Method for estimating fatigue life of laminated object, method for inspecting laminated object, method for manufacturing laminated object, device for inspecting laminated object, and program

Abstract

To provide a fatigue life estimation method of a laminated molding, an inspection method of the laminated molding, a manufacturing method of the laminated molding, an inspection device of the laminated molding, and a program capable of precisely estimating a fatigue life of the laminated molding.SOLUTION: A fatigue life estimation method of a laminated molding molded by laminating a molding material on the basis of design data of the laminated molding executes: an acquisition process of acquiring a three-dimensional profile of a measurement object part in a molded laminated molding; an extraction process of overlapping the three-dimensional profile and the design data and extracting defects from a difference between the design data and the three-dimensional profile; a calculation process of calculating a crack development speed on the basis of the extracted defects; and an estimation process of estimating a fatigue life when a permitted crack length is reached on the basis of the crack development speed.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present disclosure relates to a method for estimating a fatigue life of a layered object, a method for inspecting a layered object, a method for manufacturing a layered object, an inspection device for a layered object, and a program. [Background technology]

[0002] For example, Patent Document 1 discloses a method for determining three-dimensional additive manufacturing conditions, which predicts internal defect dimensions and surface roughness, which are factors that reduce the fatigue life of a three-dimensional object (additive manufacturing object), and sets the manufacturing conditions to be applied based on the predicted fatigue life reduction factors. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2018 / 181306 Summary of the Invention [Problem to be solved by the invention]

[0004] In the field of additive manufacturing, there is a demand for technology that can estimate the fatigue life of additive manufactured objects with high accuracy.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a method for estimating the fatigue life of an additively manufactured object, a method for inspecting an additively manufactured object, a method for manufacturing an additively manufactured object, an inspection device for an additively manufactured object, and a program that can estimate the fatigue life of an additively manufactured object with high accuracy. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, the method for estimating the fatigue life of an additively manufactured object according to the present disclosure is a method for estimating the fatigue life of an additively manufactured object manufactured by stacking a manufacturing material based on design data of the additively manufactured object, and includes the following steps: an acquisition step for acquiring a three-dimensional profile of a measurement target portion in the manufactured additively manufactured object; an extraction step for overlaying the three-dimensional profile with the design data and extracting defects from the difference between the design data and the three-dimensional profile; a calculation step for calculating a crack growth rate based on the extracted defects; and an estimation step for estimating the fatigue life at which the allowable crack length is reached based on the crack growth rate.

[0007] The method for inspecting an additively manufactured object according to the present disclosure includes a fatigue life estimation step of executing the above-mentioned method for estimating the fatigue life of an additively manufactured object, and a first judgment step of making a pass / fail judgment on the defect based on the crack length at the design life calculated from the crack growth rate and the allowable crack length.

[0008] The method for manufacturing an additively manufactured object according to the present disclosure further includes, before executing the fatigue life estimation process, a manufacturing process for manufacturing the additively manufactured object in accordance with a plurality of manufacturing parameters, an inspection process for carrying out the above-mentioned method for inspecting the additively manufactured object, and a recording process for recording in a database the fatigue life estimated in the fatigue life estimation process, the pass / fail judgment result in the first judgment process, and the plurality of manufacturing parameters in a mutually associated state.

[0009] The inspection device for a layered object according to the present disclosure is an inspection device for a layered object manufactured by stacking a building material based on design data for the layered object, and includes an acquisition unit that acquires a three-dimensional profile of a measurement target portion in the manufactured layered object, an extraction unit that overlays the three-dimensional profile with the design data and extracts defects from the difference between the design data and the three-dimensional profile, a calculation unit that calculates a crack growth rate based on the extracted defects, and an estimation unit that estimates a fatigue life at which an allowable crack length is reached based on the crack growth rate.

[0010] The program according to the present disclosure causes a computer of an inspection device for an additively manufactured object formed by stacking a building material to execute the following steps: acquiring a three-dimensional profile of a measurement target portion in the manufactured additively manufactured object based on design data of the additively manufactured object; overlaying the three-dimensional profile with the design data and extracting defects from the difference between the design data and the three-dimensional profile; calculating a crack growth rate based on the extracted defects; and estimating a fatigue life at which an allowable crack length is reached based on the crack growth rate. [Effects of the Invention]

[0011] According to the present disclosure, it is possible to provide a method for estimating the fatigue life of an additively manufactured object, a method for inspecting an additively manufactured object, a method for manufacturing an additively manufactured object, an inspection device for an additively manufactured object, and a program that can estimate the fatigue life of an additively manufactured object with high accuracy. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram illustrating an overall configuration of an additive manufacturing system according to an embodiment of the present disclosure. FIG. [Figure 2] FIG. 1 is a functional block diagram of an inspection device according to a first embodiment of the present disclosure. [Figure 3] FIG. 2 is a diagram for explaining measurement of a layered object by a detection device according to the first embodiment of the present disclosure. [Figure 4] FIG. 10 is a diagram for explaining a defect in the layered object according to the first embodiment of the present disclosure. [Figure 5] FIG. 10 is a diagram for explaining the correspondence between the length of a crack and the number of repetitions according to the first embodiment of the present disclosure. [Figure 6] 3 is a diagram for explaining data recorded in a database by a recording unit of the inspection device according to the first embodiment of the present disclosure. FIG. [Figure 7] 1 is a flowchart illustrating a method for manufacturing a layered object according to a first embodiment of the present disclosure. [Figure 8] 4 is a flowchart showing the operation of the inspection device according to the first embodiment of the present disclosure. [Figure 9] FIG. 11 is a diagram showing a state in which the pass / fail judgment results of defects recorded in a database according to the second embodiment of the present disclosure are distributed on a graph with each of a plurality of modeling parameters as an axis. [Figure 10] FIG. 10 is a functional block diagram of an inspection device according to a third embodiment of the present disclosure. [Figure 11] FIG. 10 is a diagram showing (a) a frequency distribution of fatigue life recorded in a database according to a third embodiment of the present disclosure, and (b) a probability distribution obtained from this frequency distribution. [Figure 12] 10 is a flowchart showing a method for manufacturing a layered object according to a third embodiment of the present disclosure. [Figure 13] 10 is a flowchart showing the operation of an inspection device according to a third embodiment of the present disclosure. [Figure 14] FIG. 1 is a hardware configuration diagram illustrating a configuration of a computer according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, an additive manufacturing system and a method for manufacturing an additive manufactured object according to an embodiment of the present disclosure will be described with reference to the drawings.

[0014] First Embodiment [Additive Manufacturing System] The additive manufacturing system in this embodiment is a system that manufactures a three-dimensional additively manufactured object based on design data of the three-dimensional additively manufactured object and inspects the manufactured three-dimensional additively manufactured object. The design data in this embodiment is, for example, three-dimensional CAD data. For ease of explanation, the three-dimensional additively manufactured object manufactured by the additive manufacturing system will hereinafter be referred to simply as the "additively manufactured object."

[0015] As shown in FIG. 1, the layered manufacturing system 1 in this embodiment includes a manufacturing apparatus 10, a manufacturing support apparatus 20, a post-processing apparatus 50, an inspection apparatus 30, and a detection apparatus 40 (see FIG. 3).

[0016] (modeling equipment) The modeling apparatus 10 is a 3D printer that models the layered object 100 by layering modeling materials based on design data D of the layered object 100. The modeling apparatus 10 models the layered object 100 by a metal additive manufacturing method.

[0017] The manufacturing apparatus 10 in this embodiment has a manufacturing chamber 11, a base plate 12 provided in the manufacturing chamber 11, a recoater 13 that forms a powder layer made of metal powder as a manufacturing material on the base plate 12, a beam irradiation unit 14 that irradiates a beam onto the powder layer so as to selectively solidify the powder layer according to the shape of the layered manufactured object 100, and a controller 15 that controls the recoater 13 and the beam irradiation unit 14.

[0018] (modeling support device) The modeling support device 20 has a modeling condition input device 21 that inputs the design data D of the layered object 100 input from outside and the modeling conditions of the layered object 100 to the controller 15 of the modeling device 10, and a database 22 connected to the modeling condition input device 21. The controller 15 receives inputs from the modeling condition input device 21 the two-dimensional slice data used for modeling the layered object 100 and the modeling conditions.

[0019] The two-dimensional slice data is generated by slicing the three-dimensional shape data included in the design data D of the layered object 100 into a plurality of slices in one direction. The modeling condition input device 21 may acquire the design data D from the database 22.

[0020] The modeling conditions include a plurality of modeling parameters stored in the database 22. Examples of the modeling parameters include the output intensity of the modeling apparatus 10, the modeling speed, and the heat treatment temperature. The modeling condition input device 21 in this embodiment acquires the modeling conditions from the database 22. The modeling condition input device 21 transmits a signal indicating the plurality of modeling parameters input to the controller 15 to the inspection device 30.

[0021] The flow of manufacturing the layered object 100 by the manufacturing apparatus 10 will be described below. Under control of the controller 15 based on the two-dimensional slice data and the modeling conditions, the recoater 13 spreads metal powder (modeling material) on the base plate 12 to form a powder layer, and the metal powder corresponding to the part to be modeled is melted and solidified by a beam. After the metal powder has melted and solidified, the base plate 12 is lowered and new metal powder is spread, and the metal powder corresponding to the part to be modeled is again melted and solidified by a laser. By repeating this process, the modeling material is sequentially layered along the layering direction, which is opposite to the direction of gravity, to form the layered object 100.

[0022] 1, the controller 15 and the modeling condition input device 21 are shown as independent devices, but they may be implemented as a single device by incorporating the modeling condition input device 21 into the controller 15. Furthermore, the two-dimensional slice data may be generated by, for example, the controller 15 directly inputting the design data D, or the modeling condition input device 21 having a function for this purpose.

[0023] (Post-treatment device) The post-processing device 50 is a device that performs post-processing of the layered object 100 in order to stabilize the strength of the formed layered object 100. The post-processing device 50 in this embodiment performs post-processing on the layered object 100 by, for example, heat treatment or hot isostatic pressing (HIP).

[0024] (Inspection equipment) The inspection device 30 is a device that estimates the fatigue life of the layered object 100 that has been modeled and post-processed, and judges the pass / fail of defects formed in the layered object 100. The inspection device 30 is connected to the modeling condition input device 21 of the modeling support device 20. As shown in FIG. 2 , the inspection device 30 in this embodiment includes a fatigue life estimation unit 31, a first determination unit 32, a recording unit 33, and a memory unit 34.

[0025] (Fatigue life estimation part) The fatigue life estimation unit 31 estimates the fatigue life of the layered object 100. The fatigue life estimation unit 31 includes an acquisition unit 31a, an extraction unit 31b, a calculation unit 31c, and an estimation unit 31d.

[0026] The acquisition unit 31a acquires a three-dimensional profile of a measurement target portion 100x in the layered object 100. The "measurement target portion 100x" here refers to, for example, a plurality of regions on the surface 100a of the layered object 100 that are to be measured.

[0027] The measurement target portion 100x in this embodiment is a weak point, which is a portion where defect X is likely to occur during use of the additively-made object 100 or a portion where stable additive manufacturing is relatively difficult. Examples of the measurement target portion 100x include a stress concentration portion and an overhanging portion, which are identified by analyzing the stress acting in the additively-made object 100 in advance using finite element analysis (FEM analysis) based on the design data D. The "overhanging portion" here refers to the surface of the surface 100a of the additively-made object 100 that faces the side where gravity acts in the vertical direction during modeling. Hereinafter, data indicating the stress acting in the additively-made object 100 will be referred to as the "stress analysis result." The stress analysis result in this embodiment is, for example, included in advance in the design data D.

[0028] 3, the contour of the measurement target portion 100x on the surface 100a of the layered object 100 is detected by a detection device 40 connected to the inspection device 30. The detection device 40 transmits a signal indicating data on the surface 100a of the measurement target portion 100x detected by measurement to the inspection device 30. The acquisition unit 31a receives the signal from the detection device 40 and acquires a three-dimensional profile indicating the contour of the measurement target portion 100x. The acquisition unit 31a also acquires design data D from the modeling condition input device 21.

[0029] The detection device 40 in this embodiment is, for example, a laser scanner (3D scanner) that can detect the contour of the surface 100a of the layered object 100 as three-dimensional point cloud data using a laser. In this case, the detection device 40 detects, for example, the contour of the surface 100a of the layered object 100 when the layered object 100 is rotated. The acquisition unit 31a sends a signal indicating the acquired three-dimensional profile and a signal indicating the design data D to the extraction unit 31b.

[0030] The extraction unit 31b extracts the defect X in the measurement target portion 100x of the layered object 100 based on the three-dimensional profile and the design data D received from the acquisition unit 31a. Specifically, the extraction unit 31b superimposes the three-dimensional profile on the design data D, and extracts the defect X from the difference between the design data D and the three-dimensional profile.

[0031] The term "superimposition" here means applying (fitting) model data representing the surface 100a of the design data D to the three-dimensional profile. For example, three or more arbitrary points are selected in the three-dimensional profile, and the design data D is fitted to the three-dimensional profile by matching the coordinates of three points in the model data with those of the selected points. At this time, the design data D is superimposed on the three-dimensional profile so that the difference between the entire design data D and the entire three-dimensional profile is minimized.

[0032] 4, the defect X extracted by the extraction unit 31b means a region defined by the design data D and the three-dimensional profile. The extraction unit 31b slices the extracted defect X into a plurality of two-dimensional slice data aligned in one direction, and calculates the cross-sectional area A, the surface length L, and the depth T from each of the two-dimensional slice data.

[0033] The "one direction" here means the direction in which the stress acting on the measurement target portion 100x (three-dimensional profile) that defines the above-mentioned region acts most strongly among the stresses in the stress analysis results. Therefore, the extraction unit 31b slices the defect X into multiple two-dimensional slice data along a cross section perpendicular to the one direction.

[0034] Furthermore, the "cross-sectional area A" here refers to the area of ​​the region surrounded by the design data D and the three-dimensional profile in the two-dimensional slice data. Furthermore, the "surface length L" refers to the distance between two intersections of the design data D and the three-dimensional profile in the two-dimensional slice data. Furthermore, the "depth T" refers to the distance between the design data D and a point in the three-dimensional profile located at the farthest position in the direction perpendicular to the surface 100a indicated by the design data D.

[0035] The extraction unit 31b calculates the defect dimension a0 based on the surface length L and depth T calculated from each two-dimensional slice data. Specifically, the extraction unit 31b calculates the defect dimension a0 using the following formula (i) or (ii). a0=10^(1 / 2)·T …(i) a0=A^(1 / 2) …(ii)

[0036] The above formula (i) is used when the value obtained by dividing the surface length L by the depth T is greater than 10 (L / T>10), and the above formula (ii) is used when the value obtained by dividing the surface length L by the depth T is 10 or less (L / T≦10). The extraction unit 31b sends a signal indicating the defect size a0 calculated for each two-dimensional slice data to the calculation unit 31c.

[0037] The calculation unit 31c calculates the crack growth rate v based on the defect size a0 for each of the two-dimensional slice data received from the extraction unit 31b. The calculation unit 31c calculates the crack growth rate v by using, for example, the following formula (iii) based on linear fracture mechanics and formula (iv) showing Paris's law. ΔK=F·σ·(πa)^(1 / 2) …(iii) v=da / dN=C·ΔK^m …(iv)

[0038] Here, ΔK in the above formula (iii) is the stress intensity factor range, and its unit is MPa·m^(1 / 2). Furthermore, F is a constant determined by the shape, loading conditions, etc. Furthermore, σ is the difference between the maximum stress acting on the additive manufacturing product 100 and the minimum stress acting on the additive manufacturing product 100, and its unit is MPa. Furthermore, a in the above formulas (iii) and (iv) is the length of the crack, and its unit is m.

[0039] In addition, N in the above formula (iv) is the number of repeated load applications. Furthermore, C is a constant determined by the material, loading conditions, etc. Furthermore, m is a constant determined by the material, loading conditions, etc. Therefore, the crack growth rate v can be obtained by differentiating the crack length by the number of repeated load applications. The unit of the crack growth rate v is m / cycle.

[0040] More specifically, the calculations of the above formulas (iii) and (iv) are performed using the following formulas (v) and (vi). Δa / ΔN=C·(ΔK N-1 )^m …(v) ΔK N-1 =F σ (πa N-1 )^(1 / 2) …(vi)

[0041] Here, if N-1 is 0, the above defect size a0 is substituted for a. The crack growth rate v is calculated by repeatedly calculating the above equations (v) and (vi). The calculation unit 31c sends, for example, to the estimation unit 31d and the first determination unit 32, a signal indicating the largest crack growth rate v among the crack growth rates v calculated for each two-dimensional slice data.

[0042] The estimation unit 31d estimates the fatigue life when the crack length reaches the allowable crack length based on the crack growth rate v received from the calculation unit 31c. Here, the "allowable crack length" refers to the allowable crack length among the lengths of cracks formed in the additive manufacturing object 100. Furthermore, the "fatigue life" refers to the number of load application repetitions when the crack length reaches the allowable crack length. In other words, the estimation unit 31d uses the crack growth rate v to calculate the number of load application repetitions when the crack length reaches the allowable crack length. The estimation unit 31d sends a signal indicating the estimated fatigue life to the recording unit 33.

[0043] (First Judgment Department) The first determination unit 32 determines whether the defect X passes or fails based on the crack length at the end of the design life calculated from the crack growth rate v received from the calculation unit 31c and the allowable crack length. The "design life" here refers to the number of repetitions of load application that was determined in advance at the design stage of the layered object 100. Specifically, as shown in FIG. 5, the first determination unit 32 compares the crack length at the end of the design life with the allowable crack length.

[0044] If the length of the crack at the end of the design life is longer than the allowable crack length (curve A1 shown as an example in FIG. 5), the first determination unit 32 determines that the defect X extracted by the extraction unit 31b is unacceptable. On the other hand, if the length of the crack at the end of the design life is shorter than the allowable crack length (curve A2 shown as an example in FIG. 5), the first determination unit 32 determines that the defect X extracted by the extraction unit 31b is acceptable. The first determination unit 32 sends a signal indicating the determination result to the recording unit 33.

[0045] (Recording Department) The recording unit 33 records the design life of the measurement target portion 100x, the pass / fail determination result received from the first determination unit 32, the fatigue life received from the estimation unit 31d, and the plurality of modeling parameters received from the modeling condition input device 21 of the modeling support device 20 in the database 22 of the modeling support device 20 in a state in which these data are associated with each other. Note that the design life of the measurement target portion 100x is stored in advance in, for example, the memory unit 34.

[0046] Specifically, as shown in Figure 6, the recording unit 33 records in the database 22 to update a table in which the design life, pass / fail judgment result, fatigue life, and multiple modeling parameters for each measurement target portion 100x are associated with each other.

[0047] (Method for manufacturing additive manufacturing products) Next, a method for manufacturing the layered object 100 will be described with reference to Fig. 7. In the method for manufacturing the layered object 100 in this embodiment, a manufacturing process S1, a post-processing process S2, and an inspection process S3 are performed.

[0048] (modeling process) In the modeling process S1, the layered object 100 is modeled. In the modeling process S1, the layered object 100 is modeled by the modeling device 10 of the above-described layered object modeling system 1. Specifically, design data D and modeling conditions are input to the controller 15 of the modeling device 10 from the modeling condition input device 21 of the modeling support device 20, and the modeling device 10 models the layered object 100 based on the design data D and modeling conditions.

[0049] (Post-processing process) In the post-processing step S2, post-processing is performed on the layered object 100 manufactured in the manufacturing step S1. In the post-processing step S2, the above-described post-processing device 50 performs post-processing on the layered object 100.

[0050] (Inspection process) In the inspection step S3, the layered object 100 that has been post-processed in the post-processing step S2 is subjected to an inspection method for the layered object 100, thereby inspecting the layered object 100. In the inspection step S3, the fatigue life of the layered object 100 that has been manufactured and post-processed is estimated, and the pass / fail of the defect X formed in the layered object 100 is determined.

[0051] The method for inspecting the layered object 100 according to this embodiment includes a fatigue life estimation step S31, a first determination step S32, and a recording step S33.

[0052] In the fatigue life estimation step S31, a fatigue life estimation method for an additive manufacturing object 100 is performed on the additive manufacturing object 100 that has been post-processed in the post-processing step S2, thereby estimating the fatigue life of the additive manufacturing object 100. In the fatigue life estimation method for an additive manufacturing object 100 in this embodiment, an acquisition step S31a, an extraction step S31b, a calculation step S31c, and an estimation step S31d are performed.

[0053] In the acquisition step S31a, the detection device 40 connected to the inspection device 30 measures the measurement target portion 100x in the layered object 100. Data indicating the surface 100a of the measurement target portion 100x detected by the detection device 40 is input to the inspection device 30, and the acquisition unit 31a of the inspection device 30 acquires a three-dimensional profile of the measurement target portion 100x. Also, in the acquisition step S31a, the acquisition unit 31a acquires design data D from the modeling support device 20.

[0054] In the extraction step S31b, defect X in the measurement target portion 100x of the layered object 100 is extracted based on the three-dimensional profile and design data D acquired in the acquisition step S31a. Specifically, in the extraction step S31b, the extraction unit 31b of the inspection device 30 superimposes the three-dimensional profile on the design data D and extracts defect X from the difference between the design data D and the three-dimensional profile. In the extraction step S31b, after the extraction unit 31b extracts defect X, it slices this defect X into a plurality of two-dimensional slice data and calculates the defect dimensions from each of the two-dimensional slice data.

[0055] In the calculation step S31c, the calculation unit 31c of the inspection device 30 calculates the crack growth rate v based on the defect X (defect dimension) extracted in the extraction step S31b.

[0056] In the estimation step S31d, based on the crack growth rate v calculated in the calculation step S31c, the estimation unit 31d of the inspection device 30 estimates the fatigue life when the crack reaches an allowable crack length indicating the length of the crack.

[0057] In the first judgment step S32, the first judgment unit 32 of the inspection device 30 judges whether the defect X is acceptable or not based on the crack length at the design life obtained from the crack growth rate v calculated in the calculation step S31c and the allowable crack length.

[0058] In the recording process S33, the recording unit 33 of the inspection device 30 records the design life of the measurement target portion 100x, the pass / fail judgment result determined in the first judgment process S32, the fatigue life estimated in the estimation process S31d, and the multiple modeling parameters input by the modeling condition input device 21 of the modeling support device 20 to the controller 15 of the modeling device 10 in a state in which these data are associated with each other in the database 22 of the modeling support device 20.

[0059] After the recording step S33 is completed, the process returns to the fatigue life estimation step S31, and the inspection step S3 is performed on the next measurement target portion 100x. The inspection step S3 in this embodiment is repeated until the inspection step S3 has been performed on all measurement target portions 100x in the layered object 100.

[0060] The layered object 100 is manufactured by the layered manufacturing system 1 by executing the above-described steps (S1 to S3).

[0061] (Operation of inspection equipment) Next, the operation of the inspection device 30 will be described with reference to FIG. First, the acquisition unit 31a of the inspection device 30 acquires a three-dimensional profile of the measurement target portion 100x in the layered object 100 (step S10).

[0062] Next, the extraction unit 31b of the inspection device 30 extracts the defect X in the measurement target portion 100x of the layered object 100 based on the three-dimensional profile and design data D acquired by the acquisition unit 31a (step S11).

[0063] Next, the calculation unit 31c of the inspection device 30 calculates the crack growth rate v based on the defect X (defect dimension) extracted by the extraction unit 31b (step S12).

[0064] Next, the estimation unit 31d of the inspection device 30 estimates the fatigue life when the crack reaches an allowable crack length, which indicates the length of the crack, based on the crack growth rate v calculated by the calculation unit 31c (step S13).

[0065] Next, the first judgment unit 32 of the inspection device 30 judges whether the defect X (two-dimensional slice data) is pass or fail based on the crack length at the design life obtained from the crack growth rate v calculated by the calculation unit 31c and the allowable crack length (step S14).

[0066] Next, the recording unit 33 of the inspection device 30 records in the database 22 of the modeling support device 20 the design life of the measurement target portion 100x, the pass / fail judgment result judged by the first judgment unit 32, the fatigue life estimated by the estimation unit 31d, and the multiple modeling parameters input to the controller 15 of the modeling device 10 by the modeling condition input device 21 of the modeling support device 20 in a mutually associated state (step S15).

[0067] The above-described processing from step S10 to step S15 is repeated until all measurement target portions 100x in the layered object 100 have been inspected.

[0068] (Action and effect) According to the above, the design data D of the layered object 100 and the three-dimensional profile are superimposed, and the defect X in the measurement target portion 100x is extracted from the difference between the design data D and the three-dimensional profile. Then, the crack growth rate v is calculated based on the extracted defect X, and the fatigue life is estimated. In other words, the defect X on the three-dimensional profile based on the deviation from the design data D is used to estimate the fatigue life. Therefore, for example, the size of the defect X can be more appropriately evaluated compared to when the design data D is not used to extract the defect X on the three-dimensional profile. Therefore, the fatigue life of the layered object 100 can be estimated with high accuracy.

[0069] Furthermore, according to the above, the measurement target portion 100x of the layered object 100 is set as a stress concentration portion. This makes it possible to estimate the fatigue life based on the defect X formed in a portion of the layered object 100 where cracks are relatively likely to occur. Therefore, the fatigue life of the layered object 100 can be estimated with higher accuracy.

[0070] Furthermore, according to the above, the pass / fail of the defect X is determined based on the crack length at the time of the design life and the allowable crack length calculated from the crack growth rate v. In other words, because the crack growth rate v calculated in the process of estimating the fatigue life of the layered object 100 is used, the pass / fail of the defect X can be determined with high accuracy.

[0071] Furthermore, according to the above, the estimated fatigue life, the pass / fail determination result of the defect X, and a plurality of modeling parameters are recorded in the database 22 of the modeling support device 20 in a state in which these data are associated with each other. As a result, for example, the modeling condition input device 21 of the modeling support device 20 can acquire modeling parameters for which the estimated fatigue life satisfies the requirements and modeling parameters for which the pass / fail determination result of the defect X is pass from the database 22. Therefore, more appropriate modeling conditions can be adopted.

[0072] Second Embodiment Next, a method for manufacturing a layered object 100 according to a second embodiment of the present disclosure will be described. The same parts as those in the first embodiment will be denoted by the same reference numerals and overlapping descriptions will be omitted.

[0073] In the modeling process S1 in this embodiment, when modeling conditions are input from the modeling condition input device 21 of the modeling support device 20 to the controller 15 of the modeling device 10, multiple modeling parameters that result in a pass / fail judgment result are selected from the correspondence between the pass / fail judgment results and multiple modeling parameters pre-recorded in the database 22.

[0074] Specifically, as shown in Figure 9, in the modeling process S1, when the pass / fail judgment result of defect X pre-recorded in database 22 is distributed on a graph with each of multiple modeling parameters as an axis, multiple modeling parameters are selected from the threshold range in the graph (between curve Th1 and curve Th2 in Figure 9) where the pass / fail judgment result is pass.

[0075] Although FIG. 9 shows a graph in which two modeling parameters are used as axes, the pass / fail judgment results for defect X may be distributed on a graph in which three or more modeling parameters are used as axes.

[0076] (Action and effect) According to the above, the modeling condition input device 21 of the modeling support device 20 can acquire, from the database 22, modeling parameters that result in a pass / fail judgment result for the defect X. Therefore, it is possible to prevent the defect X from occurring in the measurement target portion 100x of the layered object 100 modeled by the modeling device 10.

[0077] Third Embodiment Next, a description will be given of an inspection device 30 and a method for manufacturing a layered object 100 according to a third embodiment of the present disclosure. The same parts as those in the first embodiment will be denoted by the same reference numerals and overlapping descriptions will be omitted.

[0078] (Inspection equipment) As shown in Figure 10, the inspection device 30 in this embodiment includes a fatigue life estimation unit 31, a first judgment unit 32, a recording unit 33, a first acquisition unit 35, a second acquisition unit 36, a second judgment unit 37, and a memory unit 34.

[0079] (First Acquisition Department) The first acquisition unit 35 acquires a probability distribution from the frequency distributions of all fatigue lives pre-recorded in the database 22. Specifically, as shown in Fig. 11, the first acquisition unit 35 acquires, for example, the probability distribution that best fits the frequency distribution. The first acquisition unit 35 calculates the average N f , and the occurrence probability S when the fatigue strength is the design life LGet. The first acquisition unit 35 acquires the average N f and the occurrence probability S L is sent to the second determination unit 37.

[0080] (Second Acquisition Department) The second acquisition unit 36 ​​acquires the standard deviation of the fatigue lives of a portion of the layered object 100 selected from the plurality of newly manufactured layered objects 100. Here, "a portion" means a plurality. Specifically, the second acquisition unit 36 ​​acquires the plurality of fatigue lives estimated by the fatigue life estimation unit 31 and recorded by the recording unit 33, and acquires the standard deviation of the fatigue lives from the plurality of fatigue lives. Hereinafter, the standard deviation acquired by the second acquisition unit 36 ​​from the plurality of fatigue lives is referred to as "S". The second acquisition unit 36 ​​sends the acquired standard deviation S to the second determination unit 37.

[0081] (Second Judgment Department) The second determination unit 37 receives the average N f and the occurrence probability S L and the standard deviation S received from the second acquisition unit 36, the second determination unit 37 performs a pass / fail determination on the plurality of layered objects 100. Specifically, the second determination unit 37 calculates an evaluation value for determining the pass / fail of the layered objects 100. Hereinafter, this evaluation value will be referred to as "V." The evaluation value V in this embodiment is expressed, for example, by the following formula (vii): V=(Nf-SL) / 6S …(vii)

[0082] The second determination unit 37 calculates an evaluation value V and compares this evaluation value V with a predetermined reference value. If the evaluation value V is equal to or greater than the reference value, the second determination unit 37 determines that the newly formed multiple layered objects 100 are acceptable. On the other hand, if the evaluation value V is less than the reference value, the second determination unit 37 determines that the newly formed multiple layered objects 100 are unacceptable.

[0083] (Method for manufacturing additive manufacturing products) Next, a method for manufacturing the layered object 100 will be described with reference to Fig. 12. The method for manufacturing the layered object 100 in this embodiment executes a manufacturing process S1, a post-processing process S2, an inspection process S3, a first acquisition process S4, a second acquisition process S5, and a second determination process S6. The manufacturing process S1, post-processing process S2, and inspection process S3 shown in Fig. 12 are the same as those described in the method for manufacturing the layered object 100 in the first embodiment, and therefore their description will be omitted.

[0084] (First acquisition process) In the first acquisition step S4 , the first acquisition unit 35 of the inspection device 30 acquires a probability distribution from the frequency distributions of all fatigue lives pre-recorded in the database 22 .

[0085] (Second acquisition process) In the second acquisition process S5, the second acquisition unit 36 ​​of the inspection device 30 acquires the standard deviation of the fatigue life obtained by executing the fatigue life estimation process S31 for each of a portion of the layered objects 100 selected from the multiple layered objects 100 newly created by repeating the creation process S1.

[0086] (Second judgment step) In the second determination step S6, the pass / fail determination of the newly formed multiple layered objects 100 is performed based on the probability distribution acquired in the first acquisition step S4 and the standard deviation acquired in the second acquisition step S5.

[0087] The layered object 100 is manufactured by the layered manufacturing system 1 by executing the above-described steps (S1 to S3).

[0088] (Operation of inspection equipment) Next, the operation of the inspection device 30 will be described with reference to Fig. 13. The processes from step S10 to step S15 shown in Fig. 13 are the same as those described in the operation of the inspection device 30 in the first embodiment, and therefore the description thereof will be omitted.

[0089] After step S15 is completed, the first acquisition unit 35 of the inspection device 30 acquires a probability distribution from the frequency distributions of all fatigue lives pre-recorded in the database 22 (step S16).

[0090] Next, the second acquisition unit 36 ​​of the inspection device 30 acquires the standard deviation of the fatigue life of some of the layered objects 100 sampled from the plurality of newly manufactured layered objects 100 (step S17).

[0091] Next, the second determination unit 37 of the inspection device 30 determines whether the plurality of layered objects 100 pass or fail based on the average and occurrence probability obtained from the probability distribution, and the standard deviation (step S18).

[0092] The processes from step S10 to step S18 described above are repeated during the manufacturing of the layered object 100.

[0093] (Action and effect) According to the above, a probability distribution is obtained from the frequency distribution of all fatigue lives pre-recorded in the database 22. Then, the fatigue lives of some of the layered objects 100 sampled from the newly produced layered objects 100 are estimated, and the standard deviation of the fatigue lives of some of the layered objects 100 is obtained. Then, based on these probability distributions and standard deviations, a pass / fail judgment is made for the plurality of layered objects 100. Therefore, the pass / fail of the entire plurality of layered objects 100 can be judged based on the layered objects 100 sampled from the newly produced plurality of layered objects 100. As a result, the number of steps in manufacturing the layered objects 100 can be reduced.

[0094] (Other embodiments) The embodiments of the present disclosure have been described in detail above with reference to the drawings, but the specific configurations are not limited to those of the embodiments, and additions, omissions, substitutions, and other modifications to the configurations are possible within the scope of the gist of the present disclosure.

[0095] 14 is a hardware configuration diagram showing the configuration of a computer 1100 according to this embodiment. The computer 1100 includes a processor 1110, a main memory 1120, a storage 1130, and an interface 1140.

[0096] The inspection device 30 described above is implemented in a computer 1100. The operations of the above-described processing units are stored in the form of a program in a storage 1130. The processor 1110 reads the program from the storage 1130, loads it into the main memory 1120, and executes the above-described processing in accordance with the program. The processor 1110 also allocates a storage area in the main memory 1120 corresponding to the above-described storage unit 34 in accordance with the program.

[0097] The program may be for realizing part of the functions performed by the computer 1100. For example, the program may be for performing the functions by combining with other programs already stored in the storage 1130 or by combining with other programs installed in other devices.

[0098] Furthermore, the computer 1100 may include a custom LSI (Large Scale Integrated Circuit) such as a PLD (Programmable Logic Device) in addition to or instead of the above configuration. Examples of PLDs include PAL (Programmable Array Logic), GAL (Generic Array Logic), CPLD (Complex Programmable Logic Device), and FPGA (Field Programmable Gate Array). In this case, some or all of the functions implemented by the processor 1110 may be implemented by the integrated circuit.

[0099] Examples of storage 1130 include a magnetic disk, a magneto-optical disk, and a semiconductor memory. Storage 1130 may be an internal medium directly connected to the bus of computer 1100, or an external medium connected to computer 1100 via interface 1140 or a communication line. Furthermore, when this program is distributed to computer 1100 via a communication line, computer 1100 that receives the program may load the program into main memory 1120 and execute the above-described processing. In the above embodiment, storage 1130 is a non-transitory tangible storage medium.

[0100] The program may also be for realizing part of the above-mentioned functions. Furthermore, the program may be a so-called differential file (differential program) that realizes the above-mentioned functions in combination with another program already stored in the storage 1130.

[0101] In the above embodiment, the stress concentration portion and the overhanging portion are exemplified as the measurement target portion 100x, but the measurement target portion 100x is not limited to these. The measurement target portion 100x may be any location in the layered object 100.

[0102] <Additional Notes> The method for estimating a fatigue life of a layered object, the method for inspecting a layered object, the method for manufacturing a layered object, the device for inspecting a layered object, and the program described in each embodiment can be understood, for example, as follows.

[0103] (1) The first aspect of the method for estimating the fatigue life of an additively manufactured object 100 is a method for estimating the fatigue life of an additively manufactured object 100 manufactured by stacking a manufacturing material based on design data D of the additively manufactured object 100, and includes the following steps: an acquisition step S31a for acquiring a three-dimensional profile of a measurement target portion 100x in the manufactured additively manufactured object 100; an extraction step S31b for overlaying the three-dimensional profile with the design data D and extracting a defect X from the difference between the design data D and the three-dimensional profile; a calculation step S31c for calculating a crack growth rate v based on the extracted defect X; and an estimation step S31d for estimating the fatigue life at which the allowable crack length is reached based on the crack growth rate v.

[0104] As a result, the defect X on the three-dimensional profile is used to estimate the fatigue life based on the deviation from the design data D. Therefore, for example, the size of the defect X can be more appropriately evaluated compared to when the design data D is not used to extract the defect X on the three-dimensional profile.

[0105] (2) A second aspect of the method for estimating a fatigue life of a layered object 100 is the method for estimating a fatigue life of a layered object 100 of (1), in which the measurement target portion 100x may be a weak point portion in the layered object 100.

[0106] This makes it possible to estimate the fatigue life based on the defects X formed in the layered object 100 in parts where cracks are relatively likely to occur.

[0107] (3) The inspection method for an additively manufactured object 100 according to the third aspect includes a fatigue life estimation step S31 for executing the fatigue life estimation method for an additively manufactured object 100 according to (1) or (2), and a first judgment step S32 for making a pass / fail judgment on the defect X based on the crack length at the design life calculated by the crack growth rate v and the allowable crack length.

[0108] As a result, the crack growth rate v obtained in the process of estimating the fatigue life of the layered object 100 is used, so that the pass / fail of the defect X can be determined with high accuracy.

[0109] (4) The manufacturing method for the layered object 100 according to the fourth aspect further includes, before executing the fatigue life estimation process S31, a manufacturing process S1 in which the layered object 100 is manufactured in accordance with a plurality of manufacturing parameters, an inspection process S3 in which the inspection method for the layered object 100 of (3) is executed, and a recording process S33 in which the fatigue life estimated in the fatigue life estimation process S31, the pass / fail judgment result in the first judgment process S32, and the plurality of manufacturing parameters are recorded in the database 22 in a mutually associated state.

[0110] This allows the database 22 to record the shaping parameters whose estimated fatigue life meets the requirements and the shaping parameters whose pass / fail judgment result for defect X is pass.

[0111] (5) The manufacturing method of the layered object 100 according to the fifth aspect is the manufacturing method of the layered object 100 of (4), and in the manufacturing process S1, the plurality of modeling parameters that result in a pass / fail judgment result may be selected from the correspondence between the pass / fail judgment result and the plurality of modeling parameters pre-recorded in the database 22.

[0112] This makes it possible to obtain from the database 22 the modeling parameters that result in a pass / fail judgment result for the defect X, thereby preventing the defect X from occurring in the measurement target portion 100x of the modeled layered object 100.

[0113] (6) The manufacturing method of the layered object 100 according to the sixth aspect is a manufacturing method of the layered object 100 according to (4) or (5), and may further include a first acquisition step S4 of acquiring a probability distribution from the frequency distribution of all of the fatigue lives pre-recorded in the database 22, a second acquisition step S5 of acquiring the standard deviation of the fatigue life obtained by performing the fatigue life estimation step S31 for each of a portion of the layered objects 100 selected from a plurality of layered objects 100 newly manufactured by repeating the manufacturing step S1, and a second judgment step S6 of making a pass / fail judgment on the plurality of layered objects 100 based on the probability distribution and the standard deviation.

[0114] This makes it possible to judge the pass / fail of the plurality of layered objects 100 as a whole based on a layered object 100 extracted from the plurality of newly fabricated layered objects 100.

[0115] (7) The inspection device 30 for an additively manufactured object 100 according to the seventh aspect is an inspection device 30 for an additively manufactured object 100 manufactured by stacking a manufacturing material based on design data D of the additively manufactured object 100, and includes an acquisition unit 31a that acquires a three-dimensional profile of a measurement target portion 100x in the manufactured additively manufactured object 100, an extraction unit 31b that overlays the three-dimensional profile with the design data D and extracts a defect X from the difference between the design data D and the three-dimensional profile, a calculation unit 31c that calculates a crack growth rate v based on the extracted defect X, and an estimation unit 31d that estimates a fatigue life at which an allowable crack length is reached based on the crack growth rate v.

[0116] (8) A program according to the eighth aspect causes a computer of an inspection device 30 for an additively manufactured object 100, which is manufactured by stacking a manufacturing material, to execute the following steps: acquiring a three-dimensional profile of a measurement target portion 100x in the manufactured additively manufactured object 100 based on design data D of the additively manufactured object 100; superimposing the three-dimensional profile on the design data D and extracting a defect X from the difference between the design data D and the three-dimensional profile; calculating a crack growth rate v based on the extracted defect X; and estimating a fatigue life at which an allowable crack length is reached based on the crack growth rate v. [Explanation of symbols]

[0117] 1...Additive manufacturing system 10...Modeling device 11...Modeling chamber 12...Base plate 13...Recoater 14...Beam irradiation unit 15...Controller 20...Modeling support device 21...Modeling condition input device 22...Database 30...Inspection device 31...Fatigue life estimation unit 31a...Acquisition unit 31b...Extraction unit 31c...Calculation unit 31d...Estimation unit 32...First judgment unit 33...Recording unit 34...Memory unit 35...First acquisition unit 36...Second acquisition unit 37...Second judgment unit 40...Detection device 50...Post-processing device 100...Additive manufactured object 100a...Surface 100x...Measurement target area 1100...Computer 1110...Processor 1120...Main memory 1130...Storage 1140...Interface D...Design data S1...Modeling process S2...Post-processing process S3...Inspection process S4...First acquisition process S5...Second acquisition process S6...Second judgment process S31...Fatigue life estimation process S31a...Acquisition process S31b...Extraction process S31c...Calculation process S31d...Estimation process S32...First judgment process S33...Recording process X...Defect

Claims

1. A method for estimating a fatigue life of a layered object manufactured by layering a building material based on design data of the layered object, comprising: an acquisition step of acquiring a three-dimensional profile of a measurement target portion in the formed layered object; an extraction step of superimposing the three-dimensional profile and the design data and extracting defects from a difference between the design data and the three-dimensional profile; a calculation step of calculating a crack growth rate based on the extracted defects; an estimation step of estimating a fatigue life when the crack length reaches an allowable length based on the crack growth rate; A method for estimating the fatigue life of an additively manufactured object.

2. The method for estimating a fatigue life of a layered object according to claim 1 , wherein the measurement target portion is a weak point portion of the layered object.

3. a fatigue life estimation step of executing the fatigue life estimation method for a layered object according to claim 1 or 2; a first determination step of determining whether the defect is acceptable or not based on the crack length at the time of design life calculated from the crack growth rate and the allowable crack length; A method for inspecting an additively manufactured object.

4. a manufacturing process of manufacturing the layered object in accordance with a plurality of manufacturing parameters before executing the fatigue life estimation process; an inspection step of executing the method for inspecting a layered object according to claim 3; a recording step of recording the fatigue life estimated in the fatigue life estimation step, the pass / fail determination result in the first determination step, and the plurality of modeling parameters in a database in a state in which the fatigue life estimated in the fatigue life estimation step, the pass / fail determination result in the first determination step, and the plurality of modeling parameters are associated with each other; The method for manufacturing an additive object further comprises:

5. 5. A method for manufacturing a layered object according to claim 4, wherein in the modeling process, the plurality of modeling parameters that result in a pass / fail judgment result are selected from the correspondence between the pass / fail judgment result and the plurality of modeling parameters pre-recorded in the database.

6. a first acquisition step of acquiring a probability distribution from all the frequency distributions of the fatigue lives pre-recorded in the database; a second obtaining step of obtaining a standard deviation of the fatigue life obtained by performing the fatigue life estimation step on each of some layered objects extracted from a plurality of layered objects newly manufactured by repeating the manufacturing step; a second determination step of determining whether the plurality of additively manufactured objects is pass or fail based on the probability distribution and the standard deviation; The method for manufacturing a layered object according to claim 4, further comprising the steps of:

7. An inspection device for an additively manufactured object that is manufactured by stacking a modeling material based on design data of the additively manufactured object, an acquisition unit that acquires a three-dimensional profile of a measurement target portion in the formed layered object; an extraction unit that overlays the three-dimensional profile and the design data and extracts defects from a difference between the design data and the three-dimensional profile; a calculation unit that calculates a crack growth rate based on the extracted defects; an estimation unit that estimates a fatigue life when an allowable crack length is reached based on the crack growth rate; An inspection device for a layered object comprising:

8. Based on the design data of the additive manufacturing object, a computer of an inspection device for the additive manufacturing object that is manufactured by layering the manufacturing material is acquiring a three-dimensional profile of a measurement target portion in the fabricated layered object; superimposing the three-dimensional profile on the design data and extracting defects from a difference between the design data and the three-dimensional profile; Calculating a crack growth rate based on the extracted defects; a step of estimating a fatigue life when an allowable crack length is reached based on the crack growth rate; A program that executes the following.

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