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

Abstract

This prevents situations where perfectly good quality molded objects are mistakenly classified as defective. [Solution] The PC54 includes a defect detection area setting unit 72 that sets an area located a predetermined amount inside the surface of the object manufactured by the 3D additive manufacturing apparatus as a defect detection area based on information input from the input unit 60, and a defect detection unit 74 that detects defects present in the object based on layer data obtained for each layer in which the object is additively manufactured, identifies the defects detected in the defect detection area as defects that lead to quality defects, and outputs defect information related to the identified defects.

Description

[Technical Field]

[0001] The present invention relates to an information processing device, a defect detection method, and a 3D additive manufacturing apparatus. [Background technology]

[0002] In recent years, 3D additive manufacturing technology, which creates objects by layering thin sheets of metal powder material one by one, has been gaining attention, and many types of 3D additive manufacturing technologies have been developed by varying the powder material and manufacturing method.

[0003] Conventional 3D additive manufacturing methods involve, for example, spreading powder material layer by layer on a base plate placed on the top surface of a stage. Next, a heating mechanism consisting of an electron beam or laser melts only the two-dimensional structural portion corresponding to one cross-section of the object from the powder material spread on the base plate. The object is then formed by stacking these layers of powder material one by one in the height direction (Z direction). Since a powder bed is formed on the base plate during the creation of each layer of the object, this manufacturing method is also called the powder bed method.

[0004] During the formation of a fabricated object, areas where sintering failure occurs do not completely melt the powder material, resulting in unevenness on the surface of the object during the manufacturing process. Since such unevenness can be considered defects, 3D additive manufacturing systems have traditionally performed defect detection based on data acquired from the surface of the fabricated object using camera imaging or BSE (Back Scattered Electron) imaging. Camera imaging is a method of photographing the surface of the fabricated object using visible light, for example. BSE imaging is a method of photographing the surface of the fabricated object by detecting backscattered electrons from an electron beam irradiated onto the surface of the fabricated object.

[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 related to such defect detection, the technology disclosed in Patent Document 1 is 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 occupied 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 Initiative] [Problems that the invention aims to solve]

[0008] However, metal powder does not melt evenly in every area; some areas are less likely to melt cleanly. For example, the area directly above the metal powder, specifically the first few layers, is less likely to melt cleanly, and defects tend to occur more frequently in this area.

[0009] Furthermore, the boundary between the powder bed and the printed object can be difficult to clearly identify in BSE images, and it is possible that non-defective areas at this boundary may be mistakenly identified as defects. In addition, if the surface roughness of the printed object is high, irregularities similar to those of a defect may appear on the surface, and these irregularities may be mistakenly identified as defects.

[0010] Furthermore, it is rare for objects created using a 3D additive manufacturing system to be used as finished products in their entirety. For example, 3D additive manufacturing systems are often used to create larger objects, and the surface of these objects is polished or further processed to create the final product. In products produced in this way, the areas directly above the metal powder, known as the downskin, and the upper layers, known as the upskin, become the material to be removed during polishing or further processing. Therefore, even if defects occur in these material removal areas, those defects are irrelevant to the final finish of the product.

[0011] In cases where there are areas prone to false defect detection or areas that require material removal, if defect detection processing is performed without considering these factors, false defects or unnecessary defects will be detected. When a defect is detected, the quality of the printed object may be judged as poor, or the 3D additive manufacturing device may be stopped.

[0012] This invention was made in view of the above circumstances, and aims to prevent situations in which molded objects that are of good quality are mistakenly judged as defective. [Means for solving the problem]

[0013] The information processing device according to the present invention includes a defect detection area setting unit that sets an area located a predetermined amount inside the surface of a three-dimensional additive manufacturing device based on information input from an input unit as a defect detection area, and a defect detection unit that detects defects present in the object based on layer data obtained for each layer in which the object is additively manufactured, identifies the defects detected in the defect detection area as defects that lead to quality defects, and outputs defect information related to the identified defects. Note that the above information processing apparatus is an aspect of the present invention, and a defect detection method and a three-dimensional layer forming apparatus reflecting one aspect of the present invention are also configured in the same manner as the above information processing apparatus.

Advantages of the Invention

[0014] According to the present invention, it is possible to prevent the occurrence of a situation in which a defective determination is erroneously made for a shaped object that is not defective in terms of quality. Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of the Drawings

[0015] [Figure 1] It is a schematic cross-sectional view schematically showing a three-dimensional layer forming apparatus according to a first embodiment of the present invention. [Figure 2] It is a block diagram showing a configuration example of a control system of a three-dimensional layer forming apparatus according to a first embodiment of the present invention. [Figure 3] It is a block diagram showing a configuration example of a control block of a PC according to a first embodiment of the present invention. [Figure 4] It is a flowchart showing an example of a procedure of defect detection area setting processing according to a first embodiment of the present invention. [Figure 5] It is a diagram showing a configuration example of a defect detection area setting screen according to a first embodiment of the present invention. [Figure 6] It is a diagram showing an example of an explanation screen of a defect detection internal area according to a first embodiment of the present invention. [Figure 7] It is a diagram showing an example of an explanation screen of a defect detection peripheral area according to a first embodiment of the present invention. [Figure 8] It is a flowchart showing an example of a procedure of defect detection processing according to Example 1 of a first embodiment of the present invention. [Figure 9] It is a flowchart showing an example of a procedure of defect candidate identification processing according to Example 1 of a first embodiment of the present invention. [Figure 10] It is a perspective view of a shaped object according to a first embodiment of the present invention. [Figure 11]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 12] 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 13] 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 14] 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 15] This figure shows examples of defect candidates A and B with extremely different sizes according to the first embodiment of the present invention. [Figure 16] This flowchart shows an example of the defect determination process procedure according to Example 1 of the first embodiment of the present invention. [Figure 17] This figure shows an example of a defect candidate spanning multiple layers according to Example 2 of the first embodiment of the present invention. [Figure 18] This figure shows an example of a defect candidate ID assigned to a defect candidate integrated by the defect detection unit according to Example 2 of the first embodiment of the present invention. [Figure 19] This figure shows an example of measuring the lengths in the XY and Z directions of a combined defect candidate by the defect detection unit according to Example 2 of the first embodiment of the present invention. [Figure 20] This flowchart shows an example of the procedure for the display control process according to the first embodiment of the present invention. [Figure 21] This flowchart shows an example of the procedure for the molding control process according to the first embodiment of the present invention. [Figure 22] This figure shows an example configuration of the defect detection area setting screen according to a second embodiment of the present invention. [Figure 23] This figure shows an example of the configuration of the defect detection exclusion area information setting unit and the defect detection exclusion stack count setting unit according to the second embodiment of the present invention. [Figure 24] This figure shows an example of the configuration of the defect detection exclusion area setting confirmation screen according to the second embodiment of the present invention. [Modes for carrying out the invention]

[0016] 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.

[0017] [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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] The recovery box 21 recovers any excess powder material 32 supplied onto the build table 18 by the powder supply device 16.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] The BSE monitor 55 is comprised of a display such as a liquid crystal display (LCD) or an organic ELD (Electroluminescence Display). The BSE monitor 55 displays the BSE image output from the PC 54 and the camera image on its display screen.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] The 3D additive manufacturing apparatus 1 shown in Figure 1 comprises an input unit 60, a PC 54, and a display unit 90.

[0076] 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.

[0077] 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.

[0078] The control unit 70 includes a molding control unit 71, a defect detection area setting unit 72, a defect candidate identification unit 73, a defect detection unit 74, and a display control unit 75. The recording unit 80 includes a parameter recording unit 81 and a defect-related information recording unit 82.

[0079] The parameter recording unit 81 records parameters such as thresholds that will be compared in each process described later. The parameter recording unit 81 also stores information about the defect detection area and defect detection exclusion area set in the defect detection area setting unit 72 as parameters. A defect detection area is an area where defects detected are identified as defects that lead to a quality defect in the molded object 38. A defect detection exclusion area is an area where defects detected are not identified as defects that lead to a quality defect in the molded object 38. Defect detection exclusion areas may include, for example, areas that are prone to being mistakenly detected as defects even though they are not, or areas that will be used for machining.

[0080] In this embodiment, the defect detection area and the defect detection exclusion area are set by the user via the defect detection area setting screen Sc1 (see Figure 5). The defect detection area setting screen Sc is displayed on the display unit 90 (see Figure 3) based on the control of the display control unit 75. An example of the configuration of the defect detection area setting screen Sc1 will be described later with reference to Figure 5.

[0081] Furthermore, the defect detection unit 74 does not include information detected as a defect in the defect detection exclusion area in the defect information. As a result, information detected as a defect in the defect detection exclusion area is not displayed as defect information on the display unit 90 screen. Also, if no defect information is output, the additive manufacturing process of the molded object 38, which is performed based on the defect information, continues without being stopped. The process of stopping the additive manufacturing process of the molded object 38 is performed when defect information is output from the defect detection unit 74, based on the control by the molding control unit 71. The molding control process by the molding control unit 71, including the process of stopping the additive manufacturing process, will be described in detail later with reference to Figure 21.

[0082] The defect-related information recording unit 82 records defect-related information, including layer data, defect candidates identified by the defect candidate identification unit 73, defect detection results, and defect information, which is information about defects determined to lead to defects. The defect-related information includes at least one of the following: the number of defects detected by the defect detection unit 74, the location of the defects, the volume of the molded object, the volume defect rate, the number of defects, and the maximum length.

[0083] 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-related information recording unit 82. However, the defect-related information recording unit 82 also records layer data in which it has not determined that irregularities have occurred on the manufacturing surface.

[0084] Furthermore, the molding control unit 71 also performs control to stop the additive manufacturing process of the molded object 38 in which a defect has been detected, if defect information is input from the defect detection unit 74.

[0085] The defect detection area setting unit 72 sets defect detection areas and defect detection exclusion areas based on the user's input to the defect detection area setting screen Sc1 (see Figure 5), which will be described later.

[0086] The defect candidate identification unit 73 reads layer data captured for each layer in which the molded object 38 is built up from the defect-related information recording unit 82, and based on this layer data, detects defective areas in each layer as defect candidates. The defect candidate identification unit 73 also identifies multiple defect candidates located at approximately the same position in multiple adjacent layers in the stacking direction.

[0087] The defect detection unit 74 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 74 integrates multiple defect candidates located at approximately the same position in the stacking direction and assigns a defect candidate identifier (e.g., ID1-4) to each of the integrated defect candidates. Furthermore, the defect detection unit 74 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 74 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). Furthermore, the defect detection unit 74 identifies the defects detected within the defect detection area as defects that could result in a quality defect of the molded object 38, and outputs defect information related to the identified defects.

[0088] The display control unit 75 controls the display format of the image displayed on the display unit 90 based on the layer data and defect-related information read from the defect-related information recording unit 82, and the input operations input from the input unit 60. For example, the display control unit 75 displays the defect detection area setting screen Sc1 (see Figure 5) on the display unit 90. The display control unit 75 also 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 the display unit 90 that displays these images. The display control unit 75 also controls the display content of the screen according to the input operations (enlarging, shrinking, moving the displayed image, etc.). The display control unit 75 then controls the display unit 90 so that the visualization image can be displayed. The display control unit 75 allows the display unit 90 to display information such as the state of the molded object 38, the shape of candidate defects (Figures 10 to 13, 17, etc., described later), whether or not defects have been detected, and various graphs, even during the additive manufacturing process, on various screens, allowing the user to check the displayed information.

[0089] 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.

[0090] 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-related information data recorded on the external recording medium. Alternatively, the control unit 70 shown in Figure 3 may consist only of a defect detection area setting unit 72 and a defect detection unit 74.

[0091] 1-5. Example of defect detection area setting process Next, an example of the defect detection area setting process according to this embodiment will be described with reference to Figure 4. Figure 4 is a flowchart showing an example of the procedure for the defect detection area setting process.

[0092] First, the display control unit 75 displays the defect detection area setting screen Sc1 (see Figure 5) on the display unit 90 (see Figure 3) (step S1). Next, the defect detection area setting unit 72 receives input from the user regarding the ranges of the defect detection area and the defect detection exclusion area via the defect detection area setting screen Sc1 (S2). Next, the defect detection area setting unit 72 records the information for the defect detection area and the defect detection exclusion area in the parameter recording unit 81 (see Figure 3) of the recording unit 80 (S3). After the processing in step S3, the defect detection area setting process is completed.

[0093] 1-6. Example of Defect Detection Area Setting Screen Configuration Next, we will explain an example configuration of the defect detection area setting screen Sc1 with reference to Figure 5. Figure 5 is a diagram showing an example configuration of the defect detection area setting screen Sc1.

[0094] As shown in Figure 5, the defect detection area setting screen Sc1 includes a defect detection area information setting unit St1, a selected stack number display unit St2, and a setting information display unit St3.

[0095] The defect detection area information setting unit St1 is a setting unit into which parameters necessary for dividing the detection area in the XY cross-section are input. Based on the parameters input to the defect detection area information setting unit St1, the detection area in the XY cross-section is divided into an internal defect detection area and a peripheral defect detection area. The internal defect detection area is the defect detection area set inward from the boundary between the molded object 38 and the powder bed (on the side of the molded object 38). The peripheral defect detection area is the area set outward from the boundary between the molded object 38 and the powder bed, and inward from the boundary between the molded object 38 and the powder bed that does not fall under the defect detection area, and is a defect detection exclusion area. The internal defect detection area and the peripheral defect detection area will now be explained with reference to Figures 6 and 7.

[0096] FIG. 6 is a diagram showing an example of an explanation screen Sc11 of the defect detection internal area, and FIG. 7 is a diagram showing an example of an explanation screen Sc12 of the defect detection peripheral area. The explanation screen Sc11 of the defect detection internal area and the explanation screen Sc12 of the defect detection peripheral area are displayed in areas such as below, left, or right of the defect detection area setting screen Sc1 shown in FIG. 5. Alternatively, an explanation screen display button or the like may be provided on the defect detection area setting screen Sc1, and it may be displayed on the screen that transitions when the button is pressed.

[0097] On the left side of FIG. 6, an explanation of the internal area parameter W in , , out (μm) is shown. Specifically, an explanation is shown that the internal area parameter W in is a parameter that defines the distance (width) between the boundary (indicated by a solid line) between the shaped object 38 and the powder bed and the outer peripheral portion (indicated by a broken line) of the defect detection internal area. On the right side of FIG. 6, a state in which the defect detection internal area is defined by the internal area parameter W in is shown. On the right side of FIG. 6, the defined defect detection internal area is shown in a black-filled pattern.

[0098] On the left side of FIG. 7, an explanation of the internal area parameter W in and the peripheral area parameter W out is shown. Specifically, an explanation is shown that the peripheral area parameter W out is a parameter that defines the distance from the boundary between the shaped object 38 and the powder bed to the outer peripheral portion of the defect detection peripheral area.

[0099] On the right side of FIG. 7, a state in which the defect detection peripheral area is defined by the internal area parameter W in and the peripheral area parameter W out is shown. More specifically, it is shown that an area with a width obtained by summing the width of the internal area parameter W in and the width of the peripheral area parameter W out is set as the defect detection peripheral area. On the right side of FIG. 7, the defined defect detection peripheral area is shown in a downward-right diagonal line pattern.

[0100] Returning to Figure 5, let's continue the explanation. The defect detection area information setting unit St1 includes an internal area parameter setting unit St11 and a peripheral area parameter setting unit St12. The internal area parameter setting unit St11 (an example of the first setting unit) sets the internal area parameter W in This is a setting unit that accepts input in [μm]. The peripheral area parameter setting unit St12 (an example of a defect detection exclusion area setting unit, second setting unit) sets the peripheral area parameter W out This is a setting unit that accepts input in [μm].

[0101] As described above, false defect detection is likely to occur in the area surrounding the boundary between the molded object 38 and the powder bed. Therefore, by setting various parameters for the internal area parameter setting unit St11 and the peripheral area parameter setting unit St12, the user can appropriately set the defect detection peripheral area, which is the defect detection peripheral area, and the defect detection exclusion area, in the area surrounding the boundary.

[0102] The defect detection exclusion area information setting unit St13 is a parameter setting unit that determines whether or not to control defect detection from the defect detection exclusion area up to the downskin n layer. If the option "No setting" (not shown) is selected in the defect detection exclusion area information setting unit St13, defect detection based on the defect detection exclusion area information will not be performed. The configuration information display unit St3 shows information about the defect detection internal area and the defect detection peripheral area in a diagram.

[0103] 1-7. Example of defect detection process 1-7-1. Example 1 Next, an example of defect detection processing according to Embodiment 1 of this embodiment will be described with reference to the flowcharts and explanatory diagrams from Figure 8 onwards. In the following description, only the operation of the defect detection part that differs from the conventional will be described, and the parts related to the other molding process will be omitted as they are the same as those described in the section on the conventional technology. The PC54 (molding control software) in this example can perform real-time defect detection during the additive manufacturing of the molded object 38.

[0104] Figure 8 is a flowchart showing an example of the defect detection process procedure according to Embodiment 1. The defect detection process is an example of a defect detection method performed by the control unit 70.

[0105] First, the molding control unit 71 shown in Figure 3 sets the value of m, which represents the m-th layer, to 0 and substitutes m+1 to create the first layer (S11). Next, the molding control unit 71 performs the additive manufacturing process described above for the first layer (S12).

[0106] Next, the molding control unit 71 determines whether the additive manufacturing process for multiple layers has been completed (S13). If the additive manufacturing process for multiple layers has not been completed (NO in S13), that is, if only the additive manufacturing process for the first layer has been completed, the process returns to step S11 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 (YES1 in S13), the process proceeds to step S14.

[0107] Next, the defect candidate identification unit 73 performs the defect candidate identification process shown in Figure 9 (S14). The defect candidate identification process will now be explained in detail. Figure 9 is a flowchart showing an example of the defect candidate identification procedure. This defect candidate identification procedure is a subroutine of step S14 in Figure 8.

[0108] First, the defect candidate identification unit 73 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 (S21). The area of ​​the defect candidates will now be explained.

[0109] The defect candidate identification unit 73 identifies the location of defect candidates based on the layer data obtained during the fabrication of each layer. The defect candidate identification unit 73 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.

[0110] However, two overlapping defect candidates in adjacent layers are not always the same size. Therefore, the defect candidate identification unit 73 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.

[0111] Here, the molded object 38 and the potential defects will be explained with reference to Figures 10 to 15. Figure 10 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-related 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 8, it acquires the BSE images of all 10 fabricated objects 38 as layer data and records them in the defect-related information recording unit 82.

[0112] Figure 11 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 73 identifies the locations of the black spots as defect candidates.

[0113] Figure 12 shows an example of a BSE image in which a candidate defect A occurring in layer N was identified. Figure 13 shows an example of a BSE image in which a candidate defect B occurring in the N+1th layer was identified.

[0114] 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 73 solely based on their size in the XY cross-section.

[0115] Figure 14 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.

[0116] 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 73 measures the area SAB of defect candidate A, defect candidate B, and overlapping portion AB in the XY cross-section for each layer.

[0117] In step S11, the defect candidate identification unit 73 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 73 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.

[0118] Next, the defect candidate identification unit 73 calculates the area ratio of the two defect candidates and the area ratio of the overlapping portion AB (S22). Here, the defect candidate identification unit 73 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 73 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)

[0119] Similarly, the defect candidate identification section 73 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)

[0120] Next, the defect candidate identification unit 73 determines whether the conditions for area ratios RA and RB and parameters α and β are met (S23). 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.

[0121] The defect candidate identification unit 73 determines that the conditions for area ratios RA, RB and parameters α, β are met (YES in S23), and identifies that the two defect candidates A and B are located in approximately the same position (S24).

[0122] 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 73 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).

[0123] For example, the defect candidate identification unit 73 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)

[0124] In step S23, if the conditions for area ratios RA, RB and parameters α, β are not met (NO in S23), the defect candidate identification unit 73 determines whether the conditions for area ratios RA, RB and parameter γ are met (S25). If the defect candidate identification unit 73 determines that the conditions for area ratios RA, RB and parameter γ are met (YES in S25), it identifies that the two defect candidates A and B are located in approximately the same position (S24).

[0125] Figure 14 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 73 to be able 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 13.

[0126] Figure 15 shows examples of defect candidates A and B with extremely different sizes. In the example shown in Figure 15, 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 15, defect candidates A and B are considered to be in approximately the same location.

[0127] 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 73 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)

[0128] The definition by which the defect candidate identification unit 73 determines approximately the same location of two defect candidates using equations (1), (2), and condition (3) or (4) is called "location definition 1".

[0129] After step S24, or if the defect candidate identification unit 73 determines that the conditions for area ratios RA,RB and parameter γ are not met (NO in S25), the defect candidate identification unit 73 records the location and size of the identified defect candidate as defect-related information in the defect-related information recording unit 82 (S26), and proceeds to step S15 in Figure 8. In step S15, the defect detection unit 74 performs defect determination processing.

[0130] Furthermore, even in the NO determination in step S25, defect-related information is recorded in the defect-related 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.

[0131] 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 74 detects defects from defect candidates will be explained with reference to Figures 16 to 19. In Figure 17 and subsequent figures, it is shown that when N is 1, the defect detection range by the defect detection unit 74 is from the 1st to the 8th layer.

[0132] Figure 16 is a flowchart showing an example of a defect detection process. This defect detection process is a subroutine of step S15 in Figure 8. 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.

[0133] First, the defect detection unit 74 reads defect-related information, including defect candidates in adjacent layers in the stacking direction (Z direction), from the defect-related information recording unit 82 (S31). However, the defect detection unit 74 may also directly acquire the defect-related information output from the defect candidate identification unit 73.

[0134] Next, the defect detection unit 74 integrates multiple defect candidates that it has determined to be located at approximately the same location across multiple layers, according to the location definition 1 described above (S32). Next, the defect detection unit 74 assigns a defect candidate ID to each integrated defect candidate (S33). Here, specific examples of the processes in steps S32 and S33 will be explained with reference to Figures 17 and 18.

[0135] Figure 17 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 73.

[0136] Figure 18 shows an example of a defect candidate ID assigned to a defect candidate integrated by the defect detection unit 74. The defect detection unit 74 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 74 assigns a defect candidate ID to each integrated defect candidate.

[0137] 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 73 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 in the location definition 1. Therefore, the defect detection unit 74 merges the two defect candidates that are in approximately the same location. The defect detection unit 74 then assigns defect candidate ID 1 and ID 2 to each of the defect candidates merged in the N+1th and N+2nd layers.

[0138] The defect detection unit 74 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 74 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 74 to integrated defect candidates will remain the same as long as they are in consecutive layers.

[0139] Returning to Figure 16, the explanation continues. After assigning a defect candidate ID in step S33, the defect detection unit 74 measures the length in the XY and Z directions for each integrated defect candidate (S34). The process by which the defect detection unit 74 measures the length in the XY and Z directions will now be explained with reference to Figure 19.

[0140] Figure 19 shows an example of measuring the lengths in the XY and Z directions of a defect candidate integrated by the defect detection unit 74. The defect detection unit 74 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 74 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 19, the defect detection unit 74 measures the length in the Y direction as Ly1.

[0141] The defect detection unit 74 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 74 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 74 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.

[0142] 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.

[0143] Returning to Figure 16, we continue the explanation. After processing in step S34, the defect detection unit 74 calculates the "maximum layer length Lxy" (S35) 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.

[0144] Next, the defect detection unit 74 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" (S36). Next, the defect detection unit 74 determines whether the maximum length exceeds the maximum length threshold δ read from the maximum parameter recording unit 81 (S37).

[0145] The defect detection unit 74 determines that the integrated defect candidate is a defect (S38) if it determines that the maximum length exceeds the maximum length threshold δ and satisfies the following condition (5) (YES in S37). Maximum length > Maximum length threshold δ …(5)

[0146] Next, the defect detection unit 74 determines that a defect within the defect detection area is a defect that leads to a quality defect in the molded object 38, and outputs defect information related to that defect (S39). After processing in step S39, the defect detection unit 74 moves on to step S16 in Figure 8.

[0147] On the other hand, if the defect detection unit 74 determines that the maximum length does not satisfy the above condition (5) which is greater than the maximum length threshold δ (NO in S37), it does not determine the integrated defect candidate as a defect and proceeds to step S16 in Figure 8.

[0148] After step S15 in Figure 8, the display control unit 75 determines whether or not there is a user input operation (S16). If the display control unit 75 determines that there is a user input operation (YES in S16), it proceeds to step S17 and performs display control processing. On the other hand, if the display control unit 75 determines that there is no user input operation (NO in S16), it proceeds to step S18.

[0149] Here, we will explain the display control process (processing in step S17) that is initiated when it is determined in step S16 that a user has made an input operation. Figure 20 is a flowchart showing an example of the display control processing procedure. This defect candidate identification process is the subroutine of step S17 in Figure 8.

[0150] First, the display control unit 75 receives user input (S41). Next, the display control unit 75 reads defect candidates and defect-related information from the defect-related information recording unit 82 in response to the input (S42).

[0151] Next, the display control unit 75 edits the defect candidates and defect-related information into an image that visualizes them (S43). At this time, the display control unit 75 edits the 2D or 3D images of the defect candidates and defect-related information, as well as graphs of various information, to create a display control process that can be displayed on the display unit 90.

[0152] Next, the display control unit 75 outputs the image visualized in step S43 to the display unit 90 (see Figure 3) (S44), and proceeds to step S18 in Figure 8. The display unit 90 displays a screen that visualizes defect candidates and defect-related information, including, for example, an image formed from multiple stacked layers into a three-dimensional image, and a graph of defect-related information for each layer. In addition, defects determined by the defect detection unit 74 are displayed on the display unit 90 as error information. Therefore, even in the middle of additive manufacturing, the user can check defect candidates and defect-related information online.

[0153] After the processing in step S17 in Figure 8, or after the NO determination in step S16, the molding control unit 71 determines whether or not the additive manufacturing of the final layer has been completed (S18). If the molding control unit 71 determines that the additive manufacturing of the final layer has not been completed (NO in S18), it returns to step S11, 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.

[0154] On the other hand, if the molding control unit 71 determines that the additive manufacturing of the final layer is complete (YES in S18), it terminates the defect detection process shown in Figure 8.

[0155] The defect detection method described above in Example 1 is referred to as "Detection Method 1". The parameters α, β, γ, and δ may be changed with each additive manufacturing process, 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.

[0156] In the defect detection method according to Example 1, if the maximum length of the integrated defect candidate (whichever is longer, the maximum length Lz or the maximum length Lxy) is greater than the maximum length threshold δ, the integrated defect candidate is determined to be a defect. Since defects that are 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.

[0157] 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.

[0158] Furthermore, information on defects 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. Therefore, the user can restart the additive manufacturing process, reset various parameters, etc., at an early stage after a defect is detected, enabling them to obtain the intended object 38.

[0159] In the defect detection method according to Example 1 (Detection Method 1), the display control processing in step S17 of Figure 8 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 S17 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.

[0160] Furthermore, the defect candidate shown as defect candidate ID2 in Figure 19 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.

[0161] 1-7-2. Example 2 Next, a defect detection method according to Example 2 of the first embodiment of the present invention will be described. The defect detection method according to Example 2 is performed using the 3D additive manufacturing apparatus 1 according to Example 1 and the control unit 70 and recording unit 80 (see Figure 3) of the PC54. In the defect detection method according to Example 2, the same process is performed from step S11 in Figure 8 to step S36 in Figure 16 according to Example 1.

[0162] In Example 2, the parameter recording unit 81 records the maximum length thresholds δz and δxy used by the defect detection unit 74 to determine defects. The defect detection unit 74 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.

[0163] For example, in step S37 of the defect detection method according to Embodiment 2 (see Figure 16), the defect detection unit 74 compares the maximum length Lz with the threshold δz if the maximum length is Lz. If the defect detection unit 74 determines that the maximum length Lz is greater than the threshold δz, it proceeds to step S38 and determines the integrated defect candidate to be a defect.

[0164] On the other hand, the defect detection unit 74 compares the maximum length Lxy (Lx max or Ly max) with the maximum layer length threshold δxy. If the defect detection unit 74 determines that the maximum length Lxy is greater than the maximum layer length threshold δxy, it proceeds to step S38 and determines the integrated defect candidate to be a defect.

[0165] The defect detection method described above in Example 2 will be referred to as "Detection Method 2". In Example 1, the threshold was unified to δ regardless of the XYZ direction for 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 electron beam intensity, the scanning motion in the XY direction, etc. On the other hand, the way the powder melts in the Z direction changes depending on the electron beam depth in the stacking direction. Therefore, in the second embodiment, the threshold used for comparison is changed for two patterns: when the maximum length is in the XY direction and when it is in the Z direction, and the maximum length in the XY direction and the maximum length in the Z direction are compared using their respective thresholds. As a result, it becomes possible to appropriately determine defects that may change depending on the way the powder material melts in the XY and Z directions.

[0166] 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.

[0167] Furthermore, in the first embodiment described above, position definition 1 was given as an example of defining the degree of overlap of defect candidates, and detection methods 1 and 2 were given as examples of detection methods after the position definition has been completed. The combination of position definition 1 and detection methods 1 and 2 is not limited to the combination of Examples 1 and 2 of the first embodiment described above, and may be used in any combination.

[0168] Furthermore, although various parameters were used in the first embodiment described above, these parameters may be changed depending on the layer. For example, in Example 1, 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.

[0169] 1-8. Example of molding control processing Next, an example of the molding control process according to the first embodiment of this embodiment will be described with reference to Figure 21. Figure 21 is a flowchart showing an example of the procedure for the molding control process. First, the molding control unit 71 (see Figure 3) instructs the 3D additive manufacturing apparatus 1 (see Figure 1) to start the 3D additive manufacturing process based on molding data (not shown) (S51). Next, the molding control unit 71 determines whether or not defect information has been input from the defect detection unit 74 (S52). If the defect detected by the defect detection unit 74 is a defect detected in the defect detection area, defect information is input from the defect detection unit 74. On the other hand, if the defect is detected in the defect detection exclusion area, defect information is not output from the defect detection unit 74. If it is determined in step S52 that no defect information has been input (NO in S52), the molding control unit 71 instructs the 3D additive manufacturing apparatus 1 to continue the molding process (S53).

[0170] Next, the molding control unit 71 determines whether or not the molding of all molding data has been completed (S54). If it is determined in step S54 that the molding of all molding data has not been completed (NO in S54), the molding control unit 71 returns to step S52 to make a determination. On the other hand, if it is determined in step S54 that the molding of all molding data has been completed (YES in S54), the molding control unit 71 terminates the molding control process.

[0171] On the other hand, if it is determined in step S52 that defect information has been entered (YES in S52), the molding control unit 71 instructs the 3D additive manufacturing apparatus 1 to stop the manufacturing process (S55). After the processing in step S55, the molding control unit 71 terminates the manufacturing control process.

[0172] The defect information determined in step S52 is not output if the area where the defect detection unit 74 detected a defect is an area excluded from defect detection. Therefore, according to this embodiment, it is possible to prevent the molding process of a molded object that has no quality issues from being stopped due to a false defect detection.

[0173] [2. Second Embodiment] Next, a defect detection method according to a second embodiment of the present invention will be described. The defect detection method according to the second 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 second embodiment, the processing shown in Example 1 or Example 2 of the first embodiment is performed. In the second embodiment as well, the display control processing shown in Figure 20 and the molding control processing shown in Figure 21 are performed.

[0174] In the first embodiment, an example was described in which the defect detection area setting in the XY cross-section is accepted via the defect detection area setting screen Sc1 (see Figure 5). In the second embodiment, the defect detection area setting in the Z (stacking) direction is also accepted. Specifically, the defect detection area setting around the downskin and / or upskin is accepted.

[0175] The downskin indicates the area where the powder first melts, either directly above the powder or directly above the support (not shown) supporting the printed object 38. The area several layers above the metal powder tends to melt poorly and is prone to defects; therefore, printed objects 38 formed in this area are often excluded from the final printed object. Printed objects 38 formed directly above the support also often have areas that do not detach properly from the support, and are therefore often excluded from the final printed object. The upskin indicates the final (uppermost) layer of melt in that area of ​​the printed object. Printed objects formed in the upskin are subject to machining.

[0176] In the second embodiment, the display control unit 75 causes the display unit 90 (see Figure 3) to display a screen having a UI (User Interface) that allows setting the number of layers from the downskin and / or the number of layers from the upskin to be targeted as a defect detection exclusion area, as a defect detection area setting screen Sc2 (see Figure 22).

[0177] 2-1. Example of Defect Detection Area Setting Screen Configuration Next, an example of the configuration of the defect detection area setting screen Sc2 according to this embodiment will be described with reference to Figures 22 to 24.

[0178] Figure 22 shows an example of the configuration of the defect detection area setting screen Sc2. As shown in Figure 22, the defect detection area setting screen Sc2 includes a defect detection area information setting unit St1, a selected stack number display unit St2, and a setting information display unit St3. The defect detection area information setting unit St1 includes an internal area parameter setting unit St11, a peripheral area parameter setting unit St12, a defect detection exclusion area information setting unit St13, and a defect detection exclusion stack count setting unit St14.

[0179] The internal area parameter setting unit St11 and peripheral area parameter setting unit St12 of the defect detection area information setting unit St1, and the selected stack number display unit St2 are the same as those in the first embodiment described above, so redundant explanations will be omitted. The defect detection exclusion area information setting unit St13 (an example of a defect detection exclusion area setting unit) is configured with information on whether or not a defect detection exclusion area is set, and, if set, the location of the defect detection exclusion area. The defect detection exclusion stack count setting unit St14 (an example of a second setting unit) is configured to set the number of stacks to be excluded from defect detection in the area set in the defect detection exclusion area information setting unit St13.

[0180] Here, with reference to Figure 23, the defect detection exclusion area information setting unit St13 and the defect detection exclusion stack count setting unit St14 will be described. Figure 23 is a diagram showing an example of the configuration of the defect detection exclusion area information setting unit St13 and the defect detection exclusion stack count setting unit St14.

[0181] The left side of Figure 23 shows the state where the drop-down list button of the defect detection exclusion area information setting unit St13 is pressed and the list is displayed. The list consists of the items "None", "Down Skin", "Up Skin", and "Up Skin and Down Skin". "None" is an option that indicates that no defect detection exclusion area will be set. "Down Skin" is an option to set the defect detection exclusion area to the area around the down skin. "Up Skin" is an option to set the defect detection exclusion area to the area around the up skin. "Up Skin and Down Skin" is an option to set the defect detection exclusion area to both the area around the up skin and the area around the down skin.

[0182] In the examples shown in Figures 22 and 23, only one input UI is provided for the defect detection exclusion layer count setting unit St14. However, if "upskin and downskin" is set in the defect detection exclusion area information setting unit St13, the number of input UIs for the defect detection exclusion layer count setting unit St14 may be increased to two. In other words, the defect detection area setting screen Sc2 may be configured to allow the number of layers targeted for the defect detection exclusion area to be changed for upskin and downskin.

[0183] The right side of Figure 23 shows the state where "Down Skin" is selected in the Defect Detection Exclusion Area Information Setting Unit St13. The number of layers in the area selected in the Defect Detection Exclusion Area Information Setting Unit St13 can be set by the user via the Defect Detection Exclusion Layer Count Setting Unit St14. The right side of Figure 23 shows that the number "5" has been entered in the Defect Detection Exclusion Layer Count Setting Unit St14. In this case, the area five layers above the down skin, including the down skin itself, is set as the defect detection exclusion area.

[0184] Returning to Figure 22, the explanation continues. In the setting information display unit St3 of the defect detection area setting screen Sc2, information on the defect detection internal area and the defect detection exclusion area is shown graphically, similar to the first embodiment. In this embodiment, the display control unit 75 changes the size of the defect detection internal area and the defect detection exclusion area shown in this graph in conjunction with the size of the parameters set in the defect detection area information setting unit St1. In the setting information display unit St3 on the right side of Figure 22, it is shown that the defect detection internal area has decreased and the defect detection exclusion area has increased according to the various parameters set in the defect detection area information setting unit St1 shown on the left side of Figure 22. With this display, the user can easily and intuitively understand how the size of the defect detection internal area and the defect detection exclusion area changes based on parameter operations. In addition, the user can appropriately set the parameters while confirming that the size of the defect detection internal area and the defect detection exclusion area changes.

[0185] Figure 24 shows an example of the configuration of the defect detection exclusion area setting confirmation screen Sc3. The defect detection exclusion area setting confirmation screen Sc3 shows the target area and number of layers of the defect detection exclusion area set in the defect detection area setting screen Sc2. At the top of Figure 24, there is an explanation of how to count the number of layers of the defect detection exclusion area. Below that, it is shown that the defect detection exclusion area setting is "ON" and the number of defect detection exclusion layers is set to "3".

[0186] Below that, an explanatory diagram of the defect detection exclusion area and defect detection area is displayed. The leftmost column of the explanatory diagram lists the layer number (Layer Ni (i is an integer greater than or equal to 1)). The column to the right of that shows the defect detection exclusion area and defect detection area for each printed object, color-coded. In all the printed objects shown in the explanatory diagram, the area from the downskin upwards, including the downskin, is set as the defect detection exclusion area, and the area above that is set as the defect detection area. Note that, as shown in the explanatory diagram, depending on the shape of the printed object 38, the defect detection exclusion area and the defect detection area may be mixed within the same layer.

[0187] By displaying such explanations on the settings confirmation screen Sc3, users can easily and visually verify the meaning of defect detection areas and defect exclusion areas, as well as whether the settings configured through parameter input are as intended.

[0188] [3. Variant] In the embodiments described above, examples were given in which the defect detection unit 74 performs defect detection even in defect detection exclusion areas set by the defect detection area setting unit 72, but the present invention is not limited to this. The defect detection unit 74 does not have to detect defects in the defect detection exclusion areas. Alternatively, the defect detection unit 74 may be configured to detect only defects in the defect detection exclusion area (defect detection peripheral area) set for the XY cross-section in Example 1 of the first embodiment, and not perform defect detection targeting defect candidates integrated in the layer direction, as set in Example 2.

[0189] Furthermore, the defect detection exclusion area may be set by freely combining the defect detection peripheral area for the XY cross-section described in the first embodiment and the defect detection area around the upskin and / or downskin in the layer direction described in the second embodiment.

[0190] Furthermore, the various parameters used to set the defect detection area and the defect detection exclusion area may be set to different values ​​depending on the type of molded object or the range of the layer layers.

[0191] 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.

[0192] 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.

[0193] Furthermore, in the embodiments described above, defect candidate identification and defect detection are performed during the additive manufacturing process, but defect candidate identification and defect detection may also be performed after the completion of the manufacturing process. In other words, the defect candidate identification unit 73 and the defect detection unit 74 can operate either during or after the additive manufacturing process.

[0194] 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 X-ray inspection and CT (Computed Tomography) inspection on the fabricated object 38.

[0195] 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]

[0196] 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 detection area setting unit, 73…Defect candidate identification unit, 74…Defect detection unit, 75…Display control unit, 80…Recording unit, 81…Parameter recording unit, 82…Defect-related information recording unit, 90…Display unit, Sc1, Sc2…Defect detection area setting screen, St11…Internal area parameter setting unit, St12…Peripheral area parameter setting unit, St13…Defect detection exclusion area information setting unit, St14…Defect detection exclusion layer count setting unit, St3…Setting information display unit

Claims

1. A defect detection area setting unit sets an area located a predetermined distance inside the surface of the object fabricated by the 3D additive manufacturing device, based on information input from the input unit, as a defect detection area. The invention comprises a defect detection unit that detects defects present in the fabricated object based on layer data obtained for each layer in which the fabricated object is built up, identifies the defects detected in the defect detection area as defects that lead to quality defects, and outputs defect information related to the identified defects. Information processing device.

2. The system further includes a display control unit that displays a defect detection area setting screen for setting the aforementioned defect detection area, The defect detection area setting screen has a first setting unit for setting a predetermined amount, with the boundary between the molded object and the powder bed in the XY cross-section of the molded object as the starting point. The information processing apparatus according to claim 1.

3. The defect detection area setting screen includes a defect detection exclusion area setting unit that sets a defect detection exclusion area, which is an area in which the defect detection unit does not identify defects that would lead to quality defects. The information processing apparatus according to claim 2.

4. The defect detection exclusion area setting unit has a second setting unit that sets the predetermined amount when the downskin of the molded object is used as the starting point, and / or when the upskin of the molded object is used as the starting point. The information processing apparatus according to claim 3.

5. The second setting unit receives input that is the number of layers stacked in the additive manufacturing process, which is the predetermined amount. The information processing apparatus according to claim 4.

6. The defect detection area setting screen has a setting information display unit that graphically shows the defect detection area and the defect detection exclusion area. The display control unit changes the size of the defect detection area and the defect detection exclusion area displayed on the setting information display unit in conjunction with a predetermined size input to the first setting unit and the second setting unit. The information processing apparatus according to claim 4.

7. The invention further includes a molding control unit that stops the additive manufacturing operation when the defect detection unit identifies a defect that could lead to a quality defect, The molding control unit, if a defect is detected by the defect detection unit in the defect detection exclusion area, will continue the molding operation by the 3D additive manufacturing apparatus. The information processing apparatus according to any one of claims 1 to 6.

8. The system further includes a defect candidate identification unit that detects defective areas in each layer based on the layer data, 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 the defect 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. The information processing apparatus according to claim 7.

9. A defect detection method performed by a defect detection area setting unit and a defect detection unit provided in an information processing device, The defect detection area setting unit sets an area located a predetermined amount inside the surface of the object fabricated by the 3D additive manufacturing apparatus as a defect detection area, based on information input from the input unit. The defect detection unit includes the steps of: detecting defects present in the fabricated object based on layer data obtained for each layer in which the fabricated object is additively fabricated; identifying the defects detected in the defect detection area as defects that lead to quality defects; and outputting defect information related to the identified defects. Defect detection method.

10. 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 detection area setting unit sets an area located a predetermined distance inside the surface of the object fabricated by the 3D additive manufacturing device, based on information input from the input unit, as a defect detection area. The invention comprises a defect detection unit that detects defects present in the fabricated object based on layer data obtained for each layer in which the fabricated object is built up, identifies the defects detected in the defect detection area as defects that lead to quality defects, and outputs defect information related to the identified defects. 3D additive manufacturing device.

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