Welding monitoring method and welding monitoring device, and additive manufacturing method and additive manufacturing device

Abstract

To check occurrence of welding defects from a captured image of a weld zone, without using existing beads or the like around the weld zone as a reference.SOLUTION: A weld monitoring method includes: an image acquisition step of acquiring image information capturing a molten pool generated in a weld zone and arc light for melting a filler metal; a contour extraction step of extracting a contour of at least one of the molten pool and the arc light from the image information; an index calculation step of determining a shape index corresponding to strain of the extracted contour; and a defect determination step of determining a state of occurrence of welding defects according to the shape index.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a welding monitoring method and a welding monitoring apparatus, and an additive manufacturing method and an additive manufacturing apparatus. [Background technology]

[0002] In arc welding, various techniques have been proposed for monitoring whether arc welding is being performed appropriately by grasping various welding conditions such as the state of arc generation, the state of the molten metal (molten pool) melted near the arc spot, etc. For example, Patent Document 1 discloses a technique for monitoring the welding condition using an image captured of the molten pool during welding. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-192524 A Summary of the Invention [Problem to be solved by the invention]

[0004] In general, it is difficult to directly confirm the occurrence of welding defects during arc welding. For example, ultrasonic testing is available as a means of identifying internal defects in welds, but this method has problems such as the need to polish the weld surface and the inclination of the surface making it difficult to detect echoes originating from defects. For this reason, a method for monitoring the welding state using a captured image of the molten pool as in Patent Document 1 has been proposed, but most of these methods are based on the premise of welding with a groove. In other words, in the conventional technology for monitoring the welding state, the molten pool is judged for welding defects according to the state of the molten pool while corresponding to the positional relationship between the molten pool and the groove or the bead adjacent to the molten pool, or the bead shape. However, the image of the groove and the adjacent bead is not clearly displayed due to a large difference in brightness with the arc light, and the position and shape of the molten pool may be misidentified. As a result, there was a risk that it would be difficult to accurately monitor the welding state. In addition, in additive manufacturing, in which a multi-layered bead is layered to produce an additively manufactured object, there is no reference object that can identify the position of the groove, as described above. Therefore, the state of the molten pool cannot be accurately grasped, and it is difficult to confirm the occurrence of welding defects from images taken during welding.

[0005] Therefore, an object of the present invention is to provide a welding defect monitoring method and welding defect monitoring device, as well as an additive manufacturing method and additive manufacturing device, which are capable of checking for the occurrence of welding defects from captured images of a welded portion without using existing beads or the like around the welded portion as a reference. [Means for solving the problem]

[0006] The present invention comprises the following configurations. (1) A welding monitoring method for monitoring a welding state using image information obtained by capturing an image of a welded portion during arc welding, comprising the steps of: an image acquiring step of acquiring image information showing a molten pool generated in the welded portion and an arc light melting the filler metal; a contour extraction step of extracting a contour of at least one of the molten pool and the arc light from the image information; an index calculation step of determining a shape index corresponding to the extracted distortion of the contour; a defect determination step of determining the occurrence status of a welding defect according to the shape index; A welding monitoring method comprising: (2) An additive manufacturing method for manufacturing a molded object composed of multiple layers of weld beads by stacking weld beads formed by melting and solidifying a filler metal, comprising the steps of: When forming the weld bead, the welding state of the weld bead is monitored by the welding monitoring method described in (1). Additive manufacturing methods. (3) A welding monitoring device for monitoring a welding state using image information obtained by capturing an image of a welded portion during arc welding, comprising: an image acquisition unit that acquires image information showing a molten pool generated in the weld and an arc light that melts the filler metal; a contour extraction unit that extracts a contour of at least one of the molten pool and the arc light from the image information; an index calculation unit that calculates a shape index according to the extracted distortion of the contour; a defect determination unit that determines the occurrence status of a welding defect according to the shape index; A welding monitoring device comprising: (4) An additive manufacturing apparatus for manufacturing a molded object composed of multiple layers of weld beads by stacking weld beads formed by melting and solidifying a filler metal, comprising: When the weld bead is formed, the welding state of the weld bead is monitored by the welding monitoring device described in (3). Additive manufacturing equipment. Effect of the Invention

[0007] According to the present invention, the occurrence of a welding defect can be confirmed from a captured image of the welded portion without using existing beads or the like around the welded portion as a reference. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram of an additive manufacturing apparatus. [Diagram 2] FIG. 2 is a schematic explanatory diagram showing how an imaging unit captures an image of a welded portion where a weld bead is formed. [Diagram 3] FIG. 3 is a schematic diagram showing an image captured by the imaging section. [Figure 4] FIG. 4 is a schematic block diagram of the control unit. [Diagram 5] FIG. 5 is a functional block diagram of the control unit shown in FIG. [Figure 6] FIG. 6 is a flowchart showing the steps of the first welding monitoring method. [Figure 7A] FIG. 7A is an explanatory diagram that illustrates a schematic shape of an arc light at the tip of a welding torch. [Figure 7B] FIG. 7B is a graph showing the distribution of the distance from the center point of the arc light shown in FIG. 7A to the contour of the arc light with respect to the azimuth angle around the center point. [Figure 8A] FIG. 8A is an explanatory diagram showing a schematic diagram of another shape of the arc light at the tip of the welding torch. [Figure 8B] FIG. 8B is a graph showing the distribution of the distance from the center point of the arc light shown in FIG. 8A to the contour of the arc light with respect to the azimuth angle around the center point. [Figure 9] FIG. 9 is a schematic perspective view showing a process of forming a new weld bead adjacent to an existing weld bead. [Figure 10A] FIG. 10A is a captured image of a welded portion in which a new weld bead has been formed at a position adjacent to an existing weld bead. [Figure 10B] FIG. 10B is an explanatory diagram showing a main part of the captured image shown in FIG. 10A. [Figure 11A] FIG. 11A is a captured image of a welded portion in which a new weld bead has been formed at a position closer to the existing weld bead than in the case shown in FIG. 10A. [Figure 11B] FIG. 11B is an explanatory diagram showing a main part of the captured image shown in FIG. 11A. [Figure 12] FIG. 12 is a schematic diagram of a bead cross section when a new weld bead is formed at a position relatively distant from the existing weld bead shown in FIG. 10A. [Figure 13] FIG. 13 is a schematic diagram of a bead cross section when a new weld bead is formed at a position relatively close to the existing weld bead shown in FIG. 11A. [Figure 14] FIG. 14 is a graph showing a schematic diagram of the amount of fluctuation in the center point of the arc light over time. [Figure 15] FIG. 15 is a flowchart showing the procedure of the second welding monitoring method. [Figure 16] FIG. 16 is a schematic diagram showing the state of the arc light and the molten pool in a captured image. [Figure 17A] FIG. 17A is a captured image of a welded portion in which a new weld bead has been formed at a position adjacent to an existing weld bead. [Figure 17B] FIG. 17B is an explanatory diagram showing a main part of the captured image shown in FIG. 17A. [Figure 18A]FIG. 18A is a captured image of a welded portion in which a new weld bead has been formed at a position closer to the existing weld bead than in the case shown in FIG. 17A. [Figure 18B] FIG. 18B is an explanatory diagram showing a main part of the captured image shown in FIG. 18A. [Figure 19] FIG. 19 is a graph showing a schematic diagram of the change in the distance from the center of the arc light to the outline of the molten pool versus the azimuth angle. [Figure 20] FIG. 20 is a graph showing a schematic diagram of the amount of change in the distance to the edge of the molten pool over time. [Figure 21] FIG. 21 is a flowchart showing the steps of the third welding monitoring method. [Figure 22] FIG. 22 is an explanatory diagram that illustrates a schematic shape of the molten pool in a captured image. [Figure 23] FIG. 23 is an explanatory diagram showing (A) to (E) how the contour of the molten pool changes over time. [Figure 24] FIG. 24 is an explanatory diagram showing a specific example of extracting a shape index of the contour of the molten pool. [Diagram 25] FIG. 25 is an explanatory diagram showing an example of calculation of local curvature calculated from the contour of the molten pool in the captured image. [Figure 26] FIG. 26 is an explanatory diagram showing examples of contour shapes and local curvatures in (A) and (B). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Here, as an example of a welding device for implementing the welding monitoring method according to the present invention, an additive manufacturing device that laminates weld beads formed by melting and solidifying a filler metal and is used to manufacture a shaped object composed of multiple layers of weld beads will be described. However, the type of welding and the configuration of the welding device are not limited to this, and the welding device may be a welding device that is applicable to various types of welding such as fillet welding and butt welding.

[0010] <Configuration of additive manufacturing device> FIG. 1 is a schematic diagram of an additive manufacturing apparatus 100. As shown in FIG. The layered modeling apparatus 100 includes a modeling unit 11 that manufactures a model by layering weld beads B, and a control unit 13 that controls each part of the modeling unit 11.

[0011] The manufacturing unit 11 includes a welding robot 17 having a welding torch 15 on its tip axis, a robot drive unit 19 that drives the welding robot 17, a filler material supply unit 21 that supplies a filler material (welding wire) M to the welding torch 15, and a welding power supply unit 23 that supplies a welding current and welding voltage to the welding torch 15.

[0012] In addition, the molding unit 11 is equipped with an imaging unit 25 having a camera that images the state of the welded portion. The imaging unit 25 is provided on the tip shaft side of the welding torch 15 or the welding robot 17, and images the vicinity of the tip of the welding torch 15.

[0013] The welding torch 15 has a shield nozzle (not shown) and is a torch for gas metal arc welding to which shielding gas is supplied from the shield nozzle. The arc welding method may be either a consumable electrode type such as a shielded metal arc welding or a carbon dioxide gas arc welding, or a non-consumable electrode type such as a TIG welding or a plasma arc welding, and is appropriately selected depending on the additive manufacturing object to be produced. For 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 a melting current is supplied is held by the contact tip. The welding torch 15 holds the filler material M and generates an arc from the tip of the filler material M in a shielding gas atmosphere.

[0014] Welding robot 17 is an articulated robot. A filler material M is continuously supplied and supported at the tip of welding torch 15 attached to the tip shaft of the robot arm. The position and posture of welding torch 15 can be set arbitrarily in three dimensions within the range of the degrees of freedom of the robot arm by commands from robot driving unit 19. Welding robot 17 is not limited to an articulated robot, and may be of another type, such as a Cartesian coordinate robot or a parallel link robot.

[0015] The filler metal supply unit 21 includes a reel 27 around which the filler metal M is wound. The filler metal M is sent from the filler metal supply unit 21 to a payout mechanism (not shown) attached to a robot arm or the like, and is fed to the welding torch 15 while being fed forward and backward by the payout mechanism as necessary.

[0016] Any commercially available welding wire can be used as the filler metal M. For example, welding wires specified in MAG and MIG welding solid wires for mild steel, high tensile steel and low temperature steel (JIS Z 3312), arc welding flux-cored wires for mild steel, high tensile steel and low temperature steel (JIS Z 3313), etc. can be used. Furthermore, filler metals M such as aluminum, aluminum alloys, nickel, nickel-based alloys, etc. can be used depending on the required characteristics.

[0017] The robot driving unit 19 drives the welding robot 17 to move the welding torch 15 and melts the continuously supplied filler metal M with an arc generated by a welding current and a welding voltage from the welding power source unit 23.

[0018] A modeling program based on a trajectory plan of a model to be produced is sent from the control unit 13 to the robot driving unit 19. The modeling program is composed of many command codes and is created based on an appropriate algorithm according to various conditions such as the shape data (CAD data, etc.) of the layered model, the material, and the amount of heat input.

[0019] More specifically, the shape model of the object corresponding to the input shape data is divided into layers for each height of a predetermined weld bead, and a bead formation procedure, such as a bead formation order and welding conditions, is determined so that each obtained layer is filled with a weld bead. Then, this bead formation procedure is made into a modeling program. The created modeling program is stored in the control unit 13 (memory unit 45 described later), and is output from the control unit 13 to the robot driving unit 19 when an output request is received from the robot driving unit 19.

[0020] The robot driving unit 19 executes the received modeling program to drive each unit such as the welding robot 17, the filler material supply unit 21, and the welding power supply unit 23, and forms the weld bead B based on the trajectory plan. That is, the robot driving unit 19 drives the welding robot 17 to move the welding torch 15 along the trajectory (bead formation trajectory) of the welding torch 15 set in the trajectory plan. At the same time, the robot driving unit 19 drives the filler material supply unit 21 and the welding power supply unit 23 according to the set welding conditions to melt and solidify the filler material M at the tip of the welding torch 15 by an arc. As a result, the weld bead B is formed on the base plate 29 along the trajectory of the welding torch 15. The weld bead B forms a weld bead layer by a group of weld beads adjacent to each other, and the next weld bead layer is layered on top of this weld bead layer, thereby forming a desired three-dimensional object.

[0021] FIG. 2 is a schematic explanatory diagram showing how imaging unit 25 images welded portion 31 where weld bead B is formed. The imaging unit 25 moves along the welding direction WD together with the welding torch 15, capturing images of the arc light 33 generated at the welded portion 31 and the molten pool 35 made of molten metal formed by the arc melting the filler material M. The imaging unit 25 is composed of a camera equipped with a camera body having a CCD or CMOS type imaging element and a lens using an appropriate filter such as an ND filter or a narrow band filter, and is capable of removing noise by filtering and capturing images with a wide dynamic range from low brightness to high brightness. The imaging unit 25 may be equipped with multiple cameras and generate a captured image by combining imaging information from the multiple cameras.

[0022] FIG. 3 is a schematic diagram showing an image captured by the imaging section 25. As shown in FIG. The image 37 captured by the imaging unit 25 shows the filler metal M protruding from the welding torch 15, the arc light 33 of the arc generated at the tip 39 of the filler metal M, and the molten pool 35 formed below the filler metal M.

[0023] Note that the imaging unit 25 may be an IR (infrared) camera (thermography) other than a camera that detects visible light, or may be configured to use an IR camera in combination.

[0024] FIG. 4 is a schematic block diagram of the control unit 13. As shown in FIG. The control unit 13 is a computer device that includes a processor 41 such as a CPU or MPU, a memory 43 such as a ROM or RAM, a storage unit 45 such as an SSD (Solid State Drive) or a hard disk drive, an input unit 47, an input / output interface 49, an image processing unit 51, a display unit 53, a communication unit 55, etc.

[0025] The memory unit 45 stores the above-mentioned molding program and various reference values ​​for determining the occurrence status of welding defects, the details of which will be described later.

[0026] The input unit 47 is an input device such as a keyboard, a mouse, an input operation panel, etc., and an operator inputs information as required.

[0027] The input / output interface 49 is connected to the imaging unit 25 , filler metal supply unit 21 , robot drive unit 19 , and welding power supply unit 23 described above, and exchanges information with each unit in response to commands from the processor 41 .

[0028] The image processing unit 51 processes the captured image 37 captured by the imaging unit 25 to extract the arc light 33, which will be described in detail later, and the contours of the molten pool 35, etc. The image processing unit 51 is preferably a dedicated processing circuit using a GPU (Graphics Processing Unit) or the like, but may also be one that performs image processing using the processor 41.

[0029] The display unit 53 is composed of a display medium such as a liquid crystal display, and is capable of displaying various types of information, such as the captured image from the imaging unit 25, information on the welding status such as the welding pass and welding conditions, and information on the judgment results of welding defects, which will be described in detail later.

[0030] The communication unit 55 communicates information between the control unit 13 and the outside. The control unit 13 may be configured to have the functions of each of the above-mentioned units provided in another computer device, such as a server, that is located away from the modeling unit 11 and to control the functions of each of the above-mentioned units through the computer device. In this case, various control signals are input / output from the communication unit 55 to the other computer device at a remote location via a communication means such as a network.

[0031] FIG. 5 is a functional block diagram of the control unit 13 shown in FIG. By using the configuration of each of the above-mentioned sections, the control unit 13 realizes the functions of the image acquisition unit 61, the contour extraction unit 63, the index calculation unit 65, and the defect determination unit 67. The functions of these units will be described in detail later.

[0032] <Procedure for the first welding monitoring method> Next, a procedure of a welding monitoring method for monitoring the welding state using image information obtained by capturing an image of a welded portion during arc welding using the additive manufacturing apparatus 100 having the above configuration will be described. 6 is a flow chart showing the steps of the first welding monitoring method. Each step will be described below with reference to FIGS.

[0033] First, the image acquisition unit 61 (see FIG. 5) images the welded portion 31 during welding as shown in FIG. 2, and acquires image information of the captured image 37 including the arc light 33 generated at the tip 39 of the filler metal M and the molten pool 35 in which the filler metal M is melted, as shown in FIG. 3 (step S11, hereinafter referred to as S11).

[0034] Next, contour extraction unit 63 (see FIG. 5) extracts the portion of arc light 33 from captured image 37 and determines the contour of arc light 33 (S12). The contour of arc light 33, that is, the coordinates of the contour of arc light 33 in captured image 37 (which are the coordinates of pixels at multiple points on the contour, but may also be an approximation of a line representing the contour), are determined by appropriate image processing by image processing unit 51.

[0035] Then, index calculation unit 65 (see FIG. 5) calculates a shape index corresponding to the extracted distortion of the contour (S13). The shape index here includes any of the following: center point Oa of arc light 33 obtained from the contour of arc light 33 shown in FIG. 3, distance r from center point Oa to the contour of arc light 33, curvature R of the contour, and goodness of fit when the shape of arc light 33 is fitted with a model curve (e.g., an ellipse).

[0036] The defect determination unit 67 determines the occurrence state of a welding defect, which indicates the presence or absence of a welding defect or the degree of the welding defect (S14), according to the shape index obtained by the index calculation unit 65. The procedure for determining the presence or absence of a welding defect will be described below.

[0037] Fig. 7A is an explanatory diagram that shows a schematic diagram of the shape of arc light 33 at the tip of the welding torch, and Fig. 7B is a graph that shows the distribution of distance r from center point Oa of arc light 33 shown in Fig. 7A to the contour of arc light 33 versus azimuth angle θ around center point Oa. Here, the azimuth angle θ is set clockwise in Fig. 7A, with the upper side in the vertical direction in the captured image being 0° and the lower side being 180°. The center point Oa of arc light 33 is determined by extracting the area of ​​arc light 33 by performing image processing such as binarization and edge processing on captured image 37, and defining the center of gravity of that area (pixel area) as the center point Oa.

[0038] The center point Oa is not limited to the center of gravity position and may be defined by other methods. For example, for the coordinate values ​​(X, Y) of the area of ​​the arc light 33, the horizontal maximum value Xmax and the horizontal minimum value Xmin, and the vertical maximum value Ymax and the vertical minimum value Ymin in the captured image are calculated, and the midpoint (Xmax-Xmin) / 2, (Ymax-Ymin) / 2) between the maximum value and the minimum value in each direction is divided into two equal parts, that is, the geometric center point is set as the coordinate of the center point Oa.

[0039] 7B shows that distance r from center point Oa of arc light 33 to the contour of arc light 33 changes periodically according to azimuth angle θ, indicating that the contour of arc light 33 has a substantially elliptical shape. In other words, the contour of arc light 33 has an axisymmetric shape centered on weld line Lw that passes through center point Oa of arc light 33 and indicates welding direction WD.

[0040] Depending on the welding conditions, the shape of arc light 33 may be distorted from the approximately elliptical shape described above. FIG. 8A is an explanatory diagram showing a schematic diagram of another shape of arc light 33 at the tip of a welding torch, and FIG. 8B is a graph showing the distribution of the distance from center point Oa of arc light 33 shown in FIG. 8A to the contour of arc light 33 versus azimuth angle θ about center point Oa.

[0041] 8A, the contour of arc beam 33 is distorted from an axisymmetric shape centered on weld line Lw, which passes through center point Oa of arc beam 33 and indicates welding direction WD. This distortion of arc beam 33 occurs due to the influence of the adjacent weld beads.

[0042] FIG. 9 is a schematic perspective view showing a process of forming a new weld bead B adjacent to an existing weld bead B0. When forming the weld bead B, if there is an existing weld bead B0 adjacent to it, the arc from the welding torch 15 will be attracted to the existing weld bead B0, causing the shape of the arc light 33 to become biased.

[0043] The change in the shape of the arc light 33 will be described below using captured images. Fig. 10A is a captured image of a welded portion when a new weld bead B is formed at a position adjacent to an existing weld bead B0. Fig. 10B is an explanatory diagram showing a main part of the captured image shown in Fig. 10A. Fig. 11A is a captured image of a welded portion when a new weld bead B is formed at a position even closer to the existing weld bead B0 than the case shown in Fig. 10A. Fig. 11B is an explanatory diagram showing a main part of the captured image shown in Fig. 11A.

[0044] As shown in Figures 10A and 10B, when a new weld bead B is formed at a position relatively distant from an existing weld bead B0, the arc is not attracted to the existing weld bead B0 and maintains an approximately elliptical shape as shown in Figure 7A. On the other hand, as shown in FIGS. 11A and 11B, when a new weld bead B is formed at a position relatively close to an existing weld bead B0, the arc is attracted to the existing weld bead B0, and the shape of the arc light 33 becomes distorted. Depending on the difference in the distance between the existing weld bead B0 and the new weld bead B, the occurrence conditions of welding defects will differ.

[0045] FIG. 12 is a schematic diagram of a bead cross section when a new weld bead B is formed at a position relatively distant from the existing weld bead B0 shown in FIG. 10A. In this case, a blowhole (air hole) DF occurs between the existing weld bead B0 and the new weld bead B. This type of welding defect is thought to occur when the molten metal does not flow sufficiently between adjacent beads during bead formation and solidifies, resulting in an unwelded area.

[0046] On the other hand, FIG. 13 is a schematic diagram of a bead cross section when a new weld bead B is formed at a position relatively close to the existing weld bead B0 shown in FIG. 11A. In the case shown in FIG. 13, the existing weld bead B0 and the new weld bead B come close to each other and are fused together, with no welding defects such as blowholes existing between them.

[0047] Therefore, as an index for judging whether the molten metal has been sufficiently poured between the weld beads, a shape index representing the shape of the arc light 33 is monitored, and abnormal behavior (low distortion state) of the shape index is extracted as a sign of a defect candidate. For example, the area of ​​the arc light 33 in the captured image 37 is extracted by performing image processing, and the contour and center point Oa of the area (pixel area) of the arc light 33 are obtained. The shape of the obtained contour is evaluated by obtaining the relationship between the azimuth angle θ and the distance r shown in Fig. 7B and Fig. 8B. In other words, it is judged that the more the contour of the arc light 33 approaches a line symmetrical shape centered on the welding line Lw that passes through the center point Oa of the arc light 33 and indicates the welding direction WD, the more likely it is that a welding defect has occurred. For this judgment, appropriate parameters such as a specific contour position, the absolute value of the distance r in the area, and the amount of change with respect to the azimuth angle θ can be used, and each parameter can be combined to make the judgment.

[0048] Furthermore, when the shape of the arc light 33 changes, the welding state becomes transient, and a welding defect may occur. Therefore, the amount of variation over time of the center point Oa of the arc light 33 may be calculated, and if the amount of variation is equal to or greater than a predetermined threshold (reference variation amount), that is, if a disturbance occurs in the arc light 33, it may be determined that there is a sign of a welding defect. In this way, robustness of the determination result can be obtained, and the detection sensitivity of the welding defect can be easily adjusted by the threshold value.

[0049] FIG. 14 is a graph showing a schematic variation in the center point Oa of the arc light 33 over time. When the position of center point Oa of arc light 33 is equal to or greater than a predetermined reference fluctuation amount TH1, it is determined that a welding defect has occurred in the region where the fluctuation amount is equal to or greater than the reference fluctuation amount TH1. This determination is not limited to the center point Oa of arc beam 33, but may be based on the curvature of the contour of arc beam 33, or on the degree of conformity between the shape of arc beam 33 and a model curve (e.g., an ellipse). By determining a welding defect using a change in the curvature of the contour and the degree of conformity of the fitting, the change in the shape of arc beam 33 can be detected with high sensitivity, and the detection accuracy of welding defects can be improved.

[0050] In general, when detecting an abnormality such as the occurrence of a welding defect, the number of abnormal cases is smaller than that in normal times. Therefore, it is also possible to use normal times as a reference and capture the degree of deviation from that state as a sign of an abnormality. For example, when welding is performed based on a welding plan in which a plurality of welding passes and welding conditions for the welding passes are previously determined, a predetermined welding pass among the welding passes in the welding plan in which welding was performed properly is set as a reference pass that serves as a criterion for determining a welding defect. Then, the shape index of the reference pass is compared with the shape index of other welding passes that are common to the welding conditions of the reference pass, and the presence or absence or degree of a welding defect is determined according to the difference between the shape indexes. The welding conditions referred to here include various conditions such as welding current, welding voltage, filler metal feed speed, welding speed, torch angle, pitch, and the presence or absence of weaving.

[0051] The above-mentioned comparison of shape indices may be, for example, a comparison of the maximum fluctuation amount, a comparison of the frequency of exceeding a threshold, etc. Furthermore, whether welding has been performed properly may be determined by verifying the captured image, or by verifying other measurement data such as the welding current, welding voltage, and bead shape profile. Furthermore, when the trajectory plan is created, predicted values ​​may be prepared in advance by simulating temperature prediction, deformation prediction, etc., and the result may be compared with the predicted values.

[0052] In particular, in additive manufacturing, passes with common welding conditions are often repeated multiple times. Therefore, by comparing the shape index of the arc light between passes with common welding conditions, it becomes easier to detect the degree of deviation from a healthy state of a reference pass or sudden abnormal phenomena. In addition, even if a database of defect occurrence conditions is not prepared in advance, weld beads in a welded workpiece can be compared to each other to identify weld beads that have signs of defect occurrence.

[0053] As described above, the characteristic shape index of the arc light 33 can be extracted from the contour of the arc light 33, and the occurrence of welding defects can be determined from the obtained shape index. Then, by continuously performing the calculation to obtain the shape index at a constant time interval, the transition of the shape index during the lamination of the weld bead can be grasped, and information for traceability in the manufacturing process can be obtained.

[0054] Therefore, this welding monitoring method can identify locations where welding defects are likely to exist non-destructively and determine the presence or absence and severity of welding defects over the entire range of the welded object. Moreover, it can determine the occurrence of welding defects in real time during welding. This can be used to review the process, such as modifying welding conditions, and to ensure the quality of the welded object (molded object).

[0055] <Procedure for the second welding monitoring method> Next, a procedure for determining the occurrence of welding defects from the shape of the molten pool will be described. FIG. 15 is a flowchart showing the procedure of the second welding monitoring method. First, the image acquisition unit 61 (see FIG. 5) images the welded portion 31 during welding as shown in FIG. 2, and acquires image information of the captured image 37 including the arc light 33 of the arc generated at the tip 39 of the filler metal M and the molten pool 35 in which the filler metal M is melted, as shown in FIG. 3 (S21).

[0056] Next, the contour extraction unit 63 (see FIG. 5) extracts the portion of the arc light 33 from the acquired captured image 37, obtains the contour of the arc light 33, and calculates the center point Oa of the arc light 33 from the obtained contour. The contour extraction unit 63 also extracts the area of ​​the molten pool 35 from the acquired captured image 37, and obtains the contour of the molten pool 35 (S22). The contour and center point Oa of the arc light 33, as well as the contour of the molten pool 35, are obtained by the image processing by the image processing unit 51 (see FIG. 4) described above.

[0057] Then, the index calculation unit 65 (see FIG. 5) calculates (S23) a shape index corresponding to the extracted distortion of the contour of the molten pool 35. The shape index here includes the distance from the center point Oa of the arc light 33 obtained from the contour of the arc light 33 shown in FIG.

[0058] Here, the distance from the center point Oa of the arc light 33 to the contour of the molten pool 35 as the shape index may be the distance between a representative position of the contour of the molten pool 35 (for example, a position close to the existing weld bead) and the center point Oa of the arc light 33. Alternatively, the distance between each of a plurality of points extracted as the contour of the molten pool 35 and the center point Oa of the arc light 33 may be found. This distance may be calculated for each azimuth angle θ about the center point of the arc light 33, or may be calculated continuously for a certain period of time to calculate the variation in distance over time.

[0059] Next, the defect determination unit 67 (see FIG. 5) determines the occurrence state of a welding defect according to the distance between the center point Oa of the arc light 33 and the contour of the molten pool 35 (S24). FIG. 16 is a schematic diagram showing the state of the arc light 33 and the molten pool 35 in a captured image 37. As shown in Fig. 9, when a new weld bead B is formed adjacent to an existing weld bead B0, it is preferable that the contour of the molten pool 35 has a shape that is offset from the weld line Lw as shown in Fig. 11B, rather than being linearly symmetric about the weld line Lw as the center as shown in Fig. 10B. In that case, as shown in Fig. 13, no unwelded welding defects occur, and high-quality welding can be performed.

[0060] Therefore, the index calculation unit 65 obtains a distribution of the distance ra from the center point Oa of the arc light 33 to the contour of the molten pool 35 with respect to the azimuth angle θ around the center point Oa, as shown in Fig. 16. Then, the defect determination unit 67 determines that the more the contour of the molten pool 35 approaches an axisymmetric shape centered on the welding line Lw that passes through the center point Oa of the arc light 33 and indicates the welding direction WD, the more likely it is that a welding defect has occurred.

[0061] Fig. 17A is a captured image of a welded portion when a new weld bead B is formed at a position adjacent to an existing weld bead B0. Fig. 17B is an explanatory diagram showing a main part of the captured image shown in Fig. 17A. Fig. 18A is a captured image of a welded portion when a new weld bead B is formed at a position even closer to the existing weld bead B0 than in the case shown in Fig. 17A. Fig. 18B is an explanatory diagram showing a main part of the captured image shown in Fig. 18A.

[0062] 17A and 17B, when a new weld bead B is formed at a position relatively far from the existing weld bead B0, the contour of the molten pool 35 is not affected by the existing weld bead B0 and maintains a substantially elliptical shape. On the other hand, when a new weld bead B is formed at a position relatively close to the existing weld bead B0 as shown in Figures 18A and 18B, the contour of the molten pool 35 is distorted in the portion close to the existing weld bead B0 because heat is removed due to remelting of the existing weld bead B0.

[0063] The occurrence of welding defects varies depending on the difference in the distance between the existing weld bead B0 and the new weld bead B. Therefore, the shape index of the molten pool 35 is monitored as an index for determining whether the molten metal has been sufficiently poured between the weld beads, and abnormal behavior of the shape index (a state with little distortion) is extracted as a candidate symptom of defect occurrence.

[0064] For example, it is determined that the more the contour of the molten pool 35 approaches a shape that is linearly symmetrical about the welding line Lw that passes through the center point Oa of the arc light 33 and indicates the welding direction WD, the more likely it is that a welding defect has occurred. This determination can be made by using appropriate parameters such as a specific contour position, the absolute value of the distance ra in a region, and the amount of change with respect to the azimuth angle θ, or by combining each parameter.

[0065] FIG. 19 is a graph showing a schematic diagram of the change in distance ra from the center of arc light 33 to the outline of molten pool 35 with respect to the azimuth angle θ. In the distribution of the distance ra illustrated in Fig. 19, when there is no sign of a welding defect, a change in the distance ra (local depression caused by a decrease in the distance ra) is observed in the area close to the adjacent existing weld bead B0, but when there is a sign of a welding defect, the distance is generally constant regardless of the azimuth angle θ. Therefore, by determining the occurrence of a welding defect from the distribution profile of the distance ra, it is possible to distinguish the presence or absence or the degree of the welding defect. In this case, since the distance ra is calculated at multiple points on the contour, even if the local detection position accuracy is insufficient, it is possible to determine without reducing reliability by using the detection results at other points.

[0066] In addition, when the shape of the contour of the molten pool 35 changes, the welding state becomes a transient state, and welding defects may occur. When observing the behavior of the contour of the molten pool 35, it is confirmed that when welding defects occur, the shape of the end of the molten pool 35 on the side of the adjacent existing weld bead B0 is unstable, and irregularities occur repeatedly on the contour of the molten pool 35. In other words, when the shape of the molten pool 35 is unstable, it is estimated that the molten metal does not flow sufficiently to the toe of the adjacent bead, causing blowholes. Therefore, it is also possible to extract signs of defect occurrence by setting a threshold value for the amount of variation of the distance ra over time. In this case, since it is possible to make a judgment from trends at multiple times compared to a case where a simple distance threshold is used, the judgment result is more robust. In addition, the defect detection sensitivity can be easily adjusted by the threshold value.

[0067] FIG. 20 is a graph showing a schematic variation in the distance ra to the edge of the molten pool 35 over time. When the amount of variation in the distance ra becomes equal to or greater than a predetermined reference amount of variation TH2, it is determined that a welding defect has occurred in the region where the amount of variation is equal to or greater than the reference amount of variation TH2. This determination is not limited to the distance ra to the contour of the molten pool 35, but may be based on the curvature of the contour of the molten pool 35, or on the degree of fit when fitting the shape of the molten pool 35 with a model curve (e.g., an ellipse, etc.). By making the determination based on the change in the curvature of the contour and the degree of fit, the change in the shape of the molten pool 35 can be detected with high sensitivity, and the accuracy of detecting welding defects can be improved.

[0068] As in the first welding defect monitoring method, a normal state can be used as a reference, and the degree of deviation from that state can be taken as a sign of an abnormality. In other words, when welding is performed based on a welding plan in which a plurality of welding passes and the welding conditions for the welding passes are previously determined, a predetermined welding pass among the welding passes in the welding plan that was properly welded is set as a reference pass that serves as a criterion for determining welding defects. Then, the shape index of the reference pass is compared with the shape indexes of other welding passes that share the welding conditions of the reference pass, and the presence or absence or the degree of a welding defect is determined based on the difference between the shape indexes. The welding conditions referred to here include various conditions such as welding current, welding voltage, filler metal feed speed, welding speed, torch angle, pitch, and the presence or absence of weaving.

[0069] The comparison of the shape indices described above may be a comparison of the maximum fluctuation amount or a comparison of the frequency of exceeding a threshold value. In this case, whether welding has been performed properly may be determined by verifying the captured image, or by verifying other measurement data such as the welding current, welding voltage, and bead shape profile. Furthermore, when the trajectory plan is created, predicted values ​​may be prepared in advance by simulating temperature prediction, deformation prediction, etc., and the predicted values ​​may be compared to make a judgment.

[0070] In this way, by comparing the shape index of the molten pool 35 between passes with common welding conditions, it becomes easier to detect the degree of deviation from a sound state of a reference pass or a sudden abnormal phenomenon. Also, by comparing weld beads in a weldment, it is possible to identify weld beads that have symptoms of defect occurrence, without having to prepare a database of defect occurrence conditions in advance.

[0071] This welding monitoring method can identify locations where there is a possibility of unwelded defects non-destructively, and can determine the presence or absence and severity of welding defects over the entire range of the welded object. Moreover, the method can be performed in real time during welding. This can be used to review the process, such as modifying the building conditions, and to ensure the quality of the built object.

[0072] As described above, the characteristic shape index of the molten pool 35 can be extracted from the contour of the molten pool 35, and the occurrence of welding defects can be determined from the obtained shape index. Then, by continuously performing the calculation to obtain the shape index at regular time intervals, the transition of the shape index during the deposition of the weld bead can be grasped, and information for traceability in the manufacturing process can be obtained.

[0073] <Procedure for the third welding monitoring method> Next, a procedure for determining the occurrence of welding defects from the amount of change in the shape index of the molten pool will be described. FIG. 21 is a flowchart showing the steps of the third welding monitoring method. First, the image acquisition unit 61 (see FIG. 5) images the welded portion 31 during welding as shown in FIG. 2, and acquires image information of the captured image 37 including the molten pool 35 in which the filler metal M is molten as shown in FIG. 3 (S31).

[0074] Next, the contour extraction unit 63 (see FIG. 5) extracts the area of ​​the molten pool 35 from the acquired captured image 37, and obtains the contour of the molten pool 35 (S32). The contour of the molten pool 35 is obtained by the image processing performed by the image processing unit 51 (see FIG. 4) as described above.

[0075] Then, the index calculation unit 65 (see FIG. 5) calculates (S33) a shape index corresponding to the extracted distortion of the contour of the molten pool 35. The shape index here includes the amount of change over time of any of the following: the curvature of the contour of the molten pool 35, the inclination of the tangent to the contour, the normal direction to the contour, the number of irregularities appearing on the contour, and the size of the irregularities.

[0076] The defect determination unit 67 (see FIG. 5) determines the occurrence state of a welding defect according to the amount of change over time of the shape index obtained by the index calculation unit 65 (S34). The procedure for determining this welding defect will be described below. FIG. 22 is an explanatory diagram that illustrates a schematic shape of the molten pool 35 in a captured image. As shown in Fig. 9, when an adjacent existing weld bead B0 exists, the contour of the molten pool 35 is affected by the distance to the adjacent weld bead B0. When an unwelded welding defect occurs, an uneven portion 71 tends to appear on the contour of the molten pool 35 near the bead toe, which is the boundary between the existing weld bead B0 and the new weld bead B. This uneven portion 71 appears and disappears discontinuously with the passage of time.

[0077] 23A to 23E are explanatory diagrams showing how the contour of the molten pool 35 changes over time. Fig. 23 shows the tip of the molten pool 35 in the welding direction WD during welding.

[0078] When a weld bead is formed, the contour of the tip of the molten pool 35 changes from a smooth state as shown in Fig. 23(A) to a shrink in the direction opposite to the welding direction WD on the side of the existing weld bead (not shown) as shown in Fig. 23(B).Then, as shown in Fig. 23(C), it further shrinks in the opposite welding direction, and then changes to a protruding direction in the welding direction WD as shown in Fig. 23(D), and then returns to the smooth state as shown in Fig. 23(E).In this manner, the contour of the molten pool 35 may repeatedly change between protruding and shrinking over time.

[0079] During the period when such a change in the contour of the molten pool 35 occurs, there is a high possibility that the molten metal does not flow sufficiently between the adjacent weld beads B0, resulting in the occurrence of a non-welded welding defect as shown in Fig. 12. Therefore, whether or not an uneven portion 71 has occurred on the contour of the molten pool 35 is monitored, and if it has, this is extracted as a sign of the occurrence of a welding defect, and the occurrence status of the welding defect is judged.

[0080] FIG. 24 is an explanatory diagram showing a specific example of extracting a shape index of the contour of the molten pool 35. For example, suppose that a depression is formed near point P2, which is part of the molten pool 35. In this case, the local curvature of the contour of the molten pool 35 changes more greatly near point P2 than near points P1 and P3. In this way, the part where the local curvature of the contour changes can be extracted as an indication of the occurrence of a welding defect. This indication is found as a three-dimensional extraction region for the contour of the molten pool 35 that is continuously formed along the welding direction.

[0081] The above-mentioned local curvature can be calculated, for example, as follows. FIG. 25 is an explanatory diagram showing an example of calculation of a local curvature calculated from the contour of the molten pool 35 in the captured image. First, edge components are extracted from the captured image by applying mask processing such as a first-order differential filter (e.g., a Prewitt operator), and pixels with high edge strength are designated as edge pixels. In addition, a two-dimensional edge gradient is calculated for the extracted edge pixels. For example, the edge strength E(x, y) of edge pixel P(x, y) is calculated by equation (1), and the edge gradient φ(x, y) is calculated by equation (2). Note that x and y refer to the coordinates of the Cartesian coordinate system on the plane of the captured image.

[0082]

number

[0083] Here, Δx(x,y) and Δy(x,y) are first derivatives in the x and y directions due to mask processing.

[0084] A circular local area A(x,y) with a radius rb is set with the above-mentioned edge pixel P(x,y) as the center. The standard deviation of the edge gradients in all edge pixels present in this local area is defined as the local curvature. In other words, if the edge gradient in each edge pixel Pi(xi,yi) (i=0,1,2,...) present in the local area A(x,y) is defined as φi, the local curvature ω(x,y) in the edge pixel P(x,y) can be calculated by the following formula (3).

[0085]

number

[0086] Here, NB is the number of leading edge pixels in local region A, and φA is the average edge gradient of these edge pixels.

[0087] FIG. 26 is an explanatory diagram showing examples of contour shapes and local curvatures in (A) and (B). As shown in Fig. 26A, when the contour of the molten pool 35 composed of multiple edge pixels is close to a straight line, the edge gradient φA of each edge pixel is approximately constant, so the value of the local curvature ω(x, y) according to formula (3) is small. On the other hand, as shown in Fig. 26B, when the curvature of the contour of the molten pool 35 in the local region is large, the edge gradient φA of each edge pixel varies widely, so the value of the local curvature ω(x, y) according to formula (3) is large.

[0088] The local curvature ω(x, y) obtained as described above is a shape index of the contour of the molten pool 35. The shape index is not limited to the amount of change in the local curvature ω(x, y), but may be any of the amount of change over time in the slope of the tangent to the contour, the normal direction to the contour, the number of irregularities appearing on the contour, and the size of the irregularities.

[0089] Also in this case, the degree of deviation from a normal state can be regarded as a sign of an abnormality. When welding is performed based on a welding plan in which a plurality of welding passes and the welding conditions of the welding passes are determined in advance, a predetermined welding pass in which welding was performed soundly is set as a reference pass that serves as a criterion for determining welding defects, as in the case of the first and second welding defect monitoring methods described above. In addition, the shape index of the reference pass is compared with the shape indexes of other welding passes that share the welding conditions of the reference pass, and the presence or absence or degree of a welding defect is determined according to the difference between the shape indexes. The comparison of shape indexes and other actions and effects described above are the same as those in the case of the first and second welding defect monitoring methods described above.

[0090] As described above, according to this welding monitoring method, the occurrence of a welding defect can be determined by extracting the characteristic shape index of the molten pool 35 from the contour of the molten pool 35 and determining the amount of change in the obtained shape index over time. Then, by continuously performing the calculation to obtain the shape index at regular time intervals, the change in the shape index during the deposition of the weld bead can be grasped and information for traceability in the manufacturing process can be obtained.

[0091] In addition, it can identify locations where there is a possibility of unwelded defects non-destructively, and determine the presence or absence and severity of welding defects over the entire range of the welded object. Moreover, it can make the determination in real time during welding. This can be used to review the process, such as modifying welding conditions, and to ensure the quality of the welded object (model).

[0092] <Extraction of shape indices> In the procedures of the first to third welding defect monitoring methods described above, the shape index is extracted from the captured image by image processing, but it is also possible to extract the shape index using a machine learning model.

[0093] For example, supervised learning is performed using a plurality of teacher data including captured images including images of a molten pool and an arc in arc welding and state-related information related to the state of the arc welding, and a machine learning model is constructed in which the image information of the captured images is input and the state-related information is output. The captured images obtained by a camera are then input to the machine learning model, and the arc welding is controlled based on the state-related information output from the machine learning model.

[0094] By using such a machine-learned model to extract shape indices from captured images, it is possible to capture subtle changes in the image information that would be missed by simple image processing, making it possible to extract shape indices accurately and reliably.

[0095] As described above, according to the present welding monitoring method, the occurrence of welding defects can be determined from the shape of the arc light, the shape of the molten pool, or the shapes of both, without having to correspond to the positional relationship between the groove or the molten pool and the adjacent bead, or the bead shape. This makes it possible to highly accurately determine welding defects and to constantly and stably monitor the welding condition.

[0096] As such, the present invention is not limited to the above-described embodiments, and the present invention also contemplates mutual combinations of the various components of the embodiments, as well as modifications and applications by those skilled in the art based on the descriptions in the specification and well-known techniques, and these are included in the scope of the protection sought.

[0097] As described above, the present specification discloses the following: (1) A welding monitoring method for monitoring a welding state using image information obtained by capturing an image of a welded portion during arc welding, comprising the steps of: an image acquiring step of acquiring image information showing a molten pool generated in the welded portion and an arc light melting the filler metal; a contour extraction step of extracting a contour of at least one of the molten pool and the arc light from the image information; an index calculation step of determining a shape index corresponding to the extracted distortion of the contour; a defect determination step of determining the occurrence status of a welding defect according to the shape index; A welding monitoring method comprising: This welding monitoring method can identify locations where there is a possibility of unwelded defects non-destructively. It can also determine the occurrence of welding defects over the entire range of the welded object. It also enables real-time judgment during welding. Therefore, it can be used to review the process, such as modifying welding conditions, and to ensure the quality of the welded object (molded object).

[0098] (2) in the contour extraction step, a contour of the arc light is extracted from the image information; The welding monitoring method described in (1), wherein the shape index calculated in the index calculation step includes any one of a center point of the arc light calculated from the contour of the arc light, a distance from the center point to the contour of the arc light, a curvature of the contour of the arc light, and a goodness of fit when the shape of the arc light is fitted with a model curve. According to this welding monitoring method, various characteristics of the arc light can be used as indications of the occurrence of a welding defect, thereby determining the occurrence of a welding defect.

[0099] (3) in the contour extraction step, a contour of the arc light is extracted from the image information; The welding monitoring method described in (1), wherein in the defect determination process, it is determined that the more the contour of the arc light approaches an axisymmetric shape centered on a weld line that passes through a center point of the arc light and indicates the welding direction, the more likely it is that the welding defect has occurred. According to this welding monitoring method, by correlating the distortion of the arc light contour with the welding defect, the welding defect can be easily determined.

[0100] (4) in the contour extraction step, a contour of the arc light and a contour of the molten pool are extracted from the image information; The welding monitoring method according to (1), wherein the shape index calculated in the index calculation process includes a distance from a center point of the arc light to a contour of the molten pool, the distance being calculated from the contour of the arc light. According to this welding monitoring method, welding defects are judged according to the distance from the center of the arc light to the contour of the molten pool, so that welding defects can be judged according to subtle changes in the shape of the molten pool.

[0101] (5) The welding monitoring method according to (4), wherein the defect determination step determines that the welding defect has occurred when the distance is equal to or greater than a predetermined reference distance. According to this welding monitoring method, the occurrence of a welding defect can be easily determined by comparing the distance from the center of the arc light to the outline of the molten pool with a reference distance. In addition, the accuracy of the determination result can be further improved by fine-tuning the reference distance.

[0102] (6) in the index calculation step, the distance is calculated in a plurality of directions different from each other around a center point of the arc light, The welding monitoring method described in (4), wherein in the defect determination process, it is determined that the welding defect has occurred if the rate of change of the distance in a distribution profile of the distance in multiple directions around the center point of the arc light is always smaller than a predetermined reference rate of change. According to this welding monitoring method, the more constant the distance from the center of the arc light to the contour of the molten pool is around the center of the arc light, the more likely a welding defect is to occur. Therefore, the occurrence of a welding defect can be easily determined by using the fact that the above distance does not exceed a standard change rate as a judgment criterion.

[0103] (7) A welding monitoring method as described in (4), in which the defect determination process determines that the greater the likelihood that the welding defect has occurred, the closer the contour of the molten pool in front of the welding direction approaches an axisymmetric shape centered on a weld line that passes through the center point of the molten pool and indicates the welding direction. According to this welding monitoring method, the probability of occurrence of a welding defect can be quantitatively evaluated.

[0104] (8) A welding monitoring method according to any one of (4) to (7), wherein the center point of the molten pool is a center of gravity position of the area of ​​the molten pool in the image information. According to this welding monitoring method, the center point of the molten pool can be found with high accuracy by simple calculation.

[0105] (9) In the index calculation step, a fluctuation amount of the shape index over time is calculated, The welding monitoring method according to any one of (1) to (8), wherein in the defect determination step, it is determined that the welding defect has occurred when the amount of fluctuation is equal to or greater than a predetermined reference amount of fluctuation. According to this welding monitoring method, the shape index fluctuates over time due to unstable behavior of the arc light or molten pool, and by determining the amount of this fluctuation, the occurrence of welding defects can be accurately evaluated.

[0106] (10) The welding monitoring method according to any one of (2) to (8), wherein the center point of the arc light is a center of gravity position of an area of ​​the arc light in the image information. According to this welding monitoring method, the center point of the arc light can be found with high accuracy by simple calculation.

[0107] (11) In the contour extraction step, a contour of the molten pool is extracted from the image information; The welding monitoring method according to (1), wherein the shape index calculated in the index calculation process includes an amount of change over time in the contour of the molten pool. According to this welding monitoring method, since welding defects are likely to occur when unstable behavior occurs in the molten pool, the occurrence of welding defects can be accurately evaluated by determining the amount of change in the contour of the molten pool over time.

[0108] (12) A welding monitoring method as described in (11), wherein the shape index calculated in the index calculation process is the amount of change over time of any one of the following: the curvature of the contour of the molten pool, the slope of the tangent to the contour, the normal direction to the contour, the number of irregularities appearing on the contour, and the size of the irregularities. According to this welding monitoring method, various features of the weld pool contour can be used as indications of the occurrence of welding defects to determine welding defects.

[0109] (13) The welding monitoring method according to (12), wherein in the defect determination step, it is determined that the welding defect has occurred when the amount of change over time of the shape index is equal to or greater than a predetermined reference amount of change over time. According to this welding monitoring method, robustness of the judgment result of the occurrence of a welding defect can be obtained, and the accuracy of the judgment result can be further improved by fine-tuning the reference amount of change over time.

[0110] (14) When welding is performed based on a welding plan in which a plurality of welding passes and welding conditions for the welding passes are defined in advance, In the defect determination step, Among the welding passes of the welding plan, a reference pass that is a criterion for determining the welding defect is set; The welding monitoring method according to any one of (1) to (13), further comprising comparing the shape index in the reference pass with shape indexes of other welding passes that share the same welding conditions as the reference pass, and determining the welding defect based on the difference between the shape indexes. According to this welding monitoring method, it is possible to identify weld beads that have signs of defect occurrence by comparing the welding passes in the welded workpiece without having to prepare a database of conditions for occurrence of welding defects in advance. Therefore, even if cases in which welding defects occur are not fully understood, it is possible to determine the presence of welding defects simply by comparing with the normal state.

[0111] (15) An additive manufacturing method for manufacturing a molded object composed of multiple layers of weld beads by stacking weld beads formed by melting and solidifying a filler metal, comprising the steps of: An additive manufacturing method, comprising: monitoring a welding state of the weld bead by the welding monitoring method according to any one of (1) to (14) when the weld bead is formed. According to this additive manufacturing method, even in additive manufacturing where there is no reference object to identify the position of a weld groove, etc., the occurrence and extent of welding defects can be accurately evaluated simply by understanding the state of the arc light or molten pool.

[0112] (16) A welding monitoring device for monitoring a welding state using image information obtained by capturing an image of a welded portion during arc welding, comprising: an image acquisition unit that acquires image information showing a molten pool generated in the weld and an arc light that melts the filler metal; a contour extraction unit that extracts a contour of at least one of the molten pool and the arc light from the image information; an index calculation unit that calculates a shape index according to the extracted distortion of the contour; a defect determination unit that determines the occurrence status of a welding defect according to the shape index; A welding monitoring device comprising: This welding monitoring device can identify locations where there is a possibility of unwelded defects non-destructively. It can also determine the occurrence of welding defects over the entire range of the welded object. It also enables real-time judgment during welding. Therefore, it can be used to review the process, such as modifying welding conditions, and to guarantee the quality of the welded object (molded object).

[0113] (17) The contour extraction unit extracts a contour of the arc light from the image information, The welding monitoring device according to (16), wherein the shape index calculated by the index calculation unit includes any one of a center point of the arc light calculated from a contour of the arc light, a distance from the center point to the contour of the arc light, and a curvature of the contour of the arc light. According to this welding monitoring device,

[0114] (18) The contour extraction unit extracts a contour of the arc light and a contour of the molten pool from the image information, The welding monitoring device according to claim 16, wherein the shape index calculated by the index calculation unit includes a distance from a center point of the arc light to a contour of the molten pool, the distance being calculated from a contour of the arc light. This welding monitoring device can determine welding defects by using various characteristics of the arc light as indications of the occurrence of welding defects.

[0115] (19) The contour extraction unit extracts a contour of the molten pool from the image information, The welding monitoring device according to claim 16, wherein the shape index calculated by the index calculation unit is the amount of change over time in the contour of the molten pool. According to this welding monitoring device, since welding defects are likely to occur when unstable behavior occurs in the molten pool, the occurrence of welding defects can be accurately evaluated by determining the amount of change in the contour of the molten pool over time.

[0116] (20) An additive manufacturing apparatus for manufacturing a molded object composed of multiple layers of weld beads by stacking weld beads formed by melting and solidifying a filler metal, comprising: When the weld bead is formed, a welding state of the weld bead is monitored by a welding monitoring device according to any one of (16) to (19). Additive manufacturing equipment. With this welding monitoring device, even in additive manufacturing where there is no reference object to identify the position of the weld groove, etc., it is possible to accurately evaluate the occurrence and extent of welding defects simply by grasping the state of the arc light or molten pool. [Explanation of symbols]

[0117] 11 Modeling Department 13 Control section 15 Welding torch 17 Welding robot 19 Robot drive unit 21 Filler metal supply section 23 Welding power supply unit 25 Imaging unit 27 Reels 29 Base Plate 31 Welding 33 Arc Light 35 Molten Pool 37 Captured images 39 Tip 41 Processors 43 Memory 45 Storage section 49 Input / Output Interface 51 Image processing section 53 Display section 55 Communications Department 61 Image acquisition unit 63 Contour Extraction Unit 65 Index calculation section 67 Defect Judgment Section 71 Uneven part 100 Additive manufacturing equipment B Weld bead M filler metal

Claims

1. A welding monitoring method for monitoring a welding state using image information obtained by capturing an image of a welded portion during arc welding, comprising: an image acquiring step of acquiring image information showing a molten pool generated in the welded portion and an arc light melting the filler metal; a contour extraction step of extracting a contour of at least one of the molten pool and the arc light from the image information; an index calculation step of determining a shape index corresponding to the extracted distortion of the contour; a defect determination step of determining the occurrence status of a welding defect according to the shape index; Equipped with In the contour extraction step, a contour of the arc light is extracted from the image information, In the defect determination step, it is determined that the more the contour of the arc light approaches an axisymmetric shape centered on a weld line that passes through a center point of the arc light and indicates a welding direction, the more likely it is that the welding defect has occurred. Welding monitoring methods.

2. A welding monitoring method for monitoring a welding state using image information obtained by capturing an image of a welded portion during arc welding, comprising: an image acquiring step of acquiring image information showing a molten pool generated in the welded portion and an arc light melting the filler metal; a contour extraction step of extracting a contour of at least one of the molten pool and the arc light from the image information; an index calculation step of determining a shape index corresponding to the extracted distortion of the contour; a defect determination step of determining the occurrence status of a welding defect according to the shape index; Equipped with In the contour extraction step, a contour of the arc light and a contour of the molten pool are extracted from the image information, the shape index calculated in the index calculation step includes a distance from a center point of the arc light to a contour of the molten pool, the distance being calculated from a contour of the arc light. Welding monitoring methods.

3. The defect determination step determines that the welding defect has occurred when the distance is equal to or greater than a predetermined reference distance. The method of claim 2 .

4. The index calculation step calculates the distance in a plurality of different directions around a center point of the arc light, 3. The welding monitoring method according to claim 2, wherein in the defect determination process, it is determined that the welding defect has occurred when a rate of change in the distance in a distribution profile of the distance in multiple directions around a center point of the arc light is always smaller than a predetermined reference rate of change.

5. the defect determination step determines that the likelihood of the occurrence of the welding defect is high as the contour of the molten pool in the welding direction approaches an axisymmetric shape centered on a weld line that passes through a center point of the molten pool and indicates the welding direction; The method of claim 2 .

6. The center point of the molten pool is a center of gravity of the area of ​​the molten pool in the image information. The welding monitoring method according to any one of claims 2 to 5.

7. The index calculation step calculates a change amount of the shape index over time, 7. The welding monitoring method according to claim 1, wherein in the defect determination step, it is determined that the welding defect has occurred when the amount of fluctuation is equal to or greater than a predetermined reference amount of fluctuation.

8. In the contour extraction step, a contour of the molten pool is extracted from the image information, The welding monitoring method according to claim 1 or 2, wherein the shape index calculated in the index calculation step includes an amount of change over time of a contour of the molten pool.

9. The shape index calculated in the index calculation step is a change over time in any one of the curvature of the contour of the molten pool, the inclination of a tangent line to the contour, the normal direction of the contour, the number of irregularities appearing on the contour, and the size of the irregularities. The method of monitoring a weld as recited in claim 8.

10. In the defect determination step, it is determined that the welding defect has occurred when the amount of change over time of the shape index is equal to or greater than a predetermined reference amount of change over time. The method of monitoring a weld as recited in claim 9.

11. When welding is performed based on a welding plan in which a plurality of welding passes and welding conditions for the welding passes are determined in advance, In the defect determination step, Among the welding passes of the welding plan, a reference pass that is a criterion for determining the welding defect is set; comparing the shape index in the reference pass with shape indexes of other welding passes that have the same welding conditions as the reference pass, and determining the welding defect based on a difference between the shape indexes; The welding monitoring method according to any one of claims 1 to 10.

12. An additive manufacturing method for manufacturing a molded object composed of multiple layers of weld beads by stacking weld beads formed by melting and solidifying a filler material, comprising: When the weld bead is formed, the welding state of the weld bead is monitored by the welding monitoring method according to any one of claims 1 to 11. Additive manufacturing methods.

13. A welding monitoring device that monitors a welding state using image information obtained by capturing an image of a welded portion during arc welding, an image acquisition unit that acquires image information showing a molten pool generated in the weld and an arc light that melts the filler metal; a contour extraction unit that extracts a contour of at least one of the molten pool and the arc light from the image information; an index calculation unit that calculates a shape index according to the extracted distortion of the contour; a defect determination unit that determines the occurrence status of a welding defect according to the shape index; Equipped with The contour extraction unit extracts a contour of the arc light from the image information, The defect determination unit determines that the likelihood of the occurrence of the welding defect is high as the contour of the arc light approaches an axisymmetric shape centered on a weld line that passes through a center point of the arc light and indicates a welding direction. Welding monitoring equipment.

14. A welding monitoring device that monitors a welding state using image information obtained by capturing an image of a welded portion during arc welding, an image acquisition unit that acquires image information showing a molten pool generated in the weld and an arc light that melts the filler metal; a contour extraction unit that extracts a contour of at least one of the molten pool and the arc light from the image information; an index calculation unit that calculates a shape index according to the extracted distortion of the contour; a defect determination unit that determines the occurrence status of a welding defect according to the shape index; Equipped with the contour extraction unit extracts a contour of the arc light and a contour of the molten pool from the image information; the shape index calculated by the index calculation unit includes a distance from a center point of the arc light to a contour of the molten pool, the distance being calculated from a contour of the arc light. Welding monitoring equipment.

15. An additive manufacturing apparatus for manufacturing a molded object composed of multiple layers of weld beads by stacking weld beads formed by melting and solidifying a filler material, comprising: When the weld bead is formed, a welding state of the weld bead is monitored by the welding monitoring device according to claim 13 or 14. Additive manufacturing equipment.

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