Quality control method for laminated object, quality control device for laminated object, program, welding control device, and welding device

Abstract

To provide a quality management method of a laminated molding which can foresee quality of a laminated molding in a stage of molding planning before production, a quality management device of a laminated molding, a program, a welding control device and a welding device.SOLUTION: A quality management method of a laminated molding comprises the steps of: acquiring a limit value of a stress intensity factor of a laminated molding; obtaining an allowable defect dimension of a defect included in the laminated molding by using a condition value including a variation range of a load stress that is assumed on design and the limit value of the laminated molding; setting at least one of a prediction value of the defect dimension of the defect that may be generated in the laminated molding when manufacturing the laminated molding on the basis of a molding plan and an actual value of the defect dimension of the defect that is generated in a test body manufactured on the basis of the molding plan; and determining quality of a bead or the laminated molding by comparing the defect dimension with the allowable defect dimension.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a quality control method for a layered object, a quality control device for a layered object, a program, a welding control device, and a welding device. [Background technology]

[0002] Metal additive manufacturing is known as one of the metal processing technologies known as additive manufacturing. This technology enables the production of three-dimensional additive objects by stacking sliced ​​two-dimensional layers one by one based on three-dimensional data such as 3D-CAD. In addition, during the production of the additive object, the modeled shape is measured by a sensor, and the modeling conditions for the next layer to be formed are adjusted according to the measurement results, thereby improving the quality of the additive object.

[0003] In quality control of such additively manufactured objects, a technology is disclosed in Patent Document 1 in which data measured by a sensor during the manufacturing process is matched with load information and force flow information, and the sensor measurement values ​​are classified and evaluated in terms of their impact on quality. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-194031 A Summary of the Invention [Problem to be solved by the invention]

[0005] In the above-mentioned metal additive manufacturing, defects may occur due to insufficient fusion of metal during the repeated deposition of metal. These defects include harmful defects that have a fatal impact on quality and harmless defects that do not particularly affect the required specifications of the additive manufactured product. The criteria for determining whether a defect is harmful or harmless are usually set according to the quality assurance or management criteria. It is preferable that these criteria be specified before manufacturing, rather than clarifying them by carrying out various tests after the actual manufacturing, and this makes it easier to make a good manufacturing plan.

[0006] However, it is difficult to predict the quality of an additive manufacturing object at the modeling planning stage before manufacturing. In Patent Document 1, the position of insufficient melting is identified based on the sensor value that detects the shape and size of the actual melt spot, and it is determined that a defect has occurred if the identified position exists in a load support area or a critical load area, and quality evaluation is not performed at the modeling planning stage.

[0007] Therefore, an object of the present invention is to provide a quality control method for an additively molded object, a quality control device for an additively molded object, a program, a welding control device, and a welding device, which are capable of predicting the quality of the additively molded object at the modeling planning stage before production. [Means for solving the problem]

[0008] The present invention comprises the following configurations. (1) A quality control method for an additive manufacturing object formed by repeatedly stacking layers of beads made of melted and solidified filler metal based on a predetermined manufacturing plan, comprising the steps of: acquiring a limit value of a stress intensity factor of the additive manufacturing object; determining an allowable defect size of a defect contained in the layered object by using a variation range of load stress assumed in the design of the layered object and condition values ​​including the limit value; setting, as a defect size, either a predicted value of a defect size of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect size of a defect that has occurred in a test specimen produced based on the modeling plan; a step of comparing the defect size with the allowable defect size to determine whether the quality of the bead or the layered object is good or bad; A method for quality control of an additively manufactured object, comprising: (2) A program for causing a computer to execute a procedure of a quality control method for an additive manufacturing object formed by repeatedly stacking layers of beads made by melting and solidifying a filler metal based on a predetermined modeling plan, the program comprising: On the computer, obtaining a limit value of a stress intensity factor of the additive manufacturing object; determining an allowable defect size of a defect contained in the layered object by using a variation range of load stress assumed in the design of the layered object and condition values ​​including the limit value; setting, as a defect size, either a predicted value of a defect size of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect size of a defect that has occurred in a test specimen produced based on the modeling plan; a step of comparing the defect size with the allowable defect size to determine whether the quality of the bead or the layered object is good or bad; A program for executing the above. (3) A quality control device for an additive manufacturing object that is formed by repeatedly stacking layers of beads made by melting and solidifying a filler metal based on a predetermined modeling plan, comprising: a limit value acquisition unit that acquires a limit value of a stress intensity factor of the layered object; an allowable defect size setting unit that determines an allowable defect size of a defect included in the layered object by using a range of variation of a load stress assumed in the design of the layered object and a condition value including the limit value; a defect dimension setting unit that sets, as a defect dimension, either a predicted value of a defect dimension of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect dimension of a defect that has occurred in a test specimen produced based on the modeling plan; a determination unit that determines whether quality of the bead or the layered object is good or bad by comparing the defect size with the allowable defect size; A quality control device for additive manufacturing comprising: (4) A quality control device for a layered object according to (3), a control unit that controls continuation or stop of the arc welding in response to the result of the quality control device; A welding control device comprising: (5) The welding control device according to (4), A welding robot that performs arc welding; A welding device comprising: Effect of the Invention

[0009] According to the present invention, the quality of a layered object can be predicted at the modeling planning stage before manufacturing. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is an overall configuration diagram of a welding device. [Diagram 2] FIG. 2 is a schematic functional block diagram of the control information generating device. [Diagram 3] FIG. 3 is a flowchart showing the procedure of the control information generating method. [Figure 4] FIG. 4 is a graph showing a schematic diagram of the relationship between the crack growth rate and the stress intensity factor range. [Diagram 5] FIG. 5 is an explanatory diagram showing a cross-sectional view of the state of lamination of beads in a layered object. [Figure 6] FIG. 6 is a cross-sectional view that illustrates one narrow portion when beads are arranged in a staggered pattern. [Figure 7A] FIG. 7A is a schematic cross-sectional view showing how a defective narrow portion is remelted. [Figure 7B] FIG. 7B is a schematic cross-sectional view showing the state of the narrow portion of FIG. 7A after it has been repaired. [Figure 8A] FIG. 8A is a schematic cross-sectional view showing how a defect in a narrow portion is mechanically removed. [Figure 8B] FIG. 8B is a schematic cross-sectional view showing the state of the narrow portion of FIG. 8A after it has been repaired. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, a configuration example of the present invention will be described in detail with reference to the drawings. Here, an example of application to a welding device that manufactures a three-dimensional additive model by stacking beads made of melted and solidified filler metal will be described. However, the present invention is not limited to this, and can also be applied to general welding such as fillet welding and butt welding.

[0012] <Welding system> FIG. 1 is an overall configuration diagram of a welding system. Welding system 100 includes a welding device 110 and a welding control device 120. Welding control device 120 includes a control unit 11 and a quality control device .

[0013] (Welding equipment) First, the configuration of the welding device 110 will be described. The welding device 110 includes a welding robot 13, a robot driving unit 15, a filler metal supply unit 17, a welding power supply unit 19, and a shape detection unit 21. The welding robot 13, the robot driving unit 15, the filler metal supply unit 17, the welding power supply unit 19, and the shape detection unit 21 are each connected to a control unit 11. A machining unit 30 to which a tool 29 such as a drill or a milling tool, the details of which will be described later, can be attached is also connected to the control unit 11, and desired machining such as cutting can be performed in response to a command from the control unit 11. The machining unit 30 and the welding device 110 also function as a defect repair unit, which will be described later.

[0014] Welding robot 13 is an articulated robot, and a welding torch 27 is attached to the tip shaft of the robot. Robot driver 15 outputs a command to drive welding robot 13, and arbitrarily sets the position and posture of welding torch 27 three-dimensionally within the range of the degree of freedom of the robot arm. In addition, a filler material (welding wire) M that is continuously supplied is supported at the tip of welding torch 27.

[0015] The welding torch 27 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 shielded metal arc welding or carbon dioxide gas arc welding, or a non-consumable electrode type such as TIG welding or plasma arc welding, and is appropriately selected depending on the additive manufacturing object (welded structure) 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 27 holds the filler material M and generates an arc from the tip of the filler material M in a shielding gas atmosphere.

[0016] The filler metal supply unit 17 includes a reel 17a around which the filler metal M is wound. The filler metal M is sent from the filler metal supply unit 17 to a payout mechanism (not shown) attached to a robot arm or the like, and is fed to the welding torch 27 while being fed in the forward and reverse directions by the payout mechanism as necessary.

[0017] 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.

[0018] Welding power supply unit 19 supplies welding current and welding voltage to welding torch 27 for generating an arc from the tip of the torch.

[0019] Shape detection unit 21 is provided on or near the tip shaft of welding robot 13, and the measurement area is the vicinity of the tip of welding torch 27. Shape detection unit 21 may be another detection means provided at a position separate from welding torch 27.

[0020] Shape detection unit 21 of this configuration is moved together with welding torch 27 by the driving of welding robot 13, and measures the shape of bead B and the portion that will be the base when forming bead B. For example, a laser sensor that obtains reflected light of an irradiated laser beam as height information can be used as this shape detection unit 21. Also, other detection means such as a camera for three-dimensional shape measurement may be used as shape detection unit 21.

[0021] In the welding device 110 configured as above, a modeling program corresponding to the layered object to be manufactured is sent from the control unit 11 to the robot driving unit 15. The modeling program is composed of a large number of 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 object, the material, and the amount of heat input.

[0022] The robot driving unit 15 executes the received modeling program to drive the welding robot 13, the filler material supply unit 17, the welding power supply unit 19, etc., and forms the bead B along the trajectory set in the modeling program. That is, the robot driving unit 15 drives the welding robot 13 to move the welding torch 27 along the trajectory (bead formation trajectory) of the welding torch 27 set in the modeling program. At the same time, the filler material supply unit 17 and the welding power supply unit 19 are driven according to the set welding conditions, and the filler material M at the tip of the welding torch 27 is melted and solidified by the arc. As a result, the bead B is formed on the base plate P along the trajectory of the welding torch 27. Then, a bead layer consisting of a plurality of beads B is formed, and the next bead layer is layered on this bead layer, etc., to form an additively shaped object W of a desired three-dimensional shape.

[0023] Although not shown, welding control device 120 is configured with a computer device including a processor such as a CPU, memories such as a ROM and a RAM, and storage units such as a HD (hard disk drive) and an SSD (solid state drive). Each of the components of welding control device 120 described above operates according to commands from the CPU to perform each function. Welding control device 120 may be configured to be disposed separately from welding device 110 and connected to welding device 110 from a remote location via a communication means such as a network.

[0024] The control unit 11 constituting the welding control device 120 integrally controls the robot driving unit 15, the filler metal supply unit 17, the welding power source unit 19, and the shape detection unit 21 shown in Fig. 1. The control unit 11 executes a driving program prepared in advance or a driving program created under desired conditions to drive each unit such as the welding robot 13. In this manner, the welding torch 27 is moved according to the driving program, and multiple layers of beads B are stacked on the base plate P based on the created modeling plan, thereby forming a layered object W having a multi-layer structure.

[0025] <Quality control device> FIG. 2 is a schematic functional block diagram of the quality control device 130. As shown in FIG. The quality control device 130 predicts the presence or absence of defects and the defect size that will occur in the layered object when the layered object is manufactured by stacking a plurality of beads formed by melting and solidifying the filler material M by the welding device 110 in layers, and judges the quality of the layered object. In addition, the quality control device 130 corrects a modeling program (i.e., a modeling plan) for driving each part of the welding device 110 based on a predetermined modeling plan according to the size of the defect as necessary. For example, when a bead is formed according to the predetermined modeling plan, the bead height becomes uneven, and an unwelded portion (defect) occurs inside the bead. This can be prevented by changing and adjusting the modeling plan. The quality control device 130 generates control information for updating such a modeling plan, and outputs the control information to the control unit 11. The quality control device 130 includes a limit value acquisition unit 31, an allowable defect size setting unit 33, a defect size setting unit 35, and a judgment unit 37, each of which will be described in detail later. The quality control device 130 may also include a repair instruction unit 39, the details of which will be described later. The quality control device 130, which is configured as a computer device, is configured to perform the above-mentioned functions by executing a program prepared in advance.

[0026] <Quality control procedure> Next, the operation of each part of the quality control device 130 having the above configuration will be described. The quality control device 130 controls the quality of a layered object (or bead) formed by repeatedly stacking beads B, which are formed by melting and solidifying a filler material M, in layers, based on a predetermined modeling plan.

[0027] The procedure of this quality control method roughly includes the following steps (1) to (4). (1) A step in which the limit value acquisition unit 31 acquires a lower threshold stress intensity factor range of the stress intensity factor required for the additive manufacturing object or a limit value of the stress intensity factor such as a fracture toughness value. (2) A process in which the allowable defect dimension setting unit 33 determines the allowable defect dimension of a defect contained in the additive manufacturing object using the range of load stress fluctuation assumed in the design of the additive manufacturing object and condition values ​​including the above-mentioned limit values. (3) A process in which the defect dimension setting unit 35 sets the defect dimension to either a predicted value of the defect dimension of a defect that may occur in an additively molded object when manufacturing the additively molded object based on the modeling plan, or an actual measured value of the defect dimension of a defect that occurred in a test specimen produced based on the modeling plan. (4) A step in which the judgment unit 37 compares the defect size with the allowable defect size and judges the quality of the bead or the layered object as good or bad.

[0028] Each of the above steps will be described with reference to FIG. FIG. 3 is a flow chart showing the steps of the quality control method. First, a threshold stress intensity factor range, which is the limit value of the stress intensity factor of the additive manufacturing object to be manufactured, is obtained (step S1: hereinafter referred to as S1). The material of the additive manufacturing object is selected at the product design stage, and the stress intensity factor K of the material may be obtained, for example, experimentally by a fatigue test of a test specimen, by a stress analysis of crack growth, or by machine learning.

[0029] FIG. 4 is a graph showing a schematic relationship between the crack growth rate (da / dn) and the stress intensity factor range ΔK. As shown in Figure 4, generally, when the stress intensity factor range ΔK is reduced, the crack growth rate (da / dn) suddenly decreases. th At this point, no crack growth is observed. th is called the threshold stress intensity factor range. Threshold stress intensity factor range ΔK th In the smaller area, the crack will not grow even if a load is applied.

[0030] Threshold stress intensity factor range ΔK th For , a value obtained by subjecting a test specimen formed from an additive manufacturing product to a fatigue crack growth test may be used, or a value predicted from past manufacturing results, etc. This test specimen may be prepared by producing a block test specimen under various conditions such as the additive manufacturing product to be produced, the material of the filler metal M, the welding conditions, etc., and cutting out a portion containing a defect from the produced block test specimen.

[0031] Next, the allowable defect size of the defect contained in the additive manufacturing object is calculated (S2). The allowable defect size is determined based on the stress fluctuation range Δσ of the load stress assumed in the design of the additive manufacturing object and the lower limit stress intensity factor range ΔK th The condition is calculated using a value including:

[0032] The relationship between the stress intensity factor K and the defect (crack) dimension a can be expressed by equation (1). K = F σ√(π a) (1) Here, F is a dimensionless constant that depends on the crack size ratio and the loading type.

[0033] According to equation (1), the lower limit stress intensity factor range ΔK th It can be said that the stress fluctuation range Δσ of the load stress corresponds to equation (2). Therefore, the allowable defect (crack) size a0 is calculated from equation (2). ΔK th =F Δσ√(π a0) (2) When there are multiple types of stress acting on an additive manufacturing object, multiple fracture modes, and multiple possible defect shapes, the allowable defect size a0 may be calculated for each type. For example, the maximum value of the stress intensity factor K max is the fracture toughness value K of the material IC The allowable defect size a0 may be calculated from the fracture strength when the fracture strength reaches K max takes the form of equation (3). σ max is the maximum value of the applied stress. K max = F σ max √(π a) (3) The defects referred to here may include voids formed inside the layered product, as well as cracks and dents that occur on the surface of the layered product. Furthermore, oxides or inclusions derived from the components of the building material may be contained in the voids or cracks. IC is a value obtained in accordance with a standardized test evaluation method, for example, a value according to ASTM standard E399 is used for comparison.

[0034] Next, a predicted value of the defect size that will occur during modeling or an actual measured value of the defect size is obtained (S3). First, as a method for predicting the size of defects that will occur during manufacturing, for example, a method using machine learning can be used. Specifically, the relationship between the welding conditions (welding speed, filler metal feed speed, bead spacing, target position, etc.) used in additive manufacturing and the size of defects that will occur in samples manufactured under these welding conditions is learned, and an estimated model that represents the relationship between the two is generated.

[0035] By using this trained estimation model to obtain defect dimensions corresponding to the welding conditions actually used, it is possible to predict defect dimensions that are likely to occur before the actual manufacturing process. This estimation model can be trained using actual measurement values ​​obtained by observing defects on test specimens actually fabricated for each welding condition, or analytical values ​​obtained by calculation using an analytical method such as FEM.

[0036] Furthermore, the estimation model may be a model that learns the relationship between the bead shape of the bead constituting the additively molded object observed during molding and the defect size that occurs, instead of or in addition to the welding conditions. In this case, it is possible to predict the defect size taking into account the influence of narrow parts formed by the bead. The bead shape referred to here may be a shape predicted from the trajectory of the welding torch 27 defined in the molding plan, or may be an actually measured bead shape obtained by scanning with the laser sensor of the shape detection unit 21.

[0037] A known method can be used to obtain an estimation model by the above-mentioned learning. Examples include decision trees, linear regression, random forests, support vector machines, Gaussian process regression, and neural networks. The defect size may be predicted collectively for all passes forming the additive manufacturing object, but more precise consideration is possible if it is predicted for each pass.

[0038] Next, as a method for obtaining the measured value of the defect dimension, known detection means can be used, for example, non-destructive inspection methods such as ultrasonic flaw detection, CT, and inspection. Thereby, the defect dimension of the defect formed in the test body (for example, inside the laminated beads or between the beads) manufactured based on the shaping plan can be detected.

[0039] As described above, the predicted value or the measured value of the defect is obtained, and this is set as the defect dimension a exp If the predicted value is set as the defect dimension a exp the update of the shaping plan described later can be easily performed by calculation. Also, if the measured value is set as the defect dimension a exp accurate defect evaluation according to the state of the actually formed beads can be performed, and the update of the shaping plan can be performed more appropriately. When both the predicted value and the measured value are obtained, either one may be adopted according to the purpose of quality control, or each value may be averaged.

[0040] Next, the set defect dimension a exp is compared with the allowable defect dimension a0 to determine whether the quality of the bead or the laminated shaped object is good or bad (S4). In this good or bad determination, when the defect dimension a exp is smaller than the allowable defect dimension a0 (a exp < a0), the quality of the formed bead or laminated shaped object is determined to be good, and when the defect dimension a exp is less than the allowable defect dimension a0, it is determined to be bad.

[0041] And when the defect dimension a exp is larger than the allowable defect dimension a0, the shaping conditions are corrected or the shaping plan is corrected in order to increase the allowable defect dimension a0 or decrease the defect dimension a exp Furthermore, insertion of a repair plan, which will be described in detail later, may be performed (S5). For example, in order to increase the allowable defect size a0, the bead line direction (bead formation trajectory) in the modeling plan may be changed so that the intersection angle between the direction in which the bead is formed and the direction of the principal stress assumed to be applied to the additive model is reduced. In addition, if the material can be changed, the modeling plan may be revised by changing to a material with higher toughness. Here, the principal stress direction of the load acting on the additive model can be calculated using a method such as stress analysis based on the load conditions assumed for the main loads at the design stage of the additive model.

[0042] In addition, the defect size a exp In order to reduce this, the welding current and welding voltage may be adjusted so that the heat input to the bead is increased, and various conditions such as the welding speed may be changed. Furthermore, the trajectory of the welding torch in the modeling plan may be changed.

[0043] FIG. 5 is an explanatory diagram showing a cross-sectional view of the state of lamination of beads in a layered object. When the beads B are stacked, if an upper bead B is formed directly above a lower bead B, defects such as voids or foreign matter are likely to occur due to narrow portions (valleys) 41 formed between adjacent beads B. In addition, since the narrow portions 41 are arranged at the same positions in the arrangement direction of the beads B in the bead layer, there is a risk that the defects will be connected in the stacking direction. Therefore, it is preferable to arrange the beads B in a staggered manner in the stacking direction to suppress the height of defects extending in the stacking direction.

[0044] FIG. 6 is a cross-sectional view that shows a schematic diagram of one narrow portion 41 when beads are arranged in a staggered pattern. When the beads B are arranged in a staggered pattern in the stacking direction, a new bead B is formed directly above the narrow portion 41 between the beads B of the lower layer. Therefore, remelting occurs in the narrow portion 41 due to the formation of the new bead B, the cavity disappears, and the foreign matter is removed. This makes it difficult for defects to occur in the additive manufacturing object.

[0045] In step S5, if the welding conditions are modified, the process returns to step S3, in which the defect size corresponding to the modified welding conditions is predicted or measured. Also, in step S5, if the manufacturing plan is modified, the lower threshold stress intensity factor range ΔK corresponding to the modified manufacturing plan is calculated. th Return to step S1 to obtain the

[0046] The above steps S1 to S4 and S5 are repeated for all passes (S6), and the pass / fail judgment of defect dimensions for all passes is completed. This pass / fail judgment is based on the defect size a exp In addition to the relationship between the defect size a0 and the allowable defect size a0, information on the difference between the two may be used. For example, exp is sufficiently larger than the allowable defect size a0, for example, exp is equal to or greater than a specific threshold value, such as 5% or more, 10% or more, 30% or more, of the allowable defect size a0, the shaping plan may be modified to allow a larger correction range, and when it exceeds a range below the specific threshold value, only the welding conditions may be modified. In this case, appropriate adjustment according to the degree of correction becomes possible, and the defect size can be efficiently reduced.

[0047] According to this quality control method, the defect size is set from either the predicted value of the defect obtained from the modeling plan or the actual measurement value of the defect of a test specimen produced based on the modeling plan, and the allowable defect size is obtained from condition values ​​including the fluctuation range of the load stress of the additively molded object and the limit value of the stress intensity factor such as the lower threshold stress intensity factor range or the fracture toughness value. The obtained allowable defect size is compared with the above-mentioned defect size to judge whether the quality of the additively molded object when molded according to the modeling plan is good or bad. Therefore, the quality of the additively molded object can be predicted from the modeling plan stage before modeling. Furthermore, since the allowable defect size is obtained from the characteristics of the limit value of the stress intensity factor, the allowable defect size that serves as the judgment criterion can be appropriately set according to the material properties, improving the accuracy of the pass / fail judgment.

[0048] The allowable defect size may be calculated for each type of defect shape, which can accommodate various types of defects such as blowholes, elongated defects along the bead line direction, etc.

[0049] Furthermore, by measuring the shape of the bead that has actually been formed and setting the defect size based on the measurement results, more precise prediction is possible, improving the accuracy of pass / fail judgment.

[0050] Furthermore, the threshold stress intensity factor range or fracture toughness value may be determined according to the surface roughness of the formed bead. In this case, the allowable defect size can be specified taking into consideration the case where the surface that cannot be cut remains after molding. Note that the surface roughness referred to here refers to the roughness specified in JIS B 0601:1994 or JIS B 0031:1994.

[0051] In addition, by using an estimation model obtained through machine learning for the defect dimension prediction process, it is possible to extract predicted values ​​for defect dimensions corresponding to various molding cases as long as the learning data is prepared.

[0052] When the defect size is larger than the allowable defect size, the bead formation of the layered object may be temporarily stopped. This prevents unnecessary modeling and prevents the repair of defects from becoming complicated. The step of correcting the manufacturing conditions of the layered object may be performed before the manufacturing of the layered object, or during the inter-pass time between beads to be formed during the manufacturing of the layered object. In this case, the correction of the manufacturing conditions can be completed before the start of modeling or before the lamination of the next bead, and the bead can be formed under the corrected manufacturing conditions. This makes it possible to reduce the predicted defect size or suppress the occurrence of defects themselves.

[0053] If the criteria for determining whether a defect is harmful or harmless are specified before modeling, it becomes easier to determine the effect of the defect during modeling in cooperation with the shape detection information from the shape detection unit 21. In addition, if the criteria are specified, it is expected that the design freedom of the modeling plan will be improved without excessive consideration to quality.

[0054] In addition, the above defect size a expThe comparison of the allowable defect size a0 with the allowable defect size a0 may be performed each time bead formation along the path set in the molding plan is completed, or may be performed during the path to perform continuous processing in real time. In this case, it is possible to contribute to shortening the takt time, and the pass / fail judgment result of the generated defect can be obtained early, and the action for the defect judged to be bad can be started early.

[0055] <Repair of laminated objects> The procedure of the quality control method described above serves to predict, before modeling, whether or not the additively molded object produced based on the modeling plan will have a dimensional defect that would impair its strength, and to notify workers accordingly or to temporarily halt modeling. However, if a problematic defect is predicted, it is more preferable to repair the defect.

[0056] To repair the defect, the repair instruction unit 39 shown in Fig. 2 outputs a repair instruction signal to the control unit 11 shown in Fig. 1 to execute the repair. The control unit 11 drives the welding robot 13, the machining unit 30, etc. in response to the input repair instruction signal to carry out repair work on the layered object W in the middle of being modeled. Examples of this repair work include a method of remelting the defect by heating, and a method of mechanically removing the defect and then filling it with molten metal.

[0057] Fig. 7A is a schematic cross-sectional view showing how defective narrow portion 41 is remelted. Fig. 7B is a schematic cross-sectional view showing the state after narrow portion 41 in Fig. 7A has been repaired. As shown in FIG. 7A, if a defect occurs in a narrow portion 41 between adjacent beads B and the defect is determined to be larger than the allowable defect size by the above-mentioned quality control method, repair work is carried out to repair the defect.

[0058] To remove the defect, first, the position of the defect size larger than the allowable defect size is identified as the defect position, and the bead at the defect position is remelted by a heat source that provides thermal energy. Specifically, the repair instruction unit 39 outputs a repair instruction signal to the control unit 11. The control unit 11 drives each unit of the welding device 110 in response to the input repair instruction signal. In response to a command from the control unit 11, the welding device 110 functions as a defect repair unit, the welding torch 27 is placed at the defect position, and an arc is generated from the tip of the torch. In the case of TIG welding, an arc is generated from the TIG electrode. As a result, the narrow portion 41 is remelted by the heat input from the arc, and the gap that existed in the narrow portion 41 is filled with molten metal. In addition, the existing foreign matter is expelled from the narrow portion 41 by the molten metal. As a result, as shown in FIG. 7B, the defects are removed between the adjacent beads B, and a smooth bead connection surface is formed on the surface. In this way, any defects that occur can be removed simply by melting the bead again. Furthermore, after removing the defects, lamination can be continued without any complicated repair work.

[0059] If necessary, for example, when the narrow portion 41 is deep, the molten metal may be supplied by feeding a filler metal M. When feeding the filler metal M, it is preferable to first apply an arc to the narrow portion 41 using only the TIG electrode and then feed the filler metal M, since this allows the arc to be prevented from being blocked by the filler metal M and a good molten pool to be obtained. Gouging may also be performed by feeding compressed air along with the arc, which allows the defective portion to be removed by gouging it out. Gouging using a plasma arc may also be performed to remove the defective portion.

[0060] It is also possible to remove the defects mechanically. Fig. 8A is a schematic cross-sectional view showing a state in which a defect in narrow portion 41 is mechanically removed. Fig. 8B is a schematic cross-sectional view showing a state after the narrow portion in Fig. 8A has been repaired. First, the defect position is specified as in the case shown in FIG. 7A, and the repair instruction unit 39 outputs a repair instruction signal to the control unit 11. The control unit 11 drives the machining unit 30 and the welding device 110 in response to the input repair instruction signal. In response to the command from the control unit 11, the machining unit 30 and the welding device 110 function as defect repair units and remove the defect at the defect position. Specifically, the machining unit 30 is driven by the repair command from the control unit 11 to place the tool 29 in the narrow portion 41. Then, the narrow portion 41 is cut by the tool 29 to form a cavity 43. Next, the machining unit 30 retreats from the narrow portion 41, and the welding torch 27 moves to the position of the cavity 43. Then, an arc is generated from the tip of the torch, and the molten metal 45 in which the filler material M is melted is filled into the cavity 43. As a result, a bead layer from which the defect has been removed is formed as in FIG. 7B.

[0061] In this way, after the defects are mechanically removed, the cavity 43 is filled with the molten metal 45, so that a layered product without defects or foreign matter can be easily manufactured. In this case, even if the shape is complicated or the structure is thick, the repair work is not complicated. Furthermore, since the repair area is limited to a small area, the influence on the surroundings of the defect position is small. In addition, when a drill is used as the tool 29, the bead can be cut deeply, so that even if the defect position becomes deep as the layering progresses for a while after the defect occurs, the defect can be easily repaired. Note that, depending on the defect, only a part of the bead including the part where the defect size becomes large may be repaired by removing it by cutting.

[0062] The above-mentioned arc welding for remelting and arc welding for filling cavity 43 may be performed using welding robot 13 or may be performed manually. As for the welding conditions, a pulse mode that can obtain deep penetration in a short time is preferable, but other modes may be used depending on the thickness.

[0063] The repair instruction unit 39 may also output a repair instruction signal to perform repair before manufacturing the additively molded object or during the bead pass time during manufacturing the additively molded object. This allows the defect to be repaired while bead formation is stopped. This makes it possible to complete the correction of the manufacturing conditions before the start of modeling or before the next bead is layered, making it possible to reduce the predicted defect size or suppress the occurrence of the defect itself.

[0064] 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.

[0065] As described above, the present specification discloses the following: (1) A quality control method for an additive manufacturing object formed by repeatedly stacking layers of beads made of melted and solidified filler metal based on a predetermined manufacturing plan, comprising the steps of: acquiring a limit value of a stress intensity factor of the additive manufacturing object; determining an allowable defect size of a defect contained in the layered object by using a variation range of load stress assumed in the design of the layered object and condition values ​​including the limit value; setting, as a defect size, either a predicted value of a defect size of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect size of a defect that has occurred in a test specimen produced based on the modeling plan; a step of comparing the defect size with the allowable defect size to determine whether the quality of the bead or the layered object is good or bad; A method for quality control of an additively manufactured object, comprising: According to this method for quality control of an additively molded object, a defect size is set from either a predicted value of the defect obtained from a modeling plan or an actual measurement value of the defect of a test specimen produced based on the modeling plan, and an allowable defect size is obtained from condition values ​​including the range of variation of the load stress of the additively molded object and the limit value of the stress intensity factor. The obtained allowable defect size is compared with the above-mentioned defect size to judge whether the quality of the additively molded object when molded according to the modeling plan is good or bad. This makes it possible to predict the quality of the additively molded object from the modeling planning stage before modeling. Furthermore, since the allowable defect size is obtained from the characteristics of the limit value of the stress intensity factor, the allowable defect size that serves as the judgment criterion can be appropriately set according to the material properties, improving the accuracy of the pass / fail judgment.

[0066] (2) The quality control method for an additive manufacturing object described in (1), wherein the limit value is either a lower threshold stress intensity factor or a fracture toughness value of the additive manufacturing object. According to this quality control method for an additive manufacturing product, the allowable defect size can be determined from the lower threshold stress intensity factor or the fracture toughness value.

[0067] (3) The quality control method for an additive manufacturing object according to (1) or (2), further comprising setting the allowable defect size for each shape type of the defect. According to this method for quality control of an additive manufacturing object, quality control can be performed to deal with various types of defects, such as blowhole defects and elongated defects along the bead line direction.

[0068] (4) The quality control method of an additive manufacturing object according to any one of (1) to (3), wherein the predicted value is set based on the manufacturing plan and a result of measuring a shape of the bead. According to this quality control method for additive manufacturing, the defect size is predicted taking into account the bead shape, enabling more accurate prediction.

[0069] (5) The quality control method of an additive manufacturing object according to any one of (1) to (4), wherein the limit value is determined according to a surface roughness of the bead. According to this quality control method for additive manufacturing, it is possible to specify the allowable defect size taking into account cases where the surface remains after manufacturing that cannot be completely cut.

[0070] (6) A quality control method for an additive manufacturing object described in any one of (1) to (5), in which the predicted value is predicted using an estimation model obtained by machine learning of the relationship between at least one of the manufacturing conditions of the additive manufacturing object and the shape of the bead, and the defect dimensions. According to this method for quality control of additive manufacturing, it is possible to extract predicted values ​​of defect dimensions corresponding to various manufacturing cases as long as the learning data is prepared.

[0071] (7) A quality control method for an additive manufacturing object described in any one of (1) to (6), further comprising a step of comparing the defect dimension with the allowable defect dimension, and modifying the manufacturing conditions of the additive manufacturing object if the defect dimension is larger than the allowable defect dimension. According to this quality control method for a layered object, the modification of the manufacturing conditions can be completed before the start of modeling or before the lamination of the next bead, and the bead can be formed under the modified manufacturing conditions. This makes it possible to reduce the predicted defect size or suppress the occurrence of defects themselves.

[0072] (8) identifying a position of the defect having a size larger than the allowable defect size as a defect position; remelting the bead at the defect location with a heat source that provides thermal energy; The quality control method of the layered object according to (7), further comprising: According to this method for quality control of an additive manufacturing object, defects can be eliminated by remelting.

[0073] (9) identifying a position of the defect having a size larger than the allowable defect size as a defect position; The quality control method for an additive manufacturing object according to (7), further comprising the step of removing a portion of the bead including the defect position by cutting. According to this quality control method for layered objects, defects are mechanically removed by cutting, making it possible to easily produce layered objects that are free of foreign matter and defects.

[0074] (10) A program for causing a computer to execute a procedure of a quality control method for an additive manufacturing object formed by repeatedly stacking layers of beads made by melting and solidifying a filler metal based on a predetermined modeling plan, comprising: On the computer, obtaining a limit value of a stress intensity factor of the additive manufacturing object; determining an allowable defect size of a defect contained in the layered object by using a variation range of load stress assumed in the design of the layered object and condition values ​​including the limit value; setting, as a defect size, either a predicted value of a defect size of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect size of a defect that has occurred in a test specimen produced based on the modeling plan; and a procedure for determining whether the quality of the bead or the layered object is good or bad by comparing the defect size with the allowable defect size. According to this program, the defect size is set from either the predicted value of the defect obtained from the modeling plan or the actual measurement value of the defect of a test specimen produced based on the modeling plan, and the allowable defect size is obtained from condition values ​​including the fluctuation range of the load stress of the additively molded object and the limit value of the stress intensity factor. The obtained allowable defect size is compared with the above-mentioned defect size to judge whether the quality of the additively molded object when molded according to the modeling plan is good or bad. This makes it possible to predict the quality of the additively molded object from the modeling planning stage before modeling. In addition, since the allowable defect size is obtained from the characteristics of the limit value of the stress intensity factor, the allowable defect size that serves as the judgment criterion can be appropriately set according to the material properties, improving the accuracy of the pass / fail judgment.

[0075] (11) A quality control device for an additive manufacturing object that is formed by repeatedly stacking layers of beads made by melting and solidifying a filler metal based on a predetermined manufacturing plan, comprising: a limit value acquisition unit that acquires a limit value of a stress intensity factor of the layered object; an allowable defect size setting unit that determines an allowable defect size of a defect included in the layered object by using a range of variation of a load stress assumed in the design of the layered object and a condition value including the limit value; a defect dimension setting unit that sets, as a defect dimension, either a predicted value of a defect dimension of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect dimension of a defect that has occurred in a test specimen produced based on the modeling plan; a determination unit that determines whether the quality of the bead or the layered object is good or bad by comparing the defect size with the allowable defect size. According to this quality control device for additive manufacturing objects, the defect size is set from either the predicted value of the defect obtained from the modeling plan or the actual measurement value of the defect of a test specimen produced based on the modeling plan, and the allowable defect size is obtained from condition values ​​including the fluctuation range of the load stress of the additive manufacturing object and the limit value of the stress intensity factor. The obtained allowable defect size is compared with the above-mentioned defect size to judge whether the quality of the additive manufacturing object when modeled according to the modeling plan is good or bad. Therefore, it is possible to predict the quality of the additive manufacturing object from the modeling planning stage before modeling. Furthermore, since the allowable defect size is obtained from the characteristics of the limit value of the stress intensity factor, the allowable defect size that serves as the judgment criterion can be appropriately set according to the material properties, improving the accuracy of the pass / fail judgment.

[0076] (12) A quality control device for a layered object according to (11), a control unit that controls continuation or stop of arc welding depending on the result of the pass / fail judgment output from the quality control device. According to this welding control device, if the occurrence of a defect is detected, arc welding is temporarily stopped, thereby making it possible to prevent unnecessary molding.

[0077] (13) The welding control device according to (12), A welding robot that performs arc welding. This welding device makes it possible to achieve arc welding that is less susceptible to defects.

[0078] (14) a defect repair unit that repairs the defect occurring in the layered object; The welding device described in (13) further includes a repair instruction unit that compares the defect size with the allowable defect size, and if the defect size is larger than the allowable defect size, outputs a repair instruction signal to the defect repair unit to repair the portion where the defect size becomes larger. According to this welding device, when a defect larger than the allowable defect size occurs, a repair instruction signal for repairing the defect is output to the defect repair unit, so that the defect repair unit can carry out the repair at an appropriate time.

[0079] (15) The welding device described in (14), wherein the repair instruction unit causes the defect repair unit to repair the defect before manufacturing the additive object or during an inter-pass time of the bead during manufacturing the additive object. This welding device allows the defect to be repaired while bead formation is stopped, so that the manufacturing conditions can be corrected before the start of manufacturing or before the next bead is layered, reducing the predicted defect size or preventing the defect from occurring at all.

[0080] (16) The welding device according to (14) or (15), wherein the defect repair portion remelts the area where the defect size becomes larger using a heat source. This welding device can efficiently eliminate defects by remelting.

[0081] (17) The welding device according to (14) or (15), wherein the defect repair portion removes a portion of the bead including a portion where the defect dimension becomes large by cutting. With this welding device, defects are mechanically removed by cutting, and then the cavity is filled with molten metal, making it easy to create an additive manufacturing object that is free of foreign objects and defects. [Explanation of symbols]

[0082] 11 Control section 13 Welding robot 15 Robot drive unit 17 Filler metal supply section 17a Reel 19 Welding power supply unit 21 Shape detection section 27 Welding Torch 29 Tools 30 Machining Department 31 Limit value acquisition section 33 Allowable defect size setting section 35 Defect size setting section 37 Judgment section 39 Repair Instructions Section 41 Narrow area 43 Cavity 45 Molten Metal 100 Welding System 110 Welding equipment 120 Welding control device 130 Quality control equipment a Defect size a0 allowable defect size a exp Defect size B Bead K stress concentration factor M filler metal P Base plate W Laminated Model ΔK Stress concentration factor range ΔK th Lower limit stress concentration factor range (limit value)

Claims

1. A quality control method for an additive manufacturing object formed by repeatedly stacking layers of beads made by melting and solidifying a filler material based on a predetermined manufacturing plan, comprising the steps of: acquiring a limit value of a stress intensity factor of the additive manufacturing object; determining an allowable defect size of a defect contained in the layered object by using a variation range of load stress assumed in the design of the layered object and condition values ​​including the limit value; setting, as a defect size, either a predicted value of a defect size of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect size of a defect that has occurred in a test specimen produced based on the modeling plan; a step of comparing the defect size with the allowable defect size to determine whether the quality of the bead or the layered object is good or bad; A method for quality control of an additively manufactured object, comprising:

2. The limit value is either a lower threshold stress intensity factor or a fracture toughness value of the additive manufacturing object; The method for quality control of a layered object according to claim 1.

3. setting the allowable defect size for each shape type of the defect; The method for quality control of a layered object according to claim 1.

4. setting the allowable defect size for each shape type of the defect; The method for quality control of a layered object according to claim 2.

5. The predicted value is set based on the molding plan and a result of measuring the shape of the bead. The method for quality control of a layered object according to claim 1.

6. The predicted value is set based on the molding plan and a result of measuring the shape of the bead. The method for quality control of a layered object according to claim 4.

7. The limit value is determined according to the surface roughness of the bead. The method for quality control of a layered object according to any one of claims 1 to 6.

8. The predicted value is predicted using an estimation model obtained by machine learning of a relationship between at least one of a manufacturing condition of the additive manufacturing object and a shape of the bead, and the defect size. The method for quality control of a layered object according to any one of claims 1 to 6.

9. The predicted value is predicted using an estimation model obtained by machine learning of a relationship between at least one of a manufacturing condition of the additive manufacturing object and a shape of the bead, and the defect size. The method for quality control of a layered object according to claim 7.

10. The method further includes a step of comparing the defect size with the allowable defect size, and modifying a manufacturing condition of the layered object when the defect size is larger than the allowable defect size. The method for quality control of a layered object according to any one of claims 1 to 6.

11. The method further includes a step of comparing the defect size with the allowable defect size, and modifying a manufacturing condition of the layered object when the defect size is larger than the allowable defect size. The method for quality control of a layered object according to claim 9.

12. identifying a position of the defect having a size larger than the allowable defect size as a defect position; remelting the bead at the defect location with a heat source that provides thermal energy; The method for quality control of an additive manufactured object according to claim 10, further comprising:

13. identifying a position of the defect having a size larger than the allowable defect size as a defect position; remelting the bead at the defect location with a heat source that provides thermal energy; The method for quality control of an additive manufactured object according to claim 11, further comprising:

14. identifying a position of the defect having a size larger than the allowable defect size as a defect position; The quality control method for an additive manufactured object according to claim 10, further comprising the step of removing a part of the bead including the defect position by a cutting process.

15. identifying a position of the defect having a size larger than the allowable defect size as a defect position; The quality control method for an additive manufactured object according to claim 11 , further comprising the step of removing a part of the bead including the defect position by a cutting process.

16. identifying a position of the defect having a size larger than the allowable defect size as a defect position; The quality control method for an additive manufactured object according to claim 12 , further comprising the step of removing a part of the bead including the defect position by a cutting process.

17. identifying a position of the defect having a size larger than the allowable defect size as a defect position; The quality control method for an additive manufactured object according to claim 13 , further comprising the step of removing a part of the bead including the defect position by a cutting process.

18. A program for causing a computer to execute a procedure of a quality control method for an additive manufacturing object formed by repeatedly stacking layers of beads made by melting and solidifying a filler material based on a predetermined modeling plan, the program comprising: On the computer, obtaining a limit value of a stress intensity factor of the additive manufacturing object; determining an allowable defect size of a defect contained in the layered object by using a variation range of load stress assumed in the design of the layered object and condition values ​​including the limit value; setting, as a defect size, either a predicted value of a defect size of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect size of a defect that has occurred in a test specimen produced based on the modeling plan; a step of comparing the defect size with the allowable defect size to determine whether the quality of the bead or the layered object is good or bad; A program for executing the above.

19. A quality control device for a layered object formed by repeatedly stacking beads made by melting and solidifying a filler metal in layers based on a predetermined modeling plan, comprising: a limit value acquisition unit that acquires a limit value of a stress intensity factor of the layered object; an allowable defect size setting unit that determines an allowable defect size of a defect included in the layered object by using a variation range of a load stress assumed in the design of the layered object and a condition value including the limit value; a defect dimension setting unit that sets, as a defect dimension, either a predicted value of a defect dimension of a defect that may occur in the layered object when manufacturing the layered object based on the modeling plan or an actual measured value of a defect dimension of a defect that has occurred in a test specimen produced based on the modeling plan; a determination unit that determines whether quality of the bead or the layered object is good or bad by comparing the defect size with the allowable defect size; A quality control device for additive manufacturing comprising:

20. The quality control device for a layered object according to claim 19; a control unit that controls continuation or stop of the arc welding in response to the result of the quality control device; A welding control device comprising:

21. The welding control device according to claim 20 ; A welding robot that performs arc welding; A welding device comprising:

22. a defect repair unit that repairs the defect occurring in the layered object; and a repair instruction unit that compares the defect size with the allowable defect size, and when the defect size is larger than the allowable defect size, outputs a repair instruction signal to the defect repair unit to repair a portion where the defect size becomes larger; 22. The welding apparatus of claim 21 further comprising:

23. the repair instruction unit causes the defect repair unit to repair the defect during an inter-bead pass time before manufacturing the additively manufactured object or during manufacturing the additively manufactured object.

23. The welding apparatus of claim 22.

24. The defect repair portion remelts the portion where the defect size increases using a heat source.

24. Welding apparatus according to claim 22 or 23.

25. The defect repair portion is formed by removing a part of the bead including a portion where the defect size becomes large by cutting.

24. Welding apparatus according to claim 22 or 23.

Images