Forging flaw judgment system, forging flaw judgment device, and forging flaw judgment method

Abstract

To efficiently determine presence or absence of a flaw in forging molding.SOLUTION: A forging flaw determination system includes a forging analysis device 10 and a forging flaw determination device 20. The forging analysis device 10 generates a mesh for analysis of a formed object, calculates distance between the forming object and a die, and calculates strain rate in each mesh for analysis. The forging flaw determination device 20 calculates surface angles of all adjacent mesh surfaces based on mesh data. Then, the forging flaw determination device 20 generates a prediction formula of logistic regression analysis in which an objective function is set to presence or absence of a flaw and an explanatory function is set to the face angle and the strain rate, calculates a predicted value based on the face angle and the strain rate, and determines the presence or absence of the flaw based on the predicted value and the distance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a forging flaw determination system, a forging flaw determination device, and a forging flaw determination method that can efficiently determine whether or not a flaw phenomenon has occurred during forging. [Background technology]

[0002] Conventionally, forging is often performed by striking an ingot or cylindrical metal block with a hammer or die to apply a large force and plastically deform it to form a shape. During this forging process, the raw material may come together during the die engraving process, resulting in a closed flaw.

[0003] For this reason, there are known techniques for predicting in advance where flaws will occur during forging using analytical techniques. For example, Patent Document 1 discloses a defect risk assessment method that uses a numerical simulation of the forging process using the finite element method (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-035154 Summary of the Invention [Problem to be solved by the invention]

[0005] However, when using the analysis technology typified by the above-mentioned Patent Document 1, if mesh reconstruction occurs due to plastic deformation, the shape of the flaw becomes invisible, so in order to find the flaw, it is necessary to observe the molding process sequentially, which requires a lot of human labor. In particular, there is also the problem that the timing and location of the flaw depend on the experience of the worker, so there is a problem that the number of workers who can do this is limited.

[0006] The present invention has been made to solve the problems (issues) associated with the above-mentioned conventional technology, and aims to provide a forging flaw judgment system, a forging flaw judgment device, and a forging flaw judgment method that can efficiently determine whether or not a flaw phenomenon has occurred during forging forming. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the objectives, a forging flaw judgment system is provided which includes a forging analysis device which generates a formed product model for multiple forming processes of forging, and a forging flaw judgment device which predicts the presence or absence of a flaw phenomenon when forming a formed product at each forming process of the forging based on the formed product model, wherein the forging analysis device calculates a strain rate based on an analysis mesh of the formed product model, and the forging flaw judgment device judges the presence or absence of the flaw phenomenon based on the strain rate and the face angle of adjacent faces of the analysis mesh.

[0008] In addition, in the present invention, in the above invention, the forging analysis device includes a mesh generation unit that generates the analysis mesh of the molded product model, a distance calculation unit that calculates the distance between the molded product model and a mold, and a strain rate calculation unit that calculates a strain rate based on the amount of change in the molded product model between forming processes.

[0009] In addition, in the above invention, the forging flaw detection device of the present invention includes a face angle calculation unit that calculates the face angle of adjacent faces of the analysis mesh, a predicted value calculation unit that calculates a predicted value of the flaw phenomenon based on the strain rate and the face angle, and a determination unit that determines the presence or absence of the flaw phenomenon based on the predicted value and the distance.

[0010] Further, in the present invention, in the above invention, the predicted value calculation unit calculates the predicted value of the scratch phenomenon by logistic regression analysis using the strain rate and the face angle as explanatory variables and the occurrence of the scratch phenomenon as a target variable.

[0011] The present invention also provides a forging flaw judgment device that generates a product model for multiple forming processes in forging and predicts the presence or absence of flaws that occur when forming a product in each forming process of the forging based on the product model, and includes a strain rate calculation unit that calculates a strain rate based on an analysis mesh of the product model, and a judgment unit that judges the presence or absence of flaws based on the strain rate and the face angle of adjacent faces of the analysis mesh.

[0012] The present invention also provides a forging flaw determination method executed by a forging flaw determination system including a forging analysis device that generates a formed product model for multiple forming processes of forging, and a forging flaw determination device that predicts the presence or absence of a flaw phenomenon when forming a formed product in each forming process of the forging based on the formed product model, wherein the forging analysis device includes a strain rate calculation step in which the forging analysis device calculates a strain rate based on an analysis mesh of the formed product model, and a determination step in which the forging flaw determination device determines the presence or absence of the flaw phenomenon based on the strain rate and the face angle of adjacent faces of the analysis mesh. [Effects of the Invention]

[0013] According to the present invention, the presence or absence of flaws in forging can be efficiently determined. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing an overview of a forging flaw determination system according to an embodiment. [Figure 2] FIG. 2 is a functional block diagram showing the configuration of the forging analysis device shown in FIG. [Figure 3] FIG. 3 is a diagram showing an example of the generation of an analytical mesh. [Figure 4] FIG. 4 is a diagram showing an example of the calculation result of the distance between the molded product model and the mold. [Figure 5] FIG. 5 is a diagram showing an example of the calculation results of the strain rate. [Figure 6] FIG. 6 is a functional block diagram showing the configuration of the forging flaw detection device shown in FIG. [Figure 7] FIG. 7 is a diagram showing an example of the forging data shown in FIG. [Figure 8] FIG. 8 is an explanatory diagram for explaining calculation of the threshold value. [Figure 9] FIG. 9 is an explanatory diagram for explaining calculation of the face angle. [Figure 10] FIG. 10 is a diagram showing an example of the calculation result of the face angle. [Figure 11] FIG. 11 is a flowchart showing the processing procedure of the forging analysis device shown in FIG. [Figure 12] FIG. 12 is a flowchart showing the processing procedure of the forging flaw determination device shown in FIG. [Figure 13] FIG. 13 is a diagram showing an example of a display of the presence or absence of flaws by the forging flaw detection device. DETAILED DESCRIPTION OF THE INVENTION

[0015] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of a forging flaw determination system, a forging flaw determination device, and a forging flaw determination method according to the present invention will be described in detail with reference to the accompanying drawings.

[0016] An overview of the forging flaw determination system according to this embodiment will be described below. Fig. 1 is an explanatory diagram for explaining the overview of the forging flaw determination system according to this embodiment.

[0017] <Outline of forging flaw detection system> As shown in Figure 1(a), conventional forging technology involves manufacturing a product through multiple forming processes. Here, the product is prepared in forming process 1, and each part is repeatedly formed in each forming process, passing through forming process 30, and finally being formed in forming process 100. During forging, the shape created when the raw material comes together and closes inside the mold used for forging is called a "flaw," and analysis is generally used to predict where this will occur.

[0018] However, in analysis, the shape of the flaws becomes invisible when the analysis mesh is reconstructed due to plastic deformation, so in order to detect the flaws, it is necessary to observe the forming process sequentially.

[0019] As shown in FIG. 1(b), the present invention calculates predicted flaw values ​​based on a forming model at each forging process and determines whether flaws exist. The forging flaw determination system includes a forging analysis device 10 and a forging flaw determination device 20, which are connected via a network N. The forging analysis device 10 generates a mesh for analyzing the formed product model. The forging analysis device 10 then calculates the distance between the formed product model and the mold. The forging analysis device 10 then calculates the strain rate in each analysis mesh. Here, the strain rate is a value obtained by dividing the deformation amount per unit time by the original length.

[0020] The forging flaw detection device 20 then acquires mesh data, distance data, and strain rate data from the forging analysis device 10. The forging flaw detection device 20 then calculates the face angles of all adjacent mesh faces based on the mesh data. The forging flaw detection device 20 then calculates a predicted value based on the face angles and strain rate, and determines whether or not there are flaws based on the predicted value and the distance. The predicted value, which will be described in detail later, is a numerical value calculated using a prediction formula for logistic regression analysis in which the objective function is the presence or absence of flaws and the explanatory functions are the face angles and strain rate.

[0021] <Configuration of forging analysis device 10> Next, the configuration of the forging analysis device 10 shown in Fig. 1 will be described. Fig. 2 is a functional block diagram showing the configuration of the forging analysis device 10 shown in Fig. 1. As shown in Fig. 2, the forging analysis device 10 has a display unit 11, an input unit 12, a communication I / F unit 13, a memory unit 14, and a control unit 15. The display unit 11 is a display device such as a liquid crystal display that displays various information. The input unit 12 is an input device such as a mouse or keyboard. The communication I / F unit 13 is a communication interface unit for communicating with the forging flaw detection device 20 via the network N.

[0022] The storage unit 14 is a storage device such as a hard disk drive or nonvolatile memory, and stores forging process data 14a, mesh data 14b, distance data 14c, and strain rate data 14d. The forging process data 14a is data on a product model in each forming process using forging technology. The mesh data 14b is data on a mesh generated for the surface of the product model in order to analyze the product model. The distance data 14c is data on the distance between the mold used for forging and the product model. The strain rate data 14d is data on the strain rate calculated in the analysis mesh.

[0023] The control unit 15 is a control unit that controls the entire forging analysis device 10, and includes a mesh generation unit 15a, a distance calculation unit 15b, a strain rate calculation unit 15c, and a data transmission unit 15d. In practice, these programs are loaded into a CPU and executed, causing the mesh generation unit 15a, the distance calculation unit 15b, the strain rate calculation unit 15c, and the data transmission unit 15d to execute their respective corresponding processes.

[0024] The mesh generation unit 15a is a processing unit that generates a mesh for analysis for the molded product model. The size of the generated mesh varies depending on the shape of the molded product model. A dense mesh is generated in a portion where the shape of the molded product model changes, and a coarse mesh is generated in a portion where the shape of the molded product model does not change much (see FIG. 3). The generated mesh data is associated with the forging generation process ID and stored as mesh data 14b in the storage unit 14. Note that, although a case where a triangular mesh is generated using the diagonal lines of a rectangle is described here, any triangular mesh may be generated. The shape of the mesh may also be quadrangular or hexagonal.

[0025] The distance calculation unit 15b is a processing unit that calculates the distance between the product model 110 and the die used for forming in each forging process. Specifically, as shown in FIG. 4, the distance calculation unit 15b calculates the distance between the product model 110 and the die (not shown) according to the forging process. A distance close to 0 indicates that the product model 110 and the die are close to each other, and a distance close to 1 indicates that the product model 110 and the die are far from each other. Although not shown, a negative distance indicates that the product model 110 and the die are in contact with each other. The calculated distance data is stored in the storage unit 14 as distance data 14c in association with the forging process ID.

[0026] The strain rate calculation unit 15c is a processing unit that calculates the strain rate in each mesh. The strain rate is a numerical value obtained by dividing the deformation amount per unit time of each mesh by the original length. Specifically, the strain rate in the forming process that generated the mesh is calculated while taking into account the deformation in the forming process that generated the mesh, while inheriting the previous value.

[0027] 5, the strain rate calculation unit 15c calculates the strain rate corresponding to each mesh of the formed product model 110. A large strain rate is calculated for a portion where the shape of the formed product model 110 has changed during the forging process, and a small strain rate is calculated for a portion where the shape of the formed product model 110 has changed little. The calculated strain rate is stored in the storage unit 14 as strain rate data 14d in association with the forging process ID.

[0028] The data transmission unit 15d is a processing unit that transmits the mesh data 14b, the distance data 14c, and the strain rate data 14d to the forging flaw determination device 20 via the communication I / F unit 13.

[0029] <Configuration of forging flaw detection device 20> Next, the configuration of the forging flaw detection device 20 shown in Fig. 1 will be described. Fig. 6 is a functional block diagram showing the configuration of the forging flaw detection device 20 shown in Fig. 1. As shown in Fig. 6, the forging flaw detection device 20 has a display unit 21, an input unit 22, a communication I / F unit 23, a memory unit 24, and a control unit 25. The display unit 21 is a display device such as a liquid crystal display that displays various information. The input unit 22 is an input device such as a mouse or keyboard. The communication I / F unit 23 is a communication interface unit for communicating with the forging analysis device 10 via the network N.

[0030] The storage unit 24 is a storage device such as a hard disk drive or nonvolatile memory, and stores forging data 24a, mesh data 24b, face angle data 24c, distance data 24d, strain rate data 24e, and predicted value data 24f. The forging data 24a is data on the presence or absence of scratches on a formed product obtained by actually performing forging in advance, and the face angle and strain rate obtained by analyzing the scratched portion, such as the data shown in FIG.

[0031] Here, the presence or absence of scratches "1" is associated with a face angle of "98" and a strain rate of "58", the presence or absence of scratches "1" is associated with a face angle of "109" and a strain rate of "62", and the presence or absence of scratches "1" is associated with a face angle of "117" and a strain rate of "80".

[0032] Furthermore, the presence or absence of scratches "0" is associated with a face angle of "176" and a strain rate of "90", the presence or absence of scratches "0" is associated with a face angle of "137" and a strain rate of "85", and the presence or absence of scratches "0" is associated with a face angle of "158" and a strain rate of "85".

[0033] The mesh data 24b is data of a mesh generated for the surface of the molded product model in order to analyze the molded product model received from the forging analysis device 10. The face angle data 24c is data of the angle formed between adjacent mesh faces. The distance data 24d is data of the distance between the mold used for forging and the molded product model received from the forging analysis device 10. The strain rate data 24e is data of the strain rate calculated in the analysis mesh received from the forging analysis device 10. The predicted value data 24f is numerical data used to determine the presence or absence of flaws, calculated from a prediction formula calculated by logistic regression analysis based on the face angle data 24c and the strain rate data 24e.

[0034] The control unit 25 is a control unit that controls the entire forging flaw determination device 20, and includes a data reception processing unit 25a, a regression coefficient calculation unit 25b, a threshold calculation unit 25c, a face angle calculation unit 25d, a predicted value calculation unit 25e, a determination unit 25f, and a display control unit 25g. In practice, by loading these programs into a CPU and executing them, the data reception processing unit 25a, the regression coefficient calculation unit 25b, the threshold calculation unit 25c, the face angle calculation unit 25d, the predicted value calculation unit 25e, the determination unit 25f, and the display control unit 25g execute their respective corresponding processes.

[0035] The data receiving processor 25a is a processor that receives data from the forging analysis device 10 and stores it in the memory unit 24. Specifically, it receives mesh data 14b from the forging analysis device 10 and stores it in the memory unit 24 as mesh data 24b, receives distance data 14c from the forging analysis device 10 and stores it in the memory unit 24 as distance data 24d, and receives strain rate data 14d from the forging analysis device 10 and stores it in the memory unit 24 as strain rate data 24e.

[0036] The regression coefficient calculation unit 25b is a processing unit that calculates a regression coefficient representing the relationship between the objective variable and the number of explanatory edges in a logistic regression analysis for calculating a predicted value for determining the presence or absence of flaws in a forged product model. Specifically, the objective variable here is the presence or absence of flaws, and the explanatory variables are the mesh surface angle and the strain rate.

[0037] Here, the probability that the objective variable, binary data Y (0 / 1 for the presence or absence of scratches), will be Y=1 is p(Y=1), the two explanatory variables X (face angle and strain rate) are x1 and x2, respectively, and the regression coefficients are β0, β1, and β2, respectively. The prediction formula for logistic regression analysis can be expressed as Equation 1.

[0038]

number

[0039] If the probability that the presence or absence of a scratch is binary data Y=1 (0 / 1) is P=p(Y=1), the logarithm of odds (logit) can be expressed as logit(P)=log(P / (1-P)).

[0040] The regression coefficient calculation unit 25b calculates the regression coefficients using the maximum likelihood method. The maximum likelihood method is a method of estimating parameters that maximize the likelihood from a given observation point. Specifically, the probability that binary data Y is Y=1 is set to P=p(Y=1), and the probability that Y=0 is set to 1-P=p(Y=0). Then, given a set of n data Y (Y1, Y2, ..., Y n ) are independent of each other and p(Y i -1)=P i In this case, the log-likelihood function is used to determine the regression coefficient β so that the log-likelihood function is maximized. i The Newton-Raphson method is used for this calculation.

[0041] The threshold calculation unit 25c is a processing unit that calculates a threshold used to determine the presence or absence of flaws. Specifically, a predicted value is calculated based on the prediction formula based on logistic regression analysis using the regression coefficient calculated by the regression coefficient calculation unit 25b and the forging data 24a, and a numerical value between the minimum predicted value when flaws are present and the maximum predicted value when flaws are absent is set as the threshold.

[0042] For example, as shown in Fig. 8, predicted values ​​are calculated based on the face angle and strain rate when there is a flaw (objective function = 1) and when there is no flaw (objective function = 0). Here, the predicted value "0.975904" is associated with the objective function "1", face angle "98", and strain rate "58", the predicted value "0.926944" is associated with the objective function "1", face angle "109", and strain rate "62", and the predicted value "0.976955" is associated with the objective function "1", face angle "117", and strain rate "80".

[0043] Furthermore, the predicted value "0.017384" is associated with the objective function "0", face angle "176", and strain rate "90", the predicted value "0.790499" is associated with the objective function "0", face angle "137", and strain rate "85", and the predicted value "0.018642" is associated with the objective function "0", face angle "158", and strain rate "70".

[0044] The threshold calculation unit 25c sets a numerical value between the minimum predicted value "0.926944" for the objective function "1" and the maximum predicted value "0.790499" for the objective function "0" as the threshold.

[0045] The face angle calculation unit 25d is a processing unit that reads the mesh data 24b and calculates the angles between the faces forming a mesh and the faces forming an adjacent mesh for that mesh. The face angle calculation unit 25d calculates the face angles of all adjacent meshes generated in the formed product model. The calculated angles of the faces forming the adjacent meshes are stored in the storage unit 24 as face angle data 24c in association with the forging process ID.

[0046] The predicted value calculation unit 25e is a processing unit that calculates a predicted value using a prediction formula of logistic regression analysis based on the face angle data 24c and the strain rate data 24e. Specifically, the predicted value calculation unit 25e reads the face angle data 24c and the strain rate data 24e from the storage unit 24 and calculates a predicted value by substituting them into the prediction formula based on the logistic regression analysis using the regression coefficients calculated by the regression coefficient calculation unit 25b. The predicted value is stored in the storage unit 24 as predicted value data 24f in association with the forging process ID.

[0047] The determination unit 25f is a processing unit that determines whether or not there is a flaw in the molded product model based on the distance data 24d and the predicted value data 24f. Specifically, the determination unit 25f reads the distance data 24d and the predicted value data 24f from the storage unit 24, and if the distance data 24d is a positive value, determines that there is a flaw if the predicted value data 24f is greater than a threshold, and determines that there is no flaw if the predicted value data 24f is equal to or less than the threshold. Also, if the distance data 24d is a negative value, the determination is skipped.

[0048] The display control unit 25g is a processing unit that controls the display of the presence or absence of scratches on a predetermined display unit 21 based on the result of the determination unit 25f.

[0049] <Angle of faces forming adjacent meshes> Next, we will explain how the surface angle calculation unit 25d of the forging flaw detection device 20 calculates the angle of the surfaces forming adjacent meshes (surface angle). Fig. 9 is an explanatory diagram for explaining how the surface angle is calculated. As shown in Fig. 9, the surface angle is the angle θ formed between the first mesh surface 41a and the second mesh surface 41b.

[0050] The plane equation of the first mesh surface 41a can be expressed as a1x+b1y+c1z+d1=0. The plane equation of the second mesh surface 41b can be expressed as a2x+b2y+c2z+d2=0. The angle θ between the first mesh surface 41a and the second mesh surface 41b can be calculated using equation (2).

[0051]

number

[0052] <Example of face angle calculation results> Next, an example of the calculation result of the face angle will be described. Fig. 10 is a diagram showing an example of the calculation result of the face angle. As shown in Fig. 10, the face angle calculation unit 25d calculates all angles of faces forming adjacent meshes generated in the molding model 110. In a portion where the shape of the molding model 110 changes little, the face angle indicates a value close to 180 degrees, and in a portion where the shape of the molding model 110 changes greatly, the face angle indicates a value close to 90 degrees.

[0053] <Processing procedure of the forging analysis device 10> Next, a processing procedure of the forging analysis device 10 shown in Fig. 1 will be described. Fig. 11 is a flowchart showing the processing procedure of the forging analysis device 10 shown in Fig. 1. As shown in Fig. 11, the forging analysis device 10 generates a mesh for analyzing a molded product model (step S101). Then, the forging analysis device 10 calculates the distance between the molded product model and the die (step S102).

[0054] Thereafter, the forging analysis device 10 calculates the strain rate (step S103). Then, the forging analysis device 10 determines whether the forging process for which the calculation was performed is the final process (step S104). If the forging analysis device 10 determines that it is the final process (step S104: Yes), it transmits the mesh data 14b, the distance data 14c, and the strain rate data 14d to the forging flaw determination device 20 (step S105), and ends the process.

[0055] On the other hand, if the forging analysis device 10 determines that it is not the final process (step S104: No), it reads out data of the product model for the next process (step S106) and proceeds to step S101.

[0056] <Processing Procedure of Forging Flaw Determination Device 20> Next, a description will be given of the processing procedure of the forging flaw detection device 20. Fig. 12 is a flowchart showing the processing procedure of the forging flaw detection device 20 shown in Fig. 1. As shown in Fig. 12, the forging flaw detection device 20 reads out the mesh data 24b from the storage unit 24 (step S201). Then, the forging flaw detection device 20 calculates the face angle (step S202).

[0057] Thereafter, the forging flaw judgment device 20 reads out the strain rate data 24e from the storage unit 24 (step S203). Then, the forging flaw judgment device 20 reads out the distance data 24d from the storage unit 24 (step S204). Thereafter, the forging flaw judgment device 20 calculates a predicted value based on the face angle and the strain rate (step S205).

[0058] Then, the forging flaw determination device 20 determines whether the distance is equal to or less than "0" (Step S206). If the distance is equal to or less than "0" (Step S206: Yes), the forging flaw determination device 20 proceeds to Step S210.

[0059] On the other hand, if the distance is not "0" or less (step S206: No), the forging flaw determination device 20 determines whether the predicted value is less than or equal to a predetermined threshold (step S207). If the predicted value is less than or equal to the predetermined threshold (step S207: Yes), the forging flaw determination device 20 determines that there is no flaw (step S209) and proceeds to step S210. If the predicted value is not less than or equal to the predetermined threshold (step S207: No), the forging flaw determination device 20 determines that there is a flaw (step S208) and proceeds to step S210.

[0060] Then, the forging flaw determination device 20 determines whether the determined forming process is the final process or not (step S210). If the forging flaw determination device 20 determines that it is the final process (step S210: Yes), it ends the processing. On the other hand, if the forging flaw determination device 20 determines that it is not the final process (step S210: No), it sets the forging process ID to the next process (step S211) and proceeds to step S201.

[0061] <Example of displaying whether or not there are scratches> Next, we will explain an example of how the forging flaw determination device 20 displays whether or not there is a flaw. Fig. 13 is a diagram showing an example of how the forging flaw determination device 20 displays whether or not there is a flaw. As shown in Fig. 13, the forging flaw determination device 20 displays a location determined to be a flaw by the determination unit 25f as a flaw occurrence region 120.

[0062] As described above, in this embodiment, the forging flaw determination system includes a forging analysis device 10 and a forging flaw determination device 20. The forging analysis device 10 generates meshes for analyzing a molded product model, calculates the distance between the molded product model and the die, and calculates the strain rate in each mesh for analysis. The forging flaw determination device 20 calculates regression coefficients of a prediction formula for logistic regression analysis that calculates a predicted value, calculates the face angles of mesh faces formed by adjacent meshes, and calculates a predicted value using the prediction formula for logistic regression analysis based on the face angles and strain rate, and determines the presence or absence of flaws based on the predicted value and the distance.

[0063] In the above embodiment, the forging analysis device 10 generates meshes, calculates distances, and calculates strain rates for all forging processes, and transmits the mesh data, distance data, and strain rate data to the forging flaw judgment device 20, and the forging flaw judgment device 20 judges the presence or absence of flaws in all forging processes based on the received data.However, it is also possible for the forging analysis device 10 to generate meshes, calculate distances, and calculate strain rates for each forging process, and transmit the mesh data, distance data, and strain rate data to the forging flaw judgment device 20 each time, and the forging flaw judgment device 20 judges the presence or absence of flaws using the data received for one forging process.

[0064] In addition, in the above embodiment, the forging flaw judgment system has been described as having a forging analysis device 10 that analyzes the molded product model during the forging process and a forging flaw judgment device 20 that judges whether or not there are flaws, but the functions of the forging analysis device 10 and the forging flaw judgment device 20 may also be realized in a single device.

[0065] Furthermore, in the above embodiment, the case where the face angle and strain rate are used as explanatory variables in the logistic regression analysis has been described, but other parameters that affect the occurrence of scratches in relation to forging, such as the processing temperature, may also be added to the explanatory variables.

[0066] The configurations illustrated in the above embodiments are merely functional schematics and are not necessarily physically configured as shown. In other words, the distribution and integration of each device is not limited to that illustrated, and all or part of the devices can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. [Industrial Applicability]

[0067] The forging flaw determination system, forging flaw determination device, and forging flaw determination method according to the present invention are suitable for efficiently determining the presence or absence of flaws in forging. [Explanation of symbols]

[0068] 10 Forging analysis device 11 Display section 12 Input section 13 Communication I / F section 14 Storage section 14a Forging process data 14b Mesh data 14c Distance Data 14d Strain rate data 15 Control Unit 15a Mesh generation section 15b Distance calculation section 15c Strain rate calculation section 15d Data transmission unit 20 Forging flaw detection device 21 Display section 22 Input section 23 Communication I / F section 24 Memory section 24a Forging data 24b mesh data 24c face angle data 24d distance data 24e Strain rate data 24f forecast data 25 Control Unit 25a Data receiving processing unit 25b Regression coefficient calculation section 25c Threshold calculation unit 25d Surface angle calculation part 25e Prediction value calculation section 25f Judgment section 25g Display control unit 40 mesh 41a, 41b mesh surface 110 Molded Product Model 120 Scratch area

Claims

1. A forging flaw determination system including a forging analysis device that generates a formed product model in a plurality of forming steps of forging, and a forging flaw determination device that predicts the presence or absence of a flaw phenomenon when forming a formed product in each forming step of the forging based on the formed product model, The forging analysis device Calculating a strain rate based on an analytical mesh of the molded product model; The forging flaw determination device is The presence or absence of the flaw phenomenon is determined based on the strain rate and the face angle between adjacent faces of the analysis mesh. A forging flaw determination system characterized by:

2. The forging analysis device a mesh generation unit that generates the analysis mesh of the molding model; a distance calculation unit that calculates the distance between the molded product model and the mold; a strain rate calculation unit that calculates a strain rate based on a change in the molded product model between molding steps; 2. The forging flaw determination system according to claim 1, further comprising:

3. The forging flaw determination device is a surface angle calculation unit that calculates the surface angle between adjacent surfaces of the analysis mesh; a predicted value calculation unit that calculates a predicted value of the flaw phenomenon based on the strain rate and the face angle; a determination unit that determines whether or not the scratch phenomenon exists based on the predicted value and the distance; 3. The forging flaw determination system according to claim 2, further comprising:

4. The predicted value calculation unit The forging flaw judgment system according to claim 3, characterized in that the predicted value of the flaw phenomenon is calculated by logistic regression analysis using the strain rate and the face angle as explanatory variables and the occurrence of the flaw phenomenon as a target variable.

5. A forging flaw determination device that generates a formed product model for a plurality of forming steps of forging, and predicts the presence or absence of a flaw phenomenon when forming a formed product in each forming step of the forging based on the formed product model, a strain rate calculation unit that calculates a strain rate based on an analysis mesh of the molded product model; a determination unit that determines whether or not the flaw phenomenon exists based on the strain rate and the face angle between adjacent faces of the analysis mesh; A forging flaw detection device comprising:

6. A forging flaw determination method executed by a forging flaw determination system including a forging analysis device that generates a formed product model in a plurality of forming steps of forging, and a forging flaw determination device that predicts the presence or absence of a flaw phenomenon when forming a formed product in each forming step of the forging based on the formed product model, a strain rate calculation step in which the forging analysis device calculates a strain rate based on an analysis mesh of the formed product model; a determination step in which the forging flaw determination device determines whether or not the flaw phenomenon exists based on the strain rate and the face angle between adjacent faces of the analysis mesh; A forging flaw determination method comprising:

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