Patent Publication Number: US-2022215519-A1

Title: Processing device, welding system, processing method, and storage medium

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2021-000171, filed on Jan. 4, 2021; the entire contents of which are incorporated herein by reference. 
     FIELD 
     Embodiments described herein relate generally to a processing device, a welding system, a processing method, and a storage medium. 
     BACKGROUND 
     There is technology that extracts a feature from an image that is imaged when welding and detects a defect based on the feature. It is desirable to improve the convenience of such technology. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic view illustrating a configuration of a welding system that includes a processing device according to an embodiment; 
         FIGS. 2A and 2B  are schematic views for describing the operations of the processing device according to the embodiment; 
         FIGS. 3A and 3B  are schematic views for describing the operations of the processing device according to the embodiment; 
         FIGS. 4A and 4B  are images illustrating output examples of the first model; 
         FIGS. 5A and 5B  are images illustrating output examples of the first model; 
         FIG. 6  is a flowchart illustrating processing according to the processing device according to the embodiment; 
         FIGS. 7A and 7B  are schematic views illustrating images used to train the first model; 
         FIGS. 8A and 8B  are schematic views illustrating images used to train the first model; 
         FIG. 9  is a flowchart illustrating the processing according to the processing device according to the modification of the embodiment; 
         FIG. 10  is a schematic view illustrating the configuration of another welding system that includes the processing device according to the embodiment; 
         FIGS. 11A to 11C  are schematic views illustrating display examples of the processing device according to the modification of the embodiment; and 
         FIG. 12  is a schematic view illustrating a hardware configuration. 
     
    
    
     DETAILED DESCRIPTION 
     According to one embodiment, the processing device acquires a first detection result and a second detection result by inputting a first image to a first model. The first model detects a welding element and a defect according to an input of a welding image. The first image is imaged when welding. The first detection result relates to the welding element. The second detection result relates to the defect. The processing device determines an appropriateness of the second detection result by using the first detection result. 
     Various embodiments are described below with reference to the accompanying drawings. 
     The drawings are schematic and conceptual; and the relationships between the thickness and width of portions, the proportions of sizes among portions, etc., are not necessarily the same as the actual values. The dimensions and proportions may be illustrated differently among drawings, even for identical portions. 
     In the specification and drawings, components similar to those described previously or illustrated in an antecedent drawing are marked with like reference numerals, and a detailed description is omitted as appropriate. 
       FIG. 1  is a schematic view illustrating a configuration of a welding system that includes a processing device according to an embodiment. 
     The welding system  1  includes the processing device  10 , a welding device  20 , and a memory device  30 . 
     The welding device  20  joins two or more members by welding. For example, the welding device  20  performs arc welding or laser welding. Specifically, arc welding is tungsten inert gas (TIG) welding, metal inert gas (MIG) welding, metal active gas (MAG) welding, carbon dioxide gas arc welding, etc. Mainly herein, an example is described in which the welding device  20  performs TIG welding. 
     The welding device  20  includes, for example, a torch  21 , an arm  22 , an electrical power supplier  23 , a gas supplier  24 , a wire  25 , an imager  26 , an illuminator  27 , and a controller  28 . 
     The torch  21  includes an electrode  21   a  that is made of tungsten. The tip of the electrode  21   a  is not covered with the torch  21 . For example, the torch  21  is mounted to the arm  22  that is articulated and includes multiple links. Or, the torch  21  may be gripped by a worker. 
     The electrical power supplier  23  is electrically connected with at least one of the electrode  21   a  or a welding object S. A voltage is applied between the electrode  21   a  and the welding object S by the electrical power supplier  23 ; and an arc discharge is generated. One of the electrode  21   a  or the welding object S may be set to a common potential (e.g., a ground potential); and the electrical power supplier  23  may control only the potential of the other of the electrode  21   a  or the welding object S. In the example of  FIG. 1 , the electrical power supplier  23  controls only the potential of the electrode  21   a.    
     The gas supplier  24  is connected to the torch  21 . The gas supplier  24  supplies an inert gas to the torch  21 . Or, the gas supplier  24  may supply a gas mixture of an inert gas and an active gas. The gas that is supplied to the torch  21  is blown from the tip of the torch  21  where the electrode  21   a  is exposed toward the welding object S. 
     The tip of the wire  25  is located in the space in which the arc discharge is generated. The tip of the wire  25  is melted by the arc discharge and drops onto the welding object S. The welding object S is welded by the molten wire  25  solidifying. For example, the wire  25  is fixed with respect to the arm  22  and is automatically supplied to match the progress of the melting. 
     When welding, the imager  26  images the spot at which the welding is performed. The imager  26  acquires a still image by imaging the welding spot. Or, the imager  26  may image a video image. The imager  26  acquires a still image by cutting out a portion of the video image. The imager  26  is, for example, a camera that includes a CCD image sensor or a CMOS image sensor. 
     The illuminator  27  illuminates the welding spot when welding to obtain a clearer image from the imager  26 . If a sufficiently bright image is obtained without illuminating the welding spot, the illuminator  27  may not be included. 
     The controller  28  controls the operations of the components of the welding device  20  described above. For example, the controller  28  welds the welding object S along a prescribed direction by generating the arc discharge while driving the arm  22 . The controller  28  also may control the setting of the imager  26 , the setting of the illuminator  27 , etc. 
     The controller  28  stores the image acquired by the imager  26  in the memory device  30 . For example, the controller  28  associates the image that is imaged, the welding parameters when imaging, and the imaging conditions with the position and stores the result in the memory device  30 . 
     The welding parameter includes, for example, at least one selected from the group consisting of the speed in the movement direction (a first direction) of the torch  21 , the position of the torch  21  in the width direction (a second direction) perpendicular to the movement direction, the current supplied to the torch  21 , the voltage supplied to the torch  21 , and the supply rate of the wire  25 . The imaging conditions include, for example, settings of the imager  26  such as the exposure time, the aperture stop, the sensitivity (ISO), etc. The imaging conditions may include the settings of the illuminator  27 . 
       FIGS. 2A and 2B  and  FIGS. 3A and 3B  are schematic views for describing the operations of the processing device according to the embodiment. 
     The processing device  10  according to the embodiment checks whether or not a defect exists at the welded spot from the image that is imaged when welding. First, the processing device  10  accesses the memory device  30  and acquires the first image that is imaged when welding. The processing device  10  inputs the first image to a first model. 
     The first model detects the defect and the weld pool in the welding image according to the input of the welding image in which the weld pool is visible. The weld pool is a pool of liquid metal formed by melting the metal (the wire  25 ). As an example, the defect is a circular hole defect that is formed by the welding object S partially melting excessively when welding, piercing the welding object S, and dropping away as molten metal. In another example, the defect is overlap, undercut, or a crack. 
       FIG. 2A  is a schematic view illustrating an example of an image that is imaged when welding. A first member  101  and a second member  102  are visible in the image  100  illustrated in  FIG. 2A . The first member  101  and the second member  102  are joined by welding. The torch  21  and the not-illustrated wire  25  are located proximate to the boundary between the first member  101  and the second member  102 . The wire  25  is melted by the electric discharge generated at the tip of the torch  21 ; and a weld pool  111  is formed. A bead  113  is formed by the metal of the weld pool  111  solidifying. In the example of  FIG. 2A , a defect  112  is formed by partial burn-through of the first member  101  or the second member  102  in a circular shape due to excessive melting in the weld pool  111 . 
     When the image of  FIG. 2A  is input, the first model detects a first feature that indicates the outer edge of the weld pool  111 , and a second feature that indicates the outer edge of the defect  112 . The detection of the first feature indicates that the weld pool  111  exists in the image  100 . The detection of the second feature indicates that a defect exists in the image  100 . Also, the second feature indicates the level of the defect  112 . For example, the magnitude of the luminance of the second feature indicates the level of the defect  112 . 
     The processing device  10  acquires the output result from the first model. The output of the first model includes a first detection result related to the weld pool of the first image, and a second detection result related to the defect of the first image. When the weld pool  111  exists in the image, the first detection result includes the first feature. In other words, the first detection result indicates the existence/nonexistence of the weld pool. When the defect  112  exists, the second detection result includes the second feature. In other words, the second detection result indicates the existence/nonexistence of the defect. 
       FIG. 2B  is a schematic view illustrating the output result of the first model for the input of the image of  FIG. 2A . An output result  200  is an image that includes the first feature  211  of the weld pool  111  and the second feature  212  of the defect  112 . In the example, the first feature  211  indicates the outer edge of the weld pool  111 . The second feature  212  indicates the outer edge of the defect  112 . 
       FIG. 3A  is a schematic view illustrating another example of an image that is imaged when welding. Similarly to the image  100 , the first member  101 , the second member  102 , the torch  21 , the weld pool  111 , the defect  112 , and the bead  113  are visible in an image  100   a  illustrated in  FIG. 3A . However, blown out highlights of a portion of the image occurs due to a light emission due to electric discharge. 
       FIG. 3B  is a schematic view illustrating the output result of the first model for the input of the image of  FIG. 3A . In the image  100   a , only portions of the weld pool  111  and the defect  112  are visible. Therefore, an output result  200   a  includes a first feature  211   a  indicating a portion of the weld pool  111  and a second feature  212   a  indicating a portion of the defect  112 . 
     The processing device  10  uses the first and second detection results to perform first to third determinations. 
     In the first determination, the processing device  10  uses the first detection result to determine the appropriateness of the second detection result. Specifically, the processing device  10  determines whether or not the weld pool is appropriately detected in the first detection result. As illustrated in  FIGS. 2B and 3B , the area (the number of pixels) of the first feature in the first detection result increases as the weld pool becomes clearer in the first image. The processing device  10  compares the number of pixels of the first feature to a preset first threshold. The pixel values of each pixel included in the first feature may change according to the weld pool  111  in the welding image. In such a case, the processing device  10  compares the cumulative sum (a first cumulative sum) of the pixel values of the pixels included in the first feature to the preset first threshold. 
     For example, in the first detection result, the first feature is represented using red in RGB color space. The pixel values of each pixel of the first feature respectively represent the luminances of RGB. The processing device  10  calculates the first cumulative sum by summing the red luminance of the first feature. The pixel values of the first feature are not limited to the example and may represent the hue, the color saturation, the lightness, etc., in HSV color space. 
     When the first cumulative sum is not less than the first threshold, the processing device  10  determines that the second detection result is appropriate. In other words, the first image is clear enough that the weld pool and the defect can be detected; and the detection result of the defect in the first image is determined to be appropriate. When the second detection result is determined to be appropriate, the processing device  10  performs the second determination. When the first cumulative sum is less than the first threshold, the processing device  10  determines that the second detection result is inappropriate. For example, even when the defect is not detected, there is a possibility that the defect may be hidden by blown out highlights in the first image. When the second detection result is determined to be inappropriate, the processing device  10  does not perform the second determination. 
     In the second determination, the processing device  10  uses the second detection result to determine an existence/nonexistence of the defect in the first image. The pixel value of each pixel of the second feature output from the first model changes according to the sureness of the defect. Higher pixel values indicate that the first model strongly estimates that the pixel is a portion of the defect. The processing device  10  compares the cumulative sum (a second cumulative sum) of the pixel values included in the second feature to a preset second threshold. The second cumulative sum is zero when the second feature is not detected. 
     For example, in the second detection result, the second feature is represented using yellow in RGB color space. The pixel values of each pixel of the second feature respectively represent the luminances of RGB. The processing device  10  calculates the second cumulative sum by summing the red luminance and the green luminance of the first feature. The pixel values of the second feature are not limited to the example and may represent the hue, the color saturation, the lightness, etc., in HSV color space. 
     When the second cumulative sum is not less than the second threshold, the processing device  10  determines that the defect exists. When the defect is determined to exist in the second determination, the processing device  10  performs a third determination. When the second cumulative sum is less than the second threshold, the processing device  10  determines that the defect does not exist. When the defect is determined not to exist in the second determination, the processing device  10  does not perform the third determination. 
     In the third determination, the processing device  10  counts the number of times that the defect is consecutively determined to exist in the second determination. The processing device  10  compares the count to a preset third threshold. When the count is not less than the third threshold, the processing device  10  confirms the existence of the defect. When the count is less than the third threshold, the processing device  10  does not confirm the existence of the defect. 
     Images are consecutively imaged when welding. The multiple first images are stored in the memory device  30 . The processing device  10  acquires the first and second detection results and performs the first determination for each of the multiple first images. When the second detection result is determined to be appropriate in the first determination, the processing device  10  further performs the second determination. When the defect is determined to exist in the second determination, the processing device  10  refers to the recent previous determination result. The existence of the defect is confirmed when the defect is consecutively determined to exist in the newest determination and in the recent determination, and the consecutive determination count is not less than the third threshold. 
     For example, the existence of the defect is confirmed for the multiple first images of the consecutive determinations. After the defect is determined to exist in the second determination, the count is reset when the second detection result is determined to be inappropriate in the first determination. Or, the count may not be reset when the second detection result is determined to be inappropriate in many images in the first determination. That is, a user can select whether or not to perform the reset as appropriate according to the necessary detection accuracy of the defect. 
     Even when a hole that may become a defect occurs in the weld pool, there are cases where the defect is repaired by metal subsequently flowing into the hole. If the existence of the defect is confirmed based on the result of one second determination, there is a possibility that the existence of the defect may be confirmed even though a defect actually does not exist. 
     The processing device  10  records the quality when the processing described above ends. Specifically, the processing device  10  associates the imaging position of the first image and the quality for the imaging position, and stores the result. For example, the controller  28  refers to the memory region in which the position (the coordinate) of the tip of the arm  22  is stored. The controller  28  drives the arm  22  so that the tip of the arm  22  is positioned at the coordinate. The imager  26  moves conjunctively with the torch  21 . The coordinate of the tip of the arm  22  is used as the imaging position. Or, the imager  26  may be driven by a drive system other than the arm  22 . In such a case, the position (the coordinate) of the imager  26  in the other drive system may be used as the imaging position. Or, the position of the electrode  21   a  tip in the first image or the position of the weld pool  111  in the first image may be calculated, and one of these positions may be used as the imaging position. 
     For example, when the defect is determined not to exist in the second determination, the processing device  10  determines the quality to be “good” (a first quality) at the imaging position. When the defect is determined to exist in the second determination and the defect is not confirmed in the third determination, the processing device  10  determines the quality to be “good” at the imaging position. When the defect is confirmed in the third determination, the processing device  10  determines the quality to be “defective” (a third quality) at the imaging position. When the second detection result is determined to be inappropriate in the first determination, the processing device  10  determines the quality to be “invalid” (a second quality) at the imaging position. The processing device  10  may generate and output quality data that includes multiple positions and the quality for each position. 
     The first to third thresholds are preset by the user. The first threshold and the second threshold may be automatically set based on the size of the first image. The third threshold may be automatically set based on the interval between imaging the first image. 
       FIGS. 4A and 4B  and  FIGS. 5A and 5B  are schematic views illustrating output examples of the first model. 
     In  FIGS. 4A and 4B  and  FIGS. 5A and 5B , the first feature  211  is illustrated by a line segment marked with dots. In  FIG. 5B , the second feature  212  is illustrated by a line segment marked with dots. The pixel values increase as the density of the dots increases. 
     In the examples of  FIGS. 4A and 4B , the weld pool is clearly visible in the first image input to the first model. Therefore, in output results  200   b  and  200   c  illustrated in  FIGS. 4A and 4B , the line segment of a first feature  211   b  and the line segment of a first feature  211   c  that are detected are sufficiently long. In other words, the area of the first feature  211   b  and the area of the first feature  211   c  are sufficiently large. The second detection result is determined to be appropriate in the first determination based on the output result of  FIG. 4A  and in the first determination based on the output result of  FIG. 4B . 
     When the second detection result is determined to be appropriate, the existence/nonexistence of the defect in the first image input to the first model is determined based on the second detection result. The second feature  212  of the defect is not detected in the output results  200   b  and  200   c  of  FIGS. 4A and 4B . Therefore, the defect is determined not to exist in the first image. The processing device  10  determines the quality to be “good” at these imaging positions of the first image. 
     In an output result  200   d  illustrated in  FIG. 5A , blown out highlights occurs in a portion of the image input to the first model. Therefore, multiple divided line segments are detected as a first feature  211   d . Also, the area of the first feature  211   d  is small compared to the examples of  FIGS. 4A and 4B . For example, the second detection result is determined to be inappropriate in the first determination based on the output result  200   d  of  FIG. 5A . 
     In an output result  200   e  illustrated in  FIG. 5B , similarly to the examples of  FIGS. 4A and 4B , the area of a first feature  211   e  is sufficiently large. Therefore, the second detection result is determined to be appropriate. A second feature  212   e  is detected in the example of  FIG. 5B . For example, the defect is determined to exist based on the second feature  212   e  in the second determination. The existence of the defect is confirmed in the third determination when the number of times that the defect is consecutively determined to exist is not less than the third threshold. 
       FIG. 6  is a flowchart illustrating processing according to the processing device according to the embodiment. 
     The processing device  10  acquires the first image (step S 1 ). The processing device  10  inputs the first image to the first model (step S 2 ). The processing device  10  acquires the output result from the first model (step S 3 ). The processing device  10  determines whether or not the first cumulative sum is not less than the first threshold in the first determination (step S 4 ). When the first cumulative sum is less than the first threshold, the processing device  10  determines the quality to be “invalid” (step S 5 ). 
     When the first cumulative sum is not less than the first threshold, the processing device  10  determines whether or not the second cumulative sum is not less than the second threshold in the second determination (step S 6 ). When the second cumulative sum is less than the second threshold, the processing device  10  determines the quality to be “good” (step S 7 ). When the second cumulative sum is not less than the second threshold, the processing device  10  determines whether or not the number of times that the defect is consecutively determined to exist in the third determination is not less than the third threshold (step S 8 ). When the count is less than the third threshold, the processing device  10  does not confirm the existence of the defect and determines the quality to be “good” (step S 7 ). When the count is not less than the third threshold, the processing device  10  confirms the existence of the defect and determines the quality to be “defective” (step S 9 ). The processing device  10  stores the quality and the results of the first to third determinations in the memory device  30  (step S 10 ). 
     Advantages of embodiments will now be described. 
     Conventionally, attempts have been made to extract a feature from the image that is imaged when welding and estimate the existence of the defect by using the feature. However, when such a method is used, it is necessary to prepare a database of the relationship between the defect and the extracted feature. For example, the feature that is extracted may change each time the object of the welding, the welding parameters, or the imaging conditions change; therefore, it is necessary to update the database. 
     For this first problem, according to the embodiment, the first model that detects the weld pool and the defect according to the input of the welding image is used. In other words, the defect is directly detected from the image that is imaged when welding. By using the first model, a database of the relationship between the feature and the defect is unnecessary. The convenience of the user can be improved. It is unnecessary for the user to perform the conventional complex defect detection based on the relationship between the feature and the defect. 
     On the other hand, when the defect is directly detected, there is a possibility that the reliability may degrade compared to when the defect is detected based on the feature. For example, generally, the defect is small compared to the weld pool, etc.; therefore, there are cases where the defect is not displayed in the welding image due to blown out highlights or blocked up shadows. In such a case, although there is a possibility that the defect can be detected based on the feature, it is difficult to directly detect the defect. Then, erroneous quality data is generated when the defect is confirmed not to exist in such a case. 
     For the second problem, according to the embodiment, the appropriateness of the second detection result is determined by using the first detection result. For example, when the second detection result is inappropriate, the processing device  10  does not employ the second detection result. The reliability of the determination result relating to the defect can be increased thereby. The reliability of the quality data that includes the determination result relating to the defect can be increased. 
       FIGS. 7A and 7B  and  FIGS. 8A and 8B  are schematic views illustrating images used to train the first model. 
     The training of the first model will now be described. The first model is trained using multiple sets of teaching data. The sets of teaching data each include an input image and a teaching image. 
       FIG. 7A  illustrates an input image  300 . The torch  21 , the weld pool  111 , the bead  113 , etc., are visible in the input image  300 .  FIG. 7B  illustrates a teaching image  400 . The teaching image  400  includes a line segment  411  that indicates the outer edge of the weld pool  111 . For example, the pixel values of the line segment  411  are set to (R, G, B)=(255, 0, 0). 
     In the input image of  FIG. 7A , the defect does not exist in the weld pool  111  interior. Therefore, the defect is not taught in the teaching image of  FIG. 7B . Even when the defect exists in the weld pool  111  interior, the defect may not be taught when the area of the defect is small compared to the area of the weld pool. For example, the defect is not taught when the area of the defect is less than 10% of the area of the weld pool. This is because the likelihood of a small defect being repaired is high. Also, in the example, a defect that is outside the weld pool  111  is not taught. For example, the first model is trained not to detect defects outside the weld pool  111 . 
     By using the input image  300  as input data and the teaching image  400  as teaching data, the first model is trained to output the teaching image based on the input image. 
       FIGS. 8A and 8B  illustrate other teaching images. A teaching image  400   a  of  FIG. 8A  includes a line segment  411   a  indicating the outer edge of the weld pool and a line segment  412   a  indicating the outer edge of the defect. A teaching image  400   b  of  FIG. 8B  includes a line segment  411   b  indicating the outer edge of the weld pool and a line segment  412   b  indicating the outer edge of the defect. Multiple defects exist in the example of  FIG. 8B . Therefore, multiple line segments  412   b  are shown. The first model is trained using the teaching images shown in  FIGS. 8A and 8B . 
     Modification 
     The processing device  10  may perform feedback to the welding device  20  based on the determination relating to the defect. The processing device  10  acquires the welding parameters when welding. When the existence of the defect is confirmed in the third determination, the processing device  10  selects a welding parameter to be modified. The processing device  10  modifies the selected welding parameter. The processing device  10  transmits the modified welding parameter to the controller  28 . When the processing device  10  does not confirm the existence of the defect in the third determination, the processing device  10  does not modify the welding parameters. The processing device  10  transmits the original welding parameters to the controller  28 . 
     For example, the welding is performed by a heat source moving along the first direction. The welding parameter that is modified includes at least one selected from the group consisting of the speed of the heat source in the first direction, the position of the heat source in the second direction perpendicular to the first direction, and the output of the heat source. The modification of the speed of the heat source in the first direction includes reducing the speed in the travel direction of the heat source, stopping the travel of the heat source, or moving the heat source in a direction opposite to the travel direction. 
     When the welding is arc welding, the welding parameter that is modified includes at least one selected from the group consisting of the speed of the torch  21  in the first direction, the position of the torch  21  in the second direction perpendicular to the first direction, the current supplied to the torch  21 , the voltage supplied to the torch  21 , and the supply rate of the wire  25 . 
     When the welding is laser welding, the welding parameter that is modified includes at least one selected from the group consisting of the speed of laser light in the first direction, the position of the laser light in the second direction perpendicular to the first direction, and the intensity of the laser light. 
     By modifying the welding parameter, molten metal can be easily supplied to the position of the defect; and the likelihood of the defect being repaired can be improved. 
     The processing device  10  may calculate the position at which the defect is detected and may modify the welding parameter according to the position. For example, the processing device  10  modifies the welding parameter so that the heat source approaches the defect. The heat source moves in the opposite direction or the second direction based on the modified welding parameter. The processing device  10  may increase the output of the heat source as the distance increases. 
     The likelihood of the defect being repaired can be further improved thereby. 
     After the welding parameter is modified, the processing device  10  may determine whether or not the defect is repaired. For example, the first image is acquired after performing the welding based on the modified welding parameter. The processing device  10  performs the determination processing based on the output result of the first model for the first image and determines whether or not the existence of the defect is reconfirmed at the calculated position. When the existence of the defect is not confirmed at the calculated position, the processing device  10  determines the defect to be repaired. When the defect is repaired, the processing device  10  determines the quality to be “acceptable” (a fourth quality) at the imaging position. When the defect is not repaired, the processing device  10  determines the quality to be “defective” for the position of the defect. 
     When the existence of the defect is confirmed, the processing device  10  may determine whether or not the defect is repairable. When the defect is too large, the likelihood that the repair cannot be performed by modifying the welding parameter is high. If the welding parameter is modified even though the repair cannot be performed, there is a possibility that other spots that have good quality may be unfavorably affected. Also, the time necessary for welding is uselessly increased. 
     For example, the possibility of the repair of the defect is determined based on the size of the second feature. The size of the second feature is determined based on at least one of the first length in the first direction of the second feature or the second length in the second direction of the second feature. For example, the first length or the second length is used as the size of the second feature. The larger value of the first length or the second length may be used as the size of the second feature. The product of the first length and the second length may be used as the size of the second feature. When the defect is circular or elliptical, the product of 0.5 times the first length, 0.5 times the second length, and π may be used as the size of the second feature. 
     The processing device  10  compares the size of the second feature to a preset fourth threshold. When the size of the second feature is less than the fourth threshold, the processing device  10  modifies the welding parameter. When the size of the second feature is not less than the fourth threshold, the processing device  10  determines the defect to be unrepairable. The processing device  10  continues the welding by using the same welding parameters as before confirming the defect without modifying the welding parameter. 
       FIG. 9  is a flowchart illustrating the processing according to the processing device according to the modification of the embodiment. 
     The processing device  10  performs steps S 1  to S 9  similarly to the flowchart illustrated in  FIG. 6 . The processing device  10  determines whether or not the defect is repairable when the count is not less than the third threshold in step S 8  (step S 21 ). When the defect is unrepairable, the processing device  10  determines the quality to be “defective” without modifying the welding parameter (step S 9 ). When the defect is repairable, the processing device  10  modifies the welding parameter (step S 22 ). Thereby, welding based on the modified welding parameter is performed by the welding device  20 . 
     The processing device  10  determines whether or not the confirmed defect is repaired (step S 23 ). When the defect is repaired, the processing device  10  determines the quality to be “acceptable” (step S 24 ). When the defect is not repaired, the processing device  10  determines the quality to be “defective” (step S 9 ). 
     According to the modification, the likelihood of the defect being repaired by modifying the welding parameter can be improved. The quality of the joined body that is made can be improved thereby. 
     In the example described above, the torch  21  is held by the arm  22 . The torch  21  may be gripped by a worker performing the welding. In such a case, the processing device  10  may output data of the modification of the welding parameter to the worker. 
       FIG. 10  is a schematic view illustrating the configuration of another welding system that includes the processing device according to the embodiment. 
     The welding system  1   a  illustrated in  FIG. 10  includes the processing device  10 , the torch  21 , and a control device  40 . The torch  21  includes the electrode  21   a , an imager  21   b , a position sensor  21   c , a tilt sensor  21   d , and a gas supply port  21   e.    
     The user welds by gripping the torch  21 . The imager  21   b  images the weld pool when welding. The position sensor  21   c  detects the position of the torch  21 . The tilt sensor  21   d  detects the tilt of the torch  21 . For example, the position sensor  21   c  is an optical position sensor or an ultrasonic position sensor. The tilt sensor  21   d  is a gyro sensor or an acceleration sensor. An inert gas is forced from the gas supply port  21   e  toward the tip of the electrode  21   a.    
     The control device  40  functions as the electrical power supplier  23  of the welding device  20 , the gas supplier  24 , the controller  28 , and the memory device  30  illustrated in  FIG. 1 . The control device  40  includes a power supply that supplies electrical power to the processing device  10  and the torch  21 . In the example, the control device  40  further includes a display device  41 . 
     The processing device  10  performs processing by using the first image that is imaged by the imager  21   b . The processing device  10  transmits the data obtained by the processing to the control device  40 . The control device  40  causes the display device  41  to display the data. 
       FIGS. 11A to 11C  are schematic views illustrating display examples of the processing device according to the modification of the embodiment. 
       FIG. 11A  illustrates a display example when the defect is determined not to exist in the second determination. A determination result  501  that relates to the defect, a time  502 , a position  503  of the torch  21 , a tilt  504  of the torch  21 , welding parameters  505 , data  506  that relates to the defect, and an instruction  507  to the user are displayed in a screen  500 . 
       FIGS. 11B and 11C  illustrate display examples when the existence of the defect is confirmed in the third determination. The determination result  501  shows the detection of the defect and the size of the defect in screens  500   a  and  500   b . The user is instructed to repair the defect in the instruction  507 . Also, a position  508  of the defect is displayed in the screens  500   a  and  500   b.    
     The processing device  10  may determine the size of the defect based on the size of the second feature. The processing device  10  may output the determined size of the defect as illustrated in  FIGS. 11B and 11C . For example, the processing device  10  determines the size of the defect by comparing the size of the second feature to one or more preset thresholds. 
     When the existence of the defect is confirmed, the processing device  10  transmits the instruction of the repair of the defect to the control device  40  and modifies the welding parameter. The processing device  10  may calculate the position of the tip of the torch  21  based on the detection results of the position sensor  21   c  and the tilt sensor  21   d . The processing device  10  may modify the welding parameter when the position of the tip of the torch  21  approaches the position of the defect. The control device  40  automatically modifies the welding parameter based on the determination result of the processing device  10 . 
     A button  509  is displayed in the screens  500   a  and  500   b . For example, the display device  41  is a touch panel. The user touches the button  509  when the repair of the defect is completed. The user may use a mouse or the like to operate a pointer and click the button  509 . 
     An icon  510  may be displayed to get the attention of the user according to the size of the defect. In the example, the icon  510  is displayed when the size of the defect is large. 
     An example that relates mainly to arc welding is described above. The invention according to embodiments also is similarly applicable to laser welding. The weld pool and the defect are similarly imaged in the image that is imaged in the laser welding. The first model is trained to detect the weld pool and the defect from the image of the laser welding. The processing device  10  performs the first to third determinations by using the output result of the first model. 
     An example in which the first model detects the weld pool is described above. The first model may detect another welding element. The welding element is an element that is unique to welding and exists when welding. The welding element is at least one selected from the group consisting of a weld pool, a groove, a wire, a torch, and a bead. Even when the first model detects a welding element other than the weld pool, the processing described above can be performed using the first feature that is the detection result of the welding element. 
     It is favorable for the first model to detect the weld pool. For example, there are cases where the wire and the torch are visible at positions that are separated from the defect. There are cases where the wire or the torch is clearly visible even when blown out highlights of the defect occurs in the welding image due to electric discharge. In such a case, erroneous quality is recorded when no defect is determined. Because the defect occurs in the weld pool, the likelihood of the defect being unclear is high when the weld pool is unclear. By detecting the weld pool, the quality can be determined with higher accuracy. The reliability of the quality data can be increased. 
       FIG. 12  is a schematic view illustrating a hardware configuration. 
     The processing device  10  can be realized by the hardware configuration illustrated in  FIG. 12 . A computer  90  illustrated in  FIG. 12  includes a CPU  91 , ROM  92 , RAM  93 , a memory device  94 , an input interface  95 , an output interface  96 , and a communication interface  97 . 
     The ROM  92  stores programs that control the operations of the computer  90 . A program that is necessary for causing the computer  90  to realize the processing described above is stored in the ROM  92 . The RAM  93  functions as a memory region into which the programs stored in the ROM  92  are loaded. 
     The CPU  91  includes a processing circuit. The CPU  91  uses the RAM  93  as work memory to execute the programs stored in at least one of the ROM  92  or the memory device  94 . When executing the program, the CPU  91  performs various processing by controlling configurations via a system bus  98 . 
     The memory device  94  stores data necessary for executing the programs and data obtained by executing the programs. 
     The input interface (I/F)  95  connects the computer  90  and an input device  95   a . The input I/F  95  is, for example, a serial bus interface such as USB, etc. The CPU  91  can read various data from the input device  95   a  via the input I/F  95 . 
     The output interface (I/F)  96  connects the computer  90  and an output device  96   a . The output I/F  96  is, for example, an image output interface such as Digital Visual Interface (DVI), High-Definition Multimedia Interface (HDMI (registered trademark)), etc. The CPU  91  can transmit the data to the output device  96   a  via the output I/F  96  and can cause the output device  96   a  to display the image. 
     The communication interface (I/F)  97  connects the computer  90  and a server  97   a  that is outside the computer  90 . The communication I/F  97  is, for example, a network card such as a LAN card, etc. The CPU  91  can read various data from the server  97   a  via the communication I/F  97 . A camera  99  images the weld pool and the defect when welding and stores the image in the server  97   a.    
     The memory device  94  includes not less than one selected from a hard disk drive (HDD) and a solid state drive (SSD). The input device  95   a  includes not less than one selected from a mouse, a keyboard, a microphone (audio input), and a touchpad. The output device  96   a  includes not less than one selected from a monitor and a projector. A device such as a touch panel that functions as both the input device  95   a  and the output device  96   a  may be used. 
     The computer  90  functions as the processing device  10 . The memory device  94  and the server  97   a  function as the memory device  30 . The camera  99  functions as the imager  26  included in the welding device  20 . The output device  96   a  functions as the display device  41 . 
     By using the processing device, the welding system, or the processing method described above, the convenience of the user relating to the defect detection can be improved, and the reliability of the determination result relating to the defect can be increased. Similar effects also can be obtained by using a program to cause a computer to operate as the processing device. 
     The processing of the various data described above may be recorded, as a program that can be executed by a computer, in a magnetic disk (a flexible disk, a hard disk, etc.), an optical disk (CD-ROM, CD-R, CD-RW, DVD-ROM, DVD±R, DVD±RW, etc.), semiconductor memory, or a recording medium (non-transitory computer-readable storage medium) that can be read by another nontemporary computer. 
     For example, information that is recorded in the recording medium can be read by a computer (or an embedded system). The recording format (the storage format) of the recording medium is arbitrary. For example, the computer reads the program from the recording medium and causes the CPU to execute the instructions recited in the program based on the program. In the computer, the acquisition (or the reading) of the program may be performed via a network. 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the invention. The above embodiments can be practiced in combination with each other.