application can use a powder-falling powder-laying laser selective melting experimental device to deposit a cuboid required for printing the defect part on a TA15 titanium alloy substrate layer by layer. First, the powder can be set to use aerosolized TA15 titanium alloy powder with a particle size of 15-40 μm. Then, a three-dimensional model is established according to the designed prefabricated cavity size, and all prefabricated cavities are embedded in the printed defect part model in combination with the designed position and size, to obtain a defect part as shown in Figure 2 Here, the device parameters of the powder-falling powder-laying laser selective melting experimental device need to be set to ensure the formation of a dense structure to prevent natural defects generated in the printing process from affecting the artificial prefabricated defects.

[0060] Here, the printer deposits on the TA15 titanium alloy substrate layer by layer to obtain a high-throughput detection defect part, and uses wire electrical discharge machining to separate the printed defect part from the substrate to prepare for μCT scanning. Among them, the main part of the μCT device is as shown inFigure 3 As shown, during the test, the X-ray tube emits high-energy rays that penetrate the object being tested, i.e., as... Figure 3 The defective component shown is fed to an X-ray receiver. As the stage rotates, the receiver records X-ray imaging information at each rotation until the scan is complete. During the scan, the geometric information of the X-ray-penetrated portion of the defective component is recorded and processed, resulting in a sequence of two-dimensional projection images containing the internal geometry of the defective component. This sequence, combined with high-throughput defect detection, enables non-destructive high-throughput inspection. Before using μCT to inspect defective components, adjustments need to be made for different defective components, such as... Figure 3 The horizontal distances L1 (the horizontal distance between the X-ray tube and the defective part) and L2 (the horizontal distance between the defective part and the X-ray receiver) are calculated to maximize scanning accuracy. Furthermore, Figure 3 The X-ray receiver shown is typically a flat panel detector, which is usually located opposite the defective part and is the core imaging component used to receive and record the X-ray signal after penetrating the defective part and convert the defective part into a digital image.

[0061] In the embodiments of this application, the following are adopted: Figure 3 After the μCT system scans the defective component, it first uses data processing and reconstruction software to reconstruct the two-dimensional projection image sequence containing the internal geometric information of the defective component, generating a three-dimensional digital model of the defective component. Then, the three-dimensional digital model is imported into Avizo data post-processing software to extract and analyze the pre-fabricated cavity information within the three-dimensional digital model. In Avizo data post-processing software, the SandBox algorithm can be used for noise reduction. Based on grayscale, the pre-fabricated cavity and the metal solid within the defective component are distinguished. Here, it is necessary to ensure that the SandBox algorithm noise reduction parameters remain constant throughout the processing of different defective components to guarantee consistency. The geometric information of the pre-fabricated cavity is calculated and visualized in the three-dimensional digital model, such as... Figure 4 The visualization result of high-throughput inspection of the prefabricated cavity shown in the figure shows defects 1 to 5 in the defective component (i.e., the prefabricated cavity in the embodiment of this application).

[0062] In addition, a quantitative evaluation method for molding effect based on the size of prefabricated cavities can be adopted to analyze the geometric data of prefabricated cavities in defective parts, focus on Lengh3D data and use it to characterize molding quality. At the same time, in order to reduce algorithm error, the three-dimensional model of artificial defects can be extracted separately according to the prefabricated cavity number (i.e. defect identity document, defect ID) and compared with the design model to subjectively judge the molding quality.

[0063] In some embodiments of the present application, the defective part can be manufactured by using the powder falling powder laser selective melting experimental equipment, wherein the artificial prefabricated defect can be formed by establishing a prefabricated cavity inside the solid model of the printed defective part. Due to the small size of the artificially designed prefabricated cavity, in order to prevent distortion and other problems during the format conversion of the three-dimensional model, the STL file conversion precision error is specified to be not more than 0.005 mm, and the angle error is not more than 10° in the corresponding manufacturing link, and further μCT combined with high-throughput detection means is used to detect and evaluate the prefabricated cavity inside the printed defective part. Here, reference can be made to the specific flowchart of the high-throughput nondestructive detection method for prefabricated metal additive defective parts provided by the embodiments of the present application as shown in Figure 5 The specific flowchart of the high-throughput nondestructive detection method for prefabricated metal additive defective parts provided by the embodiments of the present application as shown in 501. Design a three-dimensional model of the prefabricated cavity and a defective part model according to the set size range parameters.

[0064] Here, in order to screen the prefabricated defective parts with the best forming quality, on the basis of a large number of experiments in the early stage, the parameter range of the size and morphology of the prefabricated cavity is summarized. For the diameter of the prefabricated cavity (for spherical prefabricated cavity) or the long axis (for ellipsoidal prefabricated cavity), a ten-level parameter sequence of 150, 200, 250, 300, 350, 400, 450, 500, 550, 600 μm is set. In addition, for the ellipsoidal prefabricated cavity, a three-level shape control ratio of 2:1, 3:1, 5:1 is set. Therefore, by using the above size range, prefabricated cavity size parameters with good forming quality can be efficiently screened under the premise of meeting the commonly used prefabricated cavity size and morphology, thereby reducing the trial and error cost.

[0065] Among them, for the artificial prefabricated cavity, the prefabricated cavity model can be directly printed and formed, and the defective part with the prefabricated cavity is subjected to slicing treatment, and the three-dimensional information of the internal prefabricated cavity is recognizable by the printer. Here, when the layer-by-layer printing process reaches the position of the prefabricated cavity, the laser will not scan the prefabricated cavity area, and the other areas will be normally melted and solidified, thereby forming a prefabricated cavity in the prefabricated cavity area. The prefabricated cavity area is filled with loose powder particles due to the layer-by-layer powder laying, and finally forms an artificial prefabricated cavity. The above prefabricated cavity modeling method can maximize the simulation of the real defect morphology.

[0066] In addition, the high-throughput detection of defective parts is designed to meet the high accuracy and small scanning range of the μCT, and as many prefabricated cavities as possible are accommodated. The size of the defective part is designed to be 12 mm x 7.3 mm x 4 mm. In order to achieve the purpose of high-throughput detection, multiple prefabricated cavities are arranged in one defective part, and the spatial distance between any two prefabricated cavities is not less than 1 mm, so as to ensure that the prefabricated cavities do not affect each other. The prefabricated cavity space position is set to five levels of depth of 0.15, 0.5, 1, 1.5, and 2.5 mm from the center of the prefabricated cavity to the upper surface of the printed defective part, in order to verify and characterize the influence of surface effects on the prefabricated cavity forming topography. Here, the spatial layout of the printed defective part and the prefabricated cavity inside it can be further referred to Figure 2

[0067] 502. Set the device parameters of the powder falling and powder laying laser selective melting experimental device and print the forming to obtain a high-throughput monitoring test piece: this link gives the specific device parameters, wherein, in the process of printing the defective part, the TA15 spherical particle powder is deposited layer by layer on the TA15 substrate to manufacture, the printing layer thickness of the powder falling and powder laying laser selective melting experimental device is set to 0.14 mm, and the laser scanning strategy is 67° rotary scanning. That is, the selected device parameters need to ensure that the formed structure is dense to prevent the natural printing of the prefabricated cavity from affecting the prefabricated cavity. After deposition, the printed defective part is separated from the substrate by wire electrical discharge machining to complete the preparation of the high-throughput detection defective part.

[0068] 503. μCT non-destructive testing and post-processing to obtain prefabricated cavity related data.

[0069] The μCT non-destructive testing associated μCT detection system is composed of RX Solutions computer tomography high-performance X-ray system EasyTom 230, μCT data processing reconstruction software, and Avizo data post-processing software. Before detection, the printed defective part is placed vertically to the rotating sample stage according to the length direction, to ensure that all prefabricated cavities in the defective part can be scanned at the same time under the premise of improving the scanning accuracy as much as possible, to obtain a two-dimensional projection image sequence containing the geometric information of the defective part, thereby achieving the purpose of high-throughput detection. In addition, during the detection process, the scanning accuracy can be improved as much as possible by adjusting the μCT scanning system parameters, thereby ensuring the accuracy of the μCT scanning results.

[0070] ​After the scanning of the defective part is completed, the two-dimensional projection image sequence containing the internal geometric information of the defective part can be reconstructed by the μCT data processing reconstruction software, a gray three-dimensional model of the printed defective part (i.e., a three-dimensional digital model) is generated, and the gray three-dimensional model of the defective part is imported into the Avizo data post-processing software for geometric information analysis of the gray three-dimensional model. After noise reduction processing is performed in the Avizo data post-processing software using the SandBox algorithm, the preformed cavity and the metal entity of the defective part are distinguished according to the gray scale, and it is necessary to ensure that the SandBox noise reduction parameters remain unchanged in the processing of different parts to ensure consistency. After the Avizo data post-processing software distinguishes the entity and the preformed cavity, the geometric information of the preformed cavity is calculated and visualized in the three-dimensional model.

[0071] 504. Calculate the preformed cavity Length3D data and use it to evaluate the forming quality.

[0072] Here, due to the preformed cavity principle, there is un-melted or partially melted metal powder in the formed preformed cavity, and the three-dimensional data of the preformed cavity obtained by μCT scanning will be distorted due to the interference of the powder, and the conventional forming effect representation parameters cannot accurately describe the defect morphology. Therefore, the embodiments of the present application use Length3D to evaluate the forming effect of the scanning processing result of the preformed cavity of the defective part to screen the size and morphology parameters of the preformed cavity; wherein Lengh3D represents the maximum length in the spatial size of the preformed cavity. Compared with parameters such as sphericity, volume, and equivalent diameter, the embodiments of the present application use Lengh3D to quantify the morphology of the defect, which can maximize the influence of processing and detection errors on the forming effect of the defect. At the same time, the three-dimensional model of the artificial preformed cavity can also be extracted according to the defect ID and compared with the design model to subjectively judge the forming quality.

[0073] According to the above description, the advantages of the present application compared with the prior art are as follows: (1) The method proposed in the present application can effectively optimize the parameters of the preformed defect of additive manufacturing.

[0074] (2) The method of directly modeling and printing the preformed defect adopted in the present application has the advantages of high realizability and simple operation.

[0075] (3) The present application fully considers the defect parameters and spatial layout parameters that may be used in the research process, and is suitable for most related researches of preformed defects.

[0076] (4) The high-throughput μCT detection technology proposed in the present application can greatly improve the detection efficiency and reduce the printing and detection cost under the premise of ensuring the printing quality and scanning accuracy.

[0077] The defect forming quality evaluation method adopted by the application considers the interference of the pre-cavity collapse in the printing process on the geometric size data, and can effectively evaluate the forming quality of the mu CT defect scanning result.

[0078] Although the specific embodiments of the application are described above, so that those skilled in the art can understand the application, it should be clear that the application is not limited to the scope of the specific embodiments, and for those skilled in the art, as long as various changes are within the spirit and scope of the application defined and determined by the appended claims, all the inventions utilizing the concept of the application are included in the protection.

[0079] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that the size of the sequence number of the above processes in various embodiments of the application does not mean the order of execution, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application. The sequence number of the above embodiments of the application is only for description, not representing the advantages and disadvantages of the embodiments.

[0080] It should be noted that in this paper, the term "include", "contain" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the sentence "including a…" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0081] In several embodiments provided by the application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division, and actual implementation can have another division manner. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0082] The units described as separate components above can or can not be physically separate, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; and part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0083] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

[0084] Alternatively, the integrated unit of the present application, if realized in the form of a software function module and sold or used as an independent product, can also be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing an apparatus to perform all or part of the methods described in the embodiments of the present application. The storage medium mentioned above includes: mobile storage devices, ROM, magnetic discs or optical discs and various media that can store program codes.

[0085] The methods disclosed in the several method embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments.

[0086] The features disclosed in the several method or device embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments or device embodiments.

[0087] The above is only an implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for high throughput non-destructive testing of a pre-fabricated metal additive defective piece, characterized in that, The method is executed by a micro-computed tomography detection system, the micro-computed tomography detection system at least includes: an X-ray tube, a rotating stage and an X-ray receiver, and the method includes: Placing the defective piece on the rotating stage in a manner that the length direction of the defective piece is perpendicular to the rotating stage, wherein the defective piece is generated by implanting a plurality of preformed cavities in a metal cuboid of a preset size in a Boolean operation manner; Rotating the rotating stage according to a preset angle step, and at each rotation step, emitting high-energy rays from the X-ray tube to the defective piece to the X-ray receiver, the X-ray receiver records X-ray imaging to obtain a two-dimensional projection image of the defective piece, until the scanning is completed after one rotation to obtain a two-dimensional projection image sequence of the defective piece; Reconstructing data of the two-dimensional projection image sequence by using data processing reconstruction software to obtain a three-dimensional digital model containing internal information of the defective piece; Performing preformed cavity size and geometric feature analysis on the three-dimensional digital model by using data post-processing software, determining feasible cavity preformed parameters of the defective piece according to the analysis result and combining the design size of the preformed cavity of the defective piece.

2. The method for high throughput non-destructive testing of pre-manufactured metal additive defective pieces of claim 1, wherein, The method is executed by a micro-computed tomography detection system, the micro-computed tomography detection system at least includes: an X-ray tube, a rotating stage and an X-ray receiver, and the method includes: Placing the defective piece on the rotating stage in a manner that the length direction of the defective piece is perpendicular to the rotating stage, wherein the defective piece is generated by implanting a plurality of preformed cavities in a metal cuboid of a preset size in a Boolean operation manner; Rotating the rotating stage according to a preset angle step, and at each rotation step, emitting high-energy rays from the X-ray tube to the defective piece to the X-ray receiver, the X-ray receiver records X-ray imaging to obtain a two-dimensional projection image of the defective piece, until the scanning is completed after one rotation to obtain a two-dimensional projection image sequence of the defective piece; 3. The method for high throughput non-destructive testing of pre-manufactured metal additive defect pieces of claim 1, wherein, Reconstructing data of the two-dimensional projection image sequence by using data processing reconstruction software to obtain a three-dimensional digital model containing internal information of the defective piece; Performing preformed cavity size and geometric feature analysis on the three-dimensional digital model by using data post-processing software, determining feasible cavity preformed parameters of the defective piece according to the analysis result and combining the design size of the preformed cavity of the defective piece. The method is executed by a micro-computed tomography detection system, the micro-computed tomography detection system at least includes: an X-ray tube, a rotating stage and an X-ray receiver, and the method includes: Placing the defective piece on the rotating stage in a manner that the length direction of the defective piece is perpendicular to the rotating stage, wherein the defective piece is generated by implanting a plurality of preformed cavities in a metal cuboid of a preset size in a Boolean operation manner; 4. The method for high throughput non-destructive testing of pre-manufactured metal additive defect pieces of claim 3, wherein, Rotating the rotating stage according to a preset angle step, and at each rotation step, emitting high-energy rays from the X-ray tube to the defective piece to the X-ray receiver, the X-ray receiver records X-ray imaging to obtain a two-dimensional projection image of the defective piece, until the scanning is completed after one rotation to obtain a two-dimensional projection image sequence of the defective piece; Reconstructing data of the two-dimensional projection image sequence by using data processing reconstruction software to obtain a three-dimensional digital model containing internal information of the defective piece; Performing preformed cavity size and geometric feature analysis on the three-dimensional digital model by using data post-processing software, determining feasible cavity preformed parameters of the defective piece according to the analysis result and combining the design size of the preformed cavity of the defective piece. A phase segmentation is performed on the three-dimensional digital model by using a data segmentation submodule of the data post-processing software, and preformed cavity data of the defective part after quantization is obtained. A quantitative analysis is performed on the preformed cavity data of the defective part after quantization by using a quantitative analysis submodule of the data post-processing software, and quantitative indexes of the size, geometric characteristics and grid quality of the preformed cavity of the defective part are obtained.

5. The method for high throughput non-destructive testing of pre-manufactured metal additive defect pieces of claim 1, wherein, The preformed cavity comprises: a first preformed sub-cavity in a spherical shape, wherein the diameter of the first preformed sub-cavity is any size in a size sequence; a second preformed sub-cavity in an ellipsoidal shape, wherein the long axis of the second preformed sub-cavity is any size in the size sequence, and the ratio of the long axis to the short axis of the second preformed sub-cavity is 2:1 or 3:1 or 5:1; The size sequence at least comprises 150 μm, 200 μm, 250 μm, 300 μm, 350 μm, 400 μm, 450 μm, 500 μm, 550 μm and 600 μm.

6. The method for high throughput non-destructive testing of pre-manufactured metal additive defect pieces of claim 1, wherein, The feasible cavity preformed parameters comprise a feasible spacing between any two preformed cavities and a feasible size of the preformed cavities.

7. The method for high-throughput non-destructive testing of pre-manufactured metal additive defect pieces according to any one of claims 1 to 6, characterized in that, The acquisition process of the defective part comprises: acquiring a preset cuboid constructed in 12 mm x 7.3 mm x 4 mm; determining the spacing between any two preformed cavities, wherein the spacing comprises a horizontal spacing not less than 1 mm and a vertical spacing of a first preset size, and the first preset size is any one of 0.15 mm, 0.5 mm, 1 mm, 1.5 mm and 2.5 mm; constructing a defective part model according to the preset cuboid, the plurality of preformed cavities and the spacing between any two preformed cavities; printing by using a powder-falling and powder-laying laser selective melting additive manufacturing equipment based on the defective part model to obtain the defective part.

8. The method for high throughput non-destructive testing of pre-manufactured metal additive defect pieces of claim 7, wherein, The equipment parameters of the powder-falling and powder-laying laser selective melting additive manufacturing equipment at least comprise: the particle size distribution range of the spherical powder of the metal is 15 μm to 40 μm; the single-layer forming thickness of the metal is 0.14 mm; the laser scanning parameter is that the scanning direction of each layer is rotated by 67° relative to the previous layer; the material of the substrate is a metal material compatible with the powder-falling and powder-laying laser selective melting additive manufacturing equipment; the parallelism error of the upper surface of the substrate is not greater than 0.04 mm.

Images