Defect detection device and additive manufacturing system

Abstract

To enable high-speed defect detection with no detection omission, which can easily detect a specific shape characteristic portion from a measurement result of a surface shape of an additive manufactured article while suppressing a data amount that needs to be stored.SOLUTION: Welding defects that occur in an additive manufactured article are detected, in molding an additive manufactured article by layering beads that are formed by melting and coagulating a filler material. A defect detection method comprises: a height detection step for detecting the height distribution of a surface shape in an additive manufactured article being molded; an image generating step for representing information on the detected height distribution as a variable of a luminance value for each pixel in a two-dimensional image, and generating a height information image obtained by converting the height distribution information into luminance value distribution information; a characteristic portion detection step for detecting a shape characteristic portion having a specific shape characteristic, in accordance with the highs / lows of the luminance values in the height information image; and a determination step for determining the possibility that the detected shape characteristic portion will be a welding defect.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a defect detection method, a manufacturing method of a layered object, a defect detection device, and a layered object manufacturing device. [Background technology]

[0002] There is a known technology for manufacturing an additively manufactured object formed from multiple layers of beads by stacking beads formed by melting and solidifying a filler material. For example, Patent Document 1 discloses an inspection device that inspects beads by measuring the surface shape of the formed bead during the manufacturing process of the additively manufactured object and comparing the measured surface shape with a reference shape. The inspection device in Patent Document 1 captures an image of a bright line when a linear laser beam is irradiated onto the bead, and detects the height of the bead surface from the position of the bright line in the captured image. Measurement devices using various methods, including but not limited to the light-section method described above, are widely used for measuring such three-dimensional shapes. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-215297 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when measuring three-dimensional shapes using various methods, for example, when using the light-section method described in Patent Document 1, the measurement data is coordinate information of bright lines, which indirectly represents the shape of the object being measured. Therefore, when performing post-processing such as determining desired shape features from the measurement data, the calculations required are complicated, making it difficult to speed up the processing. Furthermore, because the measurement data is indirect information, the amount of data can easily become enormous, making it necessary to always ensure sufficient storage capacity for storing the data.

[0005] In additive manufacturing (AM), if a narrow portion exists in the substrate where the bead is to be formed, the filler material will not penetrate sufficiently into the narrow portion, increasing the likelihood of welding defects such as unwelded areas. Therefore, it is conceivable to automatically detect specific shape features (narrow portions) by measuring the substrate shape during manufacturing and then modify the welding conditions for the narrow portion or the bead formation path in the narrow portion to prevent welding defects. However, as mentioned above, this requires high-speed computational processing of a huge amount of measurement data, and it is often practically difficult to change the welding conditions and bead formation path in real time based on the shape measurement results. Furthermore, measurement data that indirectly represents the shape of the object to be measured may not accurately reproduce the original shape, making it particularly difficult to reliably detect minute shapes without omissions.

[0006] Therefore, the present invention aims to provide a defect detection method that can easily detect specific shape features from the measurement results of the surface shape of an additively molded object while minimizing the amount of data that needs to be stored, and that enables high-speed, complete defect detection, a method for manufacturing an additively molded object using this method, as well as a defect detection device, an additively molded object, and a program. [Means for solving the problem]

[0007] The present invention comprises the following configurations. (1) A defect detection method for detecting welding defects that occur in an additively manufactured object when the additively manufactured object is manufactured by stacking beads formed by melting and solidifying a filler material, the method comprising: a height detection step of detecting a height distribution of a surface shape of the layered object during modeling; an image generating step of expressing the detected information of the height distribution as a variable of the brightness value of each pixel of a two-dimensional image and generating a height information image by converting the information of the height distribution into information of the distribution of the brightness values; a feature portion detection step of detecting a shape feature portion having a specific shape feature depending on the level of the luminance value of the height information image; The detected shape feature is the weld defect. Possibility of becoming a determination step of determining A defect detection method comprising: (2) based on information about the welding defects detected by the defect detection method according to (1), modifying a modeling plan for manufacturing the additively manufactured object so as to suppress the occurrence of the welding defects; manufacturing the layered object based on the changed manufacturing plan. A method for manufacturing additively manufactured objects. (3) A defect detection device that detects welding defects that occur in an additively manufactured object that is formed by stacking beads formed by melting and solidifying a filler metal, a shape measuring unit that detects a height distribution of a surface shape of the layered object; an image generating unit that expresses the detected information of the height distribution as a variable of the brightness value of each pixel of a two-dimensional image and generates a height information image by converting the information of the height distribution into information of the distribution of the brightness values; a feature portion detection unit that detects a shape feature portion having a specific shape feature depending on the level of the luminance value of the height information image; The detected shape feature is the weld defect. Possibility of becoming a defect determination unit that determines 、 A defect detection device comprising: (4) The defect detection device according to (3), a molding control device that changes a molding plan for manufacturing the layered object so as to suppress the occurrence of the welding defect detected by the defect detection device; and a modeling device that models the layered object based on the changed modeling plan; and An additive manufacturing system comprising: (5) A program for causing a computer to execute a procedure of a defect detection method for detecting welding defects occurring in an additively manufactured object when the additively manufactured object is manufactured by stacking beads formed by melting and solidifying a filler material, the program comprising: On the computer, a height detection step of detecting a height distribution of a surface shape of the layered object during modeling; an image generation step of expressing the detected information of the height distribution as a variable of the brightness value of each pixel of a two-dimensional image and generating a height information image by converting the information of the height distribution into information of the distribution of the brightness values; a feature portion detection step of detecting a shape feature portion having a specific shape feature depending on the level of the luminance value of the height information image; The detected shape feature is the weld defect. Possibility of becoming a determination procedure for determining A program that executes the following. [Effects of the Invention]

[0008] According to the present invention, specific shape features can be easily detected from the measurement results of the surface shape of a layered object while minimizing the amount of data that needs to be stored, enabling high-speed, flawless defect detection. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram showing the overall configuration of an additive manufacturing system. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of the object-forming control device. [Figure 3] FIG. 3 is a flowchart showing the procedure for forming a layered object. [Figure 4] FIG. 4 is a flowchart showing the procedure of the welding defect detection method. [Figure 5] FIG. 5 is an explanatory diagram showing an example of feature quantities of a narrow portion in a cross section perpendicular to the bead formation direction of a bead. [Figure 6A] FIG. 6A is an explanatory diagram showing a state in which the interval between the pair of beads shown in FIG. 5 is changed. [Figure 6B] FIG. 6B is an explanatory diagram showing a state in which the interval between the pair of beads shown in FIG. 5 is changed. [Figure 7] FIG. 7 is a schematic diagram showing how the shape of a bead is measured by a shape detector. [Figure 8] FIG. 8 is an image of height information image data obtained by converting the height information of each bead shown in FIG. 7 into brightness information. [Figure 9A] FIG. 9A is an explanatory diagram showing the result of image processing in which the height information image shown in FIG. 8 is subjected to extraction of the contour of the bead shape in accordance with changes in brightness, etc., and also extraction of narrow portions. [Figure 9B] FIG. 9B is an explanatory diagram showing the result of image processing in which the height information image shown in FIG. 8 is subjected to extraction of the contour of the bead shape in accordance with changes in brightness, etc., and also extraction of narrow portions. [Figure 10] FIG. 10 is a diagram showing the configuration of the torch and shape detector. [Figure 11] FIG. 11 is an explanatory diagram showing the shape detection result by the shape detector when the torch and the shape detector move together. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Here, the defect detection method according to the present invention will be described by taking as an example a case where it is applied to an additive manufacturing system that manufactures additively manufactured objects. Fig. 1 is a schematic diagram showing the overall configuration of the additive manufacturing system.

[0011] The additive manufacturing system 100 according to this embodiment includes a manufacturing control device 11, a manipulator 13, a filler metal supply device 15, a manipulator control device 17, a heat source control device 19, and a shape detector 21.

[0012] The manipulator control device 17 controls the manipulator 13 and the heat source control device 19. A controller (not shown) is connected to the manipulator control device 17, and an operator can instruct any operation of the manipulator control device 17 via the controller.

[0013] The manipulator 13 is, for example, an articulated robot, and a torch 23 attached to the tip shaft supports a filler material (welding wire) M so that the filler material M can be continuously supplied. The torch 23 holds the filler material M protruding from the tip. The position and posture of the torch 23 can be set arbitrarily in three dimensions within the range of the degrees of freedom of the robot arm constituting the manipulator 13. The manipulator 13 preferably has six or more degrees of freedom, and is preferably one that can arbitrarily change the axial direction of the heat source at the tip. The manipulator 13 may be in various forms, such as a four- or more-axis articulated robot as shown in FIG. 1, or a robot equipped with angle adjustment mechanisms on two or more orthogonal axes.

[0014] The torch 23 has a shield nozzle (not shown), through which a shielding gas is supplied. The shielding gas blocks the atmosphere and prevents oxidation and nitridation of the molten metal during welding, thereby suppressing welding defects. The arc welding method used in this configuration may be either a consumable electrode type such as shielded metal arc welding or carbon dioxide gas arc welding, or a non-consumable electrode type such as TIG (Tungsten Inert Gas) welding or plasma arc welding, and is appropriately selected depending on the additively manufactured object Wk to be manufactured. Here, gas metal arc welding will be used as an example. In the case of a consumable electrode type, a contact tip is disposed inside the shield nozzle, and a filler material M to which an electric current is supplied is held by the contact tip. The torch 23 holds the filler material M and generates an arc from the tip of the filler material M in a shielding gas atmosphere.

[0015] The filler material supply device 15 supplies the filler material M toward the torch 23 of the manipulator 13. The filler material supply device 15 includes a reel 15a around which the filler material M is wound, and a payout mechanism 15b that pays out the filler material M from the reel 15a. The filler material M is fed to the torch 23 by the payout mechanism 15b in the forward or reverse direction as needed. The payout mechanism 15b is not limited to a push type that is disposed on the filler material supply device 15 side and pushes out the filler material M, but may also be a pull type or push-pull type that is disposed on a robot arm or the like.

[0016] The heat source control device 19 is a welding power source that supplies the power required for welding by the manipulator 13. The heat source control device 19 adjusts the welding current and welding voltage supplied when forming a bead by melting and solidifying the filler metal. In addition, the filler metal supply speed of the filler metal supply device 15 is adjusted in conjunction with the welding conditions such as the welding current and welding voltage set by the heat source control device 19.

[0017] The heat source for melting the filler material M is not limited to the arc described above. Other heat sources may also be used, such as a heating method that combines an arc and a laser, a heating method that uses plasma, or a heating method that uses an electron beam or laser. Heating with an electron beam or laser allows for more precise control of the amount of heat, which can more appropriately maintain the state of the formed bead and contribute to further improving the quality of the layered structure. The material of the filler material M is also not particularly limited. The type of filler material M used may vary depending on the characteristics of the additively shaped object Wk, such as mild steel, high-tensile steel, aluminum, aluminum alloy, nickel, or nickel-based alloy.

[0018] The shape detector 21 is provided on or near the tip axis of the manipulator 13, and its measurement area is near the tip of the torch 23. The shape detector 21 is moved together with the torch 23 by the manipulator 13, and detects the shape of the bead B and the base (the surface of the base 25 or the existing bead B) that will serve as the base for forming a new bead B. The shape detector 21 may be another detection means provided at a position separate from the torch 23. For example, the shape detector 21 may use a laser sensor that irradiates the bead B with laser light and detects reflected light from the bead surface. Various methods for detecting height include a light-section method, a phase difference detection method, a triangulation method, and a TOF (Time of Flight) method. Any one of these methods may be used, or a combination of these methods may be used. Since these methods are well known, their description will be omitted here. To obtain information on the height distribution within a predetermined area using the shape detector 21, the laser light can be scanned over a predetermined area using a reflecting mirror such as a galvanometer mirror, or the irradiation area of ​​the laser light can be expanded by operating the manipulator 13. By using such an optical shape detection method, non-contact, high-speed shape detection becomes possible.

[0019] The additive manufacturing system 100 configured as described above operates according to a manufacturing program created based on a manufacturing plan for the additively manufactured object Wk. The manufacturing program is composed of numerous command codes and is created based on an appropriate algorithm depending on various conditions, such as the shape, material, and heat input of the object. According to this manufacturing program, the torch 23 is moved while the supplied filler material M is melted and solidified, forming a linear bead, which is a molten solid of the filler material M, on the base 25. That is, the manipulator control device 17 drives the manipulator 13 and the heat source control device 19 based on a predetermined set of programs provided by the manufacturing control device 11. In response to a command from the manipulator control device 17, the manipulator 13 moves the torch 23 while melting the filler material M with an arc to form a bead B. By sequentially forming and stacking the beads B in this manner, an additively manufactured object Wk having the desired shape is obtained.

[0020] Although a flat base 25 is used here, the shape of the base 25 is not limited to this. For example, the base 25 may be cylindrical, with beads formed on the outer periphery of the side surface of the cylinder.

[0021] Furthermore, the coordinate system of the modeling shape data handled by the additive manufacturing system 100 corresponds to the coordinate system on the base 25 on which the additive manufacturing object Wk is manufactured. For example, three axes of the coordinate system may be set so that an arbitrary position is set as the origin and a position in three-dimensional space is specified. If the base 25 is configured as a cylinder, a cylindrical coordinate system may be set, or in some cases, a spherical coordinate system may be set. Here, the explanation will be given by defining a Cartesian coordinate system having X-, Y-, and Z-axes, in which the top surface of the base 25 is the XY plane and the normal direction to the top surface of the base 25 is the Z direction.

[0022] The forming control device 11 is configured by an information processing device such as a PC (Personal Computer). Each function of the forming control device 11, which will be described later, is realized by a control unit (not shown) reading and executing a program having a specific function stored in a storage device (not shown). Examples of the storage device include memories such as RAM (Random Access Memory), which is a volatile storage area, and ROM (Read Only Memory), which is a non-volatile storage area, and storage devices such as HDD (Hard Disk Drive) and SSD (Solid State Drive). Examples of the control unit include processors such as CPU (Central Processing Unit) and MPU (Micro Processor Unit), or dedicated circuits.

[0023] <Functional configuration of the molding control device> 2 is a block diagram showing the functional configuration of the forming control device 11. The forming control device 11 includes an input unit 31, a memory unit 33, a forming program creation unit 35, a forming plan modification unit 37, an output unit 39, and a defect detection device 41. The defect detection device 41, which will be described in detail later, may be included in the forming control device 11, or may be configured separately from the forming control device 11, or may be configured to be connected to the forming control device 11 via communication or the like.

[0024] The input unit 31 acquires various types of information from the outside, for example, via an appropriate network or by an appropriate input device. Examples of the information acquired here include shape data including shape information of the object to be subjected to additive manufacturing, such as CAD / CAM data, setting data for welding conditions, output data from the shape detector 21, instruction information from the worker, etc. Details of the various types of information will be described later.

[0025] The storage unit 33 stores various information acquired by the input unit 31 and the above-mentioned modeling program. The storage unit 33 also holds a database 33a that stores information such as driving conditions such as the operating speed and operable range of the manipulator 13 when modeling various shapes, and various welding conditions that can be set by the heat source control device 19.

[0026] The manufacturing program creation unit 35 determines a manufacturing plan, such as a bead formation trajectory representing the path along which the torch 23 is moved to form a bead, and welding conditions during bead formation, by referring to each database in the memory unit 33. Furthermore, based on the created manufacturing plan, a manufacturing program is created according to the type and specifications of the manipulator 13 and heat source control device 19.

[0027] The output unit 39 outputs the modeling program created by the modeling program creation unit 35 to the manipulator control device 17, the heat source control device 19, etc. The output unit 39 may further be configured to display the processing results for the shape data using an output device (not shown), such as a display provided in the modeling control device 11.

[0028] Furthermore, the defect detection device 41 grasps the base shape when forming the bead from the information of the output data of the shape detector 21, and determines whether there is a high possibility of a welding defect occurring in the bead to be formed. If there is a risk of a welding defect occurring, the manufacturing plan modification unit 37 sends a modified manufacturing plan obtained by modifying the welding plan to the manufacturing program creation unit 35. The manufacturing program creation unit 35 creates a manufacturing program based on the input modified manufacturing plan, and outputs it to the output unit 39.

[0029] As described above, the defect detection device 41 has a function of predicting the occurrence of welding defects from the information of the output data from the shape detector 21. Specifically, the defect detection device 41 includes a shape identification unit 43, a height information image generation unit 45, a feature detection unit 47, a feature extraction unit 49, a defect size prediction unit 51, a prediction model 53, and a defect determination unit 55. The function of each unit will be described in detail below.

[0030] 3 is a flowchart showing the steps of forming a layered object. Each step is performed based on a command from the forming control device 11. When forming the layered object Wk, the forming control device 11 first acquires shape data of the layered object Wk (Step 11, hereinafter abbreviated as S11).

[0031] In the memory unit 33 of the manufacturing control device 11, drive conditions and welding conditions corresponding to the type and specifications of the manipulator 13 and the heat source control device 19 are input in advance to a database 33a, and the manufacturing program creation unit 35 creates a manufacturing plan for the layered object Wk in accordance with the input shape data while referring to the database 33a (S2). The creation of this manufacturing plan includes, in accordance with a specific algorithm, a process of slicing the shape of the layered object Wk to a predetermined thickness, determining a bead formation trajectory so as to fill the shape of each sliced ​​layer with a bead having a predetermined width, and setting the welding conditions for each bead. The algorithm for creating such a manufacturing plan is not particularly limited and may be a conventionally known one.

[0032] The molding program creation unit 35 creates a molding program that drives each part, such as the manipulator 13 and the heat source control device 19, based on the created molding plan (S13). The created molding program is output from the output unit 39 to the manipulator control device 17 (S14). Then, the manipulator control device 17 drives each part, such as the manipulator 13, the filler material supply device 15, and the heat source control device 19, in accordance with the input molding program, and moves the torch 23 while generating an arc from the tip thereof, thereby forming a bead B along the bead formation trajectory as per the molding plan (S15).

[0033] As the bead B is formed, the shape detector 21 detects height information and outputs the detection result as output data to the input unit 31 of the forming control device 11. The defect detection device 41 reads the output data from the shape detector 21 input to the input unit 31. Based on the read output data, the defect detection device 41 detects characteristic portions having specific shapes that are prone to welding defects, specifically, narrow portions formed between the formed beads. The position of the detected narrow portion is then set as a candidate for a welding defect. If the defect candidate satisfies predetermined conditions, it is determined that a welding defect will occur if the next bead is formed at the position of the narrow portion (S16). Note that although the detection of welding defects is performed in parallel with the bead formation here, it may also be performed layer-by-layer, in which the shape of the bead surface of this layer is detected collectively after all the beads B of that layer are formed, and narrow portions are extracted.

[0034] If there is a narrow portion that may cause a defect, the defect detection device 41 outputs position information (coordinate values) of the narrow portion to the manufacturing plan modification unit 37. The manufacturing plan modification unit 37 modifies the manufacturing plan described above so that the welding defects caused by the narrow portion are at an acceptable level (S18). The manufacturing plan modification unit 37 outputs the modified manufacturing plan to the manufacturing program creation unit 35. Then, the manufacturing program creation unit 35 creates a manufacturing program based on the modified manufacturing plan (S13).

[0035] If no narrow portion is detected, or if a narrow portion is detected but does not satisfy the predetermined conditions, the process continues with the formation of the bead B. The above procedure is repeated until the formation of the layered object Wk is completed (S19).

[0036] Next, the procedure of the method for detecting welding defects, which corresponds to step S16 described above, will be described in detail with reference to Figures 1, 2 and 4. Figure 4 is a flowchart showing the procedure of the method for detecting welding defects.

[0037] First, the shape specification unit 43 reads the output data from the shape detector 21. This output data is data according to the height detection method of the shape detector 21, and does not necessarily directly record height information. Therefore, the shape specification unit 43 analyzes the information of the output data and converts it into height distribution information to specify the shape (S21). The conversion into height distribution information is performed according to the height detection method of the shape detector 21. For example, in the light section method, the height distribution is found by geometric calculation according to the protrusion lengths (or depression lengths) of irregularities in the profile of the detected light emission line.

[0038] The shape specification unit 43 outputs the converted height distribution information to the height information image generation unit 45. The height information image generation unit 45 converts the input height distribution information into a two-dimensional image. That is, the height distribution information is expressed as a variable of the brightness value of each pixel in the two-dimensional image, and an image is generated in which the height distribution information is converted into information on the distribution of brightness values. This image is composed of a large number of pixels in N rows and N columns, and the brightness value of each pixel represents the height of the bead surface or base detected by the shape detector 21 at that pixel position. That is, the shape specification unit 43 converts the three-dimensional shape information of the additively manufactured object detected by the shape detector 21 into height distribution information, and the height information image generation unit 45 further generates a two-dimensional image (S22). This image is also called a "height information image" or "height information image data."

[0039] The height information image generating unit 45 outputs the generated height information image to the feature portion detecting unit 47. The feature portion detecting unit 47 performs image processing on the input height information image to detect feature portions having specific shape characteristics. (S23) As the characteristic portion, a narrow portion formed as a valley between the beads B is used, but it is not limited to this, and any shape in which a welding defect may occur may be used. in Other shapes of portions may also be used as long as they are present. Here, narrow portions are detected according to the level of brightness value of the height information image. Various image processing methods, such as general contour extraction, binarization, and mask processing, can be used to detect narrow portions, enabling accurate detection in a short time. In this way, by treating height as brightness information and converting height distribution information into a height information image represented by brightness distribution, narrow portions and the like can be easily detected using general image processing techniques. Furthermore, since the detection algorithm can be easily changed, it is easy to achieve optimal detection depending on the detection target.

[0040] The feature detection unit 47 outputs information about the detected narrow portions together with the height information image to the feature extraction unit 49. The feature extraction unit 49 uses the input narrow portion information and height information image to extract the feature amount of each narrow portion (S24).

[0041] Fig. 5 is an explanatory diagram showing an example of a feature quantity of a narrow portion in a cross section perpendicular to the bead formation direction of a bead. When a pair of adjacent beads B1 and B2 are formed on a base surface FL, which represents the surface of the base 25 (or an underlying bead), the distance between the bead edges at the bottom of the pair of beads B1 and B2 in the cross section shown in Fig. 5 is defined as the bottom distance U, the distance between the bead apex Pt1 of bead B1 and the bead apex Pt2 of bead B2 is defined as the bead distance W, and the average height of the height from the base surface FL to the bead apex Pt1 and the height from the base surface FL to the bead apex Pt2, i.e., the valley depth to the bottom formed by the pair of beads B1 and B2, is defined as H. The bottom distance U, bead distance W, and valley depth H can be used as feature quantities of the narrow portion.

[0042] 6A and 6B are explanatory diagrams showing how the spacing between the pair of beads shown in FIG. 5 is changed. As shown in FIG. 6A, when beads B1 and B2 approach each other to the point where they touch, the bottom spacing U is 0, and the valley depth H is the depth of the valley between the beads, represented by the triangle Pt1-Pt2-P1 (P2) shown by the dotted line. As shown in FIG. 6B, when beads B1 and B2 overlap each other, the bottom spacing U is 0, and the valley depth H is shallower than in the cases shown in FIGS. 5 and 6A. In this way, by including a combination of the bottom spacing U, bead spacing W, and valley depth H as feature quantities, the shape of the valley in the narrow portion can be identified. Note that the above feature quantities are merely examples, and other parameters, such as the cross-sectional area of ​​the recess shape and the inclination angle of the bead end, may also be used.

[0043] The feature extraction unit 49 outputs information about the feature of the narrow portion to the defect size prediction unit 51. The defect size prediction unit 51 determines the target position of the next bead to be formed near the input narrow portion by referring to the manufacturing plan determined by the manufacturing program creation unit 35, and calculates the distance between the position of the narrow portion and the target position of the next bead to be formed (S25). The defect size prediction unit 51 also determines the welding conditions for forming the bead by referring to the manufacturing plan. These welding conditions include at least one of the welding speed, welding current, welding voltage, and filler metal feed rate. Based on the thus-obtained information about the feature of the narrow portion, the distance between the narrow portion and the target position of the bead, and the welding conditions, the defect size of a welding defect that will occur at the position of the narrow portion when a bead is formed near the narrow portion is predicted (S26). By predicting the defect size based on the above-mentioned conditions, improved prediction accuracy and reliability can be expected.

[0044] The defect size prediction unit 51 predicts the defect size using a prediction model 53 generated by machine learning using the above-mentioned feature quantities of the narrow portion, the welding conditions for forming the bead, and the defect sizes corresponding to the feature quantities and the welding conditions, which are obtained experimentally or by simulation. Examples of machine learning methods for generating the prediction model 53 include decision trees, linear regression, random forests, support vector machines, Gaussian process regression, and neural networks. Note that multiple prediction models 53 may be generated for each type of filler metal. When consolidating the data into one prediction model, information on some or all of the components of the filler metal may be added to the training data for learning.

[0045] The defect size prediction unit 51 predicts the defect size of a welding defect that may occur due to the detected narrow portion and outputs the prediction result to the defect determination unit 55. The defect determination unit 55 compares the input defect size prediction result with a preset tolerance (S27). If it is determined that the predicted value of the defect size exceeds the tolerance, it outputs information about the narrow portion to the manufacturing plan modification unit 37 (S28). If the predicted defect size result is equal to or less than the tolerance, it removes the narrow portion from candidates for welding defects, and the above-mentioned defect size prediction and determination are repeated for all detected narrow portions (S29, S30). This determination removes narrow portions that are not particularly problematic from candidates for welding defects, thereby improving the accuracy of defect detection.

[0046] In the above-described welding defect detection method, height distribution information is obtained from the output data from the shape detector 21, and this information is expressed as a variable of the brightness value of each pixel in a two-dimensional image, thereby generating a height information image in which the height distribution information is converted into brightness value distribution information. By performing image processing using this height information image, it is possible to easily detect narrow areas from the height information image and extract feature values ​​of the narrow areas. Furthermore, by customizing the image processing content and using general image processing tools, processing can be performed more simply and with higher accuracy. Furthermore, since the large amount of output data output by the shape detector 21 is aggregated as height information image data, the amount of data to be stored can be reduced. Furthermore, the computational burden of feature values, etc. is reduced, and particularly high-performance computing power is not required, thereby reducing equipment costs.

[0047] Furthermore, by using a machine-learned prediction model 53 to predict defect size, defect size can be predicted quickly and with high accuracy. By displaying various information such as the determined height information image, detected narrow areas, and predicted welding defects on the monitor of the output unit 39 of the forming control device 11, the forming status can be visualized and notified to the worker, improving convenience. These factors make it easier to automate the detection of welding defects.

[0048] Then, based on the information on welding defects detected by this welding defect detection method, the manufacturing plan for manufacturing the additive manufacturing object is modified to suppress the occurrence of welding defects, and the additive manufacturing object is manufactured based on the modified manufacturing plan, thereby enabling the stable production of high-quality additive manufacturing objects with suppressed occurrence of welding defects.

[0049] <Image processing example> Here, we will explain the process of generating a height information image by converting height information into brightness information and identifying the position of a welding defect. Fig. 7 is a schematic diagram showing how the shape of a bead B is measured by the shape detector 21. The object to be measured here is a total of four beads B, which are formed on the base 25 along one direction (X direction), and each pair of beads B is formed adjacent to each other in a direction perpendicular to the one direction (Y direction), and each pair of beads B is formed adjacent to each other at two locations along the one direction. The shape detector 21 detects information about the height of each bead B protruding from the surface of the base 25.

[0050] FIG. 8 is an image of height information image data obtained by converting the height information of each bead B shown in FIG. 7 into brightness information. The brightness levels in the image shown in FIG. 8 correspond to the height of each bead B from the base 25, with higher brightness indicating a greater height from the base 25. FIGS. 9A and 9B are explanatory diagrams showing the results of image processing in which the contours of the bead shape are extracted from the height information image shown in FIG. 8 according to changes in brightness, etc., and narrow portions are extracted. In each of the explanatory diagrams in FIGS. 9A and 9B, the contours of the beads are indicated by dashed lines so that the positions of the extraction results can be confirmed. In the image processing results, as shown in FIG. 9A, numerous narrow portions (black dots other than the dashed lines) are detected along the contours of the beads. The defect sizes of these numerous narrow portions are predicted from the feature quantities of the narrow portions in steps S24 to S27 of FIG. 4, and narrow portions with predicted defect sizes below the tolerance are removed. FIG. 9B is a schematic diagram showing narrow portions after such a screening process has been performed. As shown in Figure 9B, the narrow portion finally extracted is the position of the valley formed by the intersection of four beads, and the valley with the steepest slope on the surface of each bead B is detected.

[0051] Narrow sections can be accurately detected by using a height information image, which is created by converting detected height information into trajectory information, and then processing this height information image. The narrow section detection algorithm can be easily changed by appropriately replacing or modifying the image processing content depending on the situation, making it easy to optimize the detection algorithm. Similarly, the extraction algorithm for the process of extracting the feature values ​​of narrow sections can also be easily optimized.

[0052] Furthermore, when additive manufacturing is performed, as shown in Fig. 1, the shape detector 21 detects the bead height and the like while moving integrally with the torch 23 at a position offset from the tip of the torch 23, so if the trajectory along which the torch 23 moves is curved or if the torch 23 is tilted, the direction in which the shape detector 21 detects the height also changes. As a result, the shape of the narrow portion between beads may be detected as having a gentler slope than the actual slope, resulting in inaccurate feature values ​​for the narrow portion.

[0053] FIG. 10 is a configuration diagram of the torch 23 and the shape detector 21. FIG. 11 is an explanatory diagram showing the shape detection result by the shape detector 21 when the torch 23 and the shape detector 21 move together. As shown in FIG. 10, the bead formation position by the torch 23 and the shape detection position by the shape detector 21 do not coincide, and there is a deviation of a predetermined distance L. Therefore, as shown in FIG. 11, problems may arise when, for example, forming a bead along two curved, parallel paths PS1 and PS2. That is, after forming a bead on path PS1, when forming a bead on path PS2 along that bead, the shape detector 21, at torch position P1, detects a line L a distance L behind the torch position P1. p1 The bead shape above is detected, and at torch position P2, line L p2 The bead shape above is detected, and at torch position P3, line L p3 Detect the bead shape on line L p1 is approximately perpendicular to the direction of movement of paths PS1 and PS2, and line L p1 The bead shape detected by line L is close to the actual bead cross section. p2 ,L p3 In this case, the torch positions P2 and P3 are shifted from the direction perpendicular to the moving direction of passes PS1 and PS2, so that the slope of the valley is gentler than the actual bead shape. p2 ,L p3 However, the control required for this correction becomes complicated, making it difficult to detect the shape in real time.

[0054] However, in the welding defect detection method according to this embodiment, by converting the output data from the shape detector 21 into a height information image, even when a bead is formed and its shape is detected while moving along a path, the information detected by the shape detector 21 is converted into height information successively. Therefore, height information detected by a plurality of paths can be easily combined, and as a result, a height information image, which is a two-dimensional height information map, can be easily formed. By generating a height information image, it is also possible to geometrically convert the detected height information, making it easier to grasp the shape. Therefore, the above-mentioned line L p1 ,L p2 ,L p3 It is possible to easily generate a shape profile in any direction, not just a shape profile on a line such as the above, thereby making it possible to grasp the shape accurately.

[0055] The above description has been given of an example in which the welding defect detection method according to this embodiment is applied to additive manufacturing in which a bead B is formed on a base 25 using a manipulator 13 holding a torch 23, but the application is not limited to additive manufacturing. For example, the method can be applied to general welding such as fillet welding, butt welding, and multi-layer welding in a groove, and further, can be similarly applied to cases in which predetermined processing is performed after shape detection or height detection.

[0056] As such, the present invention is not limited to the above-described embodiments, and the present invention also contemplates the mutual combination of the various components of the embodiments, as well as modifications and applications by those skilled in the art based on the description in the specification and well-known techniques, and these modifications and applications are included in the scope of protection sought.

[0057] As described above, the present specification discloses the following: (1) A defect detection method for detecting welding defects that occur in an additively manufactured object when the additively manufactured object is manufactured by stacking beads formed by melting and solidifying a filler material, the method comprising: a height detection step of detecting a height distribution of a surface shape of the layered object during modeling; an image generating step of expressing the detected information of the height distribution as a variable of the brightness value of each pixel of a two-dimensional image and generating a height information image by converting the information of the height distribution into information of the distribution of the brightness values; a feature portion detection step of detecting a shape feature portion having a specific shape feature depending on the level of the luminance value of the height information image; The detected shape feature is the weld defect. Possibility of becoming a determination step of determining A defect detection method comprising: This defect detection method uses a height information image, which converts the height distribution of the surface shape of an additive manufacturing object into a brightness value distribution, making it possible to easily detect shape features while reducing the amount of data that needs to be stored, thereby enabling welding defect detection to be achieved in a short takt time.

[0058] (2) The defect detection method according to (1), wherein the height detection step detects the shape characteristic portion by image processing the height information image. According to this defect detection method, shape features are detected by image processing, so that detection can be performed easily and with high accuracy by customizing the image processing content and using general image processing tools, etc., thereby enabling accurate and fast detection of welding defects.

[0059] (3) The defect detection method according to (1) or (2), wherein the shape characteristic portion is a narrow portion in which a valley portion lower than the height of the surrounding bead is formed. According to this defect detection method, by detecting narrow portions where welding defects are likely to occur, it is possible to accurately identify the location where welding defects occur.

[0060] (4) A defect detection method according to any one of (1) to (3), wherein the height distribution is detected by one of a light-sectioning method, a phase difference detection method, a triangulation method, and a TOF method, which detects reflected light from the bead surface when light is irradiated onto the bead. According to this defect detection method, the height distribution is detected by an optical shape detection method, thereby enabling non-contact and high-speed shape detection.

[0061] (5) A prediction step of predicting a defect size of the welding defect that will occur when the bead is formed at a position including the shape characteristic portion, The defect detection method described in any one of (1) to (4), wherein the judgment process compares the predicted defect size with a predetermined tolerance, and if the defect size exceeds the tolerance, judges the shape feature to be the welding defect. According to this defect detection method, welding defects are determined according to the predicted defect size, so that shape features that are not particularly problematic are excluded from candidates for welding defects, thereby improving the accuracy of defect detection.

[0062] (6) A defect detection method according to (5), wherein the prediction step predicts the defect size based on a prediction model that has previously learned the relationship between the feature quantities of the shape feature portion, the welding conditions for forming the bead, and the defect sizes corresponding to the feature quantities and the welding conditions. According to this defect detection method, the defect size is predicted based on a prediction model, which enables high-precision and high-speed prediction.

[0063] (7) A defect detection method according to (6), wherein the feature quantity of the shape feature portion includes at least one of a shape parameter representing the shape of a narrow portion in which a valley lower than the height of the surrounding beads is formed, and a distance between the position of the narrow portion and the target position of the bead to be formed next. This defect detection method predicts defect size based on various conditions, such as the shape parameters of the narrow area and the distance between the narrow area where welding defects are likely to occur and the target position of the bead, so it is expected that prediction accuracy and reliability will be improved.

[0064] (8) The defect detection method according to (6) or (7), wherein the welding conditions include at least one of a welding speed, a welding current, a welding voltage, and a filler metal feed rate. According to this defect detection method, the defect size corresponding to each welding condition can be predicted.

[0065] (9) Based on information about the welding defect detected by the defect detection method according to any one of (1) to (8), a modeling plan for manufacturing the additive manufacturing object is changed so as to suppress the occurrence of the welding defect; manufacturing the layered object based on the changed manufacturing plan. A method for manufacturing additively manufactured objects. According to this method for manufacturing an additive manufacturing object, the additive manufacturing object is manufactured in such a way that predicted welding defects do not occur, and therefore high-quality additive manufacturing objects in which the occurrence of welding defects is suppressed can be consistently obtained.

[0066] (10) A defect detection device for detecting welding defects occurring in an additively manufactured object formed by stacking beads formed by melting and solidifying a filler metal, a shape measuring unit that detects a height distribution of a surface shape of the layered object; an image generating unit that expresses the detected information of the height distribution as a variable of the brightness value of each pixel of a two-dimensional image and generates a height information image by converting the information of the height distribution into information of the distribution of the brightness values; a feature portion detection unit that detects a shape feature portion having a specific shape feature depending on the level of the luminance value of the height information image; The detected shape feature is the weld defect. Possibility of becoming a defect determination unit that determines The process and A defect detection device comprising: This defect detection device uses a height information image, which converts the height distribution of the surface shape of the additive manufacturing object into a brightness value distribution, to easily detect shape features while reducing the amount of data that needs to be stored, thereby enabling welding defect detection to be achieved in a short takt time.

[0067] (11) a feature extraction unit that extracts a feature related to at least one of the shape and the position of the detected shape feature; a prediction model that has learned the relationship between the welding conditions for forming the bead, the feature amount, and the defect size of the welding defect corresponding to the welding conditions and the feature amount; a defect size prediction unit that predicts the defect size based on the prediction model from the feature amount of the detected shape characteristic portion and information on welding conditions of the bead that forms the shape characteristic portion; Equipped with The defect detection device described in (10), wherein the defect determination unit compares the predicted defect size with a predetermined tolerance and determines the shape feature portion to be a welding defect if the defect size exceeds the tolerance. According to this defect detection device,

[0068] (12) The defect detection device according to (11), a molding control device that changes a molding plan for manufacturing the layered object so as to suppress the occurrence of the welding defect detected by the defect detection device; and a modeling device that models the layered object based on the changed modeling plan; and An additive manufacturing system comprising: This additive manufacturing system produces additively manufactured objects in a way that prevents predicted welding defects from occurring, thereby consistently producing high-quality additively manufactured objects with reduced welding defects.

[0069] (13) A program for causing a computer to execute a procedure of a defect detection method for detecting welding defects occurring in an additively manufactured object when the additively manufactured object is manufactured by stacking beads formed by melting and solidifying a filler material, the program comprising: On the computer, a height detection step of detecting a height distribution of a surface shape of the layered object during modeling; an image generation step of expressing the detected information of the height distribution as a variable of the brightness value of each pixel of a two-dimensional image and generating a height information image by converting the information of the height distribution into information of the distribution of the brightness values; a feature portion detection step of detecting a shape feature portion having a specific shape feature depending on the level of the luminance value of the height information image; The detected shape feature is the weld defect. Possibility of becoming a determination procedure for determining A program that executes the following. This program uses a height information image, which converts the height distribution of the surface shape of an additive manufacturing object into a brightness distribution, to easily detect shape features while reducing the amount of data that needs to be stored. This enables the detection of welding defects in a short takt time. [Explanation of symbols]

[0070] 11. Modeling control device 13 Manipulator 15 Filler metal supply device 15a reel 17 Manipulator control device 19 Heat source control device 21 Shape detector 23 Torch 25 base 31 Input section 33 Storage section 33a Database 35 Modeling Programming Department 37. Design Planning and Change Department 39 Output section 41 Defect detection equipment 43 Shape identification part 45 Height information image generation unit 47 Feature detection unit 49 Feature Extraction Unit 51 Defect size prediction section 53 Predictive Models 100 Additive Manufacturing System M filler metal

Claims

1. A defect detection device for detecting welding defects that occur in an additively manufactured object that is manufactured by stacking beads formed by melting and solidifying a filler material, a shape measurement unit that is provided on a tip axis of the manipulator or in the vicinity of the tip axis, moves integrally with the torch, and detects a height distribution of a surface shape of the layered object; an image generating unit that expresses the detected information of the height distribution as a variable of the brightness value of each pixel of a two-dimensional image and generates a height information image by converting the information of the height distribution into information of the distribution of the brightness values; a feature portion detection unit that detects a shape feature portion having a specific shape feature depending on the level of the luminance value of the height information image; a defect determination unit that determines the possibility that the detected shape characteristic portion will become the welding defect; a feature extraction unit that extracts a feature related to at least one of the shape and position of the shape feature detected by the feature detection unit; a prediction model that has learned the relationship between the welding conditions for forming the bead, the feature amount, and the defect size of the welding defect corresponding to the welding conditions and the feature amount; a defect size prediction unit that predicts the defect size based on the prediction model from the feature amount of the detected shape characteristic portion and information on welding conditions of the bead that forms the shape characteristic portion; Equipped with the defect determination unit compares the defect size predicted by the defect size prediction unit with a predetermined tolerance, and determines the shape characteristic portion to be a welding defect when the defect size exceeds the tolerance. Defect detection equipment.

2. The defect detection device according to claim 1 ; a molding control device that changes a molding plan for manufacturing the layered object so as to suppress the occurrence of the welding defect detected by the defect detection device; and a modeling device that models the layered object based on the changed modeling plan; and An additive manufacturing system comprising:

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