Information processing device, defect detection method, and 3D additive manufacturing apparatus

Abstract

This invention provides an information processing device that can confirm during additive manufacturing whether potential defects remain as defects in the fabricated object based on layer data obtained from multiple layers. [Solution] The PC54 includes a defect candidate identification unit 72 that identifies multiple defect candidates located at substantially the same position in multiple adjacent layers in the stacking direction, a defect detection unit 73 that detects defects remaining in the fabricated object based on the size in the stacking direction and the size within the layer of the multiple defect candidates located at substantially the same position in multiple layers identified, and a fabrication control unit 71 that controls the fabrication to stop or output a warning if the size of a defect exceeds a preset threshold during the fabrication process, at least for the fabricated object in which a defect has been detected.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, a defect detection method, and a three-dimensional layer manufacturing apparatus.

Background Art

[0002] In recent years, three-dimensional layer manufacturing technology that forms by stacking layers of thinly laid metal powder materials one by one has been in the spotlight, and many types of three-dimensional layer manufacturing technologies have been developed depending on the material of the powder material and the manufacturing method.

[0003] The manufacturing method of a conventional three-dimensional layer manufacturing apparatus is, for example, to spread a powder material layer by layer on a base plate installed on the upper surface of a stage. Next, only a two-dimensional structure portion corresponding to one cross-section of the object to be manufactured is melted by a heating mechanism composed of an electron beam or a laser on the powder material spread on the base plate. Then, the object to be manufactured is formed by stacking such powder material layers one by one in the height direction (Z direction). Since a powder bed is formed on the base plate during the manufacturing of each layer of the object to be manufactured, this manufacturing method is also called the powder bed method.

[0004] When forming the object to be manufactured, in a portion where sintering failure occurs, the powder material does not melt completely, and unevenness occurs on the surface of the object to be manufactured during manufacturing. Since such unevenness can become a defect, in a three-dimensional layer manufacturing apparatus, defect detection has been performed based on data obtained from the surface of the object to be manufactured by camera imaging or BSE (Back Scattered Electron) imaging. Camera imaging is, for example, a method of imaging the surface of the object to be manufactured with visible light. BSE imaging is a method of imaging the surface of the object to be manufactured by detecting reflected electrons of an electron beam irradiated on the surface of the object to be manufactured.

[0005] Layer data obtained through camera photography or BSE photography is displayed on the monitor as an XY cross-sectional view of each layer of the object being fabricated. The user can use this layer data to check the state of the XY cross-section immediately after melting the powder material. The user could also identify potential defects (referred to as defect candidates) based on the surface irregularities of the object being fabricated. Examples of defect candidates include areas with irregularities in the layers and areas where melting was insufficient, resulting in voids.

[0006] As a technology for detecting such defects, the technology disclosed in Patent Document 1 was known. Patent Document 1 states that "the determination unit calculates the height of the protrusions and at least one of the total area of ​​the region where the protrusions occurred and the proportion of the protrusions in the sintered region from the state of the protrusions measured by the surface state measurement unit, and compares these with corresponding thresholds to determine whether the sintered layer is good or bad." [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2020-200501 [Overview of the project] [Problems that the invention aims to solve]

[0008] As mentioned above, even if a user could check the state of the XY cross-section layer by layer immediately after melting using conventional defect detection functions, hundreds to thousands of layers are added during the manufacturing process, making it difficult for the user to visually check the state of all layers. After the additive manufacturing is complete, the information necessary for defect detection cannot be obtained without inspecting the manufactured object with other measuring equipment (for example, a device using X-ray CT (Computed Tomography)). Therefore, it was usually impossible to know whether a problematic defect occurred during additive manufacturing until after the process was completed. Furthermore, conventional defect detection functions did not provide defect information, such as the location of the defect.

[0009] Due to the circumstances described above, it was not possible to prevent cases where, after several days of additive manufacturing had been completed, problems with the quality of the printed object were discovered. When quality problems occur after the manufacturing is complete, users lose a significant amount of time and cost in the production process of the printed object.

[0010] This invention was made in view of these circumstances, and aims to prevent the loss of significant time and cost in the production process of molded objects. [Means for solving the problem]

[0011] The information processing device according to the present invention includes: a defect candidate identification unit that detects defective areas in each layer as defect candidates based on layer data obtained for each layer in which the molded object is additively manufactured, and identifies a plurality of defect candidates located at substantially the same position in a plurality of adjacent layers in the stacking direction; a defect detection unit that detects defects remaining in the molded object based on the size in the stacking direction and the size within the layer of the plurality of defect candidates located at substantially the same position identified in the plurality of layers; and a molding control unit that controls the additive manufacturing of at least the molded object in which defects have been detected to stop or output a warning if the size of a defect exceeds a preset threshold during the additive manufacturing process. The above-described information processing device is one aspect of the present invention, and a defect detection method and a 3D additive manufacturing apparatus that reflect one aspect of the present invention are configured in the same manner as the above-described information processing device. [Effects of the Invention]

[0012] According to the present invention, if a problematic defect is detected in the manufactured object during the additive manufacturing process, the additive manufacturing of at least the defective object is stopped or a warning is issued, thereby preventing significant time and cost losses in the production process of the manufactured object. Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0013] [Figure 1] This is a schematic cross-sectional view illustrating a three-dimensional additive manufacturing apparatus according to the first embodiment of the present invention. [Figure 2] This is a block diagram showing an example of the configuration of a control system for a three-dimensional additive manufacturing apparatus according to the first embodiment of the present invention. [Figure 3] This is a block diagram showing an example configuration of a control block for a PC according to the first embodiment of the present invention. [Figure 4] This flowchart shows an example of a defect detection process according to the first embodiment of the present invention. [Figure 5] This flowchart shows an example of a defect candidate identification process according to the first embodiment of the present invention. [Figure 6] This is a perspective view of a molded object according to the first embodiment of the present invention. [Figure 7] This figure shows an example of a BSE image of a fabricated object viewed from above in the Z direction during additive manufacturing according to the first embodiment of the present invention. [Figure 8] This figure shows an example of a BSE image in which a candidate defect A occurring in the Nth layer according to the first embodiment of the present invention has been confirmed. [Figure 9] This figure shows an example of a BSE image in which a candidate defect B occurring in the N+1th layer according to the first embodiment of the present invention is confirmed. [Figure 10] This figure shows examples of BSE images of defect candidate A and defect candidate B, which were confirmed when the Nth layer and the N+1th layer were superimposed according to the first embodiment of the present invention. [Figure 11] This figure shows examples of defect candidates A and B with extremely different sizes according to the first embodiment of the present invention. [Figure 12] This flowchart shows an example of a defect detection process according to the first embodiment of the present invention. [Figure 13] This figure shows an example of a defect candidate spanning multiple layers according to the first embodiment of the present invention. [Figure 14] This figure shows an example of a defect candidate ID assigned to a defect candidate integrated by the defect detection unit according to the first embodiment of the present invention. [Figure 15] It is a diagram showing an example in which the lengths in the XY direction and the Z direction of a defect candidate integrated with a defect detection unit according to the first embodiment of the present invention are measured. [Figure 16] It is a flowchart showing an example of display control processing according to the first embodiment of the present invention. [Figure 17] It is a diagram showing an example of a GUI screen for setting the content of mold control when a defect detected by the defect detection processing according to the first embodiment of the present invention exceeds a threshold value. [Figure 18] It is a flowchart showing an example of mold control processing according to the first embodiment of the present invention. [Figure 19] It is a flowchart showing an example of mold control processing according to the second embodiment of the present invention. [Figure 20] It is a flowchart showing an example of mold control processing according to the third embodiment of the present invention. [Figure 21] It is a diagram showing an example of display on a result table display screen on which a result table according to the third embodiment of the present invention is displayed. [Figure 22] It is a diagram showing a BSE image of a plurality of molded objects viewed from above in the Z direction during layer molding and an example of the melting order of the plurality of molded objects. [Figure 23] It is a diagram showing a conventional example in which the molding process of some of the molded objects is stopped during layer molding and the melting order of the remaining molded objects is changed. [Figure 24] It is a diagram showing an example in which the molding process of some of the molded objects is stopped during layer molding according to the fourth embodiment of the present invention and the melting order of the remaining molded objects is changed to an appropriate order. [Figure 25] It is a flowchart showing an example of defect determination processing according to the sixth embodiment of the present invention. [Figure 26] It is a flowchart showing an example of defect determination processing according to the seventh embodiment of the present invention.

Modes for Carrying Out the Invention

[0014] Hereinafter, embodiments of the information processing apparatus, 3D additive manufacturing apparatus, and defect detection method of the present invention will be described with reference to the figures. In each figure, common components are denoted by the same reference numerals, and redundant explanations will be omitted.

[0015] [1. First Embodiment] 1-1. Configuration of a 3D additive manufacturing system First, a three-dimensional additive manufacturing apparatus according to the first embodiment of the present invention (hereinafter referred to as "this example") will be described with reference to Figure 1.

[0016] Figure 1 is a schematic cross-sectional view illustrating the 3D additive manufacturing apparatus of this example. In the following explanation, in order to clarify the shape and positional relationships of the various parts of the 3D additive manufacturing apparatus, the left-right direction in Figure 1 will be referred to as the X direction, the depth direction as the Y direction, and the up-down direction as the Z direction. The X, Y, and Z directions are mutually orthogonal. Furthermore, the X and Y directions are parallel to the horizontal direction, and the Z direction is parallel to the vertical direction.

[0017] The 3D additive manufacturing apparatus 1 shown in Figure 1 is a device that melts a powder material 32, which consists of metal powders such as titanium, copper, and tungsten, by irradiating it with an electron beam 15 (an example of a manufacturing beam), and then builds a three-dimensional object by stacking the layers of solidified powder material 32.

[0018] As shown in Figure 1, the 3D additive manufacturing apparatus 1 comprises a vacuum chamber 3, a beam irradiation device 2 (an example of an electron gun), a powder supply device 16, a build table 18, a build box 20, and a recovery box 21. The 3D additive manufacturing apparatus 1 also comprises a build plate 22, an inner base 24, a plate moving device 26, a radiation shield cover 28, a mask cover 30, a camera 42, and a shutter 44. Furthermore, the 3D additive manufacturing apparatus 1 is equipped with multiple detection units 46 for detecting backscattered electrons.

[0019] Vacuum chamber 3 is a chamber that creates a vacuum by exhausting the air inside the chamber using a vacuum pump (not shown). The inside of vacuum chamber 3 is maintained under vacuum. A beam irradiation device 2 is also installed in vacuum chamber 3.

[0020] The beam irradiation device 2 is a device that irradiates an electron beam 15 onto the molding surface 32a of the molding plate 22 or the powder layer formed from the powder material 32, and comprises an electron gun (an example of a beam irradiation unit) and an electron optical system. The molding surface 32a corresponds to the upper surface of the powder layer. The state of the powder layer changes as the 3D additive manufacturing process progresses. Although not shown in the figures, the beam irradiation device 2 has an electron gun that is the source of the electron beam 15, a focusing lens that focuses the electron beam 15 generated by the electron gun, and a deflection lens that deflects the electron beam 15 focused by the focusing lens as an electron optical system.

[0021] A focusing lens is constructed using a focusing coil, and the magnetic field generated by the focusing coil focuses the electron beam 15. A deflecting lens is constructed using a deflecting coil, and the magnetic field generated by the deflecting coil deflects the electron beam 15. Therefore, the electron optical system scans the electron beam 15 according to the melting conditions for melting the powder material, and melts the top layer of powder material (an example of a powder bed) spread on the build plate 22 with the electron beam 15.

[0022] The powder supply device 16 (an example of a powder supply system) supplies powder material 32, as an example of powder material to be used as raw material for the molded object 38, onto the build table 18, spreads it on the build plate 22, and forms a powder layer. The powder supply device 16 includes a hopper 16a, a powder dispenser 16b, and a squeegee 16c.

[0023] The hopper 16a is a container for storing metal powder. The powder dispenser 16b is a device that dispenses the powder material 32 stored in the hopper 16a onto the build table 18. The squeegee 16c is a long, elongated member in the Y direction and has a blade 16d for spreading powder. The squeegee 16c spreads the powder material 32, which is dropped by the powder dispenser 16b, onto the build table 18 and the build plate 22. The squeegee 16c is provided to be movable in the X direction in order to spread the powder material 32 over the entire surface of the build table 18.

[0024] The build table 18 is positioned horizontally inside the vacuum chamber 3. The build table 18 is positioned below the powder supply device 16. The central part of the build table 18 is open. The shape of the opening of the build table 18 is circular or rectangular in plan view (for example, a quadrilateral in plan view).

[0025] The build box 20 forms the space for builds. A build box 20 with a circular or angular cross-section is provided inside the vacuum chamber 3. The upper end of the build box 20 is connected to the opening edge of the build table 18. The lower end of the build box 20 is connected to the bottom wall of the vacuum chamber 3.

[0026] The recovery box 21 recovers any excess powder material 32 supplied onto the build table 18 by the powder supply device 16.

[0027] The build plate 22 forms a molded object 38 using powder material 32. The molded object 38 is formed by stacking on the build plate 22. The build plate 22 is formed in a circular or rectangular shape in plan view to match the opening shape of the build table 18. The build plate 22 is connected (grounded) to the inner base 24 by an earth wire 34 to prevent it from becoming electrically floating. The inner base 24 is maintained at GND (ground) potential.

[0028] During the creation of each layer of the molded object 38, a powder bed is formed on the build plate 22 and the inner base 24. The powder bed is made by spreading powder material 32 to a position several millimeters higher than the build table 18 installed on the build box 20. This powder bed is formed by a powder supply device 16 filled with powder material 32 and a squeegee 16c.

[0029] The inner base 24 is provided to be movable in the vertical direction (Z direction). The build plate 22 moves vertically together with the inner base 24. The inner base 24 has larger external dimensions than the build plate 22. The inner base 24 slides vertically along the inner surface of the build box 20. A sealing member 36 is attached to the outer circumference of the inner base 24. The sealing member 36 is a member that maintains sliding properties and airtightness between the outer circumference of the inner base 24 and the inner surface of the build box 20. The sealing member 36 is made of a material that is heat-resistant and elastic. Powder material 32 is spread on the inner base 24, and a build plate 22 on which the molded object 38 is formed is placed there.

[0030] The plate moving device 26 moves the build plate 22 and inner base 24 in the vertical direction. The plate moving device 26 is located inside the lower part of the build box 20. The plate moving device 26 comprises a shaft 26a and a drive mechanism 26b. The shaft 26a is connected to the lower surface of the inner base 24. The drive mechanism 26b comprises a motor (not shown) and a power transmission mechanism, and by driving the power transmission mechanism, which is driven by the motor, the build plate 22 and inner base 24 are moved vertically (Z direction) together with the shaft 26a. The power transmission mechanism is composed of, for example, a rack and pinion mechanism or a ball screw mechanism.

[0031] The radiation shield cover 28 is positioned between the build plate 22 and the beam irradiation device 2 in the Z direction. The radiation shield cover 28 is made of a metal such as stainless steel. The radiation shield cover 28 shields against the radiant heat generated when the electron beam 15 is irradiated onto the powder material 32 by the beam irradiation device 2.

[0032] The electron beam 15 is irradiated onto the powder material 32, melting it. At this time, if the radiant heat emitted from the fabrication surface 32a of the powder layer diffuses widely within the vacuum chamber 3, the thermal efficiency will decrease. In contrast, when the radiant shield cover 28 is placed above the build plate 22, the heat radiated from the build surface 32a is shielded by the radiant shield cover 28, and the shielded heat is reflected back to the build plate 22 by the radiant shield cover 28. Therefore, the heat generated by the irradiation of the electron beam 15 can be utilized efficiently.

[0033] Furthermore, the radiation shield cover 28 has the function of preventing evaporated material generated when the powder material 32 is irradiated with the electron beam 15 from adhering (depositing) onto the inner wall of the vacuum chamber 3. Here, the deposited material refers to metal vapor and metal sputtering using fireworks, etc. In other words, when the powder material 32 is irradiated with the electron beam 15, some of the molten metal becomes a mist-like vapor and rises from the build surface 32a. The radiation shield cover 28 is positioned to cover the space above the build surface 32a so that this vapor does not diffuse into the vacuum chamber 3.

[0034] The fabricated object 38 is constructed by two-dimensionally melting a layer of powder material 32 in the area of ​​the fabricated object 38 using an electron beam 15 from the beam irradiation device 2, and then layering the melted material. The areas of the powder material 32 spread on the fabrication plate 22 other than the fabricated object 38 are pre-sintered bodies 35 in which the powder material 32 has been pre-sintered, and these bodies are conductive due to the electron beam 15 irradiated from the beam irradiation device 2.

[0035] A mask cover 30 is mounted below the radiation shield cover 28. The mask cover 30 has an opening 30a and a mask portion 30b. When forming the molded object 38, the mask cover 30 is placed over the upper surface of the powder material 32, i.e., the molding surface 32a. In this process, the opening 30a exposes the powder material 32 that is spread on the molding plate 22. The mask portion 30b is a conductive material that shields the unsintered region of the powder material 32 located outside the opening 30a. It is preferable that the mask portion 30b be made of the same type of metal material as the powder material.

[0036] The shape of the opening 30a is formed to match the shape of the build plate 22. For example, if the build plate 22 is circular in plan view, the plan view shape of the opening 30a is formed to be circular, and if the build plate 22 is rectangular in plan view, the plan view shape of the opening 30a is formed to be rectangular.

[0037] The mask cover 30 is positioned below the radiation shield cover 28. The opening 30a and mask portion 30b of the mask cover 30 are positioned between the build plate 22 and the radiation shield cover 28 in the Z direction. The mask cover 30 has an enclosure portion 30c. The enclosure portion 30c is positioned to enclose the space above the opening 30a. A portion (upper part) of the enclosure portion 30c overlaps with the radiation shield cover 28 in the Z direction.

[0038] The enclosure 30c has the function of shielding against radiant heat generated from the build surface 32a and suppressing the diffusion of evaporated material generated from the build surface 32a. In other words, the enclosure 30c has the same function as the radiant shield cover 28. Although not shown in the diagram, the mask cover 30 is equipped with an up and down drive mechanism that allows it to rise during squeegeeing to avoid interference with the squeegee 16c.

[0039] The mask cover 30 is made of a metal with a higher melting point than the powder material 32 used as the raw material for the molded object 38. Furthermore, the mask cover 30 is made of a material with low reactivity with the powder material 32. For example, titanium can be used as a constituent material for the mask cover 30. Alternatively, the mask cover 30 may be made of the same metal material as the powder material 32 used. Although not shown in the diagram, the mask cover 30 is electrically grounded to GND. During the preheating process before the main sintering process, described later, when the powder material 32 is pre-sintered by irradiation with the electron beam 15, the mask cover 30 provides an electrical shielding function that minimizes powder scattering.

[0040] A camera 42 for photographing the fabrication surface and a shutter 44 for preventing deposition on the camera 42 are mounted on the upper surface of the radiation shield cover 28. The camera 42 is capable of photographing the fabrication surface 32a of the powder layer. The camera 42 is positioned offset in the Y direction from the beam irradiation device 2 so as not to interfere with its position.

[0041] The camera 42 is preferably configured as a visible light camera, such as a digital video camera. The camera 42 photographs the fabrication surface 32a of the powder layer and generates an image (image data) of the powder layer. Therefore, the image generated by the camera 42 is an image showing the state of the fabrication surface 32a of the powder layer. Note that the photography by the camera 42 is performed with illumination light emitted from an illumination light source (not shown) provided by the 3D additive manufacturing apparatus 1 shining on the fabrication surface 32a of the powder layer.

[0042] The shutter 44 protects the camera 42 and the observation window from evaporated material generated from the fabrication surface 32a when the powder material 32 is melted by the irradiation of the electron beam 15, preventing it from adhering to the camera 42 and the observation window. The camera 42 takes pictures of the fabrication surface 32a with the shutter 44 open. The camera 42 basically continues to take pictures, and the shutter 44 is closed only during the melting process. In addition, in processes where evaporated material is easily generated or in processes where a large amount of evaporated material is generated, i.e., in the process of melting the powder material 32 with the electron beam 15, the camera 42 takes pictures with the shutter 44 closed.

[0043] Multiple detection units 46 for detecting backscattered electrons are located below the beam irradiation device 2. Specifically, the detection units 46 are positioned between the beam irradiation device 2 and the molding surface 32a of the molded object 38 formed on the molding plate 22. The detection units 46 measure the backscattered electrons generated by the electron beam 15 irradiated onto the molded object 38 or the pre-sintered body 35, and output the backscattered electron signal data as layer data to the PC 54 or hard computer (not shown) shown in Figure 2, which will be described later.

[0044] In recent years, a 3D additive manufacturing apparatus 1 has been provided that includes a surface monitoring function for the fabricated object 38 for the purpose of quality control of the fabricated object 38. In the 3D additive manufacturing apparatus 1, the surface of the fabricated object 38 can be visualized not only from image data taken of the surface of the fabricated object 38 using the camera 42 described above, but also from data detected by the detection unit 46 from backscattered electrons reflected from the surface of the fabricated object 38. However, the 3D additive manufacturing apparatus 1 can be configured to have only a camera 42, or to have a camera 42 and a detection unit 46.

[0045] In the following embodiment, an example is given in which defects are detected using a BSE image formed based on backscattered electrons detected by the detection unit 46. In backscattered electron monitoring using an electron beam, the PC 54 or a hard computer performs processing such as calculating and imaging the backscattered electron signals to monitor the state of the powder bed and the fabricated object 38. When detecting defects by monitoring using backscattered electrons, the timing at which the PC 54 acquires monitoring data is usually immediately after the pre-sintered body is melted.

[0046] 1-2. Example of a control system configuration for a 3D additive manufacturing device Next, we will describe an example of the control system configuration for the 3D additive manufacturing apparatus 1. Figure 2 is a block diagram showing an example of the control system configuration of the 3D additive manufacturing apparatus 1 in this example. The 3D additive manufacturing apparatus 1 is equipped with the detection unit 46 described above. Furthermore, Figure 2 includes additional hardware and software components for manufacturing control, which are located outside the configuration of the 3D additive manufacturing apparatus 1 shown in Figure 1.

[0047] As shown in Figure 2, the 3D additive manufacturing apparatus 1 includes a polarization amplifier control circuit 51, an ADC (analog-to-digital converter) 52, a preamplifier 53, a PC (personal computer) 54 (an example of a control unit), and a BSE monitor 55, all of which are electron beam control units.

[0048] The polarization amplifier control circuit 51 is connected to the beam irradiation device 2 and the PC 54. The polarization amplifier control circuit 51 controls the beam irradiation device 2 based on the set beam scanning information. As a result, the beam irradiation device 2 irradiates the electron beam 15 to a predetermined position. The polarization amplifier control circuit 51 also transmits beam irradiation position information, which indicates the irradiation position of the electron beam 15, to the PC 54.

[0049] The preamplifier 53 is connected to the detection unit 46 and the ADC 52. The preamplifier 53 converts the reflected electron current detected by the detection unit 46 from a current signal to a voltage signal. The voltage signal converted by the preamplifier 53 is transmitted to the ADC 52. The ADC 52 converts the reflected electron signal, which has become a voltage signal, from an analog signal to a digital signal and transmits it to the PC 54.

[0050] PC54 is an example of an information processing device and includes a CPU (Central Processing Unit) 54a, ROM (Read Only Memory) 54b, RAM (Random Access Memory) 54c, and a recording device 54d.

[0051] The CPU 54a reads the program code of the software that implements each function according to this embodiment from the ROM 54b, loads it into the RAM 54c, and executes it. Variables and parameters that occur during the calculation process of the CPU 54a are temporarily written to the RAM 54c, and these variables and parameters are read out by the CPU 54a as appropriate.

[0052] The image processing unit, a function of the CPU 54a, captures the image generated by the camera 42 and performs predetermined image processing on the captured image. The PC 54 then outputs the camera image processed by the image processing unit to the BSE monitor 55. The molding control software program (hereinafter abbreviated as "molding control software"), which the CPU 54a reads from the ROM 54b and executes, controls the polarization amplifier control circuit 51. The molding control software also controls the preamplifier 53 via the ADC 52.

[0053] Examples of recording devices 54d include HDDs (Hard Disk Drives) and SSDs (Solid State Drives). A non-volatile recording medium such as a drive, optical disc, magneto-optical disc, or flash memory is used. This recording device 54d stores the OS (Operating System), various parameters, and a program for operating the PC 54.

[0054] The ROM 54b and the recording device 54d store programs and data necessary for the operation of the CPU 54a, and are used as an example of a non-transient storage medium that can be read by a computer that stores programs executed by the PC 54. The recording device 54d stores camera images generated by the image processing unit. Furthermore, the recording device 54d stores beam irradiation position information transmitted from the polarization amplifier control circuit 51.

[0055] The CPU 54a selects a predetermined calculation formula from multiple calculation formulas according to the molding process. Then, the CPU 54a performs calculation processing on the backscattered electron signal using the selected calculation formula and calculates the calculation signal. The image processing unit then converts the calculation signal into an image and acquires a backscattered electron composition image (BSE Image). The CPU 54a outputs the acquired backscattered electron composition image (BSE Image) to the BSE monitor 55. The backscattered electron composition image (BSE Image) is also stored in the recording device 54d.

[0056] The BSE monitor 55 is comprised of a display such as a liquid crystal display (LCD) or an organic ELD (Electro-Luminescence Display). The BSE monitor 55 displays the BSE image output from the PC 54 and the camera image on its display screen.

[0057] The input device 56 can be, for example, a keyboard or a mouse. The user can use the input device 56 to perform predetermined input operations and give instructions to the PC 54.

[0058] 1-3. Examples of operation of a 3D additive manufacturing device Next, we will describe an example of the operation of the 3D additive manufacturing apparatus 1.

[0059] First, the user uses the molding control software located in the PC54 to create molding data for melting the desired object 38 according to its shape. At this time, the user inputs appropriate melting conditions, including correction functions. Once the user has finished creating the molding data, they start the 3D additive manufacturing device 1 based on the molding data.

[0060] Next, the beam irradiation device 2 operates based on control commands provided by the PC 54 and heats the build plate 22. The beam irradiation device 2 irradiates the build plate 22 with the electron beam 15 through the opening 30a of the mask cover 30 and scans the build plate 22 with the electron beam 15. As a result, the build plate 22 is heated to a temperature at which the powder material 32 is partially sintered.

[0061] Next, powder material 32 is spread onto the build plate 22. The plate moving device 26 operates based on control commands provided by the PC 54, lowering the build plate 22 by a predetermined amount.

[0062] Next, the user positions the top surface of the build plate 22, which is covered with powder material 32, at approximately the same height as the top surface of the powder material 32 spread on the build table 18. Then, the user lowers the mask cover 30 to the top surface of the build plate 22 and makes contact with the powder material 32 so as to cover the powder material 32 present on the outer perimeter of the build plate 22.

[0063] Next, the beam irradiation device 2 irradiates the electron beam 15 to an area slightly smaller than the entire upper surface of the build plate 22. That is, the area slightly smaller than the entire upper surface of the build plate 22 is the area where the electron beam 15 does not hit the opening inside the mask cover 30, and the beam irradiation device 2 irradiates this area with the electron beam. In this way, the beam irradiation device 2 preheats the build plate 22 to a temperature sufficient for the powder material 32 to be completely pre-sintered by irradiating the electron beam 15 to an area slightly smaller than the entire upper surface of the build plate 22.

[0064] At the start of the printing process, the plate moving device 26 lowers the inner base 24 by a predetermined amount so that the upper surface of the printing plate 22 is slightly lower than the upper surface of the powder material 32 spread on the printing table 18. At this time, the printing plate 22 lowers by a predetermined amount ΔZ together with the inner base 24. This slight lowering of ΔZ corresponds to the subsequent layer thickness in the Z direction. After that, the plate moving device 26 moves the mask cover 30 upward.

[0065] Next, the powder supply device 16 drops the powder material 32 supplied from the hopper 16a to the powder dispenser 16b onto the build table 18 using the powder dispenser 16b. Subsequently, the powder supply device 16 spreads the powder material 32 onto the inner base 24 by moving the squeegee 16c from one end to the other in the X direction, forming a powder bed. As a result, the powder material 32 is spread on the build table 18 to a thickness equivalent to ΔZ. Any excess powder material 32 is collected in the recovery box 21. After the squeegee 16c exits the mask cover 30, the powder supply device 16 lowers the mask cover 30 back to the build surface, so that the mask cover 30 covers the powder material 32 present on the outer periphery of the build plate 22.

[0066] Next, the beam irradiation device 2 preheats the powder layer on the build plate 22 by operating based on control commands provided by the polarization amplifier control circuit 51 and PC 54. In other words, the beam irradiation device 2 powder heats (PH) the powder layer on the build plate 22 and pre-sintersects the powder material 32. Pre-sintering the powder material 32 allows it to become conductive. Therefore, the powder material 32 can suppress powder scattering during the main sintering process that follows the preheating process.

[0067] The beam irradiation device 2 irradiates the powder material 32 spread on the build plate 22 with an electron beam 15. The beam irradiation device 2 also scans the electron beam 15 over a wider area than the area for forming the build object 38 (hereinafter also referred to as the "build area"), but in an area where the electron beam does not hit the inner opening of the mask cover 30. As a result, both the powder material 32 present in the build area and the powder material 32 present around the build area are partially sintered.

[0068] The beam irradiation device 2 melts the two-dimensional shape region of a pre-prepared, designed object, according to the two-dimensional shape obtained by slicing it at ΔZ intervals, using the electron beam 15. After melting and solidifying one layer of powder material 32, the beam irradiation device 2 again irradiates an area slightly smaller than the build plate 22 with the electron beam 15 to raise the temperature and prepare to spread the powder material 32. After raising the temperature to a predetermined level, the beam irradiation device 2 turns off the electron beam 15 and moves the mask cover 30 upward.

[0069] The plate moving device 26 lowers the inner base 24 by ΔZ and moves the squeegee 16c again along the upper surface of the powder material 32 spread on the build table 18 to the opposite side. Then, the plate moving device 26 spreads the powder material 32 by ΔZ on top of the previous layer and lowers the mask cover 30 back to the build surface.

[0070] The beam irradiation device 2 irradiates an area of ​​the mask cover 30 where the electron beam 15 does not hit the inner opening, thereby ensuring that the newly laid powder material 32 is pre-sintered, and then melts the two-dimensional region corresponding to that layer. The beam irradiation device 2 repeats this process to form the fabricated object 38.

[0071] Furthermore, the beam irradiation device 2 acquires a backscattered electron composition image (BSE image) as described below. First, the beam irradiation device 2 operates based on control commands provided by the polarization amplifier control circuit 51 and PC 54, scanning the electron beam 15 over the pre-sintered region where the pre-sintered powder material 32 exists. At this time, the beam irradiation device 2 reduces the electron beam current of the electron beam 15 as much as possible and focuses it on the fabrication surface 32a for irradiation. The detection unit 46 detects backscattered electrons generated by the electron beam 15. The detection unit 46 outputs the detected backscattered electron signal to the PC 54 via the preamplifier 53 and ADC 52.

[0072] 1-4. Example of Control Block Configuration Figure 3 is a block diagram showing an example configuration of the control block of PC54. The CPU 54a of PC54 executes the molding control software, enabling the defect detection process performed in cooperation with each functional block shown in Figure 3. Furthermore, it is possible to incorporate each functional block of PC54 shown in Figure 3 into the 3D additive manufacturing apparatus 1.

[0073] The 3D additive manufacturing apparatus 1 shown in Figure 1 comprises an input unit 60, a PC 54, and a display unit 90.

[0074] The input unit 60 has a function that enables user input operations, and this function is realized by the input device 56 shown in Figure 2. The input unit 60 is used by the user to perform input operations. The input unit 60 is the functional part of the input device 56 shown in Figure 2. User input operations include setting various parameters, which will be described later, and selecting the display of defects or defect candidates. In addition, the user's input operations can also instruct the display unit 90 to display the printed object 38 in 2D (2 Dimensions) or 3D (3 Dimensions) during or after printing.

[0075] The PC54 includes a control unit 70 and a recording unit 80. The control unit 70 has the functions of calculation and control for each process in this example, and these functions are realized by the CPU 54a, ROM 54b, and RAM 54c shown in Figure 2. The recording unit 80 has the function of recording each parameter, each data, each image, etc. in this example, and this function is realized by the recording device 54d shown in Figure 2.

[0076] The control unit 70 includes a molding control unit 71, a defect candidate identification unit 72, a defect detection unit 73, and a display control unit 74. The recording unit 80 includes a parameter recording unit 81 and a defect information recording unit 82.

[0077] The parameter recording unit 81 records parameters such as thresholds that will be compared in each of the processes described later.

[0078] The defect information recording unit 82 records defect information, including layer data, defect candidates identified by the defect candidate identification unit 72, and defect detection results. The defect information includes at least one of the following: the number of defects detected by the defect detection unit 73, the location of the defects, the volume of the molded object, the volume defect rate, the area defect rate, the number of defects, and the maximum length.

[0079] The molding control unit 71 controls the additive manufacturing processes of the three-dimensional additive manufacturing apparatus 1, including the beam irradiation device 2 and the plate moving device 26 described above. After the main melting process is completed in the additive manufacturing process, the molding control unit 71 controls the beam irradiation device 2 to scan the manufacturing surface with an emission current low enough to detect backscattered electrons, and the detection unit 46 detects the generated backscattered electrons and acquires a backscattered electron image. When the height of recesses or protrusions on the layer surface exceeds a predetermined threshold, the molding control unit 71 determines that irregularities have occurred on the manufacturing surface and records the layer data in the defect information recording unit 82. However, the defect information recording unit 82 also records layer data in which it has not determined that irregularities have occurred on the manufacturing surface.

[0080] The defect candidate identification unit 72 reads layer data captured for each layer in which the molded object 38 is built up from the defect information recording unit 82, and based on this layer data, detects defective areas in each layer as defect candidates. The defect candidate identification unit 72 also identifies multiple defect candidates that are located at approximately the same position in multiple adjacent layers in the stacking direction.

[0081] The defect detection unit 73 detects defects remaining in the fabricated object 38 based on the size in the stacking direction and the size within the layer of multiple defect candidates located at approximately the same position across multiple layers. Here, the defect detection unit 73 integrates multiple defect candidates located at approximately the same position in the stacking direction and assigns a defect candidate identifier (e.g., ID1 to ID4) to each of the integrated defect candidates. Furthermore, the defect detection unit 73 measures the stacking direction length (Lz) of the integrated defect candidate, and the first direction length (X direction length Lx) and second direction length (Y direction length Ly) within the layer intersecting the stacking direction of the integrated defect candidate, for each layer, and sets the maximum length of the first direction length (Lx) and the second direction length (Ly) as the maximum layer length (Lxy). Then, the defect detection unit 73 sets the longer of the stacking direction length (Lz) and the maximum layer length (Lxy) as the maximum length, and detects the integrated defect candidate as a defect if the maximum length exceeds the maximum length threshold (δ).

[0082] The display control unit 74 controls the display format of the image displayed on the display unit 90 based on the layer data and defect information read from the defect information recording unit 82 and the input operations input from the input unit 60. For example, the display control unit 74 generates 2D images, 3D images, graphs, etc. of the molded surface of the molded object 38 and generates a screen (an example of a visualization image) on which these images are displayed on the display unit 90. The display control unit 74 also controls the display content of the screen according to the input operations (enlarging, shrinking, moving the displayed image, etc.). The display control unit 74 then controls the display unit 90 so that the visualization image can be displayed. The display control unit 74 allows the display unit 90 to display information such as the state of the molded object 38, the shape of candidate defects (Figures 8 to 11, 15, etc., described later), whether or not defects have been detected, and various graphs, even during the additive manufacturing process, so that the user can check the displayed information.

[0083] The display unit 90 has the function of displaying images processed by the PC 54, and this function is realized by the BSE monitor 55 shown in Figure 2. For example, a liquid crystal display device, an organic EL display device, etc., can be used for the display unit 90. When a touch panel display is used for the PC 54, the input unit 60 and the display unit 90 are formed as a single unit.

[0084] In Figure 3, the PC 54 is configured to include a recording unit 80, but the recording unit 80 may be configured as an external recording medium. In this case, the control unit 70 reads and processes the parameter, layer data, and defect information data recorded on the external recording medium.

[0085] 1-5. Example of defect detection process Next, an example of defect detection processing will be explained with reference to the flowcharts and explanatory diagrams from Figure 4 onwards. In the following explanation, only the operation of the defect detection part that differs from conventional methods will be described, and the parts related to the other manufacturing process will be omitted as they are the same as those described in the section on conventional technology. In this example, PC54 (manufacturing control software) can perform real-time defect detection during the additive manufacturing of the manufactured object 38.

[0086] Figure 4 is a flowchart showing an example of defect detection processing. Defect detection processing is an example of a defect detection method performed by the control unit 70.

[0087] First, the molding control unit 71 shown in Figure 3 sets the value of m, which represents the mth layer, to 0 and substitutes m+1 to create the first layer (S1). Next, the molding control unit 71 performs the additive manufacturing process described above for the first layer (S2).

[0088] Next, the molding control unit 71 determines whether the additive manufacturing process for multiple layers has been completed (S3). If the additive manufacturing process for multiple layers has not been completed (NO in S3), that is, if only the additive manufacturing process for the first layer has been completed, the process returns to step S1 and the additive manufacturing process for the second layer and subsequent layers is performed. On the other hand, if the additive manufacturing process for multiple layers has been completed (YES in S3), the process proceeds to step S4.

[0089] Next, the defect candidate identification unit 72 performs the defect candidate identification process shown in Figure 5 (S4). The defect candidate identification process will now be explained in detail. Figure 5 is a flowchart showing an example of a defect candidate identification process. This defect candidate identification process is a subroutine of step S4 in Figure 4.

[0090] First, the defect candidate identification unit 72 measures the area of ​​each of two adjacent defect candidates in the stacking direction, where at least a portion of the XY cross-section overlaps (S11). The area of ​​the defect candidates will now be explained.

[0091] The defect candidate identification unit 72 identifies the location of defect candidates based on the layer data obtained during the fabrication of each layer. The defect candidate identification unit 72 assumes that the defect candidates are located at approximately the same position in the XY cross-section of multiple layers, and does not consider the position in the Z direction (Z coordinate). Here, we focus on the positions in the XY cross-section of multiple layers and consider the area where multiple defect candidates in adjacent layers overlap.

[0092] However, two overlapping defect candidates in adjacent layers are not always the same size. Therefore, the defect candidate identification unit 72 designates the larger defect candidate in the XY cross-section as defect candidate A, and the smaller defect candidate in the XY cross-section as defect candidate B. Here, as an example of adjacent layers in the Z direction, we take the powder layer of the Nth layer (where N is an integer greater than or equal to 1) and the powder layer of the N+1th layer. In the following explanation, the powder layer of the Nth layer and the powder layer of the N+1th layer will be abbreviated as the Nth layer and the N+1th layer, respectively.

[0093] Here, the molded object 38 and the potential defects will be explained with reference to Figures 6 to 11. Figure 6 is a perspective view of the fabricated object 38. This perspective view is a 3D representation of multiple fabricated objects 38 that are fabricated in a single additive manufacturing process based on CAD (Computer Aided Design) data. In this example, it is shown that a total of 10 fabricated objects 38 are fabricated. The fabrication control unit 71 shown in Figure 3 acquires the BSE image of the fabricated object 38 as layer data each time the melting of one layer of the fabricated object 38 is completed, and records it in the defect information recording unit 82. For example, when the fabrication control unit 71 is simultaneously performing additive manufacturing of 10 fabricated objects 38 as shown in Figure 6, it acquires the BSE images of all 10 fabricated objects 38 as layer data and records them in the defect information recording unit 82.

[0094] Figure 7 shows an example of a BSE image of the fabricated object 38 viewed from above in the Z direction during additive manufacturing. Because additive manufacturing is in progress, the fabricated surface of the fabricated object 38 is visible in the BSE image. Also, faint black spots are visible on the upper part of the fabricated object 38. These black spots are irregularities that occurred on the fabricated surface during additive manufacturing and are recorded in the layer data by the fabrication control unit 71 as described above. Then, the defect candidate identification unit 72 identifies the locations of the black spots as defect candidates.

[0095] Figure 8 shows an example of a BSE image in which a candidate defect A occurring in layer N was identified. Figure 9 shows an example of a BSE image in which a candidate defect B occurring in the N+1th layer was identified.

[0096] As described above, the defect candidate occurring in the Nth layer has a larger area than the defect candidate occurring in the N+1th layer. Therefore, the defect candidate occurring in the Nth layer is called defect candidate A, and the defect candidate occurring in the N+1th layer is called defect candidate B. The relative positions of defect candidate A and defect candidate B in the Z direction are not important; defect candidate A and defect candidate B are distinguished by the defect candidate identification unit 72 solely based on their size in the XY cross-section.

[0097] Figure 10 shows examples of BSE images for defect candidate A and defect candidate B, which were identified when layer N and layer N+1 were superimposed.

[0098] Although the positions of defect candidate A and defect candidate B in the XY cross-section are different, parts of defect candidate A and defect candidate B overlap. The overlapping portion of defect candidate A and defect candidate B is called overlapping portion AB. Based on the layer data, the defect candidate identification unit 72 measures the area SAB of defect candidate A, defect candidate B, and overlapping portion AB in the XY cross-section for each layer.

[0099] In step S11, the defect candidate identification unit 72 measures the area (SA) of the defect candidate in the Nth layer identified in the Nth layer and the area (SB) of the defect candidate in the N+1th layer identified in the N+1th layer, from among multiple defect candidates that overlap in multiple adjacent layers in the stacking direction. The defect candidate identification unit 72 also calculates the overlapping area (SAB) of the overlapping portion where the defect candidate in the Nth layer and the defect candidate in the N+1th layer overlap in the stacking direction.

[0100] Next, the defect candidate identification unit 72 calculates the area ratio of the two defect candidates and the area ratio of the overlapping portion AB (S12). Here, the defect candidate identification unit 72 calculates the area ratio of the overlapping area to the area of ​​the defect candidate of the Nth layer (RA) and the area ratio of the overlapping area to the area of ​​the defect candidate of the N+1th layer (RB) as area ratios. For this reason, the defect candidate identification unit 72 expresses the area ratio of the overlapping portion AB to defect candidate A as RA. The area ratio RA is calculated by the following formula (1). RA = SAB / SA …(1)

[0101] Similarly, the defect candidate identification section 72 expresses the area ratio of the overlapping section AB to the defect candidate B as RB. The area ratio RB is calculated by the following formula (2). RB = SAB / SB …(2)

[0102] Next, the defect candidate identification unit 72 determines whether the conditions of the area ratios RA and RB and the parameters α and β are met (S13). Parameter α is an example of an area ratio threshold compared with area ratio RA, and parameter β is an example of an area ratio threshold compared with area ratio RB.

[0103] The defect candidate identification unit 72 determines that the conditions for area ratios RA and RB and parameters α and β are met (YES in S13), and identifies that the two defect candidates A and B are located in approximately the same position (S14).

[0104] For example, the parameter recording unit 81 records two area ratio thresholds, one each for area ratio RA and RB. Let α be the area ratio threshold parameter for area ratio RA, and β be the area ratio threshold parameter for area ratio RB. Area ratio threshold parameter α is used as an example of the first area threshold, and area ratio threshold parameter β is used as an example of the second area threshold. The defect candidate identification unit 72 identifies the defect candidate of the Nth layer and the defect candidate of the N+1th layer as being in approximately the same location if the overlapping area ratio RA is the same as or exceeds the first area threshold (α), and the overlapping area ratio RB to the area of ​​the defect candidate of the N+1th layer is the same as or exceeds the second area threshold (β).

[0105] For example, the defect candidate identification unit 72 defines that two defect candidates A and B are located in approximately the same position when the following condition (3) is met. RA ≥ α and RB ≥ β, or RA≧α and RB>β, or RA > α and RB > β, or RA > α and RB ≥ β …(3)

[0106] In step S13, if the conditions for area ratios RA, RB and parameters α, β are not met (NO in S13), the defect candidate identification unit 72 determines whether the conditions for area ratios RA, RB and parameter γ are met (S15). If the defect candidate identification unit 72 determines that the conditions for area ratios RA, RB and parameter γ are met (YES in S15), it identifies that the two defect candidates A and B are located in approximately the same position (S14).

[0107] Figure 10 illustrates the case where the sizes of defect candidates A and B do not change drastically. However, in actual additive manufacturing processes, the sizes of defect candidates A and B can change drastically. In order for the defect candidate identification unit 72 to identify that defect candidates A and B are in approximately the same location even in such cases, the parameter recording unit 81 records an area ratio threshold parameter γ in addition to the area ratio threshold parameters α and β. Here, parameter γ will be explained with reference to Figure 11.

[0108] Figure 11 shows examples of defect candidates A and B with extremely different sizes. In the example shown in Figure 11, the location of defect candidate B is contained within the location of defect candidate A, and defect candidate A is significantly larger than defect candidate B. In this case, the area ratio RA is smaller than the area ratio threshold parameter α, but the area ratio RB is larger than the area ratio threshold parameter β, so condition (3) is not satisfied. However, as can be seen from Figure 11, defect candidates A and B are considered to be in approximately the same location.

[0109] Therefore, the parameter recording unit 81 records an area ratio threshold parameter γ in addition to the area ratio threshold parameters α and β. The defect candidate identification unit 72 defines that if the following condition (4) is met and either area ratio RA or RB exceeds the area ratio threshold parameter γ, then the two defect candidates are located in approximately the same position, regardless of the value of the other. RA > γ, or RB > γ …(4)

[0110] The definition by which the defect candidate identification unit 72 determines approximately the same location of two defect candidates using such equations (1), (2), and condition (3) or (4) is called "location definition 1".

[0111] After step S14, or if the defect candidate identification unit 72 determines that the conditions for area ratios RA,RB and parameter γ are not met (NO in S15), the defect candidate identification unit 72 records the location and size of the identified defect candidate as defect information in the defect information recording unit 82 (S16), and proceeds to step S5 in Figure 4. In step S5, the defect detection unit 73 performs defect determination processing.

[0112] Furthermore, even in the NO determination in step S15, defect information is recorded in the defect information recording unit 82 because there is a possibility that the defect candidate identified during the sintering of the next layer may overlap with the current defect candidate.

[0113] Next, using an xz cross-sectional view of a portion of the fabricated object 38, which has been stacked from the Nth layer to the N+7th layer, as an example, the process by which the defect detection unit 73 detects defects from defect candidates will be explained with reference to Figures 12 to 14. In Figure 13 and subsequent figures, it is shown that when N is 1, the defect detection range by the defect detection unit 73 is from the 1st to the 8th layer.

[0114] Figure 12 is a flowchart showing an example of a defect detection process. This defect detection process is a subroutine of step S5 in Figure 4. In the following flowchart and xz cross-sectional diagram, for explanatory purposes, the x, y, and z directions will be expressed in lowercase, but each direction represents the same orientation as the X, Y, and Z directions shown in Figure 1, etc.

[0115] First, the defect detection unit 73 reads defect information, including defect candidates from adjacent layers, from the defect information recording unit 82 (S21). However, the defect detection unit 73 may also directly acquire the defect information output from the defect candidate identification unit 72.

[0116] Next, the defect detection unit 73 integrates multiple defect candidates that are located at approximately the same position across multiple layers (S22). Then, the defect detection unit 73 assigns a defect candidate ID to each integrated defect candidate (S23). Here, specific examples of the processes in steps S22 and S23 will be explained with reference to Figures 13 and 14.

[0117] Figure 13 shows an example of a defect candidate that spans multiple layers. The dashed lines in the diagram represent the boundaries of each layer. The rectangular frames separated by solid lines represent potential defects in each layer. These potential defects have already been identified by the defect candidate identification unit 72.

[0118] Figure 14 shows an example of a defect candidate ID assigned to a defect candidate integrated by the defect detection unit 73. The defect detection unit 73 integrates defect candidates that are located at approximately the same location across multiple layers, according to the location definition 1 described above. The defect detection unit 73 assigns a defect candidate ID to each integrated defect candidate.

[0119] In additive manufacturing, powder layers are stacked in the order of the Nth layer, the N+1th layer, and so on. The defect candidate identification unit 72 has determined that the defect candidate identified in the N+1th layer and the defect candidate identified in the N+2nd layer are in approximately the same location as defined by the location definition 1. Therefore, the defect detection unit 73 merges the two defect candidates that are in approximately the same location. The defect detection unit 73 then assigns defect candidate ID 1 and ID 2 to each of the defect candidates merged in the N+1th and N+2nd layers.

[0120] The defect detection unit 73 integrates the defect candidate identified in the N+3 layer, which is located at approximately the same position as the defect candidate first identified in the N+2 layer, and assigns defect candidate ID 3 to the integrated defect candidate. Similarly, the defect detection unit 73 integrates the defect candidate identified in the N+6 layer, which is located at approximately the same position as the defect candidate first identified in the N+5 layer, and assigns defect candidate ID 4 to the integrated defect candidate. The defect candidate IDs assigned by the defect detection unit 73 to integrated defect candidates will remain the same as long as they are in consecutive layers.

[0121] Returning to Figure 12, the explanation continues. After assigning a defect candidate ID in step S23, the defect detection unit 73 measures the length in the XY and Z directions for each integrated defect candidate (S24). The process by which the defect detection unit 73 measures the length in the XY and Z directions will now be explained with reference to Figure 15.

[0122] Figure 15 shows an example of measuring the lengths in the XY and Z directions of a defect candidate integrated by the defect detection unit 73. The defect detection unit 73 measures the length in the XY direction and the length in the Z direction Lz of each integrated defect candidate. For example, the defect detection unit 73 measures the length in the Z direction as Lz1 and the length in the X direction as Lx1 for the integrated defect candidate with defect candidate ID 1. Although the length in the Y direction, which is the depth direction of the drawing, is not shown in Figure 15, the defect detection unit 73 measures the length in the Y direction as Ly1.

[0123] The defect detection unit 73 measures the Z-direction length of the integrated defect candidate with defect candidate ID 2 as Lz2 and the X-direction length as Lx2, similar to the defect candidate with defect candidate ID 1. The defect detection unit 73 also measures the Z-direction length of the integrated defect candidate with defect candidate ID 3 as Lz3 and the X-direction length as Lx3. The defect detection unit 73 also measures the Z-direction length of the integrated defect candidate with defect candidate ID 4 as Lz4 and the X-direction length as Lx4.

[0124] In the following explanation, if we do not distinguish between defect candidate IDs, we will refer to the length of the integrated defect candidate in the X direction as "Lx", the length in the Y direction as "Ly", and the length in the Z direction as "Lz", excluding the defect candidate ID.

[0125] Returning to Figure 12, we continue the explanation. After processing in step S24, the defect detection unit 73 calculates the "maximum layer length Lxy" (S25) by determining the longer of the X-direction length Lx and the Y-direction length Ly measured for each layer. The maximum layer length Lxy is the longer of the maximum X-direction layer length Lx max or the maximum Y-direction layer length Ly max of the defect candidate. Here, the maximum layer length Lx max represents the maximum length among multiple lengths Lx, and the maximum layer length Ly max represents the maximum length among multiple lengths Ly.

[0126] Next, the defect detection unit 73 compares the length Lz in the Z direction with the maximum layer length Lxy and sets the longer of the two as the "maximum length" (S26). Next, the defect detection unit 73 determines whether the maximum length exceeds the maximum length threshold δ read from the maximum parameter recording unit 81 (S27).

[0127] The defect detection unit 73 determines that the following condition (5) is met when the maximum length exceeds the maximum length threshold δ (YES in S27), and determines the integrated defect candidate to be a defect (S28), and proceeds to step S6 in Figure 4. Maximum length > Maximum length threshold δ …(5)

[0128] On the other hand, if the defect detection unit 73 determines that the maximum length does not satisfy the above condition (5) which exceeds the maximum length threshold δ (NO in S27), it does not determine the integrated defect candidate as a defect and proceeds to step S6 in Figure 4. The integrated defect candidate that the defect detection unit 73 did not determine as a defect is still referred to as a defect candidate. This is because a defect candidate that was not considered a defect in the determination in step S27 may be newly determined as a defect by the defect detection unit 73 as a result of subsequent additive manufacturing processes after the defect determination process shown in Figure 12.

[0129] After step S5 in Figure 4, the display control unit 74 determines whether or not there is a user input operation (S6). If the display control unit 74 determines that there is a user input operation (YES in S6), it proceeds to step S7 and performs display control processing. On the other hand, if the display control unit 74 determines that there is no user input operation (NO in S6), it proceeds to step S8.

[0130] Here, we will explain the display control process (processing in step S7) that is initiated when it is determined in step S6 that a user has made an input operation. Figure 16 is a flowchart showing an example of the display control process. This defect candidate identification process is the subroutine of step S7 in Figure 4.

[0131] First, the display control unit 74 receives user input (S31). Next, the display control unit 74 reads defect candidates and defect information from the defect information recording unit 82 in response to the input (S32).

[0132] Next, the display control unit 74 edits the defect candidates and defect information into an image that visualizes them (S33). At this time, the display control unit 74 edits the 2D or 3D images of the defect candidates and defect information, and the graphs of various information, and performs display control processing to generate a screen that can be displayed on the display unit 90. The display control unit 74 may also generate a defect detection result table (referred to as a result table) that includes defect information determined for each layer based on the layer data, and information including the progress of the additive manufacturing process on the molded object 38. The result table will be described later with reference to Figure 21.

[0133] Next, the display control unit 74 outputs the image visualized in step S33 to the display unit 90 (see Figure 3) (S34), thereby controlling the display unit 90 to display the visualized image during additive manufacturing. After that, the display control unit 74 moves on to step S8 in Figure 4. The display unit 90 displays a screen that includes an image visualizing defect candidates and defect information, such as an image formed from multiple stacked layers into a 3D image, and a graph of defect information for each layer. In addition, defects determined by the defect detection unit 73 are displayed on the display unit 90 as error information. Therefore, even during additive manufacturing, the user can check defect candidates and defect information online.

[0134] Returning to the explanation of Figure 4. After the processing in step S7 in Figure 4, or after the NO determination in step S6, the molding control unit 71 determines whether or not the additive manufacturing of the final layer has been completed (S8). If the molding control unit 71 determines that the additive manufacturing of the final layer has not been completed (NO in S8), it returns to step S1, increments the m of the m-th layer, and continues the additive manufacturing of subsequent layers, defect candidate identification processing, defect determination processing, and display control processing. In the defect determination processing, the defect candidates are integrated using the layer data from which the additive manufacturing process has been performed up to the time the m of the m-th layer is incremented, and the process of determining defects is repeated until the additive manufacturing of the final layer is completed.

[0135] On the other hand, if the molding control unit 71 determines that the additive manufacturing of the final layer is complete (YES in S8), it terminates the defect detection process shown in Figure 4.

[0136] The defect detection method according to the first embodiment described above will be referred to as "detection method 1". The parameters α, β, γ, and δ mentioned above may be changed each time additive manufacturing is performed, or they may be fixed once determined. Furthermore, the parameter values ​​may be changed for each type of powder used in the additive manufacturing process.

[0137] In the defect detection method according to the first embodiment, if the longer of the combined defect candidate's maximum length Lz or maximum length Lxy is greater than the maximum length threshold δ, the combined defect candidate is determined to be a defect. Since defects formed to be long in the layering direction are likely to remain in the fabricated object 38 after the fabrication is complete, measures such as stopping the fabrication process of the fabricated object 38 in which a defect has been detected can be taken even in the middle of the fabrication process. By taking such measures, it is possible to prevent the consumption of materials and power due to unnecessary fabrication processes.

[0138] Conventionally, only the XY cross-sectional data of the layer in which defect detection was performed was used for the layer in which defect detection was performed. As a result, even though defects detected in a certain layer may disappear as the additive manufacturing process progresses, the conventional defect detection method incorrectly detected that defects remained in the manufactured object 38. On the other hand, in this embodiment, in addition to the XY cross-sectional data of the layer in which defect detection was performed, the XY cross-sectional data of layers that were added after the layer in which defect detection was performed was also used for defect detection. As a result, defect candidates that disappeared after multiple layers had been added were not incorrectly detected as defects. As a result, it is possible to improve the accuracy of defect detection remaining in the manufactured object 38.

[0139] Furthermore, defect information detected during the additive manufacturing process is displayed on the display unit 90, allowing the user to check the defect status, the shape of potential defects, etc. This enables the user to restart the additive manufacturing process, reset various parameters, etc., at an early stage after a defect is detected, thereby obtaining the intended object 38.

[0140] In the defect detection method according to the first embodiment (detection method 1), the display control processing in step S7 of Figure 4 is shown as an example of being performed as an online real-time process, but the display control processing may also be performed after the molding process. In this case, the display control processing in step S7 is performed independently in response to user input. Furthermore, the display control processing may be performed on an external server (such as a cloud server) different from the 3D additive manufacturing apparatus 1. In this case, a user located at a different location from the facility where the 3D additive manufacturing apparatus 1 is installed can check the results of the additive manufacturing process.

[0141] Furthermore, the defect candidate shown as defect candidate ID2 in Figure 15 has a shape in which defect candidates are connected over multiple layers at approximately the same location. Defect candidates are not limited to this shape and may branch out along the way. Also, two defect candidates identified in one layer may connect in the next layer that is stacked. Even with such various shapes of defect candidates, the defect detection method according to this embodiment can reliably detect any defect that affects the quality of the fabricated object 38.

[0142] 1-6. Example of molding control processing Next, an example of the molding control process will be explained with reference to the explanatory diagram in Figure 17 and the flowchart in Figure 18. First, before explaining the molding control process, we will explain the GUI screen for setting the molding control content when a defect is detected.

[0143] Figure 17 shows an example of a GUI screen for setting the molding control content when a defect detected by the defect detection process shown in Figure 4 exceeds a threshold. The settings screen 100 shown in Figure 17 is an example of a GUI screen that has items for the maximum allowable defect length [μm], the allowable volume defect rate [%], and the molding stop operation. The user can input thresholds for the maximum allowable defect length [μm] and the allowable volume defect rate [%] by operating the input device 56 shown in Figure 2. The user inputs the threshold to be used to determine if there is a quality problem. The threshold for the maximum allowable defect length is assumed to be greater than or equal to the maximum length threshold δ in step S27 shown in Figure 12, but it may also be the same value as the maximum length threshold δ. In Figure 17, the threshold for the maximum allowable defect length is set to 400 [μm], and the threshold for the allowable volume defect rate is not set.

[0144] In this example, the maximum allowable defect length and the volume defect rate are used as thresholds, but the thresholds are not limited to these, and other thresholds may be freely adopted. For example, the number of defects could be used as another threshold. Alternatively, the printing process could be stopped if defects occur in a predetermined percentage, for example, 80%, of the multiple objects being printed simultaneously.

[0145] The "Printing Stop Operation" setting configures the control actions that the PC54 will take regarding the additive manufacturing process if the size of a defect exceeds a pre-set threshold during the process. In this example, the PC54 will be instructed to either stop the additive manufacturing of at least the defective parts or to output a warning. For example, the options for the printing stop operation include "All Stop," "Parts Stop," and "Warning." The user can configure the additive manufacturing control actions to be taken when a defect is detected during the process by using the pull-down buttons to select an option. Figure 17 shows an example where "All Stop" is selected.

[0146] As described above, the display control unit 74 according to this embodiment controls the first to third control options, which can be displayed on the display unit, as options for manufacturing control when the size of a defect exceeds a preset threshold during additive manufacturing. For example, the first control is "stop all manufacturing," which stops the additive manufacturing of all manufactured objects. The second control is "stop manufacturing only for the affected object," which stops the additive manufacturing of the object in which a defect has been detected and continues the additive manufacturing of the remaining objects. Furthermore, the third control is "display only warning message," which outputs a warning indicating that a defect has been detected.

[0147] Next, the molding control process according to the first embodiment of the present invention will be described. Figure 18 is a flowchart showing an example of the molding control process according to the first embodiment. The following description will focus on the operation of the parts related to molding control that differ from the conventional method, and other parts related to the molding process will be described simply or omitted as they are the same as those described in the section on the conventional technology. In this example, PC54 (molding control software) performs real-time defect detection during the additive manufacturing of the molded object 38, and when a defect is detected, it executes the control content of the molding stop operation set on the setting screen 100. Here, it is assumed that "All Stop" is set on the setting screen 100.

[0148] First, the molding control unit 71 shown in Figure 3 heats the molding plate 22 with the beam irradiation device 2 after molding the first layer of the object to prepare for the next layer (S41). Next, the molding control unit 71 spreads powder material 32 onto the molding plate 22 with the powder supply device 16 (hereinafter referred to as "recoating") (S42). Next, the molding control unit 71 uses the beam irradiation device 2 to powder heat (PH) the powder layer on the molding plate 22, pre-sintering the powder material 32 (S43). Finally, the molding control unit 71 uses the beam irradiation device 2 to melt and solidify the powder material 32 according to the two-dimensional shape of the object (S44).

[0149] Next, the molding control unit 71 acquires a BSE image of the molding surface 32a using the detection unit 46 (S45). Next, the molding control unit 71 afterheats (AH) the powder material 32 on the molding plate 22 using the beam irradiation device 2, preparing to spread the powder material 32 (S46). Next, the molding control unit 71 moves the inner base 24 using the plate moving device 26 to lower the molding plate 22 by one layer in the Z direction (S47).

[0150] In parallel with the processing in steps S46 and S47, the defect detection unit 73 executes the defect detection process shown in Figure 4 (S48). The defect detection unit 73 outputs the result of the defect detection process to the molding control unit 71.

[0151] After the processing in step 47, if the result of the defect detection process in step S48 is that there is a defect, the molding control unit 71 refers to the threshold set on the setting screen 100 shown in Figure 17. The molding control unit 71 then determines whether the state of the detected defect exceeds the threshold (S49). If the result of the defect detection process in step S48 is that there is no defect, the molding control unit 71 branches to NO in step 49 without performing any determination processing.

[0152] If the detected defect is below a threshold (NO in S49), the molding control unit 71 determines whether or not additive manufacturing has been performed up to the final layer (S50). If additive manufacturing has been performed up to the final layer (YES in S50), the molding control unit 71 determines that the manufactured object 38 is complete and terminates the additive manufacturing.

[0153] On the other hand, if additive manufacturing has not been performed up to the final layer (NO in S50), the manufacturing control unit 71 moves to step S41 and performs the manufacturing process for the next layer.

[0154] In step S49 described above, if the defect status exceeds a threshold (YES in S49), the molding control unit 71 controls the process to immediately stop the molding of all objects in accordance with the control content "Stop all molding" set in the molding stop operation on the setting screen 100 (S51). When the molding process is stopped, the results table in Figure 21 may be configured to indicate that the molding process for all objects has been stopped.

[0155] Furthermore, if two thresholds are set on the settings screen 100, in step S49, the printing process may be stopped if the defect status exceeds both thresholds, or if the defect status exceeds either of the two thresholds.

[0156] The molding control process according to the first embodiment described above stops the molding process for all objects if it detects the occurrence of a problematic defect during the additive manufacturing process. This prevents significant time and cost losses in the production process of the objects.

[0157] In the flowchart shown in Figure 18, defect detection, threshold checks, and complete halt of the entire build process (if the defect exceeds the threshold) are completed before recoating the next layer. However, in reality, the computation time required for defect detection may be long depending on the performance of the PC54 and the number of defects, and the process up to complete halt of the entire build process may not be completed before recoating the next layer. Therefore, the process shown in Figure 18 is just one example, and in reality, the series of processes—defect detection, threshold checks, and complete halt of the entire build process (if the defect exceeds the threshold)—do not necessarily need to be completed before recoating the next layer; some delay is acceptable.

[0158] [2. Second Embodiment] Next, the molding control process according to the second embodiment of the present invention will be described. The molding control process according to the second embodiment is performed using the 3D additive manufacturing apparatus 1 according to the first embodiment and the control unit 70 and recording unit 80 of the PC54. In the molding control process according to the second embodiment, the same processes as in Figure 18 of the first embodiment, from steps S41 to S43 and from steps S45 to S50, are performed. Then, in the molding control process according to the second embodiment, steps S44 and S51 in Figure 17 of the first embodiment are modified. Here, it is assumed that "Stop molding only the relevant parts (Parts Stop)" is set on the setting screen 100.

[0159] Figure 19 is a flowchart showing an example of the molding control process according to the second embodiment. The molding control unit 71 executes the processes from heating the molding plate 22 (S41) to powder heating (PH) (S43).

[0160] Next, if no defects were detected in the previous defect detection process (S48), the molding control unit 71 melts and solidifies the powder material 32 to match the two-dimensional shape of the molded object (S44A), similar to step S44 in Figure 18.

[0161] Next, the molding control unit 71 executes the process from acquiring the BSE image (S45) to lowering the molding plate 22 by one layer in the Z direction (S47). In parallel with the processes in steps S46 and S47, the defect detection unit 73 executes the defect detection process shown in Figure 4 (S48) and outputs the result of the defect detection process to the molding control unit 71.

[0162] If the defect detection process in step S48 results in a defect, the molding control unit 71 refers to the threshold set on the setting screen 100 shown in Figure 17. The molding control unit 71 then determines whether the detected defect exceeds the threshold (S49).

[0163] If any of the printed objects have a defect status that exceeds the threshold (YES in S49), the printing control unit 71 controls the printing process to immediately stop only the affected objects, in accordance with the control content "Stop printing only the affected objects" set in the printing stop operation on the setting screen 100 (S51A). For objects whose defect status does not exceed the threshold, the printing process continues. When the printing process is stopped, it is preferable to indicate in the results table in Figure 21 that the printing process for the affected object has been stopped. For example, if the number of objects to be stopped increases, the user may decide to stop all objects based on the results table.

[0164] If the detected defect status is below a threshold (NO in S49), or after processing in step S51A, the molding control unit 71 determines whether or not additive manufacturing has been performed up to the final layer (S50). If additive manufacturing has been performed up to the final layer (YES in S50), the molding control unit 71 terminates the additive manufacturing.

[0165] On the other hand, if additive manufacturing has not been performed up to the final layer (NO in S50), the manufacturing control unit 71 moves to step S41 and starts the manufacturing process for the next layer. Then, the manufacturing control unit 71 executes the processes in steps S41 to S43.

[0166] Next, after the processing in step S43, if a defect was detected in the previous defect detection process (S48), the molding control unit 71 melts and solidifies the molded object in which the defect state did not exceed the threshold (S44A). At this time, the molding control unit 71 does not melt the molded object in which the defect state exceeds the threshold.

[0167] Next, the molding control unit 71 executes the processes in steps S45 to S50, and if necessary (if YES is determined in step S49), executes the control content "Stop molding only for the relevant molded object" set in the molding stop operation on the setting screen 100 (S51A). After that, if the molding control unit 71 has completed additive manufacturing up to the final layer (YES in S50), it terminates the additive manufacturing.

[0168] The molding control process according to the second embodiment described above stops the molding of only the affected object if a problematic defect is detected during the additive manufacturing process. Therefore, it is possible to prevent significant time and cost losses in the production process of the manufactured object due to the manufacturing of an object containing a problematic defect. Furthermore, since the molding process continues for manufactured objects that do not have a problematic defect, it is possible to use manufactured objects that do not have quality issues instead of discarding them, compared to the control in the first embodiment which uniformly stops the molding process of all objects.

[0169] Furthermore, if the system is configured to display in the results table (Figure 21) that the printing process for a particular object has been stopped, the user can notice an anomaly in the 3D additive manufacturing apparatus 1 from the results table when the number of objects whose printing process is stopped increases. Therefore, if the user determines that the number of objects whose printing process is stopped is increasing, they can input a command to the PC 54 to stop the printing process for all objects. Alternatively, the number of objects whose printing process is stopped may be displayed in the results table, and a warning may be displayed in the results table if the number of such objects exceeds a certain number or percentage.

[0170] In the flowchart shown in Figure 19, defect detection, threshold checks, and stopping the build process if the defect exceeds the threshold are completed before recoating the next layer. However, in reality, the computation time required for defect detection may be long depending on the performance of the PC54 and the number of defects, and the process up to stopping the build process may not be completed before recoating the next layer. Therefore, the process shown in Figure 19 is just one example, and in reality, the series of processes of defect detection, threshold checks, and stopping the build process of the corresponding object if the defect exceeds the threshold do not necessarily need to be completed before recoating the next layer, and some delay is acceptable.

[0171] [3. Third Embodiment] Next, a molding control process according to a third embodiment of the present invention will be described. The molding control process according to the third embodiment is performed using the 3D additive manufacturing apparatus 1 according to the first embodiment and the control unit 70 and recording unit 80 of the PC 54. In the molding control process according to the third embodiment, the same process from steps S41 to S50 in Figure 18 of the first embodiment is performed. Then, in the molding control process according to the third embodiment, the process is changed from step S51 in Figure 17 of the first embodiment. Here, it is assumed that "Show warning messages only (Warning)" is set on the settings screen 100.

[0172] Figure 20 is a flowchart showing an example of the molding control process according to the third embodiment. The molding control unit 71 executes the process from raising the temperature of the molding plate 22 (S41) to lowering the molding plate 22 by one layer in the Z direction (S47). In parallel with the processes in steps S46 and S47, the defect detection unit 73 executes the defect detection process shown in Figure 4 (S48) and outputs the result of the defect detection process to the molding control unit 71.

[0173] If the defect detection process in step S48 results in a defect, the molding control unit 71 refers to the threshold set on the setting screen 100 shown in Figure 17. The molding control unit 71 then determines whether the detected defect exceeds the threshold (S49).

[0174] If there is a printed object in which the detected defect status exceeds the threshold (YES in S49), the printing control unit 71 controls the display unit 90 to display a warning message according to the control content "Display warning message only" set in the printing stop operation on the setting screen 100 (S51B). At this time, the printing process of the object in question continues. If the defect status exceeds the threshold, it is preferable to display the defect status of the object in question in the results table in Figure 21. For example, if the number of printed objects with defects exceeding the threshold increases, the user may decide to stop the printing process of the object in question or all objects based on the results table and input a stop command to the PC 54.

[0175] If the detected defect status is below a threshold (NO in S49), or after processing in step S511B, the molding control unit 71 determines whether or not additive manufacturing has been performed up to the final layer (S50). If additive manufacturing has been performed up to the final layer (YES in S50), the molding control unit 71 terminates the additive manufacturing.

[0176] On the other hand, if additive manufacturing has not been performed up to the final layer (NO in S50), the manufacturing control unit 71 moves to step S41 and starts the manufacturing process for the next layer. Then, the manufacturing control unit 71 executes the processes in steps S41 to S51B.

[0177] The molding control process according to the third embodiment described above displays a warning message and continues the molding process if it detects the occurrence of a problematic defect during the additive manufacturing process. Therefore, the user can monitor the molding process that has continued due to the warning message and decide whether to stop or continue the molding process at their own discretion. Thus, this embodiment can provide an environment in which the user can stop the molding process even if a problematic defect occurs in the molded object, while the molding process continues. Therefore, it is possible to prevent significant time and cost losses in the production process of molded objects.

[0178] In the flowchart shown in Figure 20, defect detection, threshold checking, and display of a warning message if the defect exceeds the threshold are completed before recoating the next layer. However, in reality, the computation time required for defect detection may be long depending on the performance of the PC54 and the number of defects, and the process up to displaying the warning message may not be completed before recoating the next layer. Therefore, the process shown in Figure 20 is just one example, and in reality, the series of processes of defect detection, threshold checking, and displaying a warning message if the defect exceeds the threshold do not necessarily need to be completed before recoating the next layer, and some delay is acceptable.

[0179] As described above, the information processing device (PC54) according to the first to third embodiments includes: a defect candidate identification unit that detects defective areas in each layer as defect candidates based on layer data obtained for each layer in which the molded object is additively manufactured, and identifies a plurality of defect candidates located at substantially the same position in a plurality of adjacent layers in the stacking direction; a defect detection unit that detects defects remaining in the molded object based on the size in the stacking direction and the size within the layer of the plurality of defect candidates located at substantially the same position identified in the plurality of layers; and a molding control unit that controls the additive manufacturing of at least the molded object in which defects have been detected to stop or output a warning if the size of a defect exceeds a preset threshold during the additive manufacturing process.

[0180] Here, Figure 21 shows an example of the display of the results table display screen 110, where the results table is displayed. The results table display screen 110 displays the results table generated by the display control unit 74 in step S33 of Figure 16. The results table includes the name of the molded object (Part Name) and the volume of the molded object [mm²]. 3 The following items are included: volume defect rate [%], number of defects, maximum defect length [μm], and judgment result.

[0181] The names of the sculptures are identification names assigned to each of the 38 sculptures. The volume of the printed object represents the volume of the printed object 38 at the time the additive manufacturing process is completed, as assumed in the printing data. The volume defect rate represents the ratio of the volume of defects detected in the printed object 38 to the total volume of the printed object 38 at the time of completion of additive manufacturing as assumed in the printing data. The defect count represents the number of defects detected in the printed object 38.

[0182] The maximum defect length represents the maximum length of defects detected in the fabricated object 38. The left side, separated by an "@" symbol, represents the maximum defect length, and the right side represents the layer number (layer number) of the layer in which the defect occurred. Note that even if the current number of layers in the additive manufacturing process is, for example, 1300 layers, the results display screen 110 will show the layer number (1206 layers) in which the maximum length was determined.

[0183] The judgment result represents the result of the additive manufacturing process of the fabricated object 38. For example, it is generally desirable for the volume defect rate to be less than 0.1%, but if the volume defect rate exceeds 1%, for example, "BAD" will be displayed in the judgment result section.

[0184] The results table displays "BAD" in the judgment result column if any abnormal values ​​are found in important defect information (number of defects, maximum defect length, etc.) for each printed object, or if defects occurred during additive manufacturing performed before the user checked the results table. The threshold used for this defect judgment is assumed to be the maximum length threshold δ as described above, but it may also be the threshold for the allowable maximum defect length or allowable volume defect rate set on the settings screen 100. Printed objects 38 in which defects have occurred need to have the additive manufacturing process stopped or be discarded and recreated. For this reason, only the information of printed objects 38 in which "BAD" is displayed in the judgment result column may be displayed on the results table display screen 110. Alternatively, "STOP" may be displayed in the judgment result column for the relevant printed object so that it is immediately clear that the additive manufacturing process has been stopped.

[0185] On the other hand, if the additive manufacturing process is proceeding without problems, "GOOD" will be displayed in the judgment result section. In order to make it easier for the user to see when "BAD" is displayed, the display control unit 74 may create a screen in which the text color of "BAD" is red, or cells containing "BAD" are displayed in red. In this way, by displaying "GOOD" or "BAD" in the judgment result section, the user can check in real time during the additive manufacturing process whether there are any quality problems caused by defects in the manufactured object 38 that is currently undergoing additive manufacturing.

[0186] The results display screen 110 displays five items in a GUI (Graphical User Interface): the volume of the molded object, the volume defect rate, the number of defects, the maximum defect length, and the judgment result. However, the items displayed on the results display screen 110 do not have to be limited to these five items, and additional items may be added. Conversely, items that are deemed unnecessary may be deleted. Also, the text color of "BAD" displayed in the judgment result items may be kept the same as the text color of "GOOD".

[0187] [4. Fourth Embodiment] Next, the molding control process according to the fourth embodiment of the present invention will be described. The molding control process according to the fourth embodiment is an example of appropriately changing the melting order of the remaining molded objects when additive manufacturing is continued in the second and third embodiments (NO determination at S50 in Figures 19 and 20). The molding control process according to the fourth embodiment is performed using the 3D additive manufacturing apparatus 1 according to the first embodiment and the control unit 70 and recording unit 80 of the PC 54.

[0188] Figure 22 shows BSE images of multiple fabricated objects viewed from above in the Z direction during additive manufacturing, and an example of the melting sequence of multiple fabricated objects. Figure 23 shows a conventional example in which the printing process of some of the objects is stopped during additive manufacturing, and the melting order of the remaining objects is changed.

[0189] In the second embodiment, if any of the fabricated objects have a detected defect that exceeds a threshold, the fabrication process is immediately stopped only for the objects that are determined to have exceeded the threshold. Fabrication continues for the remaining objects. When melting the remaining printed objects, the erroneous objects are excluded from the melting process, which can change the melting order and consequently alter the effects of heat diffusion. To minimize such changes in heat diffusion, the melting order is adjusted to be appropriate for the remaining printed objects that continue to be melted.

[0190] As a concrete example, consider the case shown in Figure 22. The BSE image in Figure 22 shows 12 printed objects 38a to 38m. The numbers in parentheses to the left of each printed object indicate the order of melting. The initial melting order is 38a, 38m, 38g, 38f, 38b, 38k, 38h, 38e, 38c, 38j, 38i, and 38d (1st to 12th). In this case, for example, if printing stops due to defect detection of printed objects 38m, 38f, 38k, 38e, 38j, and 38d (2nd, 4th, 6th, 8th, 10th, and 12th), simply melting them in the order excluding the object that stopped printing will result in the melting order shown in Figure 23. That is, the melting order of the remaining printed objects is 38a, 38g, 38b, 38h, 38c, and 38i (1st to 6th).

[0191] However, in this case, since adjacent objects are melted sequentially, the objects being melted tend to be more strongly affected by the heat generated when adjacent objects are melted. Therefore, it is expected that the effect of heat diffusion will change before and after the melting of some objects due to defect detection. Thus, as shown in Figure 24, it is desirable to change the melting order appropriately within the objects that continue to be melted.

[0192] Figure 24 shows an example in this embodiment in which the manufacturing process of some of the manufactured objects is stopped midway through additive manufacturing, and the melting order of the remaining manufactured objects is changed to an appropriate order. The following are some possible algorithms for achieving this modified order. (A) Determine the centroid coordinates of each of the objects being manufactured using additive manufacturing. (B) Specify the object to be melted first from the remaining objects. This should be the object located as far outside as possible to the melting area of ​​the powder layer's build surface 32a (Figure 1) (for example, object 38a(1) in Figure 24). (C) Derive the distance between the centroid coordinates of the first object to melt and the centroid coordinates of all remaining objects, and find the coordinate with the longest distance among them. Determine the object with this centroid coordinate to be the second object to melt (object 38i(2) in Figure 24). (D) Once the second object to melt is determined, the centroid coordinates of the second object to melt are then derived, along with the distances between the centroids of all objects except those that melted before it. The coordinate with the longest distance among these is then determined. The object with these centroid coordinates is determined to be the third object to melt (in the case of Figure 24, object 38g(3)). (E) Repeat the process in (D) above. That is, once the nth object to be melted is determined, the centroid coordinates of the nth object to be melted are determined, and the distances between the centroids of all objects except those that have been melted before it are derived, and the coordinate with the longest distance among these distances is found. The object with these centroid coordinates is determined to be the (n+1)th object to be melted.

[0193] The algorithms (A) to (E) described above are applied before printing to determine the order in which the remaining printed parts will melt. If a defect is detected during printing and the printing of the affected part is stopped, instead of simply removing the stopped part and melting the remaining parts in the initially determined order (Figure 23), the algorithms (A) to (E) are run again on the remaining printed parts after removing the stopped part. If the melting order is changed as a result, the printing control unit 71 performs the melting in the changed melting order (Figure 24).

[0194] As described above, in the fourth embodiment, when the molding control unit 71 continues additive manufacturing of the remaining molded objects excluding the molded object in which a defect has been detected, it designates the molded object furthest from the molded object that will be melted first after the additive manufacturing continues as the next molded object to be melted. Thereafter, the molding control unit 71 determines the melting order by repeatedly designating the molded object furthest from the molded object that was melted immediately before as the next molded object for any molded objects for which the melting order has not been specified.

[0195] By changing the melting order of the remaining objects when continuing the additive manufacturing process for a defect-free object, the objects being melted become less susceptible to the heat generated when adjacent objects are melted. Therefore, the objects being manufactured through additive manufacturing can be produced properly.

[0196] The algorithm described above is merely one example, and other algorithms may be used to determine the melting order.

[0197] [5. Fifth Embodiment] Next, a defect detection method according to a fifth embodiment of the present invention will be described. The defect detection method according to the fifth embodiment is performed using the three-dimensional additive manufacturing apparatus 1 according to the first embodiment and the control unit 70 and recording unit 80 of the PC 54. In the defect detection method according to the fifth embodiment, the same process is performed from step S1 in Figure 4 to step S26 in Figure 12 according to the first embodiment.

[0198] In the fifth embodiment, the parameter recording unit 81 records the maximum length thresholds δz and δxy used by the defect detection unit 73 to determine defects. The defect detection unit 73 then detects the integrated defect candidate as a defect if the maximum length is the stacking direction length Lz and the stacking direction length Lz exceeds the maximum length threshold δz in the stacking direction, or if the maximum length is the maximum layer length Lxy and the maximum layer length Lxy exceeds the maximum layer length threshold δxy within the layer.

[0199] For example, in step S27 (see Figure 12) of the defect detection method according to the fifth embodiment, the defect detection unit 73 compares the maximum length Lz with the threshold δz if the maximum length is Lz. If the defect detection unit 73 determines that the maximum length Lz is greater than the threshold δz, it proceeds to step S28 and determines the integrated defect candidate to be a defect.

[0200] On the other hand, the defect detection unit 73 compares the maximum length Lxy (Lx max or Ly max) with the maximum layer length threshold δxy. If the defect detection unit 73 determines that the maximum length Lxy is greater than the maximum layer length threshold δxy, it proceeds to step S28 and determines the integrated defect candidate to be a defect.

[0201] The defect detection method according to the fifth embodiment described above is referred to as "detection method 2". In the first embodiment, the threshold was unified to δ regardless of the XYZ direction with respect to the maximum length. However, the way the powder material melts is likely to differ between the XY and Z directions. For example, the way the powder melts in the XY direction changes depending on the intensity of the electron beam, the movement of the scan in the XY direction, etc. On the other hand, the way the powder melts in the Z direction changes depending on the depth of the electron beam in the stacking direction. Therefore, the defect detection unit 73 in the fifth embodiment uses detection method 2 to change the threshold used for comparison in two patterns: when the maximum length is in the XY direction and when it is in the Z direction, and compares the maximum length in the XY direction and the maximum length in the Z direction with their respective thresholds. As a result, defect candidates that may change depending on the way the powder material melts in the XY and Z directions can be appropriately identified as defects.

[0202] The parameters α, β, γ, δz, and δxy mentioned above may be changed with each additive manufacturing cycle, or they may be fixed once determined. Furthermore, the parameter values ​​may be changed for each type of powder used. Also, the parameter values ​​may be changed for each type of powder used in the additive manufacturing process.

[0203] [6. Sixth Embodiment] Next, a defect detection method according to a sixth embodiment of the present invention will be described. The defect detection method according to the sixth embodiment is performed using the three-dimensional additive manufacturing apparatus 1 according to the first embodiment and the control unit 70 and recording unit 80 of the PC54. In the defect detection method according to the sixth embodiment, the same process is performed from step S1 in Figure 4 to step S25 in Figure 12 as in the first embodiment. The integrated defect candidates are shown in Figure 15.

[0204] In the sixth embodiment, the defect detection unit 73 provides a stacking direction length threshold Lz_lim and a maximum layer length threshold Lxy_lim, which the defect detection unit 73 uses to determine defects. These thresholds are recorded in the parameter recording unit 81. The defect detection unit 73 detects the integrated defect candidate as a defect when the stacking direction length Lz is greater than or equal to the stacking direction length threshold Lz_lim and the maximum layer length Lxy is greater than or equal to the maximum layer length threshold Lxy_lim. The integrated defect candidate is shown in Figure 15.

[0205] Figure 25 is a flowchart showing an example of a defect detection process according to the sixth embodiment. As described above, steps S21 to S25 in Figure 25 are the same processes as steps S21 to S25 in Figure 12, so a detailed explanation is omitted. Note that step S26 in Figure 12 is omitted in Figure 25.

[0206] After step S25, the defect detection unit 73 determines whether the Z-direction length of the integrated defect candidate measured in step S24 (referred to as the stacking direction length Lz) is greater than or equal to the stacking direction length threshold Lz_lim read from the parameter recording unit 81 (S27A).

[0207] If the defect detection unit 73 determines that the stacking direction length Lz is greater than or equal to the stacking direction length threshold Lz_lim (YES in S27A), it determines whether the maximum layer length Lxy of the integrated defect candidate calculated in step S25 is greater than or equal to the maximum layer length threshold Lxy_lim (S27B).

[0208] If the defect detection unit 73 determines that the maximum layer length Lxy is greater than or equal to the maximum layer length threshold Lxy_lim (YES in S27B), it determines the integrated defect candidate as a defect (S28) and proceeds to step S6 in Figure 4.

[0209] On the other hand, if the defect detection unit 73 determines that the stacking direction length Lz is not greater than or equal to the stacking direction length threshold Lz_lim (NO in S27A), or if it determines that the maximum layer length Lxy is not greater than or equal to the maximum layer length threshold Lxy_lim (NO in S27B), it does not determine the integrated defect candidate as a defect and proceeds to step S6 in Figure 4.

[0210] The defect detection method according to the sixth embodiment described above is referred to as "detection method 3". Detection method 3 is almost the same as detection method 1 according to the first embodiment. The only difference is that when the stacking direction length Lz is greater than or equal to the stacking direction length threshold Lz_lim, and the maximum layer length Lxy is greater than or equal to the maximum layer length threshold Lxy_lim, the integrated defect candidate is detected as a defect.

[0211] The layering direction length threshold Lz_lim and the maximum layer length threshold Lxy_lim mentioned above may be changed each time additive manufacturing is performed, or they may be fixed once determined. Furthermore, the parameter values ​​may be changed for each type of powder used in the additive manufacturing process.

[0212] [7. Seventh Embodiment] Next, a defect detection method according to a seventh embodiment of the present invention will be described. The defect detection method according to the seventh embodiment is performed using the three-dimensional additive manufacturing apparatus 1 according to the first embodiment and the control unit 70 and recording unit 80 of the PC54. In the defect detection method according to the seventh embodiment, the same process is performed from step S1 in Figure 4 to step S25 in Figure 12 according to the first embodiment.

[0213] In the seventh embodiment, the defect detection unit 73 provides a stacking direction length threshold Lz_lim and a maximum layer length threshold Lxy_lim, which the defect detection unit 73 uses to determine defects. These thresholds are recorded in the parameter recording unit 81. The defect detection unit 73 detects the integrated defect candidate as a defect if the stacking direction length Lz is greater than or equal to the stacking direction length threshold Lz_lim, or if the maximum layer length Lxy is greater than or equal to the maximum layer length threshold Lxy_lim. The integrated defect candidate is shown in Figure 15.

[0214] Figure 26 is a flowchart showing an example of the defect detection process according to the seventh embodiment. As mentioned above, steps S21 to S25 in Figure 26 are the same processes as steps S21 to S25 in Figure 12, so a detailed explanation is omitted. Note that step S26 in Figure 12 is deleted from Figure 26.

[0215] After step S25, the defect detection unit 73 determines whether the Z-direction length of the integrated defect candidate measured in step S24 (referred to as the stacking direction length Lz) is greater than or equal to the stacking direction length threshold Lz_lim read from the parameter recording unit 81 (S27A).

[0216] If the defect detection unit 73 determines that the stacking direction length Lz is greater than or equal to the stacking direction length threshold Lz_lim (YES in S27A), it determines the integrated defect candidate as a defect (S28) and proceeds to step S6 in Figure 4.

[0217] On the other hand, if the defect detection unit 73 determines that the stacking direction length Lz is not equal to or greater than the stacking direction length threshold Lz_lim (NO in S27A), it determines whether the maximum layer length Lxy of the integrated defect candidate calculated in step S25 is equal to or greater than the maximum layer length threshold Lxy_lim (S27B).

[0218] If the defect detection unit 73 determines that the maximum layer length Lxy is greater than or equal to the maximum layer length threshold Lxy_lim (YES in S27B), it determines the integrated defect candidate as a defect (S28) and proceeds to step S6 in Figure 4.

[0219] On the other hand, if the defect detection unit 73 determines that the maximum layer length Lxy is not equal to or greater than the maximum layer length threshold Lxy_lim (NO in S27B), it does not determine the integrated defect candidate as a defect and proceeds to step S6 in Figure 4.

[0220] The defect detection method according to the seventh embodiment described above is referred to as "detection method 4". Detection method 4 is almost the same as detection method 1 according to the first embodiment. The only difference is that it detects the integrated defect candidate as a defect when the stacking direction length Lz is greater than or equal to the stacking direction length threshold Lz_lim, or when the maximum layer length Lxy is greater than or equal to the maximum layer length threshold Lxy_lim.

[0221] The layering direction length threshold Lz_lim and the maximum layer length threshold Lxy_lim mentioned above may be changed each time additive manufacturing is performed, or they may be fixed once determined. Furthermore, the parameter values ​​may be changed for each type of powder used in the additive manufacturing process.

[0222] The molding control unit 71 compares the defect status detected in the fifth to seventh embodiments with the threshold value of the maximum allowable defect length set on the setting screen 100 (Figure 17). If the defect status exceeds the threshold value, the molding control unit 71 executes the control content for stopping the molding operation set on the setting screen 100 (S51 in Figure 18, S51A in Figure 19, and S51B in Figure 20).

[0223] [8. Variation] Although various parameters were used in the embodiments described above, these parameters may be changed depending on the layer. For example, in the first embodiment, four types of parameters were given: α, β, γ, and δ. Of these four types of parameters, the maximum length threshold δ can be set to δ=5 for layers 100 to 200, and δ=10 for layers 201 to 300, for example. The appropriate values ​​for these parameters may vary depending on the shape of the fabricated object 38. In such cases, it is expected that the defect detection accuracy will be further improved by changing these parameters depending on the layer.

[0224] Furthermore, in the embodiments described above, examples were given in which the present invention is applied to dedicated manufacturing control software used in powder bed type 3D additive manufacturing equipment. However, the manufacturing beam is not limited to electron beam type, and the present invention may be applied to manufacturing control software of manufacturing equipment including other types such as laser type.

[0225] Furthermore, in the embodiments described above, the defect candidates identified by the BSE monitor 55 and the detected defects were described as being displayed as BSE images. However, the defect candidates and BSE images may also be displayed as camera images captured by the camera 42.

[0226] Furthermore, since the detection method in each of the embodiments described above improves the accuracy of defect detection, it is possible to omit further steps such as performing an X-ray CT inspection on the fabricated object 38.

[0227] It should be noted that the present invention is not limited to the embodiments described above, and various other applications and modifications can be taken as long as they do not deviate from the gist of the present invention as described in the claims. For example, the embodiments described above are detailed and specific explanations of the configuration of the 3D additive manufacturing apparatus 1 and the manufacturing control software of PC54 in order to clearly explain the present invention, and are not necessarily limited to having all the configurations described. Furthermore, it is possible to replace some of the configurations of the embodiments described here with the configurations of other embodiments, and it is also possible to add the configurations of other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace some of the configurations of each embodiment with other configurations. Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it is safe to assume that almost all components are interconnected. [Explanation of Symbols]

[0228] 1...3D additive manufacturing device, 2...Beam irradiation device, 15...Electron beam, 18...Building table, 32...Powder material, 35...Temporarily sintered body, 38...Building object, 42...Camera, 46...Detection unit, 54...PC, 55...BSE monitor, 60...Input unit, 70...Control unit, 71...Building control unit, 72...Defect candidate identification unit, 73...Defect detection unit, 74...Display control unit, 80...Recording unit, 81...Parameter recording unit, 82...Defect information recording unit, 90...Display unit, 100...Setting screen

Claims

1. A defect candidate identification unit detects defective areas in each layer as defect candidates based on layer data obtained for each layer in which the fabricated object is built up, and identifies multiple defect candidates located at substantially the same position in multiple adjacent layers in the stacking direction. A defect detection unit detects defects remaining in the fabricated object based on the size in the stacking direction and the size within the layer of the multiple defect candidates located at substantially the same position in the multiple layers identified above. The system includes a molding control unit that controls the process to stop or output a warning if the size of the defect exceeds a preset threshold during the additive manufacturing process, at least for the molded object where the defect was detected. Information processing device.

2. The molding control unit performs a first control to stop the additive manufacturing of all molded objects. The information processing apparatus according to claim 1.

3. The molding control unit performs a second control, which involves stopping the additive manufacturing of the object in which the defect was detected and continuing the additive manufacturing of the remaining objects. The information processing apparatus according to claim 1.

4. The molding control unit performs a third control, which outputs a warning indicating that the defect has been detected. The information processing apparatus according to claim 1.

5. The display control unit controls the following options for controlling the build process when the size of the defect exceeds a preset threshold during the build process: a first control that stops the build process of all objects; a second control that stops the build process of the object in which the defect was detected and continues the build process of the remaining objects; and a third control that outputs a warning indicating that the defect has been detected. These options are displayed on the display unit. The molding control unit performs the control of the selected option. The information processing apparatus according to claim 1.

6. When the molding control unit continues additive manufacturing of the remaining molded objects after excluding the object in which the defect was detected, it determines the melting order by repeatedly performing the following steps: designating the object furthest from the object to be melted first after continuing additive manufacturing as the next object to be melted; and designating the object furthest from the object to be melted immediately before as the next object to be melted for objects for which the melting order has not been specified. The information processing apparatus according to any one of claims 1, 3 to 5.

7. The system further includes a display control unit that generates a result table for each fabricated object, which includes information on defects detected for each layer based on the layer data, and information on the progress of the additive manufacturing process for the fabricated object, and controls the system to enable display of this table on the display unit. The information including the progress of the additive manufacturing process includes either information indicating that the defect occurred during the additive manufacturing process, or information indicating that the additive manufacturing process was performed successfully. The information processing apparatus according to claim 1.

8. The defect candidate identification unit measures the area of ​​the defect candidate of the Nth layer (where N is an integer of 1 or more) identified in the N+1th layer, and the area of ​​the defect candidate of the N+1th layer identified in the N+1th layer, among a plurality of defect candidates that overlap in a plurality of adjacent layers in the stacking direction. It calculates the overlapping area of ​​the overlapping portion where the defect candidate of the Nth layer and the defect candidate of the N+1th layer overlap in the stacking direction. If the ratio of the overlapping area to the area of ​​the defect candidate of the Nth layer is the same as or exceeds the first area threshold, and the ratio of the overlapping area to the area of ​​the defect candidate of the N+1st layer is the same as or exceeds the second area threshold, the defect candidate of the Nth layer and the defect candidate of the N+1st layer are identified as being in substantially the same position. The information processing apparatus according to claim 1.

9. The defect detection unit integrates the plurality of defect candidates located at substantially the same position in the stacking direction, measures the stacking direction length of the integrated defect candidate, and the first direction length and second direction length within the layer intersecting the stacking direction of the integrated defect candidate for each layer, and sets the maximum length of the first direction length and the second direction length as the maximum layer length. The information processing apparatus according to claim 8.

10. The defect detection unit provides a maximum length threshold in the stacking direction and a maximum layer length threshold within the layer, and detects the integrated defect candidate as a defect when the stacking direction length of the integrated defect candidate is greater than or equal to the maximum length threshold in the stacking direction, and the maximum layer length of the integrated defect candidate within the layer is greater than or equal to the maximum layer length threshold. The information processing apparatus according to claim 9.

11. The defect detection unit provides a maximum length threshold in the stacking direction and a maximum layer length threshold within the layer, and detects the integrated defect candidate as a defect if the stacking direction length of the integrated defect candidate is greater than or equal to the maximum length threshold in the stacking direction, or if the maximum layer length of the integrated defect candidate within the layer is greater than or equal to the maximum layer length threshold. The information processing apparatus according to claim 9.

12. A defect detection method performed by a defect candidate identification unit, a defect detection unit, and a molding control unit provided in an information processing device, The defect candidate identification unit detects defective areas in each layer as defect candidates based on layer data obtained for each layer in which the fabricated object is built up, and identifies a plurality of defect candidates located at substantially the same position in a plurality of adjacent layers in the stacking direction. The defect detection unit detects a defect remaining in the fabricated object based on the size in the stacking direction and the size within the layer of a plurality of defect candidates located at substantially the same position in the plurality of layers, The molding control unit includes the step of controlling the molding control unit to stop the molding of at least the defective object or output a warning if the size of the defect exceeds a preset threshold during the molding process. Defect detection method.

13. A powder bed covered with powdered material, A powder supply system for spreading the aforementioned powder material onto the powder bed, A beam irradiation unit that irradiates the powder material spread on the powder bed with a molding beam, An electron optical system that scans the molding beam according to melting conditions for the powder material and melts the powder material spread on the powder bed, A defect candidate identification unit detects defective areas in each layer as defect candidates based on layer data obtained for each layer in which the fabricated object is built up, and identifies multiple defect candidates located at substantially the same position in multiple adjacent layers in the stacking direction. A defect detection unit detects defects remaining in the fabricated object based on the size in the stacking direction and the size within the layer of the multiple defect candidates located at substantially the same position in the multiple layers identified above. The system includes a molding control unit that controls the process to stop or output a warning if the size of the defect exceeds a preset threshold during the additive manufacturing process, at least for the molded object where the defect was detected. 3D additive manufacturing device.

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