Forging defect prediction device, forging defect prediction method, and forging defect prediction program

Abstract

A forging defect prediction device (10) is provided with a processor (81). A processor (81) configured so as to calculate a surface pressure of each analysis grid on the basis of stresses applied to a plurality of analysis grids forming the molded object model; a friction coefficient determination unit that determines a friction coefficient as coulomb friction when the surface pressure of each analysis grid is equal to or less than a predetermined threshold value, and determines the friction coefficient as shear friction when the surface pressure is higher than the predetermined threshold value; switching the determined friction coefficient, and analyzing the grid for analysis at the same time; and predicting whether or not there is a defect phenomenon in the molded object model on the basis of the surface angles of the surfaces of the adjacent analysis grids.

Description

Technical Field

[0001] This invention relates to a forging defect prediction device, a forging defect prediction method, and a forging defect prediction program. Background Technology

[0002] In forging, hammers or dies are mostly used to apply a large force to metal ingots or cylindrical metal blocks, causing them to undergo plastic deformation and thus achieve the desired shape. Analytical techniques for simulating the plastic deformation that occurs in the metal block during forging are known (e.g., see Japanese Patent Application Laid-Open No. 2013-210735, Japanese Patent Application Laid-Open No. 2005-207774, Japanese Patent Application Laid-Open No. 2004-000781, Japanese Patent Application Laid-Open No. 2009-059255).

[0003] In addition, there are known methods for analyzing billet deformation during the casting process. For example, Japanese Patent Application Publication No. 2018-118300 discloses a method for analyzing billet deformation in a die-casting process. This method uses a friction coefficient function based on casting conditions and lubrication conditions to analyze the frictional stress applied to a specified part of the billet by a fixed mold, that is, the mold opening resistance formed by the contact surface pressure of the fixed mold. Summary of the Invention

[0004] However, in the aforementioned Japanese Patent Application Publication Nos. 2013-210735, 2005-207774, 2004-000781, 2009-059255, and 2018-118300, the occurrence of defects cannot be accurately predicted when aluminum forging is being carried out. These technologies utilize Coulomb friction, meaning that frictional stress is proportional to contact pressure. However, aluminum no longer follows Coulomb's law of friction when the contact pressure increases.

[0005] Therefore, in aluminum forging, it is important to accurately and efficiently predict the occurrence of defects. Efficient defect prediction is also important in all types of forging forming.

[0006] The present invention provides a forging defect prediction device, a forging defect prediction method, and a forging defect prediction program that can accurately and efficiently predict the occurrence of defects during forging.

[0007] The forging defect prediction device according to the first aspect of this disclosure is configured to generate a model of a formed object in multiple forming processes of forging, and predict whether defects will occur when forming the formed object in each forming process of forging based on the model of the formed object. The forging defect prediction device includes a processor configured to calculate the surface pressure of each analysis grid based on the stress applied to multiple analysis grids forming the model of the formed object; determine the friction coefficient as Coulomb friction when the surface pressure of each analysis grid is below a predetermined threshold, and determine the friction coefficient as shear friction when the surface pressure is above the predetermined threshold; switch the friction coefficient while simultaneously analyzing the analysis grids; and predict whether the formed object model will produce the defect based on the face angle of adjacent analysis grid faces.

[0008] Alternatively, in the forging defect prediction device according to the first aspect of this disclosure, the analysis mesh in each forming process may contain multiple nodes, and the processor may be configured to calculate the surface pressure based on the average value of the stresses applied to the multiple nodes.

[0009] Alternatively, in the forging defect prediction device according to the first aspect of this disclosure, the processor may be configured to determine the coefficient of friction of Coulomb friction based on the surface pressure when the surface pressure is lower than a predetermined Coulomb threshold, and to reduce the coefficient of friction based on the surface pressure when the surface pressure is higher than the Coulomb threshold but lower than the predetermined threshold.

[0010] Alternatively, in the forging defect prediction device according to the first aspect of this disclosure, the processor may be configured to predict that the defect phenomenon has occurred when the face angle of the face of the adjacent analysis grid is below a predetermined angle threshold, and to predict that the defect phenomenon has not occurred when the face angle of the face of the adjacent analysis grid is above the predetermined angle threshold.

[0011] Alternatively, in the forging defect prediction device according to the first aspect of this disclosure, the processor may be configured to calculate the pressure of the gas present in the gap when there is a gap between the formed article and the forging die, and analyze the analysis grid based on the pressure of the gas.

[0012] Furthermore, the second aspect of this disclosure relates to a forging defect prediction method. The forging defect prediction device used in this method is configured to generate a model of a formed object in multiple forming processes of forging, and to determine whether defects exist during the forming of the formed object in each forming process of forging based on the model of the formed object. The forging defect prediction method includes: a surface pressure calculation step, wherein the surface pressure of each analysis grid is calculated based on the stress applied to multiple analysis grids forming the model of the formed object; a determination step, wherein when the surface pressure of each analysis grid is below a predetermined threshold, the friction coefficient is determined as Coulomb friction, and when the surface pressure is above the predetermined threshold, the friction coefficient is determined as shear friction; an analysis step, switching the friction coefficient determined by the determination step while simultaneously analyzing the analysis grids; and a prediction step, wherein the presence or absence of the defect in the model of the formed object is predicted based on the face angles of adjacent analysis grid faces analyzed by the analysis step.

[0013] Furthermore, a third aspect of this disclosure relates to a forging defect prediction program, in which a forging defect prediction device is configured to generate a model of a formed object in multiple forming processes of forging, and predict, based on the model of the formed object, whether defects will occur when forming the formed object in each forming process of forging. The forging defect prediction program is characterized in that the forging defect prediction device performs the following functions: calculating the surface pressure of each analysis grid based on the stress applied to multiple analysis grids forming the model of the formed object; determining the friction coefficient as Coulomb friction when the surface pressure of each analysis grid is below a predetermined threshold, and determining the friction coefficient as shear friction when the surface pressure is above the predetermined threshold; switching the friction coefficient while simultaneously analyzing the analysis grids; and predicting whether the formed object model will produce the defect based on the face angle of adjacent analysis grid faces.

[0014] According to the present invention, it is possible to accurately and efficiently predict the occurrence of defects during forging. Attached Figure Description

[0015] The features, advantages, and industrial applicability of preferred embodiments of the present invention are described below in conjunction with the accompanying drawings, wherein the same reference numerals denote the same parts. Figure 1A This is a diagram illustrating an outline of the forging defect prediction device according to Embodiment 1. Figure 1B This is a diagram illustrating an outline of the forging defect prediction device according to Embodiment 1. Figure 2 It is shown Figure 1A as well as Figure 1BThe diagram shown is a functional block diagram of the forging defect prediction device. Figure 3 This is a diagram illustrating an example of mesh generation for analysis. Figure 4 It is an explanatory diagram used to illustrate the relationship between surface pressure and frictional stress. Figure 5 It is an explanatory diagram used to illustrate the relationship between surface pressure and the coefficient of friction. Figure 6 It is shown Figure 2 The flowchart shows the processing steps of the forging defect prediction device. Figure 7 It is shown Figure 6 The flowchart shown illustrates the processing steps for determining the friction coefficient. Figure 8A This is a diagram showing an outline of the forging defect prediction device according to Embodiment 2. Figure 8B This is a diagram showing an outline of the forging defect prediction device according to Embodiment 2. Figure 8C This is a diagram showing an outline of the forging defect prediction device according to Embodiment 2. Figure 8D This is a diagram showing an outline of the forging defect prediction device according to Embodiment 2. Figure 8E This is a diagram showing an outline of the forging defect prediction device according to Embodiment 2. Figure 8F This is a diagram showing an outline of the forging defect prediction device according to Embodiment 2. Figure 9A It is an explanatory diagram used to illustrate the changes in gap. Figure 9B It is an explanatory diagram used to illustrate the changes in gap. Figure 9C It is an explanatory diagram used to illustrate the changes in gap. Figure 10 This is a functional block diagram showing the configuration of the forging defect prediction device according to Embodiment 2. Figure 11 It is shown Figure 10 The flowchart shows the processing steps of the forging defect prediction device. Figure 12 This is a diagram illustrating an example of hardware configuration. Detailed Implementation

[0016] Hereinafter, embodiments of the forging defect prediction device, forging defect prediction method, and forging defect prediction program involved in the present invention will be described in detail with reference to the accompanying drawings.

[0017] Implementation Method 1 This document provides an overview of the forging defect prediction device 10 according to Embodiment 1. Figure 1A as well as Figure 1B This is a diagram showing an outline of the forging defect prediction device 10 according to Embodiment 1.

[0018] Overview of the forging defect prediction device 10 like Figure 1A As shown, the manufacturing of products using conventional forging technology requires multiple forming processes. Here, the workpiece is prepared in forming process 1, and forming is repeated on different parts in each forming process. After forming process 30, the forming process is completed in forming process 100. In forging, the shape caused by raw material entrapment inside the forging die is called a "defect." Generally, the location of defects is predicted in advance using analytical techniques.

[0019] However, in analytical techniques, since the frictional stress generated by the contact between the die used in forging and the raw material is calculated using Coulomb's law of friction, defects cannot be predicted, and improving the accuracy of prediction has become a challenge.

[0020] like Figure 1B As shown, in each forming process of forging, the present invention performs numerical analysis based on the formed object model to calculate the surface pressure and determine the friction coefficient, thereby predicting the presence or absence of defects. The forging defect prediction device 10 generates multiple analysis meshes constituting the formed object model. Then, the forging defect prediction device 10 performs numerical analysis, calculates the stress at the nodes of each analysis mesh based on each analysis mesh, and calculates the surface pressure based on the stress.

[0021] Subsequently, the forging defect prediction device 10 determines the coefficient of friction between the formed workpiece and the die based on the surface pressure. Here, when the surface pressure is below a specified threshold, the coefficient of friction is determined according to Coulomb's law of friction, that is, the frictional stress increases with the surface pressure; when the surface pressure is above the specified threshold, the coefficient of friction is determined according to the law of shear friction, that is, the frictional stress is constant and independent of the surface pressure.

[0022] Then, after the final step of the forming process, the forging defect prediction device 10 calculates the face angles between adjacent faces of the analysis grid and predicts whether there is a defect based on the face angles. Specifically, if the face angle between adjacent faces of the analysis grid is higher than a predetermined angle threshold, it is predicted that there is no defect; if the face angle is lower than the predetermined angle threshold, it is predicted that there is a defect.

[0023] Composition of forging defect prediction device 10 Next, an explanation Figure 1A as well as Figure 1BThe forging defect prediction device 10 shown is configured as follows. Figure 2 It is shown Figure 1A as well as Figure 1B The diagram shows a functional block diagram of the forging defect prediction device 10. Figure 2 As shown, the forging defect prediction device 10 includes a display unit 11, an input unit 12, a storage unit 14, and a control unit 15. The display unit 11 is a display device such as a liquid crystal display (LCD) that displays various information. The input unit 12 is an input device such as a mouse or keyboard.

[0024] Storage unit 14 is a storage device such as a hard disk drive or non-volatile memory, storing forging process data 14a, friction coefficient table 14b, mesh data 14c, surface pressure data 14d, and friction coefficient data 14e. Forging process data 14a contains data on the formed object model from each forming process using forging technology. Friction coefficient table 14b shows the relationship between the friction coefficient and surface pressure.

[0025] Mesh data 14c is data from multiple meshes generated on the surface of the formed object model for analysis. Surface pressure data 14d is surface pressure data calculated at the nodes of each analysis mesh. Friction coefficient data 14e is friction coefficient data at the nodes of each mesh, determined based on the surface pressure.

[0026] The control unit 15 is the control unit for the entire forging defect prediction device 10, and includes a mesh generation unit 15a, an analysis unit 15b, a surface pressure calculation unit 15c, a friction coefficient determination unit 15d, and a defect prediction unit 15e. In practice, by loading these programs into the CPU and executing them, processing flows corresponding to the mesh generation unit 15a, the analysis unit 15b, the surface pressure calculation unit 15c, the friction coefficient determination unit 15d, and the defect prediction unit 15e are executed.

[0027] The mesh generation unit 15a is a processing unit that generates multiple meshes 40 for analysis from the shaped object model. The density of the generated meshes 40 can be adjusted according to the shape of the shaped object model. For example... Figure 3 As shown, a fine mesh 40 is generated in the region of the shaped model 110 where there are shape changes.

[0028] The generated mesh data, referred to as mesh data 14c, is stored in storage unit 14 in a manner corresponding to the forging process ID. Furthermore, the case where a triangular mesh 40 is generated using the diagonals of a rectangle is described here, but any triangular mesh 40 can also be generated. Additionally, the shape of the mesh 40 can also be quadrilateral, hexagonal, etc.

[0029] Analysis unit 15b is a processing unit that performs numerical analysis based on the nodes of the generated mesh. The numerical analysis can utilize methods such as the finite element method. Analysis unit 15b performs analysis based on the friction coefficient between the molded object and the die, as determined by friction coefficient determination unit 15d.

[0030] The surface pressure calculation unit 15c is a processing unit that calculates the surface pressure applied from the metal mold to the formed object model based on the stress at the nodes of each mesh. Specifically, the surface pressure P is calculated based on the stress σ in the X-axis direction at the mesh nodes. 11 Stress σ in the Y-axis direction 22 and the stress σ in the Z-axis direction 33 The value is obtained by calculation using equation (1). Here, the surface pressure P is the average value of the stress. [Formula 1]

[0031] The friction coefficient determination unit 15d is a processing unit that determines the friction coefficient between the molded part and the die based on the surface pressure. For example... Figure 4 As shown, when the frictional stress τ is between the surface pressure 0 (MPa) and the specified threshold P1, the friction coefficient determination unit 15d determines the friction coefficient based on Coulomb's friction law, that is, it increases according to the surface pressure. When the surface pressure is above the specified threshold P1, the friction coefficient determination unit 15d determines the friction coefficient based on the shear friction law, that is, the frictional stress τ is a constant value τ1 (MPa).

[0032] The reason is that the characteristics of the friction interface in forging are as follows: when the surface pressure is low, due to the roughness of the solid surface, the contact occurs at the top of the tiny protrusions on the solid surface (the actual contact point). When the surface pressure increases, the area of ​​the actual contact point (the actual contact area) increases accordingly, thus following Coulomb's law of friction, which states that the frictional stress changes with the surface pressure. When the surface pressure is higher than the specified threshold P1, the raw material and the die are in contact in almost the entire area, and the actual contact area no longer changes with the surface pressure. Therefore, the frictional stress follows the law of shear friction.

[0033] In addition, such as Figure 5 As shown, the friction coefficient determination unit 15d has the following characteristics: when the surface pressure P is lower than a predetermined coulomb threshold P2, the friction coefficient is a constant value μ1; when the surface pressure P is higher than the predetermined coulomb threshold P2 but lower than a predetermined threshold P1, the friction coefficient decreases as the surface pressure P increases. This characteristic is stored in the storage unit 14 as a friction coefficient table 14b, and the friction coefficient determination unit 15d determines the friction coefficient based on each surface pressure.

[0034] The defect prediction unit 15e is a processing unit that calculates the face angles of adjacent faces of the analysis mesh and predicts whether or not a defect exists based on these face angles. Specifically, if the face angle is higher than a predetermined angle threshold, it is predicted that there is no defect, and if the face angle is lower than the predetermined angle threshold, it is predicted that there is a defect.

[0035] Processing steps of forging defect prediction device 10 Next, the processing steps of the forging defect prediction device 10 will be explained. Figure 6 It is shown Figure 2 A flowchart illustrating the processing steps of the forging defect prediction device 10 is shown. Figure 6 As shown, the forging defect prediction device 10 generates multiple analysis meshes for the formed article model (step S101). Then, the forging defect prediction device 10 performs numerical analysis using the finite element method (step S102).

[0036] Next, the forging defect prediction device 10 calculates the surface pressure of each grid (step S103). Then, the forging defect prediction device 10 performs friction coefficient determination processing based on the surface pressure (step S104). Finally, the forging defect prediction device 10 determines whether it is the final process (step S105).

[0037] If the forging defect prediction device 10 determines that it is not the final process (step S105: No), it reads the data of the formed product model for the next process (step S106) and proceeds to step S102. On the other hand, if the forging defect prediction device 10 determines that it is the final process (step S105: Yes), it calculates the face angles of adjacent faces of the analysis mesh (step S107).

[0038] Then, the forging defect prediction device 10 determines whether the face angle between adjacent faces of the analysis mesh is below a predetermined angle threshold (step S108). If the face angle between adjacent faces of the analysis mesh is below the predetermined angle threshold (step S108: Yes), the forging defect prediction device 10 predicts a defect (step S109). Conversely, if the face angle between adjacent faces of the analysis mesh is not below the predetermined angle threshold (step S108: No), the forging defect prediction device 10 predicts no defect (step S110).

[0039] Processing steps for determining the friction coefficient Next, an explanation Figure 6 The steps for determining the friction coefficient are shown below. Figure 7 It is shown Figure 6 The flowchart shown illustrates the steps involved in determining the friction coefficient. Figure 7As shown, the forging defect prediction device 10 determines whether the surface pressure is above a predetermined threshold (step S201). Then, if the surface pressure is above the predetermined threshold (step S201: Yes), the forging defect prediction device 10 sets the friction coefficient to m (step S202) and proceeds to... Figure 6 Step S105.

[0040] On the other hand, if the surface pressure is not above a predetermined threshold (step S201: No), the forging defect prediction device 10 determines whether the surface pressure is below a predetermined coulomb threshold (step S203). If the surface pressure is below the predetermined coulomb threshold (step S203: Yes), the forging defect prediction device 10 sets the friction coefficient to μ1 and proceeds to... Figure 6 Step S105.

[0041] On the other hand, when the surface pressure is not lower than the specified coulomb threshold (step S203: no), the forging defect prediction device 10 determines the friction coefficient corresponding to the surface pressure (step S205) and proceeds to... Figure 6 Step S105.

[0042] As described above, in this first embodiment, the forging defect prediction device 10 generates an analysis mesh for the formed part model, performs finite element analysis, and calculates the surface pressure of each mesh. Then, the forging defect prediction device 10 determines the friction coefficient based on the surface pressure. Regarding the determination of the friction coefficient, when the surface pressure is above a predetermined threshold, the friction coefficient is determined according to the shear friction law; when the surface pressure is below a predetermined Coulomb threshold, the friction coefficient is determined according to the Coulomb friction law. Furthermore, when the surface pressure is above the predetermined Coulomb threshold but does not exceed the predetermined threshold, the friction coefficient is determined by decreasing the friction coefficient based on the surface pressure.

[0043] Implementation Method 2 In the first embodiment described above, the case where the forging defect prediction device 10 determines the friction coefficient based on the surface pressure was explained. For the forging defect prediction device 20 involved in the second embodiment, the case where the influence of the gap between the raw material and the die during forging is reflected in the analysis is explained.

[0044] Overview of the forging defect prediction device 20 Figures 8A to 8F This is a diagram showing an outline of the forging defect prediction device 20 according to Embodiment 2. Figures 8A to 8C As shown, when performing analysis, the forging defect prediction device 20 considers the factor that, when air or the like enters the gap G between the die M and the formed workpiece W, the air is compressed and pressure is applied to the formed workpiece W as the forming process proceeds.

[0045] For example, such as Figure 8AAs shown, in molding process 1, when the molded part W is pressed into the mold M, air is formed and enters the gap G between the molded part W and the mold M. Afterwards, as... Figure 8B As shown, in forming step 30, the molded article W is further pressed in, and the volume of the gap G is smaller than that in forming step 1 due to compression. In this case, because the air in the gap G is compressed, the portion of the molded article W facing the gap G bears pressure from the gap G, and the rate at which the molded article W undergoes plastic deformation slows down. Then, as... Figure 8C As shown, in the forming process 100, the formed product W is affected by the pressure of the gap G, resulting in defects.

[0046] On the other hand, if air or other substances do not enter gap G, such as Figure 8D As shown, even though the molded part W is pressed into the mold M in molding process 1, although a gap G exists in the analysis, no pressure is applied to the molded part W through the gap G. Afterwards, as... Figure 8E As shown, in forming process 30, the molded article W is further pressed in the direction of the arrow due to plastic deformation. Then, as... Figure 8F As shown, in molding process 100, the molded article W undergoes plastic deformation in the mold M. In this case, it is difficult to reproduce the defects of the molded article W in the analysis.

[0047] Pressure in gap G Next, the pressure in gap G will be explained. Figures 9A to 9C This is an explanatory diagram used to illustrate the change in gap G. For example... Figure 9A As shown, in molding process 1, the molded object model 110 is in a state where a gap G exists. Air is present in this gap G, and in molding process 1, the air pressure is, for example, one atmosphere. Furthermore, the gap G is a closed space formed by the molded object W and the mold M.

[0048] Then, as Figure 9B As shown, in forming process 30, the volume of gap G decreases due to the plastic deformation of the molded part W. Since gap G is a closed space with a constant pressure × volume, if the volume of gap G decreases, the pressure inside gap G becomes greater than one atmosphere. Then, as... Figure 9C As shown, in forming process 100, the volume of gap G is further reduced, and the pressure within gap G is further increased compared to the pressure in forming process 30. When analyzing the plastic deformation of the formed product W, the forging defect prediction device 20 takes the pressure within this gap G into account during the analysis.

[0049] Composition of forging defect prediction device 20 Next, the configuration of the forging defect prediction device 20 will be explained. Figure 10This is a functional block diagram showing the configuration of the forging defect prediction device 20 according to Embodiment 2. Furthermore, regarding... Figure 2 The same reference numerals are used to mark the same parts of the forging defect prediction device 10 shown in the figure, and detailed descriptions are omitted.

[0050] like Figure 10 As shown, the forging defect prediction device 20 includes a display unit 11, an input unit 12, a storage unit 24, and a control unit 25. The storage unit 24 is a storage device such as a hard disk drive or non-volatile memory, storing forging process data 14a, a friction coefficient table 14b, grid data 14c, surface pressure data 14d, friction coefficient data 14e, and gap pressure data 24a. The gap pressure data 24a is the pressure data of the air existing between the formed workpiece W and the gap G.

[0051] The control unit 25 is the overall control unit for the forging defect prediction device 20, and includes a mesh generation unit 15a, an analysis unit 15b, a surface pressure calculation unit 15c, a friction coefficient determination unit 15d, a defect prediction unit 15e, and a gap pressure calculation unit 25a. In practice, by loading these programs into the CPU and executing them, processing flows corresponding to the mesh generation unit 15a, analysis unit 15b, surface pressure calculation unit 15c, friction coefficient determination unit 15d, defect prediction unit 15e, and gap pressure calculation unit 25a are executed.

[0052] The gap pressure calculation unit 25a is a processing unit that calculates the pressure of the air present in the gap G between the molded part W and the mold M. Specifically, based on the formula air pressure Pa × air volume V = C, when the air volume V decreases in the finite element analysis, Pa = C / V is calculated to obtain Pa. Furthermore, the calculated air pressure Pa is set as the pressure parameter at the mesh junction of the molded part model in contact with the gap G in the finite element analysis.

[0053] Processing steps of forging defect prediction device 20 Next, the processing steps of the forging defect prediction device 20 will be explained. Figure 11 It is shown Figure 10 The flowchart shows the processing steps of the forging defect prediction device 20. (See attached flowchart.) Figure 11 As shown, the forging defect prediction device 20 generates an analysis mesh for the formed part model (step S301). Then, the forging defect prediction device 20 performs numerical analysis using the finite element method (step S302). Furthermore, in the numerical analysis, the pressure of the gap G and the friction coefficient between the formed part W and the die M are analyzed.

[0054] Next, the forging defect prediction device 20 calculates the pressure of the gap (step S303). Then, the forging defect prediction device 20 calculates the surface pressure of each grid (step S304). Next, the forging defect prediction device 20 performs friction coefficient determination processing based on the surface pressure (step S305). Then, the forging defect prediction device 20 determines whether it is the final process (step S306).

[0055] If the forging defect prediction device 20 determines that it is not the final process (step S306: No), it reads the data of the formed product model for the next process (step S307) and proceeds to step S302. On the other hand, if the forging defect prediction device 20 determines that it is the final process (step S306: Yes), it calculates the face angle between adjacent faces of the analysis mesh (step S308).

[0056] Then, the forging defect prediction device 20 determines whether the face angle between adjacent faces of the analysis grid is below a predetermined angle threshold (step S309). If the face angle between adjacent faces of the analysis grid is below the predetermined angle threshold (step S309: Yes), the forging defect prediction device 20 predicts a defect (step S310). Conversely, if the face angle between adjacent faces of the analysis grid is not below the predetermined angle threshold (step S309: No), the forging defect prediction device 20 predicts no defect (step S311). Furthermore, the processing steps for determining the friction coefficient are the same as those for the forging defect prediction device 10, therefore their detailed explanation is omitted.

[0057] As described above, in this second embodiment, the forging defect prediction device 20 generates an analysis mesh for the formed part model, performs finite element analysis, and calculates the gap pressure and the surface pressure of each mesh. Then, the forging defect prediction device 20 determines the friction coefficient based on the surface pressure. Regarding the determination of the friction coefficient, when the surface pressure is above a predetermined threshold, the friction coefficient is determined according to the shear friction law; when the surface pressure is below a predetermined Coulomb threshold, the friction coefficient is determined according to the Coulomb friction law. Furthermore, when the surface pressure is above the predetermined Coulomb threshold but below it, the friction coefficient is determined by decreasing the friction coefficient based on the surface pressure.

[0058] Relationship with hardware Next, the correspondence between the forging defect prediction device 10 according to Embodiment 1 and the main hardware configuration of the computer will be explained. Figure 12 This is a diagram illustrating an example of hardware configuration.

[0059] Generally, a computer is constructed by connecting components such as a CPU 81, ROM 82, RAM 83, and non-volatile memory 84 via a bus 85. A hard disk drive can also be used instead of the non-volatile memory 84. For ease of explanation, only the basic hardware configuration is shown.

[0060] Here, the ROM 82 or non-volatile memory 84 stores the programs required to start the operating system (hereinafter referred to as "OS"). When the power is connected, the CPU 81 reads the OS program from the ROM 82 or non-volatile memory 84 and executes it.

[0061] On the other hand, various applications running on the OS are stored in non-volatile memory 84, and the CPU 81 uses RAM 83 as main memory to execute the applications, thereby executing the processes corresponding to the applications.

[0062] Furthermore, the forging defect prediction program of the forging defect prediction device 10 according to Embodiment 1 is stored in non-volatile memory 84, etc., just like other applications, and the CPU 81 loads and executes the forging defect prediction program. In the forging defect prediction device 10 according to Embodiment 1, it includes... Figure 2 The forging defect prediction program corresponding to the mesh generation unit 15a, analysis unit 15b, surface pressure calculation unit 15c, friction coefficient determination unit 15d, and defect prediction unit 15e shown is stored in non-volatile memory 84, etc. The forging defect prediction program is loaded and executed by CPU 81, thereby generating a forging defect prediction process corresponding to the mesh generation unit 15a, analysis unit 15b, surface pressure calculation unit 15c, friction coefficient determination unit 15d, and defect prediction unit 15e.

[0063] The configurations shown in the above embodiments are merely simplified functional illustrations and do not necessarily require the physical structures depicted. That is, the methods of distributing and integrating the devices are not limited to those shown in the illustrations; all or part of the functions can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0064] The forging defect prediction device, forging defect prediction method, and forging defect prediction program involved in this invention are applicable to accurately and efficiently predicting the occurrence of defect phenomena during forging.

Claims

1. A forging defect prediction device (10) configured to generate a shaped object model of a plurality of forming processes of a forging process, and predict whether or not a defect occurs when a shaped object is formed in each forming process of the forging process based on the shaped object model, characterized by comprising a processor (81) configured to: calculate a surface pressure of each analysis grid based on a stress applied to a plurality of analysis grids that form the shaped object model; determine a friction coefficient as Coulomb friction when the surface pressure of each analysis grid is below a prescribed threshold value, and determine the friction coefficient as shear friction when the surface pressure is higher than the prescribed threshold value; switch the friction coefficient while analyzing the analysis grids; and predict whether or not the defect occurs in the shaped object model based on a face angle of a face of an adjacent analysis grid.

2. The forging defect prediction device (10) according to claim 1, characterized in that: the analysis grids in each of the forming processes include a plurality of nodes, and the processor (81) is configured to calculate the surface pressure based on an average value of stresses applied to the plurality of nodes. The processor (81) is configured to: determine a friction coefficient of Coulomb friction based on the surface pressure when the surface pressure is below a prescribed Coulomb threshold value, and reduce the friction coefficient based on the surface pressure when the surface pressure is higher than the Coulomb threshold value and lower than the prescribed threshold value. The processor (81) is configured to predict that the defect occurs when a face angle of the face of an adjacent analysis grid is below a prescribed angle threshold value, and predict that the defect does not occur when the face angle of the face of the adjacent analysis grid is higher than the prescribed angle threshold value. The processor (81) is configured to calculate a pressure of a gas present in a gap between the shaped object and a forging process die when the gap exists, and analyze the analysis grids based on the pressure of the gas. The forging defect prediction method includes: a surface pressure calculation step in which a surface pressure of each analysis grid is calculated based on a stress applied to a plurality of analysis grids that form the shaped object model; a determination step in which a friction coefficient is determined as Coulomb friction when the surface pressure of each analysis grid is below a prescribed threshold value, and the friction coefficient is determined as shear friction when the surface pressure is higher than the prescribed threshold value; an analysis step in which a friction coefficient determined by the determination step is switched while the analysis grids are analyzed; and a prediction step in which whether or not the defect occurs in the shaped object model is predicted based on a face angle of a face of an adjacent analysis grid analyzed by the analysis step. ​ ​ ​ ​ 3. The forging defect prediction device (10) according to claim 1, characterized in that ​ ​ ​ 4. The forging defect prediction device (10) according to claim 1, characterized by ​ 5. The forging defect prediction device (10) according to any one of claims 1 to 4, characterized in that, ​ 6. A forging defect prediction method applied to a forging defect prediction device (10) configured to generate a shaped object model in a plurality of shaping processes of a forging shaping, and predict, based on the shaped object model, whether a defect phenomenon occurs when a shaped object is shaped in each shaping process of the forging shaping, characterized in that, ​ ​ ​ ​ ​ 7. A forging defect prediction program which is applied to a forging defect prediction device (10) configured to generate a shaped object model in a plurality of forming processes of a forging forming, and predict whether or not a defect phenomenon occurs when a shaped object is formed in each forming process of the forging forming based on the shaped object model, characterized by, the forging defect prediction program causing the forging defect prediction device (10) to execute the following: calculating a surface pressure of each analysis grid based on a stress applied to a plurality of analysis grids forming the shaped object model; determining a friction coefficient as Coulomb friction in a case where the surface pressure of each analysis grid is below a prescribed threshold value, and determining the friction coefficient as shear friction in a case where the surface pressure is higher than the prescribed threshold value; switching the friction coefficient while analyzing the analysis grids; and predicting whether or not the defect phenomenon occurs in the shaped object model based on a face angle of a face of an adjacent analysis grid.

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