Method and model for predicting shape of welded portion

Abstract

To provide a method and a model for predicting a welded portion, which can select arc-welding conditions efficiently.SOLUTION: A method for predicting a shape of a welded portion in an object to be arc-welded (a welded object) 4, includes a step in which measurement values of a surface temperature distribution of the welded object 4 are used as a heat input distribution to the welded object 4.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a method and model for predicting the shape of a weld in arc welding. [Background technology]

[0002] The quality of arc welding is evaluated by the weld shape obtained as a result of welding. The weld shape is specified by bead width, reinforcement height, penetration depth, etc. Improvements in welding power source control technology have improved welding stability. However, the welding conditions required to obtain the optimal weld shape still rely on the experience of skilled workers. Even when skilled workers select welding conditions, they may need to conduct repeated test welding when welding with unfamiliar welding materials or new materials. This requires additional work space for the test welding, as well as welding workpieces, welding materials, and time. This increases welding costs. Furthermore, when beginners with less experience select welding conditions, the number of repeated welding tests required to obtain the optimal welding conditions is greater than when skilled workers select welding conditions. This further increases welding costs.

[0003] In light of the above-described background, methods for predicting the shape of a weld have been developed to reduce the number of test welding operations, as described in Patent Document 1 or Patent Document 2. The following describes conventional techniques for predicting the shape of a weld.

[0004] The method described in Patent Document 1 precisely calculates the arc heat input using a heat source model equipped with the function of simulating the current and voltage output by the welding power source, and then calculates the shape of the molten pool based on this arc heat input using heat conduction calculations to estimate the weld bead shape.

[0005] In the method described in Patent Document 2, in order to estimate the penetration depth by hybrid welding using laser and arc, a database is prepared in advance in which the total heat input and penetration depth are associated for each welding condition, and the penetration depth is estimated by using a correlation equation between the welding conditions and the penetration depth calculated from the database.

[0006] Another possible method is to set the arc heat input, arc heat input distribution, peak arc pressure as a physical effect of the arc, and arc pressure distribution by combining virtual parameters, and then calculate the molten pool shape based on this arc heat input and arc pressure using heat conduction calculations to estimate the weld shape. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-200691 [Patent Document 2] Patent Publication No. 2021-79430 Summary of the Invention [Problem to be solved by the invention]

[0008] In the methods described in Patent Documents 1 and 2, the weld shape is predicted based on the heat input. To improve the accuracy of the weld shape prediction, it is necessary to calculate not only the arc heat input but also the distribution of the arc heat input into the molten pool, i.e., the arc heat input distribution, more precisely and set it as a boundary condition for the simulation. However, calculating the arc heat input distribution is not easy, and a great deal of time is spent on it. As a result, a great deal of time is spent on selecting arc welding conditions.

[0009] In view of the above, an object of the present disclosure is to provide a method and a model for predicting the shape of a weld that enable efficient selection of arc welding conditions. [Means for solving the problem]

[0010] In order to achieve the above object, the method and model for predicting the shape of a weld according to the present disclosure are as follows.

[0011] (1) A method for predicting the shape of a weld in an arc welding workpiece, comprising: A method for predicting a weld shape, comprising applying a measured value of a surface temperature distribution of the workpiece to be welded as a heat input distribution to the workpiece.

[0012] (2) measuring the intensity of infrared radiation emitted from the surface of the workpiece; setting the emissivity of a solid portion and the emissivity of a molten portion of the work-pieces so that the emissivity of the molten portion is lower than the emissivity of the solid portion, and calculating a measurement value of the surface temperature distribution of the work-pieces from the measurement result of the infrared intensity; The method for predicting a weld shape according to (1) above, comprising:

[0013] (3) The method for predicting a shape of a welded portion according to (2) above, wherein the emissivity of the solid portion is set to 0.4 to 1.0, and the emissivity of the molten portion is set to 0.1 to 0.5.

[0014] (4) A model for predicting the shape of a weld in an arc welding workpiece, a prediction model for a weld shape configured to input a measured value of a surface temperature distribution of the workpiece to be welded as a heat input distribution to the workpiece to be welded; [Effects of the Invention]

[0015] According to the present disclosure, a method and a model for predicting the shape of a weld are provided that enable efficient selection of arc welding conditions. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 1 is a schematic diagram illustrating an outline of arc welding. [Figure 2]FIG. 1 is a block diagram illustrating a configuration example of a prediction system according to the present disclosure. [Figure 3] 10 is a flowchart illustrating an example of a procedure for a method for predicting a shape of a weld. [Figure 4] 1 is a thermography image showing an example of the surface temperature distribution of a welded portion. [Figure 5] 1 is a graph showing an example of a surface temperature distribution of a welded portion. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, embodiments of a weld shape prediction method and prediction model according to the present disclosure will be described with reference to the drawings. Each drawing is a schematic diagram and may differ from the actual product. Furthermore, the following embodiments exemplify an apparatus or method for embodying the technical idea of ​​the present disclosure, and are not intended to limit the configuration to that described below. In other words, the technical idea of ​​the present disclosure can be modified in various ways within the technical scope described in the claims.

[0018] (Outline of the method for predicting weld shape) By executing the weld shape prediction method according to the present disclosure, the shape of a weld obtained by arc welding can be predicted. Arc welding, as shown in FIG. 1 , is a method in which an arc is generated between workpieces 4 and a welding torch 3, and the workpieces 4 are melted and joined by the heat generated by the arc. The portion of the workpieces 4 joined by melting them is also referred to as a weld 5. The weld 5 includes a portion of the workpieces 4 that has solidified after melting. The weld 5 also includes a heat-affected zone as defined in JIS Z 3001. The heat-affected zone is a portion that has not melted due to the heat of welding but has changed in structure, metallurgical properties, mechanical properties, etc. The prediction method according to the present disclosure may predict the shape of a weld 5 that includes at least a portion of the heat-affected zone, or may predict the shape of a weld 5 that does not include the heat-affected zone.

[0019] The shape of the weld 5, i.e., the weld shape, is specified by the bead width, the reinforcement height, the penetration depth, etc. The weld shape can be controlled by the arc welding conditions. Conversely, the arc welding conditions must be selected to obtain a desired weld shape. The weld shape may include the cross-sectional shape of the weld 5. The weld shape may also include other shapes of the weld 5, such as the surface shape or the toe shape of the weld 5.

[0020] In order to select arc welding conditions, the shape of the weld may be estimated by simulating arc welding. The arc welding simulation includes calculation of heat conduction in arc welding. Specifically, the arc welding conditions used in the simulation are adjusted so that the difference between the desired weld shape and the weld shape estimated by the arc welding simulation is small. The parameters of the arc welding conditions when the difference between the desired weld shape and the estimated weld shape is sufficiently small are selected as appropriate arc welding conditions.

[0021] In an arc welding simulation, the shape of a weld is estimated by setting assumed values ​​for virtual parameters representing the arc heat input or arc pressure corresponding to the arc welding conditions and running the simulation, and each parameter is adjusted by repeatedly setting and estimating the assumed value so that the estimated weld shape approaches the actual weld shape. It is desired to reduce the number of attempts to adjust the parameters of the arc welding conditions set in the simulation so that the arc welding conditions can be selected efficiently.

[0022] A weld shape prediction model may be used for the arc welding simulation. The weld shape prediction model is configured to output a prediction result of the weld shape obtained by arc welding applying the arc welding conditions when the arc welding conditions are input. The arc welding conditions for obtaining the desired weld shape are determined by repeating a simulation using the prediction model while changing the arc welding conditions so that the prediction result of the weld shape approaches the desired weld shape.

[0023] The prediction model may be, for example, a model that applies the finite element method (FEM). When the prediction model is a model that applies the finite element method, parameters determined according to the arc welding conditions are set as boundary conditions of the finite element method. The prediction model may also be a model that applies various other methods, such as the finite difference method (FDM).

[0024] The boundary conditions determined according to the arc welding conditions include the arc heat input distribution. The arc heat input distribution is the distribution of the arc heat input to each part on the surface of the work-piece 4. The arc heat input is the amount of heat entering the work-piece 4 from the arc. Calculating the arc heat input distribution from the arc welding conditions is not easy, although it can be achieved using, for example, another model.

[0025] During arc welding, a weld pool is formed on the surface of the workpiece 4. The heat that enters the weld pool from the arc is transported into the weld pool by thermal conduction or convection, melting any solid parts of the workpiece 4 that are not yet melted, expanding the melted area. Therefore, the temperature distribution on the surface of the weld pool during arc welding reflects the distribution of heat entering from the arc, i.e., the arc heat input distribution. If the temperature distribution on the surface of the weld pool is set as the boundary condition of the weld shape prediction model, the heat transport phenomenon within the weld pool can be reproduced and the weld shape can be estimated by calculating an energy conservation equation that takes into account heat transport due to thermal conduction or convection within the weld pool.

[0026] Therefore, in the method for predicting a weld shape according to the present disclosure, the surface temperature distribution of the work-piece 4 is set as a boundary condition of the prediction model for the weld shape instead of the arc heat input distribution. The surface temperature distribution of the work-piece 4 may be measured during arc welding and stored in association with the arc welding conditions.

[0027] By replacing the arc heat input distribution with the surface temperature distribution as the parameter set for the boundary condition, there is no need to calculate the arc heat input distribution. By eliminating the need to calculate the arc heat input distribution, the time required to obtain a predicted result of the weld shape using a prediction model of the weld shape is reduced. By reducing the time required to obtain a predicted result of the weld shape, the time required to select the arc welding conditions is reduced. In other words, the method for predicting the weld shape according to the present disclosure allows for efficient selection of arc welding conditions.

[0028] (Configuration example of weld shape prediction system 1) As shown in FIG. 2, a prediction system 1 according to an embodiment of the present disclosure includes a prediction device 10, a welding device 20, and a measurement device 30.

[0029] As shown in FIG. 1 , welding device 20 includes welding torch 3, and performs arc welding by generating an arc between welding torch 3 and workpiece 4. Welding device 20 is configured to be able to control at least one of the arc current and arc voltage during welding. Welding device 20 may be configured to be able to move at least one of workpiece 4 or welding torch 3. Welding device 20 may be configured so that a stage on which workpiece 4 is mounted can move relative to welding torch 3, or so that welding torch 3 can move relative to the stage on which workpiece 4 is mounted.

[0030] The measuring device 30 measures the surface temperature distribution of the work-piece 4, including the portion melted by the arc, during arc welding by the welding device 20. The measuring device 30 may be configured to include a thermograph. The measuring device 30 may also be configured to include a two-color temperature measuring device.

[0031] The prediction device 10 inputs the welding conditions and the surface temperature distribution of the workpiece 4 into the prediction model 12. The prediction device 10 acquires the prediction result of the weld shape output from the prediction model 12 and outputs it to the outside.

[0032] The prediction device 10 and the welding device 20 or the measuring device 30 are connected to each other so that they can communicate with each other. The prediction device 10, the welding device 20, or the measuring device 30 may have a communication interface based on a wired or wireless communication standard. For example, the wireless communication standard may include a cellular phone communication standard such as 3G, 4G, or 5G. Furthermore, for example, the wireless communication standard may include IEEE 802.11 or Bluetooth (registered trademark). The communication interface may support one or more of these communication standards. The communication interface is not limited to these examples and may communicate with other devices or input and output data based on various standards. The prediction device 10, the welding device 20, or the measuring device 30 may be connected via a network or may be directly connected, for example, peer-to-peer (P2P).

[0033] The prediction device 10 may be communicatively connected to an external device so as to transmit the predicted weld shape result to the external device. The prediction device 10 may include an output device that outputs the predicted weld shape result. The prediction device 10 may include a display device for displaying the predicted weld shape result as the output device. The display device may include, for example, an LCD (Liquid Crystal Display), an organic EL (Electro-Luminescence) display or an inorganic EL display, or a PDP (Plasma Display Panel). The display device is not limited to these displays and may include various other types of displays.

[0034] The prediction device 10 may be configured to include at least one processor, such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The prediction device 10 may be configured with one processor or multiple processors. The processor constituting the prediction device 10 may realize the functions of the prediction device 10 by reading and executing a program stored in a storage unit described later.

[0035] The prediction device 10 may include a storage unit. The storage unit stores various types of information or data. The storage unit may store, for example, a program executed in the prediction device 10, or data or processing results used in processing executed in the prediction device 10. The storage unit may also function as a work memory for the prediction device 10. The storage unit may be configured to include, for example, a semiconductor memory, but is not limited to this. For example, the storage unit may be configured as an internal memory of a processor used as the prediction device 10, or as a hard disk drive (HDD) accessible from the prediction device 10. The storage unit may be configured as a non-transitory readable medium. The storage unit may be configured integrally with the prediction device 10, or may be configured separately from the prediction device 10.

[0036] The prediction device 10 may generate the prediction model 12, or may acquire the prediction model 12 from an external device. The prediction system 1 may not include the welding device 20. If the prediction system 1 does not include the welding device 20, the prediction device 10 may acquire the welding conditions from an external device that performs welding. The prediction system 1 may not include the measurement device 30. If the prediction system 1 does not include the measurement device 30, the prediction device 10 may acquire the surface temperature distribution of the workpiece 4 from an external device that performs temperature measurement.

[0037] (About the weld shape prediction model 12) The governing equations that form the basis of the prediction model 12 used in the method for predicting the shape of a weld according to the present disclosure will be described below.

[0038] First, as a comparative example, the energy conservation equation in a model where the arc heat input distribution is input is expressed as the following equation (1): ρ is the density [kg / m 3 ], H represents enthalpy [J / kg], t represents time [s], u → represents velocity [m / s], λ represents thermal conductivity [W / (m K)], T represents temperature [K], and q Arc is the arc heat input flux [W / m 2 ], and n→ is the normal vector [m -1In addition, symbols with → above them to represent vectors in mathematical formulas are to be replaced in the description with, for example, u→ or n→.

[0039]

number

[0040] The second term on the left side of the above formula (1) is a convection term, and the first term on the right side is a heat conduction term. The second term on the right side of formula (1) is an arc heat input term that reflects the arc heat input distribution. In the model according to the comparative example that uses formula (1) including the arc heat input term, q Arc To determine q Arc It is necessary to match the arc heat input, which is the integral value of , with the radius of the arc heat input distribution. The radius of the arc heat input distribution may be matched as a parameter of the Gaussian distribution when the arc heat input distribution is approximated by a Gaussian distribution. As such, it takes a lot of time to determine the parameters that represent the arc heat input distribution.

[0041] On the other hand, in the prediction model 12 used in the method for predicting the shape of a weld according to the present disclosure, the surface temperature distribution of the work-piece 4 is input instead of the arc heat input distribution. By inputting the surface temperature distribution instead of the arc heat input distribution, it is not necessary to determine parameters representing the arc heat input distribution, and the time required for prediction is reduced.

[0042] In the prediction model 12 according to the present disclosure, to which the surface temperature distribution of the work-piece 4 is input, the energy conservation equation is expressed as the following equation (2): Q EX represents external heat generation other than arc heat input.

[0043]

number

[0044] The velocity u→ used in equation (2) is calculated by solving the following equations for conservation of mass (3) and momentum (4). Among the symbols used in equations (3) and (4), P represents pressure, μ represents the viscosity coefficient, and F→ EX represents external forces including gravity, surface tension, or arc pressure.

[0045]

number

[0046]

number

[0047] The range in which the surface temperature distribution of the workpiece 4 is set as the boundary condition of the prediction model 12 is preferably wider than the range affected by the actual arc heat input in order to fully incorporate the influence of the arc heat input. For example, a range with a radius of 2 mm or more is preferable as a numerical example.

[0048] The formulas used in the above explanation are merely examples and may be modified as appropriate.

[0049] (Example of procedure for predicting weld shape) An example of the procedure of the weld shape prediction method according to the present disclosure will be described below with reference to the flowchart shown in Fig. 3. The weld shape prediction method may be executed by prediction device 10. The weld shape prediction method may be realized as a weld shape prediction program executed by a processor included in prediction device 10. The weld shape prediction program may be stored in a non-transitory computer-readable medium.

[0050] The prediction device 10 acquires welding conditions from the welding device 20 when the welding device 20 performs arc welding (step S1). The welding conditions may include an arc current or an arc voltage. The welding conditions may include a speed at which the workpiece 4 moves relative to the welding torch 3 of the welding device 20 during arc welding.

[0051] The prediction device 10 acquires the surface temperature distribution of the workpiece 4 during arc welding from the measurement device 30 (step S2). The surface temperature distribution may be represented as a temperature image generated by thermography, as illustrated in FIG. 4. The temperature image in FIG. 4 includes the molten zone of the workpiece 4. The molten zone is the portion of the workpiece 4 that is melted by the heat of arc welding. The molten zone includes the arc heat input zone. The arc heat input zone is the portion where the arc directly inputs heat when the temperature image is captured. The arc heat input zone has the highest temperature in the temperature image. The arc heat input zone corresponds to the range of white pixels in the temperature image in FIG. 4, which indicates the highest temperature. The temperature of the molten zone other than the arc heat input zone is lower than the temperature of the arc heat input zone, but higher than the temperature of the surrounding area outside the target of arc welding. The molten zone corresponds to the range of gray pixels in the temperature image in FIG. 4, which indicates a higher temperature than the surrounding area, and extends in the Y-axis direction.

[0052] Regarding the positional relationship between the temperature image in Figure 4 and the welding device 20, the welding torch 3 of the welding device 20 is located near the white pixels that represent the arc heat input area in the temperature image. In the temperature image, the white pixels that represent the arc heat input area are located in the positive direction of the Y axis among the gray pixels that represent the molten area, which means that arc welding is being performed while the work-piece 4 is moving in the negative direction of the Y axis relative to the welding torch 3. Note that the temperature image in Figure 4 was captured when the thermographic camera of the measuring device 30 was located in the negative direction of the Y axis relative to the welding torch 3.

[0053] When the measuring device 30 is a thermograph, the infrared intensity measurements obtained by photographing the molten zone and the arc heat input zone with the thermograph are affected by the light emission of the arc. In order to remove the influence of the light emission of the arc from the infrared intensity measurements, the arc may be momentarily extinguished when photographing the molten zone and the arc heat input zone with the thermograph. By having the thermograph photograph the molten zone and the arc heat input zone with the arc extinguished and no light emission, the influence of the light emission of the arc is removed from the infrared intensity measurements obtained by the thermograph.

[0054] Furthermore, it is preferable that the measuring device 30 photographs the molten zone and the arc heat input zone after the arc welding has reached a quasi-steady state, that is, after the arc welding has been performed for a sufficient period of time.

[0055] When the measuring device 30 is a thermograph, the measuring device 30 generates a temperature image by calculating the surface temperature based on the emissivity from the measured infrared intensity. The emissivity of the object 4 to be welded differs between when the object 4 to be welded is molten and when the object 4 to be welded is in a solid state. That is, the emissivity of the molten portion of the object 4 to be welded differs from the emissivity of the portion other than the molten portion. Therefore, the measuring device 30 according to the present disclosure may generate a temperature image by setting the emissivity of the molten portion and the emissivity of the portion other than the molten portion to different values. The emissivity of the molten portion may be set to a value greater than the emissivity of the portion other than the molten portion. Specifically, when the object 4 to be welded is a steel plate, the emissivity of the molten portion may be set to 0.1 to 0.5. The emissivity of the portion other than the molten portion may be set to 0.4 to 1.0. The emissivity may be set to another value depending on the physical properties of the object 4 to be welded.

[0056] The measuring device 30 may estimate the surface temperature at a specific position on the work-piece 4 with high accuracy to improve the accuracy of the surface temperature distribution in the temperature image. The measuring device 30 may generate a temperature image by correcting the overall surface temperature distribution so that the surface temperature at a specific position on the temperature image matches the highly accurately estimated surface temperature. Specifically, the measuring device 30 may estimate a position on the work-piece 4 where the surface temperature is the melting point of the work-piece 4 and a position where the surface temperature is the boiling point of the work-piece 4. The measuring device 30 may identify a position where the surface temperature is the melting point of the work-piece 4 based on the distribution of infrared intensity. A closed curve connecting the positions where the surface temperature is the melting point of the work-piece 4 corresponds to the boundary line surrounding the range of the molten zone.

[0057] The position where the surface temperature of the workpiece 4 reaches its melting point may be estimated based on the distribution of infrared intensity. As data corresponding to the infrared intensity distribution, the graph in FIG. 5 shows the infrared intensity at each position along the dashed-dotted line (A) parallel to the X-axis in the temperature image in FIG. 4, i.e., the results of calculating the temperature from the one-dimensional distribution of infrared intensity. The horizontal axis of the graph in FIG. 5 represents the X-coordinate of each position along the dashed-dotted line (A). The vertical axis represents the surface temperature calculated from the infrared intensity at each position. The graph referred to as "before conversion," which includes points plotted with solid circles (●), represents the results of calculating the surface temperature using a uniform emissivity from the infrared intensity at each position. The surface temperature at each position in the graph referred to as "before conversion" is proportional to the infrared intensity at each position because it is calculated using a uniform emissivity.

[0058] A discontinuity exists in the distribution of surface temperatures, i.e., the distribution of infrared intensity, represented by a graph called "before transformation." In this disclosure, a discontinuity is defined as a maximum point that appears as the infrared intensity rises toward a peak. The discontinuity is not limited to the above definition, and may be defined as a point where the rate of increase in infrared intensity relative to a change in the X-coordinate suddenly drops as the infrared intensity rises toward a peak. The discontinuity in infrared intensity occurs due to a sudden change in emissivity caused by a change in the state of the workpiece 4. Therefore, the discontinuity in infrared intensity may be estimated to be the boundary between the portion of the workpiece 4 in a solid state and the portion of the workpiece 4 in a molten state, i.e., the molten zone.

[0059] Furthermore, the peak points of the surface temperature distribution represented by the graph called "before conversion," i.e., the infrared intensity distribution, may be estimated to be points where the work-piece 4 evaporates from the molten zone. Therefore, the peak points of the infrared intensity may be estimated to be points where the surface temperature of the work-piece 4 reaches the boiling point.

[0060] As described above, in the graph referred to as "before conversion" in FIG. 5, the positions where the surface temperature of the workpiece 4 is at its melting point and its boiling point are estimated. The graph referred to as "after conversion," which includes the points plotted with solid squares (■) in FIG. 5, is a graph converted so that the surface temperature at the discontinuous point of the graph referred to as "before conversion" becomes the melting point of the workpiece 4, and the surface temperature at the peak point of the graph referred to as "before conversion" becomes the boiling point of the workpiece 4. Temperatures higher than the melting point and lower than the boiling point are appropriately converted so that they become continuous. The measuring device 30 may generate a temperature image using the converted graph as the surface temperature distribution of the molten zone and the arc heat input zone.

[0061] In the temperature image of FIG. 4 , the dashed-dotted line (A) set to calculate the one-dimensional distribution of the surface temperature, i.e., the infrared intensity in the X-axis direction, is set to pass through the point where the infrared intensity peaks. The measuring device 30 may calculate the one-dimensional distribution of the infrared intensity in the X-axis direction for a plurality of different Y coordinates to calculate the two-dimensional distribution of the infrared intensity. The measuring device 30 may associate the maximum value in the two-dimensional distribution of the infrared intensity with the boiling point of the workpiece 4. The measuring device 30 may identify a boundary line where the infrared intensity becomes discontinuous in the two-dimensional distribution of the infrared intensity, and associate the infrared intensity on the boundary line with the melting point of the workpiece 4. If there is variation in the infrared intensity at each point on the boundary line, the measuring device 30 may associate the minimum, maximum, or average value of the infrared intensity at each point on the boundary line with the melting point of the workpiece 4.

[0062] In the example of FIG. 5, the one-dimensional distribution of infrared intensity is calculated along the X-axis direction, but the direction in which the one-dimensional distribution of infrared intensity is calculated is not limited to the X-axis direction, and may be the Y-axis direction or another direction.

[0063] As described above, the measurement device 30 can generate the surface temperature distribution of the molten zone. The prediction device 10 may acquire the infrared intensity distribution from the measurement device 30 and generate the surface temperature distribution.

[0064] Returning to Fig. 3, prediction device 10 inputs the surface temperature distribution of the molten part generated from the surface temperature distribution of the work-piece 4 and the welding conditions for arc welding into prediction model 12 (step S3). Prediction device 10 outputs the shape of weld 5, i.e., the predicted result of the weld shape, from prediction model 12 (step S4). After executing the procedure of step S4, prediction device 10 ends execution of the flowchart in Fig. 3.

[0065] The predicted weld shape is compared with the weld shape expected when arc welding is performed on the workpiece 4. If the predicted weld shape is sufficiently close to the expected weld shape, the welding conditions used when the predicted weld shape was obtained may be used. If there is a large difference between the expected weld shape and the predicted weld shape, the welding conditions are changed and the weld shape is predicted again. The change in welding conditions and the prediction of the weld shape are repeated until the predicted weld shape is sufficiently close to the expected weld shape.

[0066] (summary) As described above, the prediction device 10 according to the present disclosure predicts the shape of a weld using the surface temperature distribution of the molten zone. As a comparative example, if the arc heat input distribution to the workpiece 4 is expressed as a Gaussian distribution and set as the boundary condition of the prediction model 12 to predict the shape of a weld, it would take a lot of time to calculate an arc heat input distribution that matches the actual situation, and it would also take a lot of time to predict the shape of the weld. On the other hand, the prediction device 10 according to the present disclosure can predict the shape of a weld without calculating the arc heat input distribution by using the surface temperature distribution instead of the arc heat input distribution. As a result, the shape of a weld is predicted efficiently.

[0067] (Example) Specific examples are described below. The weld shape prediction model 12 was configured so that the size of the workpiece 4 to be welded was 25 mm thick, 150 mm wide, and 200 mm long as geometry. The prediction model 12 was also configured to be able to calculate the arc welding phenomenon in which a wire with a diameter of 1.2 mm is dropped. The temperature-dependent physical properties of the workpiece 4 to be welded, equivalent to those of SM490 steel, were used. The welding method was bead-on-plate.

[0068] The surface temperature distribution of the workpiece 4 to be set as a boundary condition in the prediction model 12 was measured by photographing the workpiece 4 with a thermographic camera while arc welding was being performed on the workpiece 4. The surface temperature distribution of the workpiece 4 in this example was expressed as a temperature image as shown in FIG. 4. When generating the surface image, the infrared intensity distribution measured by the thermograph was converted into a surface temperature distribution, taking into account the difference in emissivity between the metal in a molten state and the metal in a solid state.

[0069] In this example, the shape of the weld was predicted by setting an assumed value for the arc pressure distribution, which is one of the boundary conditions of prediction model 12. The arc pressure distribution was then adjusted by repeatedly assuming the arc pressure distribution and predicting the shape of the weld based on the assumed boundary conditions. On the other hand, as a comparative example, the shape of the weld was predicted by assuming not only the arc pressure distribution but also the arc heat input distribution as boundary conditions of prediction model 12. The shape of the weld was then adjusted by repeatedly assuming both the arc pressure distribution and the arc heat input distribution and predicting the shape of the weld based on the assumed boundary conditions.

[0070] In both the present example and the comparative example, the convergence condition for the fitting was set to be that the difference in width and depth between the predicted weld shape and the actual weld shape be within 10%. The number of times the fitting process converged was compared between the present example and the comparative example. To reduce the influence of personal factors when fitting the arc pressure distribution, the peak value was appropriately set based on a Gaussian distribution approximating the arc pressure distribution or a distribution similar to literature values. Then, if the calculated value was smaller than the experimental value, the peak value of the arc pressure set as the boundary condition was increased by 10%. Conversely, if the calculated value was larger than the experimental value, the peak value of the arc pressure set as the boundary condition was decreased by 10%. After reaching an intermediate state, the peak value of the arc pressure was adjusted in 1% increments.

[0071] Table 1 shows the number of attempts required until convergence when fitting was performed in each of the present embodiment and the comparative example using the method described above. In Table 1, Nos. 1 to 5 show the results of the present embodiment when measured values ​​were applied to the surface temperature distribution of the molten part. Nos. 6 to 10 show the results of the comparative example when a hypothetical Gaussian distribution was applied to the surface temperature distribution of the molten part. The current value, voltage value, and welding speed were set as welding conditions. The welding conditions were set to the same values ​​for Nos. 1 to 5 of the present embodiment and Nos. 6 to 10 of the comparative example. As described above, measured values ​​were applied to the surface temperature distribution of the molten part in the present embodiment, and a Gaussian distribution was applied in the comparative example. In addition, if the number of attempts required until convergence was 9 or less, it was evaluated as good, and if it was 10 or more, it was evaluated as bad.

[0072] [Table 1]

[0073] According to Table 1, under all of the conditions of Nos. 1 to 5 of this example, the number of matching trials was 9 or less, and was judged as good. On the other hand, under all of the conditions of Nos. 6 to 10 of the comparative examples, the number of matching trials was 10 or more, and was judged as bad. According to this result, by using the measured value as the surface temperature distribution of the molten part in this example, the number of matching trials can be reduced. As a result, the time required to select welding conditions is shortened. In other words, the welding conditions can be selected efficiently.

[0074] Although the embodiments of the present disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art could make various modifications or alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included within the scope of the present disclosure. For example, the functions included in each component or step can be rearranged so as not to cause logical inconsistencies, and multiple components or steps can be combined or divided into one. The embodiments of the present disclosure can also be realized as a program executed by a processor included in an apparatus or a storage medium on which a program is recorded. It should be understood that these are also included within the scope of the present disclosure. [Explanation of symbols]

[0075] 1. Prediction System 3 welding torches 4 Welding target 5 Welded parts 10 Prediction device (12: Prediction model) 20 Welding equipment 30 Measuring Equipment

Claims

1. A method for predicting a shape of a weld in a workpiece for arc welding, comprising: A method for predicting a weld shape, comprising applying a measured value of a surface temperature distribution of the workpiece to be welded as a heat input distribution to the workpiece.

2. measuring the intensity of infrared radiation emitted from the surface of the workpiece; setting the emissivity of a solid portion and the emissivity of a molten portion of the work-pieces to be welded so that the emissivity of the molten portion is lower than the emissivity of the solid portion, and calculating a measurement value of the surface temperature distribution of the work-pieces from the measurement result of the infrared intensity; The method of claim 1 , comprising:

3. 3. The method for predicting a shape of a welded portion according to claim 2, wherein the emissivity of the solid portion is set to 0.4 to 1.0, and the emissivity of the molten portion is set to 0.1 to 0.

5.

4. A model for predicting a shape of a weld in a workpiece for arc welding, a prediction model for a weld shape configured to input a measured value of a surface temperature distribution of the workpiece to be welded as a heat input distribution to the workpiece to be welded;

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