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

Abstract

The challenge is to accurately and efficiently predict the occurrence of defects during forging. [Solution] The forging defect prediction device 10 generates multiple analysis meshes of the molded product model. The forging defect prediction device 10 then performs numerical analysis to calculate the stress at the nodes of each mesh based on the analysis mesh, and calculates the surface pressure based on the stress. Subsequently, the forging defect prediction device 10 identifies the coefficient of friction between the molded product and the mold based on the surface pressure. Here, the coefficient of friction is identified based on the Coulomb friction law, which states that frictional stress increases in accordance with the surface pressure, when the surface pressure is below a predetermined threshold, and based on the shear friction law, which states that the coefficient of friction remains constant regardless of the surface pressure, when the surface pressure is above a predetermined threshold. Subsequently, the forging defect prediction device 10 calculates the surface angle between adjacent surfaces of the analysis mesh and predicts the presence or absence of defects based on the surface angle.

Description

Technical Field

[0001] The present invention relates to a forging defect prediction device, a forging defect prediction method, and a forging defect prediction program that can appropriately and efficiently predict the occurrence of defects when performing forging forming.

Background Art

[0002] Conventionally, forging forming is often performed by hitting a lump of metal in the form of an ingot or a cylinder with a hammer or a mold to apply a large force and cause plastic deformation to form a shape. Analytical techniques for simulating the plastic deformation of the lump of metal that occurs during such forging forming are known (see, for example, Patent Documents 1 to 4).

[0003] Also, an analytical method for the deformation of rough materials in the case of casting forming is known. For example, Patent Document 5 discloses an analytical method for the deformation of rough materials in a die-casting method, and uses a friction coefficient function based on casting conditions and lubrication conditions to analyze the fixed die friction stress applied to a predetermined part of the rough material from the fixed die, that is, the die opening resistance due to the contact surface pressure of the fixed die.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Patent Document 5

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, the methods described in Patent Documents 1 to 5 above have the problem that they cannot properly predict the occurrence of defects when performing aluminum forging. These conventional techniques use Coulomb friction, which assumes that frictional stress is proportional to contact pressure, but aluminum does not follow this Coulomb friction law when the contact pressure is high.

[0006] Therefore, accurately and efficiently predicting the occurrence of defects is a crucial challenge when performing aluminum forging. This challenge arises not only in aluminum forging but also in various other forging processes.

[0007] The present invention was made to solve the problems (issues) of the above-mentioned prior art, and aims to provide a forging defect prediction device, a forging defect prediction method, and a forging defect prediction program that can predict the occurrence of defects appropriately and efficiently when performing forging. [Means for solving the problem]

[0008] To solve the above-mentioned problems and achieve the objective, the present invention provides a forging defect prediction device that generates a molded product model in a plurality of forming processes of forging and predicts the presence or absence of defect phenomena when forming a molded product in each forming process of forging based on the molded product model, comprising: a surface pressure calculation unit that calculates the surface pressure of each analytical mesh based on the stress applied to a plurality of analytical meshes forming the molded product model; a specification unit that specifies the friction coefficient as Coulomb friction when the surface pressure of each analytical mesh is below a predetermined threshold and specifies the friction coefficient as shear friction when the surface pressure is greater than the predetermined threshold; an analysis unit that performs analysis of the analytical mesh while switching the friction coefficient specified by the specification unit; and a prediction unit that predicts the presence or absence of defect phenomena occurring in the molded product model based on the surface angles of adjacent surfaces of the analytical mesh analyzed by the analysis unit.

[0009] Furthermore, the present invention is characterized in that, in the above invention, the surface pressure calculation unit calculates the surface pressure based on the average value of the stress applied to the plurality of nodes that form the analytical mesh in each molding process.

[0010] Furthermore, the present invention is characterized in that, in the above invention, the specific part determines the coefficient of friction of Coulomb friction based on the surface pressure if the surface pressure is less than a predetermined Coulomb threshold, and reduces the coefficient of friction based on the surface pressure if the surface pressure is greater than the Coulomb threshold and less than the predetermined threshold.

[0011] Furthermore, the present invention is characterized in that, in the above invention, the prediction unit predicts that the scratch phenomenon will occur if the surface angle of adjacent surfaces of the analysis mesh is smaller than a predetermined angle threshold, and predicts that the scratch phenomenon will not occur if it is greater than or equal to the predetermined angle threshold.

[0012] Furthermore, the present invention further comprises a void pressure calculation unit that calculates the pressure of the gas present in the void when a void exists between the molded product and the forging mold, and the analysis unit performs the analysis based on the pressure of the gas.

[0013] Furthermore, the present invention relates to a forging defect prediction device for which a forging defect prediction device generates a molded product model in a plurality of forming processes of forging and predicts the presence or absence of defect phenomena when forming a molded product in each forming process of forging based on the molded product model, and is characterized by including: a surface pressure calculation step of calculating the surface pressure of each analytical mesh based on the stress applied to a plurality of analytical meshes forming the molded product model; a identification step of identifying the friction coefficient as Coulomb friction if the surface pressure of each analytical mesh is below a predetermined threshold, and identifying the friction coefficient as shear friction if the surface pressure is greater than the predetermined threshold; an analysis step of performing an analysis of the analytical mesh while switching the friction coefficient identified in the identification step; and a prediction step of predicting the presence or absence of defect phenomena occurring in the molded product model based on the surface angles of adjacent surfaces of the analytical mesh analyzed in the analysis step.

[0014] Furthermore, the present invention relates to a forging defect prediction program executed by a forging defect prediction device that generates a molded product model in a plurality of forming processes of forging and predicts the presence or absence of defect phenomena when forming a molded product in each forming process of forging based on the molded product model, characterized in that the program causes a computer to execute the following steps: a surface pressure calculation step that calculates the surface pressure of each analytical mesh based on the stress applied to a plurality of analytical meshes that form the molded product model; a identification step that identifies the friction coefficient as Coulomb friction if the surface pressure of each analytical mesh is below a predetermined threshold, and identifies the friction coefficient as shear friction if the surface pressure is greater than the predetermined threshold; an analysis step that performs analysis of the analytical mesh while switching the friction coefficient identified by the identification step; and a prediction step that predicts the presence or absence of defect phenomena occurring in the molded product model based on the surface angles of adjacent surfaces of the analytical mesh analyzed by the analysis step. [Effects of the Invention]

[0015] According to the present invention, when performing forging, it is possible to predict the occurrence of defects appropriately and efficiently. [Brief explanation of the drawing]

[0016] [Figure 1] Figure 1 is a diagram showing an overview of the forging defect prediction device according to Embodiment 1. [Figure 2] Figure 2 is a functional block diagram showing the configuration of the forging defect prediction device shown in Figure 1. [Figure 3] Figure 3 is a diagram showing an example of the generation of an analysis mesh. [Figure 4] Figure 4 is an explanatory diagram for explaining the relationship between surface pressure and frictional stress. [Figure 5] Figure 5 is an explanatory diagram for explaining the relationship between surface pressure and coefficient of friction. [Figure 6] Figure 6 is a flowchart showing the processing procedure of the forging defect prediction device shown in Figure 2. [Figure 7] Figure 7 is a flowchart showing the processing procedure of the coefficient of friction identification process shown in Figure 6. [Figure 8] Figure 8 is a diagram showing an overview of the forging defect prediction device according to Embodiment 2. [Figure 9] Figure 9 is an explanatory diagram for explaining the change in voids. [Figure 10] Figure 10 is a functional block diagram showing the configuration of the forging defect prediction device according to Embodiment 2. [Figure 11] Figure 11 is a flowchart showing the processing procedure of the forging defect prediction device shown in Figure 10. [Figure 12] Figure 12 is a diagram showing an example of a hardware configuration.

Mode for Carrying Out the Invention

[0017] Hereinafter, embodiments of the forging defect prediction device, the forging defect prediction method, and the forging defect prediction program according to the present invention will be described in detail based on the drawings.

[0018] [Embodiment 1] The overview of the forging defect prediction device 10 according to Embodiment 1 will be described. Figure 1 is a diagram showing an overview of the forging defect prediction device 10 according to Embodiment 1.

[0019] <Overview of the forging defect prediction device 10> As shown in Figure 1(a), the manufacturing of products using conventional forging technology involves multiple molding processes to form the product. Here, the molding process is shown as follows: preparation of the molded product is carried out in molding process 1, molding is repeated for each part in each molding process, and after going through molding process 30, the molding is completed in molding process 100. Shapes that occur when the material is caught inside the mold used for forging during the forging process are called "defects," and generally, the location of occurrence is predicted in advance using analysis.

[0020] However, the analysis uses Coulomb's law to calculate the frictional stress generated by the contact between the die used in forging and the material, so it fails to predict defects, and improving the prediction accuracy is a challenge.

[0021] As shown in Figure 1(b), the present invention performs numerical analysis based on a molded product model in each forming step of forging to calculate surface pressure and identify the coefficient of friction, thereby predicting the presence or absence of defects. The forging defect prediction device 10 generates multiple analytical meshes that form a molded product model. The forging defect prediction device 10 then performs numerical analysis to calculate the stress at the nodes of each analytical mesh based on each analytical mesh, and calculates the surface pressure based on this stress.

[0022] Subsequently, the forging defect prediction device 10 determines the coefficient of friction between the molded product and the mold based on the surface pressure. Here, the coefficient of friction is determined based on the Coulomb friction law, which states that the frictional stress increases in proportion to the surface pressure, when the surface pressure is below a predetermined threshold, and based on the shear friction law, which states that the coefficient of friction remains constant regardless of the surface pressure, when the surface pressure is greater than the predetermined threshold.

[0023] Then, once the final stage of the molding process is complete, the forging defect prediction device 10 calculates the surface angle between adjacent surfaces of the analysis mesh and predicts the presence or absence of defects based on the surface angle. Specifically, if the surface angle between adjacent surfaces of the analysis mesh is greater than a predetermined angle threshold, it predicts that there are no defects, and if the surface angle is smaller than the predetermined angle threshold, it predicts that there are defects.

[0024] <Configuration of the forging defect prediction device 10> Next, the configuration of the forging defect prediction device 10 shown in Figure 1 will be described. Figure 2 is a functional block diagram showing the configuration of the forging defect prediction device 10 shown in Figure 1. As shown in Figure 2, the forging defect prediction device 10 has 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 that displays various information. The input unit 12 is an input device such as a mouse or keyboard.

[0025] The storage unit 14 is a storage device such as a hard disk drive or non-volatile memory, and stores forging process data 14a, friction coefficient table 14b, mesh data 14c, surface pressure data 14d, and friction coefficient data 14e. The forging process data 14a is data of the molded product model in each molding process using forging technology. The friction coefficient table 14b is data showing the relationship between the friction coefficient and surface pressure.

[0026] Mesh data 14c is data of multiple meshes generated on the surface of the molded product model for the purpose of analyzing the molded product model. Surface pressure data 14d is data of the surface pressure calculated at the nodes of each analysis mesh. Friction coefficient data 14e is data of the friction coefficient at the nodes of each mesh identified based on the surface pressure.

[0027] The control unit 15 is a control unit that controls 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 identification unit 15d, and a defect prediction unit 15e. In practice, by loading these programs into the CPU and executing them, the mesh generation unit 15a, the analysis unit 15b, the surface pressure calculation unit 15c, the friction coefficient identification unit 15d, and the defect prediction unit 15e are made to execute the processes corresponding to each of them.

[0028] The mesh generation unit 15a is a processing unit that generates multiple meshes 40 for analysis on the molded product model. The size of the generated meshes 40 is varied in density according to the shape of the molded product model. For example, as shown in Figure 3, fine meshes 40 are generated in areas of the molded product model 110 that have changes in shape.

[0029] The generated mesh data is stored in the storage unit 14 as mesh data 14c, associated with the forging process ID. While this explanation describes the generation of a triangular mesh 40 using the diagonals of a rectangle, any triangular mesh 40 may be generated. Furthermore, the shape of the mesh 40 may be a quadrilateral, hexagon, or the like.

[0030] The analysis unit 15b is a processing unit that performs numerical analysis based on the nodes of the generated mesh. Finite element methods are used for the numerical analysis. The analysis unit 15b performs the analysis based on the friction coefficient between the molded product and the mold, which is determined by the friction coefficient determination unit 15d.

[0031] The surface pressure calculation unit 15c is a processing unit that calculates the surface pressure applied from the mold to the molded product model based on the stress at each mesh node. Specifically, the surface pressure P is calculated by calculating the stress σ in the X-axis direction at the mesh nodes. 11 And, stress σ in the Y-axis direction 22 And the stress σ in the Z-axis direction 33 Based on this, it is calculated using equation (1). Here, the surface pressure P is the average value of the stress.

[0032]

number

[0033] The friction coefficient determination unit 15d is a processing unit that determines the friction coefficient between the molded product and the mold based on the surface pressure. As shown in Figure 4, the friction coefficient determination unit 15d determines the friction coefficient such that the friction stress τ follows the Coulomb friction law, which increases in accordance with the surface pressure from the surface pressure of 0 (MPa) up to a predetermined threshold P1, and follows the shear friction law, where the friction stress τ is constant at τ1 (MPa) when the surface pressure is above the predetermined threshold P1.

[0034] This is because, as a characteristic of friction interfaces in forging, when the surface pressure is low, contact occurs at the tops of fine protrusions (true contact points) on the solid surface due to the roughness of the solid surface. When the surface pressure increases, the area of ​​these true contact points (true contact area) increases, and the friction stress follows the Coulomb law, which states that the friction stress changes according to the surface pressure. When the surface pressure is greater than a predetermined threshold P1, the material and the mold are in contact over almost the entire surface, and the true contact area no longer changes with respect to fluctuations in surface pressure, so the friction stress follows the shear friction law.

[0035] Furthermore, as shown in Figure 5, the friction coefficient determination unit 15d has the characteristic that when the surface pressure P is less than a predetermined Coulomb threshold P2, the friction coefficient is kept constant at μ1, and when the surface pressure P is greater than the predetermined Coulomb threshold P2 and less than a predetermined threshold P1, the friction coefficient decreases as the surface pressure P increases. This determination 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.

[0036] The defect prediction unit 15e is a processing unit that calculates the surface angles of adjacent surfaces of the analysis mesh and predicts the presence or absence of defects based on these surface angles. Specifically, if the surface angle is greater than a predetermined angle threshold, it predicts that there are no defects, and if the surface angle is less than the predetermined angle threshold, it predicts that there are defects.

[0037] <Processing procedure for the forging defect prediction device 10> Next, the processing procedure of the forging defect prediction device 10 will be described. Figure 6 is a flowchart showing the processing procedure of the forging defect prediction device 10 as shown in Figure 2. As shown in Figure 6, the forging defect prediction device 10 generates multiple analysis meshes of the molded product model (step S101). Then, the forging defect prediction device 10 performs numerical analysis using the finite element method (step S102).

[0038] Subsequently, the forging defect prediction device 10 calculates the surface pressure for each mesh (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 or not it is the final process (step S105).

[0039] If it is not the final process (step S105: No), the forging defect prediction device 10 reads the data of the molded product model for the next process (step S106) and proceeds to step S102. On the other hand, if it is the final process (step S105: Yes), the forging defect prediction device 10 calculates the surface angles of adjacent faces of the analysis mesh (step S107).

[0040] The forging defect prediction device 10 then determines whether the surface angle between adjacent surfaces of the analysis mesh is below a predetermined angle threshold (step S108). Subsequently, if the surface angle between adjacent surfaces of the analysis mesh is below the predetermined angle threshold (step S108: Yes), the forging defect prediction device 10 predicts that there is a defect (step S109). On the other hand, if the surface angle between adjacent surfaces of the analysis mesh is not below the predetermined angle threshold (step S108: No), the forging defect prediction device 10 predicts that there is no defect (step S110).

[0041] <Processing procedure for determining the coefficient of friction> Next, the processing procedure for the friction coefficient determination process shown in Figure 6 will be explained. Figure 7 is a flowchart showing the processing procedure for the friction coefficient determination process shown in Figure 6. As shown in Figure 7, 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 a predetermined threshold (step S201: Yes), the forging defect prediction device 10 sets the friction coefficient to m (step S202) and proceeds to step S105 in Figure 6.

[0042] 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 less than a predetermined Coulomb threshold (step S203). If the surface pressure is less than a predetermined Coulomb threshold (step S203: Yes), the forging defect prediction device 10 sets the friction coefficient to μ1 and proceeds to step S105 in Figure 6.

[0043] On the other hand, if the surface pressure is not less than a predetermined Coulomb threshold (step S203: No), the forging defect prediction device 10 identifies a friction coefficient corresponding to the surface pressure (step S205) and proceeds to step S105 in Figure 6.

[0044] As described above, in this embodiment 1, the forging defect prediction device 10 generates an analysis mesh for the molded product model, performs finite element analysis, and calculates the surface pressure of each mesh. Subsequently, the forging defect prediction device 10 identifies the coefficient of friction based on the surface pressure. The coefficient of friction is determined according to the shear friction law if the surface pressure is above a predetermined threshold, and according to the Coulomb friction law if the surface pressure is below a predetermined Coulomb threshold. Furthermore, if the surface pressure is above a predetermined Coulomb threshold but below the predetermined threshold, the coefficient of friction is reduced based on the surface pressure to determine the coefficient of friction.

[0045] [Embodiment 2] By the way, in Embodiment 1 described above, the forging defect prediction device 10 was described in a case where the coefficient of friction is determined based on surface pressure. However, in Embodiment 2, the forging defect prediction device 20 will be described in a case where the effect of the gap between the material and the mold is reflected in the analysis when forming using forging.

[0046] <Overview of the Forging Defect Prediction Device 20> Figure 8 shows an overview of the forging defect prediction device 20 according to Embodiment 2. As shown in Figures 8(a) to 8(c), the forging defect prediction device 20 performs analysis taking into account the pressure applied to the molded product W as the molding process progresses, when air or the like enters the gap G between the mold M and the molded product W, and the air is compressed.

[0047] For example, as shown in Figure 8(a), when the molded product W is pressed against the mold M in molding step 1, air enters between the molded product W and the mold M, forming a void G. Then, as shown in Figure 8(b), in molding step 30, the molded product W is pressed further, and the volume of the void G becomes smaller due to compression compared to molding step 1. In this case, because the air in the void G is compressed, the portion of the molded product W facing the void G receives pressure from the void G, and the rate of plastic deformation of the molded product W slows down. Then, as shown in Figure 8(c), in molding step 100, scratches occur on the molded product W due to the influence of the pressure in the void G.

[0048] On the other hand, if there is no air or other material in the void G, as shown in Figure 8(d), even if the molded product W is pressed against the mold M in molding step 1, the void G exists in the analysis, but no pressure or other material is applied to the molded product W from the void G. Subsequently, as shown in Figure 8(e), in molding step 30, the molded product W is further pressed in by plastic deformation in the direction of the arrow. Then, as shown in Figure 8(f), in molding step 100, the molded product W undergoes plastic deformation against the mold M. In this case, it is difficult to reproduce the defects in the molded product W in the analysis.

[0049] <Pressure in void G> Next, we will explain the pressure in the void G. Figure 9 is an explanatory diagram illustrating the changes in the void G. As shown in Figure 9(a), in molding process 1, the state in which the molded product model 110 has a void G is shown. Air is present in this void G, and in molding process 1, it has, for example, an air pressure of 1 atmosphere. The void G is a closed space composed of the molded product W and the mold M.

[0050] Then, as shown in Figure 9(b), in the molding process 30, the volume of the void G decreases due to the plastic deformation of the molded product W. Since the pressure × volume of the void G, which is a closed space, is constant, when the volume of the void G decreases, the pressure inside the void G becomes greater than 1 atmosphere. Subsequently, as shown in Figure 9(c), in the molding process 100, the volume of the void G becomes even smaller, and the pressure inside the void G becomes even greater than the pressure in the molding process 30. When the forging defect prediction device 20 analyzes the plastic deformation of the molded product W, it takes this pressure inside the void G into consideration during the analysis.

[0051] <Configuration of the forging defect prediction device 20> Next, the configuration of the forging defect prediction device 20 will be described. Figure 10 is a functional block diagram showing the configuration of the forging defect prediction device 20 according to Embodiment 2. Note that parts similar to those in the forging defect prediction device 10 shown in Figure 2 are denoted by the same reference numerals, and their detailed explanations are omitted.

[0052] As shown in Figure 10, 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, and stores forging process data 14a, friction coefficient table 14b, mesh data 14c, surface pressure data 14d, friction coefficient data 14e, and void pressure data 24a. The void pressure data 24a is data on the pressure of the air present between the molded product W and the void G.

[0053] The control unit 25 is a control unit that controls the entire 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 identification unit 15d, a defect prediction unit 15e, and a void pressure calculation unit 25a. In practice, by loading these programs into the CPU and executing them, the mesh generation unit 15a, the analysis unit 15b, the surface pressure calculation unit 15c, the friction coefficient identification unit 15d, the defect prediction unit 15e, and the void pressure calculation unit 25a will each execute the corresponding processes.

[0054] The void pressure calculation unit 25a is a processing unit that calculates the pressure of the air present in the void G between the molded product W and the mold M. Specifically, based on the formula air pressure Pa × air volume V = C, if the air volume V decreases in the finite element analysis, Pa = C / V is calculated to calculate Pa. The calculated air pressure Pa is then set as a pressure parameter at the mesh contact point of the molded product model that is in contact with the void G in the finite element analysis.

[0055] <Processing procedure for the forging defect prediction device 20> Next, the processing procedure of the forging defect prediction device 20 will be described. Figure 11 is a flowchart showing the processing procedure of the forging defect prediction device 20 as shown in Figure 10. As shown in Figure 11, the forging defect prediction device 20 generates a mesh for analysis of the molded product model (step S301). Then, the forging defect prediction device 20 performs numerical analysis using the finite element method (step S302). In the numerical analysis, the analysis is performed based on the calculated pressure of the void G and the friction coefficient between the molded product W and the mold M.

[0056] Subsequently, the forging defect prediction device 20 calculates the pressure of the voids (step S303). Then, the forging defect prediction device 20 calculates the surface pressure for each mesh (step S304). After that, the forging defect prediction device 20 performs friction coefficient determination processing based on the surface pressure (step S305). Finally, the forging defect prediction device 20 determines whether or not it is the final process (step S306).

[0057] If it is not the final process (step S306: No), the forging defect prediction device 20 reads the data of the molded product model for the next process (step S307) and proceeds to step S302. On the other hand, if it is the final process (step S306: Yes), the forging defect prediction device 20 calculates the surface angles of adjacent faces of the analysis mesh (step S308).

[0058] The forging defect prediction device 20 then determines whether the surface angles of adjacent faces of the analysis mesh are below a predetermined angle threshold (step S309). Subsequently, if the surface angles of adjacent faces of the analysis mesh are below the predetermined angle threshold (step S309: Yes), the forging defect prediction device 20 predicts that there are defects (step S310). On the other hand, if the surface angles of adjacent faces of the analysis mesh are not below the predetermined angle threshold (step S309: No), the forging defect prediction device 20 predicts that there are no defects (step S311). Note that the processing procedure for determining the coefficient of friction is the same as that of the forging defect prediction device 10, so a detailed explanation is omitted.

[0059] As described above, in this second embodiment, the forging defect prediction device 20 generates an analysis mesh for the molded product model, performs finite element analysis, and calculates the pressure of the voids and the surface pressure of each mesh. Subsequently, the forging defect prediction device 20 identifies the coefficient of friction based on the surface pressure. The coefficient of friction is identified according to the shear friction law if the surface pressure is above a predetermined threshold, and according to the Coulomb friction law if the surface pressure is below a predetermined Coulomb threshold. Furthermore, if the surface pressure is above a predetermined Coulomb threshold but below the predetermined threshold, the coefficient of friction is reduced based on the surface pressure to identify the coefficient of friction.

[0060] <Relationship with hardware> Next, the correspondence between the forging defect prediction device 10 according to this embodiment 1 and the main hardware configuration of the computer will be described. Figure 12 is a diagram showing an example of the hardware configuration.

[0061] Generally, a computer consists of components such as a CPU 81, ROM 82, RAM 83, and non-volatile memory 84, connected by a bus 85. A hard disk drive may be used instead of the non-volatile memory 84. For the sake of explanation, only the basic hardware configuration is shown.

[0062] Here, the ROM 82 or non-volatile memory 84 stores programs necessary for starting the operating system (hereinafter simply referred to as "OS"), and the CPU 81 reads and executes the OS program from the ROM 82 or non-volatile memory 84 when the power is turned on.

[0063] On the other hand, various application programs executed on the OS are stored in non-volatile memory 84, and the CPU 81 executes the application programs using RAM 83 as main memory, thereby executing the processes corresponding to the applications.

[0064] Furthermore, the forging defect prediction program of the forging defect prediction device 10 according to this embodiment 1 is stored in non-volatile memory 84 or the like, just like other application programs, and the CPU 81 loads and executes this forging defect prediction program. In the case of the forging defect prediction device 10 according to this embodiment 1, the forging defect prediction program, which includes routines corresponding to the mesh generation unit 15a, analysis unit 15b, surface pressure calculation unit 15c, friction coefficient identification unit 15d, and defect prediction unit 15e shown in Figure 2, is stored in non-volatile memory 84 or the like. When the CPU 81 loads and executes the forging defect prediction program, a forging defect prediction process corresponding to the mesh generation unit 15a, analysis unit 15b, surface pressure calculation unit 15c, friction coefficient identification unit 15d, and defect prediction unit 15e is generated.

[0065] The configurations illustrated in each of the above embodiments are functional schematics and do not necessarily have to be physically represented as shown. In other words, the distributed and integrated forms of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions. [Industrial applicability]

[0066] The forging defect prediction device, forging defect prediction method, and forging defect prediction program according to the present invention are suitable for accurately and efficiently predicting the occurrence of defects when performing forging. [Explanation of Symbols]

[0067] 10. Forging defect prediction device 11 Display section 12 Input section 14 Storage section 14a Forging process data 14b Friction coefficient table 14c Mesh Data 14d Surface pressure data 14e Friction coefficient data 15 Control Unit 15a Mesh generation section 15b Analysis section 15c Surface pressure calculation section 15d Friction coefficient specification part 15e Scratch prediction section 20 Forging defect prediction device 24 Memory section 24a Void pressure data 25 Control Unit 25a Air gap calculation section 40 mesh 81 CPU 82 ROM 83 RAM 84 Non-volatile memory 85 Bus 110 Molded Models G void M type W molded product

Claims

1. A forging defect prediction device that generates molded product models for multiple forming processes in forging, and predicts the presence or absence of defect phenomena when forming the molded product in each forming process of the forging based on the molded product models, A surface pressure calculation unit calculates the surface pressure of each analytical mesh based on the stress applied to the plurality of analytical meshes forming the molded product model, A specification unit that identifies the friction coefficient as Coulomb friction when the surface pressure of each analysis mesh is below a predetermined threshold, and identifies the friction coefficient as shear friction when the surface pressure is greater than the predetermined threshold, An analysis unit that performs analysis of the analysis mesh while switching the friction coefficient identified by the specified unit, A prediction unit predicts whether or not the scratch phenomenon occurs in the molded product model based on the surface angles of adjacent surfaces of the analysis mesh analyzed by the analysis unit. A forging defect prediction device characterized by being equipped with the following features.

2. The surface pressure calculation unit is, The forging defect prediction device according to claim 1, characterized in that the surface pressure is calculated based on the average value of the stress applied to a plurality of nodes that form the analytical mesh in each molding process.

3. The specified part is, The forging defect prediction device according to claim 1, characterized in that, based on the surface pressure, if the surface pressure is less than a predetermined Coulomb threshold, the coefficient of friction of Coulomb friction is determined, and if the surface pressure is greater than the Coulomb threshold and less than the predetermined threshold, the coefficient of friction is reduced based on the surface pressure.

4. The prediction unit, The forging defect prediction device according to claim 1, characterized in that it predicts that the defect phenomenon will occur if the surface angle of adjacent surfaces of the analysis mesh is smaller than a predetermined angle threshold, and predicts that the defect phenomenon will not occur if it is greater than or equal to the predetermined angle threshold.

5. The system further includes a void pressure calculation unit that calculates the pressure of the gas present in the void when a void exists between the molded product and the forging mold. The aforementioned analysis unit, A forging defect prediction device according to any one of claims 1 to 4, characterized in that it performs analysis based on the pressure of the aforementioned gas.

6. A forging defect prediction device generates molded product models for multiple forming processes in forging, and predicts the presence or absence of defects when forming the molded product in each forming process of forging based on the molded product models, wherein the device provides a forging defect prediction method, A surface pressure calculation step, which calculates the surface pressure of each analytical mesh based on the stress applied to the plurality of analytical meshes forming the molded product model, The identification step involves identifying the coefficient of friction as Coulomb friction if the surface pressure of each analysis mesh is below a predetermined threshold, and identifying the coefficient of friction as shear friction if the surface pressure is greater than the predetermined threshold. An analysis step in which the analysis of the analysis mesh is performed while switching the friction coefficient identified in the aforementioned specific step, A prediction step that predicts whether or not the defect phenomenon occurs in the molded product model based on the surface angles of adjacent surfaces of the analysis mesh analyzed in the above analysis step, and A method for predicting forging defects, characterized by including [a specific element].

7. A forging defect prediction program executed by a forging defect prediction device that generates molded product models for multiple forming processes of forging, and predicts the presence or absence of defect phenomena when forming the molded product in each forming process of forging based on the molded product models, A surface pressure calculation procedure for calculating the surface pressure of each analytical mesh based on the stress applied to the multiple analytical meshes forming the molded product model, A procedure for identifying the coefficient of friction as Coulomb friction if the surface pressure of each analysis mesh is below a predetermined threshold, and as shear friction if the surface pressure is greater than the predetermined threshold, An analysis procedure which involves performing the analysis of the analysis mesh while switching the friction coefficient identified by the above-mentioned specific procedure, A prediction procedure that predicts the presence or absence of the defect phenomenon occurring in the molded product model based on the surface angles of adjacent surfaces of the analysis mesh analyzed by the above analysis procedure, and A forging defect prediction program characterized by having a computer perform the following actions.

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