Forging defect determination system, forging defect determination device, and forging defect determination method

Abstract

A forging defect determination system according to the present invention is provided with a forging analysis device (10) and a forging defect determination device (20). A forging analysis device (10) is provided with a first control unit (15) for generating a molded article model in a plurality of molding steps for forging molding. The first control unit (15) is disposed so as to calculate a strain rate on the basis of an analysis grid of the molded object model. The forging defect determination device (20) is provided with a second control unit (25), and is disposed so as to predict, on the basis of the molded article model, the presence or absence of a defect phenomenon when molding the molded article in each molding step of the forging molding. The second control unit (25) is arranged so as to determine the presence or absence of the defect phenomenon on the basis of the strain rate and the surface angle between the surfaces of the adjacent analysis grids.

Description

Technical Field

[0001] The present invention relates to a forging defect judgment system, a forging defect judgment device, and a forging defect judgment method. Background Art

[0002] In forging, a hammer or a die is mostly used to strike an ingot-shaped or cylindrical metal block with a large force to cause plastic deformation to produce a shape. In the above forging, there is a case where raw materials gather during the die sinking process of the die to generate a closed defect.

[0003] Therefore, there is a known technique for predicting in advance the occurrence position of defects generated in forging using analysis techniques. For example, Japanese Unexamined Patent Application Publication No. 2022-035154 discloses a flaw occurrence risk evaluation method that uses numerical simulation of a forging process based on the finite element method (for example, refer to Japanese Unexamined Patent Application Publication No. 2022-035154).

[0004] However, in the case of using analysis techniques represented by the above Japanese Unexamined Patent Application Publication No. 2022-035154, if mesh reconstruction occurs due to plastic deformation, the shape of the defect cannot be seen. Therefore, in order to detect a defect, it is necessary to observe the forming process sequentially, which has a problem of requiring labor. In particular, since the determination of the occurrence time and occurrence location of a defect depends on the experience of the operator, there is also a problem of operator limitation. Summary of the Invention

[0005] The present invention provides a forging defect judgment system, a forging defect judgment device, and a forging defect judgment method that can efficiently judge whether a defect phenomenon occurs in forging.

[0006] The forging defect judgment system according to the first aspect of the present invention includes a forging analysis device and a forging defect judgment device. The forging analysis device is configured to generate a formed object model in a plurality of forming processes of forging, and includes a first control unit. The first control unit is configured to calculate a strain rate based on the analysis mesh of the formed object model. The forging defect judgment device is configured to predict whether a defect phenomenon occurs when forming the formed object in each forming process of the forging based on the formed object model, and includes a second control unit. The second control unit is configured to judge whether there is the defect phenomenon based on the strain rate and the surface angle of the surface of adjacent analysis meshes.

[0007] In addition, based on the forging defect judgment system according to the first aspect of the present invention, the first control unit may be configured to generate the analysis mesh of the formed object model, may be configured to calculate the distance between the formed object model and the die, or may be configured to calculate the strain rate based on the change amount of the formed object model during the forming process.

[0008] In addition, based on the forging defect judgment system according to the first aspect of the present invention, the second control unit may also be configured to calculate the surface angle of the surface of the adjacent analysis grid, may also be configured to calculate a predicted value of the defect phenomenon based on the strain rate and the surface angle, and may also be configured to determine whether there is the defect phenomenon based on the predicted value and the distance.

[0009] In addition, based on the forging defect judgment system according to the first aspect of the present invention, the second control unit may also be configured to calculate the predicted value of the defect phenomenon based on a logistic regression analysis in which the strain rate and the surface angle are explanatory variables and the occurrence of the defect phenomenon is a target variable.

[0010] In addition, the forging defect judgment device according to the second aspect of the present invention is configured to predict whether there is a defect phenomenon when forming a formed object in each forming process of the forging forming based on a formed object model in a plurality of forming processes of the forging forming. The forging defect judgment device includes a second control unit. The second control unit is configured to calculate a strain rate based on an analysis grid of the formed object model, and determine whether there is the defect phenomenon based on the strain rate and the surface angle of the surface of an adjacent analysis grid.

[0011] In addition, the forging defect judgment method according to the third aspect of the present invention is executed by a forging defect judgment system. The forging defect judgment system includes: a forging analysis device configured to generate a formed object model in a plurality of forming processes of the forging forming; and a forging defect judgment device configured to predict whether there is a defect phenomenon when forming a formed object in each forming process of the forging forming based on the formed object model. The forging defect judgment method includes: the forging analysis device calculates a strain rate based on an analysis grid of the formed object model; and the forging defect judgment device determines whether there is the defect phenomenon based on the strain rate and the surface angle of the surface of an adjacent analysis grid.

[0012] According to the present invention, it is possible to efficiently judge whether there are defects in the forging forming. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Hereinafter, features, advantages, and industrial significance of embodiments of the present invention will be described with reference to the drawings, in which the same reference numerals denote the same elements. Figure 1A FIG. is a diagram showing an outline of a forging defect judgment system according to an embodiment. Figure 1B FIG. is a diagram showing an outline of a forging defect judgment system according to an embodiment. Figure 2 FIG. shows Figure 1BFunctional block diagram of the structure of the forging analysis device shown Figure 3 It is a figure showing an example of generation of an analysis mesh Figure 4 It is a figure showing an example of the calculation result of the distance between the formed object model and the mold Figure 5 It is a figure showing an example of the calculation result of the strain rate Figure 6 It shows Figure 1B Functional block diagram of the structure of the forging defect judgment device shown Figure 7 It shows Figure 6 An example of forging formation data shown Figure 8 It is an explanatory diagram for explaining the calculation of the threshold value Figure 9 It is an explanatory diagram for explaining the calculation of the surface angle Figure 10 It is a figure showing an example of the calculation result of the surface angle Figure 11 It shows Figure 1B Flowchart of the processing sequence of the forging analysis device shown Figure 12 It shows Figure 1B Flowchart of the processing sequence of the forging defect judgment device shown Figure 13 It is a figure showing an example of the display of the presence or absence of defects of the forging defect judgment device Detailed implementation mode

[0014] Hereinafter, embodiments of the forging defect judgment system, the forging defect judgment device, and the forging defect judgment method according to the present invention will be described in detail with reference to the drawings

[0015] The outline of the forging defect judgment system according to this embodiment will be described Figure 1A 、 Figure 1B It is an explanatory diagram for explaining the outline of the forging defect judgment system according to the embodiment

[0016] <Outline of the forging defect judgment system> As Figure 1AAs shown, in the manufacture of products using forging technology, the products are formed through multiple forming processes. Here, it is shown that in forming process 1 (the first forming process), the preparation of the formed object is carried out, and in each forming process, each part is repeatedly formed. After passing through forming process 30 (the 30th forming process), the forming is completed in forming process 100 (the 100th forming process). In forging, the shape formed by aggregating and closing the raw material inside the die used for forging is called a "defect", and usually, analysis is used to predict the occurrence position in advance.

[0017] However, in the analysis, if the reconstruction of the analysis grid occurs along with plastic deformation, the shape of the defect cannot be seen, and it is necessary to observe the forming process successively in the discovery of the defect.

[0018] As Figure 1B shown, in each forming process of forging in the present invention, the predicted value of the defect is calculated based on the formed object model, and the presence or absence of the defect is judged. The forging defect judgment system has a forging analysis device 10 and a forging defect judgment device 20, which are connected via a network N. The forging analysis device 10 generates an analysis grid of the formed object model. After that, the forging analysis device 10 calculates the distance between the formed object model and the die. And the forging analysis device 10 calculates the strain rate at each analysis grid. Here, the strain rate refers to the value obtained by dividing the amount of deformation per unit time by the original length.

[0019] After that, the forging defect judgment device 20 obtains the grid data, distance data, and strain rate data through the forging analysis device 10. And the forging defect judgment device 20 calculates the surface angle of all adjacent grid surfaces based on the grid data. And the forging defect judgment device 20 calculates the predicted value based on the surface angle and the strain rate, and judges the presence or absence of the defect based on the predicted value and the distance. Here, the detailed content will be described later. The predicted value is a value calculated using a prediction formula of logistic regression analysis with the objective function as the presence or absence of the defect and the explanatory functions as the surface angle and the strain rate.

[0020] <Structure of the forging analysis device 10> Next, an explanation will be given of Figure 1B the structure of the forging analysis device 10 shown. Figure 2 It is a functional block diagram showing Figure 1B the structure of the forging analysis device 10 shown. As Figure 2 shown, the forging analysis device 10 has a display unit 11, an input unit 12, a communication I / F unit 13, a storage unit 14, and a control unit 15. The display unit 11 is a display device such as a liquid crystal display for displaying various information. The input unit 12 is an input device such as a mouse and a keyboard. The communication I / F unit 13 is a communication interface unit for communicating with the forging defect judgment device 20 via the network N.

[0021] The storage unit 14 is a storage device such as a hard disk drive or a non-volatile memory, and stores forging forming process data 14a, mesh data 14b, distance data 14c, and strain rate data 14d. The forging forming process data 14a is data of a formed object model in each forming process using forging technology. The mesh data 14b is data of a mesh generated for the surface of the formed object model for analyzing the formed object model. The distance data 14c is data of the distance between the die used in forging and the formed object model. The strain rate data 14d is data of the strain rate calculated in the analysis mesh.

[0022] The control unit 15 is a control unit that controls the entire forging analysis device 10, and has a mesh generation unit 15a, a distance calculation unit 15b, a strain rate calculation unit 15c, and a data transmission unit 15d. In fact, by loading their programs into the CPU and executing them, the mesh generation unit 15a, the distance calculation unit 15b, the strain rate calculation unit 15c, and the data transmission unit 15d respectively execute corresponding processes. The control unit 15 is an example of the first control unit in the present invention.

[0023] The mesh generation unit 15a is a processing unit that generates a mesh for analysis for the formed object model. Regarding the size of the generated mesh, the density of the mesh is changed according to the shape of the formed object model. Regarding the size of the mesh, a denser mesh is generated in a part where the shape of the formed object model changes, and a sparser mesh is generated in a part where the shape of the formed object model does not change much (refer to Figure 3 ). The generated mesh data is stored in the storage unit 14 as the mesh data 14b in a manner corresponding to the forging generation process ID. In addition, the case of generating a triangular mesh using the diagonal of a rectangle is described here, but any triangular mesh can also be generated. In addition, the shape of the mesh can also be a quadrilateral or a hexagon.

[0024] The distance calculation unit 15b is a processing unit that calculates the distance between the formed object model and the die used in forming in each forming process of forging. Specifically, as Figure 4 shown, the distance calculation unit 15b calculates the distance between the formed object model 110 and a die (not shown) according to the forging forming process. When the distance is close to 0, it indicates that the formed object model 110 and the die are in a relatively close position state, and when the distance is close to 1, it indicates that the formed object model 110 and the die are in a distant position state. Although not shown, when the distance is negative, it indicates that the formed object model 110 and the die are in a contact state. The calculated distance data is stored in the storage unit 14 as the distance data 14c in a manner corresponding to the forging generation process ID.

[0025] The strain rate calculation unit 15c is a processing unit that calculates the strain rate in each grid. The strain rate is a value obtained by dividing the amount of deformation per unit time of each grid by the original length. Specifically, the strain rate of the forming process in which the grid is generated is calculated while retaining the previous value and taking into account the deformation of the forming process in which the grid is generated.

[0026] For example Figure 5 As shown, the strain rate calculation unit 15c calculates the strain rate corresponding to each grid of the formed object model 110. For the strain rate, in the forging process, a larger strain rate is calculated in the part where the shape of the formed object model 110 changes, and a smaller strain rate is calculated in the part where the shape of the formed object model 110 changes less. The calculated strain rate is stored in the storage unit 14 as the strain rate data 14d in a manner corresponding to the forging generation process ID.

[0027] The data transmission unit 15d is a processing unit that transmits the grid data 14b, the distance data 14c, and the strain rate data 14d to the forging defect judgment device 20 via the communication I / F unit 13.

[0028] <Structure of the forging defect judgment device 20> Next, Figure 1B the structure of the forging defect judgment device 20 shown will be described. Figure 6 is a functional block diagram showing Figure 1B the structure of the forging defect judgment device 20 shown. As Figure 6 shown, the forging defect judgment device 20 has a display unit 21, an input unit 22, a communication I / F unit 23, a storage 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 a keyboard. The communication I / F unit 23 is a communication interface unit for communicating with the forging analysis device 10 via the network N.

[0029] The storage unit 24 is a storage device such as a hard disk device or a non-volatile memory, and stores forging forming data 24a, grid data 24b, surface angle data 24c, distance data 24d, strain rate data 24e, and predicted value data 24f. The forging forming data 24a is data on the presence or absence of defects generated in the formed object by actually performing forging forming in advance, and data on the surface angle and strain rate obtained by analyzing the part where the defect is generated. For example, it is Figure 7 the data shown.

[0030] Here, the surface angle "98", the strain rate "58" are associated with the presence or absence of defects "1", the surface angle "109", the strain rate "62" are associated with the presence or absence of defects "1", and the surface angle "117", the strain rate "80" are associated with the presence or absence of defects "1".

[0031] In addition, the surface angle "176", strain rate "90" are associated with the presence or absence of defects "0", the surface angle "137", strain rate "85" are associated with the presence or absence of defects "0", and the surface angle "158", strain rate "85" are associated with the presence or absence of defects "0".

[0032] The mesh data 24b is data of a mesh generated for the surface of the formed object model for analyzing the formed object model received by the forging analysis device 10. The surface angle data 24c is data of the angle formed by the surfaces of adjacent meshes. The distance data 24d is data of the distance between the die used in forging and the formed object model received by the forging analysis device 10. The strain rate data 24e is data of the strain rate calculated in the analysis mesh received by the forging analysis device 10. The predicted value data 24f is numerical data used to determine the presence or absence of defects, and is calculated based on the surface angle data 24c and the strain rate data 24e according to a prediction formula calculated by logistic regression analysis.

[0033] The control unit 25 is a control unit that controls the entire forging defect determination device 20, and includes a data reception processing unit 25a, a regression coefficient calculation unit 25b, a threshold calculation unit 25c, a surface angle calculation unit 25d, a predicted value calculation unit 25e, a determination unit 25f, and a display control unit 25g. In practice, by loading their programs into the CPU and executing them, the data reception processing unit 25a, the regression coefficient calculation unit 25b, the threshold calculation unit 25c, the surface angle calculation unit 25d, the predicted value calculation unit 25e, the determination unit 25f, and the display control unit 25g respectively execute corresponding processes. The control unit 25 is an example of the second control unit in the present invention.

[0034] The data reception processing unit 25a is a processing unit that receives data from the forging analysis device 10 and stores it in the storage unit 24. Specifically, it receives the mesh data 14b from the forging analysis device 10 and stores it in the storage unit 24 as the mesh data 24b, receives the distance data 14c from the forging analysis device 10 and stores it in the storage unit 24 as the distance data 24d, and receives the strain rate data 14d from the forging analysis device 10 and stores it in the storage unit 24 as the strain rate data 24e.

[0035] The regression coefficient calculation unit 25b is a processing unit that calculates the regression coefficient representing the relationship between the target variable and the explanatory variable in logistic regression analysis, and this logistic regression analysis is used to calculate the predicted value for determining the presence or absence of defects in the formed object model using forging. Specifically, here, the target variable is the presence or absence of defects, and the explanatory variables are the mesh surface angle and the strain rate.

[0036] Here, if the probability that the binary data Y consisting of 0 / 1 representing the target variable, i.e., the presence or absence of defects, is Y = 1 is set as p(Y = 1), the two explanatory variables of the surface angle and the strain rate are respectively set as x1 and x2, and the regression coefficients are respectively set as β0, β1, and β2, the prediction formula of the logistic regression analysis can be expressed by Equation 1:

[0037] Moreover, when the probability that the binary data Y consisting of 0 / 1 representing the presence or absence of defects is Y = 1 is set as P = p(Y = 1), the logit can be expressed by logit(P) = log(P / (1 - P)).

[0038] The regression coefficient calculation unit 25b calculates the regression coefficients using the maximum likelihood method. The maximum likelihood method refers to a method of inferring the parameters that maximize the likelihood from the given observation points. Specifically, the probability that the binary data Y is Y = 1 is set as P = p(Y = 1), and the probability that Y = 0 is set as 1 - P = p(Y = 0). Then, when Y (Y1, Y2,..., Yn) composed of n data are independent of each other and satisfy p(Yi - 1) = Pi, the regression coefficient βi is calculated using the log-likelihood function to maximize the log-likelihood function. In this calculation, the Newton-Raphson method is used.

[0039] The threshold calculation unit 25c is a processing unit that calculates the threshold used in the determination of the presence or absence of defects. Specifically, based on the prediction formula of the logistic regression analysis and the forging forming data 24a, the predicted value is calculated using the regression coefficients obtained by the regression coefficient calculation unit 25b, and the value between the minimum value of the predicted value in the case of the presence of defects and the maximum value of the predicted value in the case of the absence of defects is set as the threshold.

[0040] For example Figure 8 As shown, the predicted values are calculated based on the surface angle and the strain rate in the case of the presence of defects (objective function = 1) and the case of the absence of defects (objective function = 0). Here, the predicted value "0.975904" is associated with the objective function "1", the surface angle "98", and the strain rate "58", the predicted value "0.926944" is associated with the objective function "1", the surface angle "109", and the strain rate "62", and the predicted value "0.976955" is associated with the objective function "1", the surface angle "117", and the strain rate "80".

[0041] In addition, the predicted value "0.017384" is associated with the objective function "0", the surface angle "176", and the strain rate "90", the predicted value "0.790499" is associated with the objective function "0", the surface angle "137", and the strain rate "85", and the predicted value "0.018642" is associated with the objective function "0", the surface angle "158", and the strain rate "70".

[0042] The threshold calculation unit 25c sets a value between the minimum value "0.926944" of the predicted value of the objective function "1" and the maximum value "0.790499" of the predicted value of the objective function "0" as the threshold value.

[0043] The surface angle calculation unit 25d is a processing unit that reads the mesh data 24b and calculates the angle between the surface forming the mesh of the read mesh data 24b and the surface forming the adjacent mesh. The surface angle calculation unit 25d calculates the surface angles of all adjacent meshes generated in the formed object model. The calculated angles of the surfaces forming the adjacent meshes are stored in the storage unit 24 as surface angle data 24c in a manner corresponding to the forging process ID.

[0044] 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 surface angle data 24c and the strain rate data 24e. Specifically, the surface angle data 24c and the strain rate data 24e are read from the storage unit 24, and the predicted value is calculated by substituting into the prediction formula of logistic regression analysis using the regression coefficients calculated by the regression coefficient calculation unit 25b. This predicted value is stored in the storage unit 24 as predicted value data 24f in a manner corresponding to the forging process ID.

[0045] The determination unit 25f is a processing unit that determines whether there is a defect in the formed object model based on the distance data 24d and the predicted value data 24f. Specifically, when the distance data 24d and the predicted value data 24f are read from the storage unit 24 and the distance data 24d is a positive value, it is determined that there is a defect when the predicted value data 24f is greater than the threshold value, and it is determined that there is no defect when the predicted value data 24f is less than or equal to the threshold value. In addition, when the distance data 24d is a negative value, the determination is skipped.

[0046] The display control unit 25g is a processing unit that controls the display of the presence or absence of a defect on a specified display unit 21 based on the result of the determination unit 25f.

[0047] <Angle of the surface forming the adjacent mesh> Next, the calculation of the angle of the surface forming the adjacent mesh (surface angle) performed in the surface angle calculation unit 25d of the forging defect determination device 20 will be described. Figure 9 It is an explanatory diagram for explaining the calculation of the surface angle. As Figure 9As shown, the surface angle is the angle θ formed by the first mesh surface 41a and the second mesh surface 41b.

[0048] The equation of the plane of the first mesh surface 41a is expressed as a1x + b1y + c1z + d1 = 0 and In addition, the equation of the plane of the second mesh surface 41b is expressed as a2x + b2y + c2z + d2 = 0. The angle θ formed by the first mesh surface 41a and the second mesh surface 41b can be calculated using equation (2).

[0049] <An example of the calculation result of the surface angle> Next, an example of the calculation result of the surface angle will be described. Figure 10 is a diagram showing an example of the calculation result of the surface angle. As Figure 10 shown, the surface angle calculation unit 25d calculates all the angles of the surfaces of the adjacent meshes formed in the formed object model 110. The surface angles of the parts with less shape change in the formed object model 110 show values close to 180 degrees, and in the case where the shape change of the formed object model 110 is large, the surface angles show values close to 90 degrees.

[0050] <The processing sequence of the forging analysis device 10> Next, for Figure 1B the processing sequence of the forging analysis device 10 shown will be described. Figure 11 is shown Figure 1B is a flowchart showing the processing sequence of the forging analysis device 10 shown. As Figure 11 shown, the forging analysis device 10 generates an analysis mesh of the formed object model (step S101). And the forging analysis device 10 calculates the distance between the formed object model and the mold (step S102).

[0051] After that, the forging analysis device 10 calculates the strain rate (step S103). And the forging analysis device 10 determines whether the forging process after the calculation is the final process (step S104). When the forging analysis device 10 determines that it is the final process (step S104: Yes), it sends the mesh data 14b, the distance data 14c, and the strain rate data 14d to the forging defect determination device 20 (step S105) and ends the process.

[0052] On the other hand, when the forging analysis device 10 determines that it is not the final process (step S104: No), it reads the data of the formed object model of the next process (step S106) and proceeds to step S101.

[0053] <The processing sequence of the forging defect determination device 20> Next, the processing sequence of the forging defect judgment device 20 will be described. Figure 12 shows Figure 1B a flowchart showing the processing sequence of the forging defect judgment device 20 shown. As Figure 12 shown, the forging defect judgment device 20 reads the mesh data 24b from the storage unit 24 (step S201). Then, the forging defect judgment device 20 calculates the surface angle (step S202).

[0054] After that, the forging defect judgment device 20 reads the strain rate data 24e from the storage unit 24 (step S203). And, the forging defect judgment device 20 reads the distance data 24d from the storage unit 24 (step S204). After that, the forging defect judgment device 20 calculates a predicted value based on the surface angle and the strain rate (step S205).

[0055] And, the forging defect judgment device 20 determines whether the distance is "0" or less (step S206). When the distance of the forging defect judgment device 20 is "0" or less (step S206: Yes), it proceeds to step S210.

[0056] On the other hand, when the distance of the forging defect judgment device 20 is not "0" or less (step S206: No), it determines whether the predicted value is below a specified threshold value (step S207). When the predicted value of the forging defect judgment device 20 is below the specified threshold value (step S207: Yes), it determines that there is "no" defect (step S209) and proceeds to step S210. When the predicted value of the forging defect judgment device 20 is not below the specified threshold value (step S207: No), it determines that there is "a" defect (step S208) and proceeds to step S210.

[0057] And, the forging defect judgment device 20 determines whether the determined forming process is the final process (step S210). When it is determined that it is the final process (step S210: Yes), the processing ends. On the other hand, when the forging defect judgment 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.

[0058] <Example of display of presence or absence of defect> Next, an example of display of the presence or absence of a defect in the forging defect judgment device 20 will be described. Figure 13 is a diagram showing an example of display of the presence or absence of a defect in the forging defect judgment device 20. As Figure 13 shown, the forging defect judgment device 20 displays the position determined as a defect by the judgment unit 25f as the defect occurrence area 120.

[0059] As described above, in the present embodiment, the forging defect judgment system has a forging analysis device 10 and a forging defect judgment device 20. The forging analysis device 10 generates an analysis grid of the formed object model, calculates the distance between the formed object model and the mold, and calculates the strain rate in each analysis grid. The forging defect judgment device 20 calculates the regression coefficient of the prediction formula of the logistic regression analysis for calculating the predicted value, calculates the surface angle of the grid surface formed by adjacent grids, uses the prediction formula of the logistic regression analysis to calculate the predicted value based on the surface angle and the strain rate, and judges the presence or absence of defects based on the predicted value and the distance.

[0060] In addition, in the above embodiment, it is described that in the forging analysis device 10, the generation of the grid, the calculation of the distance, and the calculation of the strain rate are performed in all the forging forming processes, and the grid data, the distance data, and the strain rate data are sent to the forging defect judgment device 20. In the forging defect judgment device 20, the presence or absence of defects in all the forging forming processes is judged based on the received data. However, it may also be that for each forging forming process, whenever the generation of the grid, the calculation of the distance, and the calculation of the strain rate are performed in the forging analysis device 10, the grid data, the distance data, and the strain rate data are sent to the forging defect judgment device 20. In the forging defect judgment device 20, the presence or absence of defects is judged using the data of one forging forming process received.

[0061] In addition, in the above embodiment, it is described that the forging defect judgment system has a forging analysis device 10 for analyzing the formed object model in the forging forming process and a forging defect judgment device 20 for judging the presence or absence of defects. However, the functions of the forging analysis device 10 and the forging defect judgment device 20 may also be implemented in one device.

[0062] In addition, in the above embodiment, it is described that the surface angle and the strain rate are used as the explanatory variables of the logistic regression analysis. However, other parameters such as the processing temperature that affect the occurrence of defects in the forging forming may also be added to the explanatory variables.

[0063] Each structure illustrated in the above embodiments is only a functional schematic diagram, and it is not necessary to physically adopt the illustrated structure. That is, the distribution and integration methods of the respective devices are not limited to the illustrated cases, and all or a part of them can be functionally or physically distributed and inherited in any unit according to various loads, usage conditions, etc.

[0064] The forging defect judgment system, the forging defect judgment device, and the forging defect judgment method according to the present invention are applicable to efficiently judging the presence or absence of defects in the forging forming.

Claims

1. A forging defect judgment system, characterized in that: include: A forging analysis device (10) configured to generate a formed object model in a plurality of forming steps of forging forming, and comprising a first control unit (15) configured to calculate a strain rate based on an analysis grid of the formed object model; and A forging defect judgment device (20) is configured to predict whether there is a defect phenomenon when the formed object is formed in each forming process of the forging forming based on the formed object model, and includes a second control unit (25), and the second control unit (25) is configured to judge whether there is the defect phenomenon based on the strain rate and the surface angle of the adjacent analysis grid surface.

2. The forging defect judgment system according to claim 1, characterized in that: The first control unit (15) is configured as follows: generating the analysis grid of the formed object model; Calculating the distance between the formed object model and the mold; as well as The strain rate is calculated based on the amount of change in the molded object model during the molding process.

3. The forging defect judgment system according to claim 2, characterized in that: The second control unit (25) is configured as follows: calculating the face angles of the faces of the adjacent analysis grids; Calculating a predicted value of the defect phenomenon according to the strain rate and the face angle; and The presence or absence of the defect phenomenon is determined based on the predicted value and the distance.

4. The forging defect judgment system according to claim 3, characterized in that: The second control unit (25) is configured as follows: The predicted value of the defect phenomenon is calculated based on a logistic regression analysis using the strain rate and the face angle as explanatory variables and the occurrence of the defect phenomenon as a target variable.

5. A forging defect judgment device (20), which is configured to predict whether there is a defect phenomenon when the formed object is formed in each forming process of forging based on a formed object model in a plurality of forming processes of forging, characterized in that: The invention comprises a second control unit (25) which is configured as follows: Calculating the strain rate according to the analysis grid of the formed object model; and The presence or absence of the defect phenomenon is determined based on the strain rate and the surface angle of the adjacent analysis grid surfaces.

6. A forging defect judgment method, performed by a forging defect judgment system, the forging defect judgment system comprising: A forging analysis device (10) configured to generate a model of a formed object in a plurality of forming steps of forging forming; and a forging defect judgment device (20) configured to predict whether there are defects when the formed object is formed in each forming process of the forging forming according to the formed object model, characterized in that the forging defect judgment method includes: The forging analysis device (10) calculates the strain rate based on the analysis grid of the formed object model; and The forging defect judgment device (20) judges whether the defect phenomenon exists based on the strain rate and the surface angle of the surface of the adjacent analysis grid.

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