Prediction device, prediction method, and program

Abstract

To enable accurate prediction of fatigue life.SOLUTION: A prediction device is provided, comprising a curve estimation unit configured to estimate a fatigue life curve based on the shape of a stress concentration zone of a structure, a distribution computation unit configured to compute a distribution of stress amplitude caused in the stress concentration zone by a fluctuating load applied to the structure, and a life prediction unit configured to predict the fatigue life of the structure based on the stress amplitude distribution and the fatigue life curve.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a prediction device, a prediction method, and a program. [Background technology]

[0002] Equipment installed in transportation vehicles can suffer fatigue failure due to random vibrations while in motion. A known method for predicting fatigue life due to vibration is to use the fatigue life curve (also called the "SN curve"), which shows the relationship between the stress amplitude and the number of repetitions until fatigue failure occurs.

[0003] For example, Patent Document 1 discloses a method for estimating the fatigue life of a copper material, which determines the number of repetitions until the copper material breaks by obtaining an SN curve when repeated stress of varying amplitude is applied to the copper material. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 11-64203 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the conventional technology has a problem that the accuracy of fatigue life prediction decreases when the structure to be predicted has points where stress is concentrated.

[0006] In view of the above technical problems, an object of the present invention is to predict fatigue life with high accuracy. [Means for solving the problem]

[0007] A prediction device according to one embodiment of the present invention includes a curve estimation unit that estimates a fatigue life curve based on the shape of a stress concentration portion of a structure, a distribution calculation unit that calculates the distribution of stress amplitudes generated in the stress concentration portion due to fluctuating loads applied to the structure, and a life prediction unit that predicts the fatigue life of the structure based on the stress amplitude distribution and the fatigue life curve. Effect of the Invention

[0008] According to one aspect of the present invention, the fatigue life can be predicted with high accuracy. [Brief description of the drawings]

[0009] [Figure 1] FIG. 2 is a schematic diagram showing an example of a structure. [Diagram 2] 1 is a block diagram showing an example of an overall configuration of a fatigue life prediction system. [Diagram 3] FIG. 2 is a block diagram showing an example of a hardware configuration of a computer. [Figure 4] 1 is a block diagram showing an example of a functional configuration of a fatigue life prediction system; [Diagram 5] FIG. 2 is a schematic diagram showing an example of a smoothing material. [Figure 6] FIG. 2 is a schematic diagram showing an example of a notched member. [Figure 7] 1 is a graph showing an example of fatigue test results. [Figure 8] 1 is a graph showing an example of a relational expression. [Figure 9] 1 is a flowchart illustrating an example of a fatigue life prediction method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0011] [Embodiment] One embodiment of the present invention is a fatigue life prediction system for predicting the fatigue life of a structure. The fatigue life prediction system in this embodiment predicts the fatigue life until damage occurs in a structure when a fluctuating load due to vibration is applied to the structure. In this embodiment, the number of repetitions of the fluctuating load until damage occurs in the structure is predicted as the fatigue life.

[0012] For example, devices mounted on transport vehicles such as automobiles or trains can suffer fatigue failure due to random vibrations while in motion. For parts mounted on transport vehicles, standards related to the power spectral density (PSD) of random vibration acceleration are established by the International Standard Organization (ISO) and other organizations depending on the type of device.

[0013] Conventionally, analysis methods in the frequency domain have been proposed for predicting fatigue life based on the finite element method (FEM). One example of an analysis method in the frequency domain is the Dirlik method. Details of the Dirlik method are described in, for example, Reference 1.

[0014] [Reference 1] T. Dirlik, "Application of computers in fatigue analysis", PhD Thesis, University of Warwick, 1985.

[0015] However, in conventional analysis methods, the predicted fatigue life of equipment with parts that undergo shape change tends to deviate significantly from the actual fatigue life. This is because local stress occurs in parts that undergo shape change, and conventional analysis methods are unable to properly handle local stress. Hereinafter, the parts where local stress occurs are referred to as "stress concentration parts."

[0016] A structure whose fatigue life is predicted in this embodiment will be described with reference to Fig. 1. Fig. 1 is a schematic diagram showing an example of a structure in this embodiment.

[0017] The structure in this embodiment is an electronic component. This electronic component is a component that constitutes an electronic circuit board of an electronic device mounted on a transport device. As shown in Fig. 1, the electronic component 9 in this embodiment is a gull-wing type surface mount component having a package 91 and a lead terminal 92.

[0018] Lead terminal 92 is formed in an L-shape, with one end connected to a side surface of package 91. The other end of lead terminal 92 is joined to an electronic circuit board on which electronic component 9 is mounted. Because lead terminal 92 is bent into an L-shape, local stress is generated at the bent portion when the entire electronic device vibrates, which often becomes the starting point of damage.

[0019] The present embodiment aims to accurately predict the fatigue life of a structure that undergoes shape changes. In this embodiment, a fatigue life curve corresponding to the shape of the structure is estimated, and the fatigue life is predicted while taking local stress into consideration. In one aspect, according to this embodiment, the fatigue life of a structure can be predicted with higher accuracy than the conventional technology.

[0020] <Overall composition> The overall configuration of the fatigue life prediction system in this embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the overall configuration of the fatigue life prediction system in this embodiment.

[0021] 2, a fatigue life prediction system 1000 in this embodiment includes a prediction device 1 and one or more terminal devices 2. The prediction device 1 and the terminal devices 2 are connected to each other so as to be able to communicate data with each other via a communication network N1 such as a LAN (Local Area Network) or the Internet.

[0022] The prediction device 1 is an information processing device such as a personal computer, a workstation, or a server that predicts the fatigue life of a structure. The prediction device 1 receives information on the structure to be predicted (hereinafter, also referred to as "structural information") from a terminal device 2. The prediction device 1 predicts the fatigue life of the structure based on the structural information, and transmits the prediction result to the terminal device 2.

[0023] The terminal device 2 is an information processing terminal such as a personal computer, a tablet terminal, or a smartphone operated by a user of the fatigue life prediction system 1000. The terminal device 2 transmits structural information input by the user to the prediction device 1. The terminal device 2 receives the fatigue life prediction result from the prediction device 1 and outputs it to the user.

[0024] The overall configuration of the fatigue life prediction system 1000 shown in Fig. 2 is an example, and various system configuration examples are possible depending on the application and purpose. For example, one or more of the prediction device 1 and the terminal device 2 may be included in the fatigue life prediction system 1000. For example, the prediction device 1 may be realized by a plurality of computers, or may be realized as a cloud computing service. The division of devices such as the prediction device 1 and the terminal device 2 shown in Fig. 2 is an example.

[0025] <Hardware configuration> The hardware configuration of each device included in the fatigue life prediction system 1000 in this embodiment will be described with reference to FIG.

[0026] <Computer hardware configuration> The prediction device 1 and the terminal device 2 in this embodiment are realized by, for example, a computer. Fig. 3 is a block diagram showing an example of the hardware configuration of a computer 500 in this embodiment.

[0027] 3, the computer 500 in this embodiment includes a CPU (Central Processing Unit) 501, a ROM (Read Only Memory) 502, a RAM (Random Access Memory) 503, a HDD (Hard Disk Drive) 504, an input device 505, a display device 506, a communication I / F (Interface) 507, and an external I / F 508. The CPU 501, the ROM 502, and the RAM 503 form a so-called computer. Each piece of hardware of the computer 500 is connected to each other via a bus line 509. The input device 505 and the display device 506 may be connected to the external I / F 508 for use.

[0028] The CPU 501 is a calculation device that reads out programs and data from a storage device such as the ROM 502 or the HDD 504 onto the RAM 503 and executes processing to realize the overall control and functions of the computer 500. The computer 500 may have a GPU (Graphics Processing Unit) in addition to or instead of the CPU 501.

[0029] The ROM 502 is an example of a non-volatile semiconductor memory (storage device) that can retain programs and data even when the power is turned off. The ROM 502 functions as a main storage device that stores various programs and data required for the CPU 501 to execute various programs installed in the HDD 504. Specifically, the ROM 502 stores boot programs such as a Basic Input / Output System (BIOS) and an Extensible Firmware Interface (EFI) that are executed when the computer 500 is started, and data such as an Operating System (OS) setting and a network setting.

[0030] The RAM 503 is an example of a volatile semiconductor memory (storage device) in which programs and data are erased when the power is turned off. The RAM 503 is, for example, a dynamic random access memory (DRAM) or a static random access memory (SRAM). The RAM 503 provides a working area in which various programs installed in the HDD 504 are expanded when the CPU 501 executes them.

[0031] The HDD 504 is an example of a non-volatile storage device that stores programs and data. The programs and data stored in the HDD 504 include an OS, which is basic software that controls the entire computer 500, and applications that provide various functions on the OS. Note that the computer 500 may use a storage device that uses a flash memory as a storage medium (e.g., an SSD (Solid State Drive) or the like) instead of the HDD 504.

[0032] The input device 505 includes a touch panel, operation keys or buttons, a keyboard or mouse, a microphone for inputting sound data such as voice, etc., which are used by the user to input various signals.

[0033] The display device 506 is composed of a display such as a liquid crystal display or an organic EL (Electro-Luminescence) display for displaying a screen, a speaker for outputting sound data such as voice, and the like.

[0034] The communication I / F 507 is an interface that connects to a communication network and enables the computer 500 to perform data communication.

[0035] The external I / F 508 is an interface with external devices, such as a drive device 510.

[0036] The drive device 510 is a device for setting the recording medium 511. The recording medium 511 here includes media that record information optically, electrically, or magnetically, such as a CD-ROM, a flexible disk, or a magneto-optical disk. The recording medium 511 may also include semiconductor memories that record information electrically, such as a ROM or a flash memory. This allows the computer 500 to read and / or write data from and to the recording medium 511 via the external I / F 508.

[0037] The various programs to be installed in the HDD 504 are installed, for example, by setting the distributed recording medium 511 in a drive device 510 connected to the external I / F 508 and reading out the various programs recorded in the recording medium 511 by the drive device 510. Alternatively, the various programs to be installed in the HDD 504 may be installed by being downloaded from a network different from the communication network via the communication I / F 507.

[0038] <Functional configuration> The functional configuration of the fatigue life prediction system 1000 in this embodiment will be described with reference to Fig. 4. Fig. 4 is a block diagram showing an example of the functional configuration of the fatigue life prediction system in this embodiment.

[0039] <Prediction device> As shown in FIG. 4, the prediction device 1 in this embodiment includes a relational equation storage unit 100, a structural information receiving unit 101, a model creation unit 102, a vibration analysis unit 103, a distribution calculation unit 104, a coefficient calculation unit 105, a strength estimation unit 106, a curve estimation unit 107, a life prediction unit 108, and a result output unit 109.

[0040] The relational equation storage unit 100 is realized by the HDD 504 shown in Fig. 3. The structural information receiving unit 101, the model creating unit 102, the vibration analysis unit 103, the distribution calculation unit 104, the coefficient calculation unit 105, the strength estimation unit 106, the curve estimation unit 107, the life prediction unit 108 and the result output unit 109 are realized by processing that is executed by the CPU 501 in accordance with a program loaded from the HDD 504 onto the RAM 503 shown in Fig. 3.

[0041] The relational equation storage unit 100 stores two predetermined relational equations. The first relational equation is a relational equation that indicates the relationship between the stress concentration factor and the fatigue strength coefficient. The second relational equation is a relational equation that indicates the relationship between the stress concentration factor and the fatigue strength index. The first relational equation and the second relational equation are derived based on the results of fatigue tests on a plurality of structures with different shapes. The method of deriving each relational equation will be described later.

[0042] The structural information receiving unit 101 receives input of structural information related to a structure to be predicted. The structural information receiving unit 101 receives the structural information from the terminal device 2, thereby receiving the input of the structural information.

[0043] The structural information includes information on the shape of the structure and information on the physical property values ​​of the structure. The information on the shape is, for example, a CAD (Computer-Aided Design) model. The physical property values ​​are, for example, density, Young's modulus, Poisson's ratio, etc. The structural information is not limited to these, and may include any information that can be used in vibration analysis.

[0044] The model creation unit 102 creates a mesh model of the entire equipment including the structure to be predicted (hereinafter also referred to as "equipment model"), a mesh model of the structure having a stress concentration part (hereinafter also referred to as "bent model"), and a mesh model of the structure not having a stress concentration part (hereinafter also referred to as "smooth model") based on the structural information input to the structural information receiving unit 101. The mesh models can be created using, for example, known structural analysis software.

[0045] The vibration analysis unit 103 performs vibration analysis (modal analysis) using the equipment model created by the model creation unit 102. The vibration analysis can be performed using, for example, known structural analysis software.

[0046] The distribution calculation unit 104 calculates the power spectrum density (hereinafter also referred to as “stress PSD”) of the stress amplitude based on the vibration analysis result by the vibration analysis unit 103.

[0047] The coefficient calculation unit 105 calculates the stress concentration coefficient using the bent model and the smooth model created by the model creation unit 102. Specifically, the coefficient calculation unit 105 first analyzes the stress when an equal load is applied to each of the bent model and the smooth model. Next, the coefficient calculation unit 105 calculates the stress concentration coefficient by dividing the stress value of the bent model by the stress value of the smooth model.

[0048] The strength estimation unit 106 estimates a fatigue strength coefficient and a fatigue strength index based on the stress concentration coefficient calculated by the coefficient calculation unit 105. Specifically, the strength estimation unit 106 estimates a fatigue strength coefficient corresponding to the stress concentration coefficient using a first relational equation read out from the relational equation storage unit 100. In addition, the strength estimation unit 106 estimates a fatigue strength index corresponding to the stress concentration coefficient using a second relational equation read out from the relational equation storage unit 100.

[0049] The curve estimation unit 107 estimates a fatigue life curve based on the shape of the structure. Specifically, the curve estimation unit 107 generates a fatigue life curve using the fatigue strength coefficient and the fatigue strength index estimated by the strength estimation unit 106.

[0050] More specifically, the curve estimator 107 calculates σ f is the fatigue strength coefficient, b is the fatigue strength index, and the fatigue strength coefficient σ f and fatigue strength index b to generate a fatigue strength curve.

[0051]

number

[0052] Where, σ a is the stress amplitude, N f is the number of cycles (the number of repeated stress amplitudes until damage occurs).

[0053] The life prediction unit 108 predicts the fatigue life of the structure based on the stress PSD calculated by the distribution calculation unit 104 and the fatigue life curve estimated by the curve estimation unit 107. In this embodiment, the life prediction unit 108 predicts the fatigue life based on the cumulative damage law.

[0054] The cumulative damage rule is a method for predicting whether or not damage will occur when stress amplitudes occur randomly. In the cumulative damage rule, the stress amplitudes σ1, σ2, . . . , σ m Assuming that each of the following occurs repeatedly, the fatigue life is predicted based on the cumulative damage level D calculated by equation (2).

[0055]

number

[0056] where n1, n2, , n m is the stress amplitude σ1,σ2,...,σ m is the number of times the load is applied, and N1, N2,...,N m are the stress amplitudes σ1,σ2,...,σ m If the cumulative damage level D is less than 1.0, it is determined that no damage will occur. On the other hand, if the cumulative damage level D is 1.0 or more, it is determined that damage will occur.

[0057] The result output unit 109 outputs the fatigue life prediction result obtained by the life prediction unit 108. The result output unit 109 transmits the fatigue life prediction result to the terminal device 2.

[0058] Terminal Device As shown in FIG. 4, the terminal device 2 in this embodiment includes a structural information input unit 201 and a result display unit 202.

[0059] The structure information input unit 201 and the result display unit 202 are realized by processing that is executed by the CPU 501 according to a program loaded onto the RAM 503 from the HDD 504 shown in FIG.

[0060] The structural information input unit 201 receives input of structural information on a structure to be predicted in response to a user operation. The structural information input unit 201 transmits the structural information to the prediction device 1.

[0061] The result display unit 202 receives the fatigue life prediction result from the prediction device 1. The result display unit 202 outputs the fatigue life prediction result to the display device 506 or the like.

[0062] <How to derive the relational equation> The method of deriving the first relational expression and the second relational expression will be described in detail with reference to Fig. 5 to Fig. 9. The first relational expression and the second relational expression are derived by performing a fatigue test using the stress concentration factor as a parameter and analyzing the relationship between the stress concentration factor and the fatigue strength coefficient or the fatigue strength index. Since the stress concentration factor depends on the shape of the structure, it is advisable to perform fatigue tests on a plurality of structures with different shapes.

[0063] The fatigue test method may be selected according to the deformation state of the structure. Since bending deformation is predominant in the lead terminals of electronic components, a rotating bending fatigue test may be performed in this embodiment. The stress amplitude in the rotating bending fatigue test may be adjusted according to the material or shape of the structure. In this embodiment, as an example, it is set to 90 to 440 MPa.

[0064] In the rotating bending fatigue test, a test piece having no stress concentration portion and a plurality of test pieces having stress concentration portions are used. FIG. 5 is a schematic diagram showing a first test piece in this embodiment. As shown in FIG. 5, the first test piece is a test piece having no stress concentration portion, and is a round bar-shaped smooth material having a smooth side surface. FIG. 6 is a schematic diagram showing a second test piece in this embodiment. As shown in FIG. 6, the second test piece is a test piece having a stress concentration portion, and is a round bar-shaped notched material having a ring-shaped notch near the center in the length direction.

[0065] Fig. 7 is a graph showing fatigue test results in this embodiment. In Fig. 7, the circular plots (smooth round bar) are actual measured values ​​of fatigue life for smooth materials. The triangular plots (notched round bar α=1.2) are actual measured values ​​of fatigue life for notched materials with a stress concentration factor α of 1.2. The square plots (notched round bar α=2.5) are actual measured values ​​of fatigue life for notched materials with a stress concentration factor α of 2.5. As shown in Fig. 7, the fatigue test results show that the fatigue strength decreases as the stress concentration factor α increases.

[0066] Next, the fatigue strength coefficient σ f and fatigue strength exponent b are derived and applied to equation (1) to generate a fatigue life curve. a is the nominal stress amplitude. This gives a fatigue life curve according to the stress concentration factor α.

[0067] Furthermore, a first relational expression and a second relational expression are constructed based on the fatigue life curve corresponding to the stress concentration factor. Fig. 8 is a graph showing an example of the relational expression in this embodiment. In Fig. 8, the horizontal axis (Stress Concentration Factor) is the stress concentration factor α, the left vertical axis (Fatigue strength index) is the fatigue strength index b, and the right vertical axis (Fatigue strength factor) is the fatigue strength coefficient σ f That is, Fig. 8 shows a two-axis graph showing both the first relational expression and the second relational expression.

[0068] Based on the first and second relational expressions derived as described above, the fatigue strength coefficient and the fatigue strength index can be estimated from the stress concentration factor according to the shape of the structure. Then, the fatigue strength coefficient and the fatigue strength index can be used to generate a fatigue life curve according to the shape of the structure.

[0069] <Processing Procedure> A process procedure of the fatigue life prediction method executed by the fatigue life prediction system 1000 in this embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing an example of the fatigue life prediction method in this embodiment.

[0070] In step S1, the structure information input unit 201 of the terminal device 2 accepts input of structure information on a structure to be predicted in response to a user operation. Next, the structure information input unit 201 transmits the structure information to the prediction device 1.

[0071] In the prediction device 1, the structural information receiving unit 101 receives the structural information from the terminal device 2. Next, the structural information receiving unit 101 receives input of the structural information. Then, the structural information receiving unit 101 transmits the structural information to the model creation unit .

[0072] In step S2, the model creation unit 102 of the prediction device 1 receives the structural information from the structural information receiving unit 101. Next, the model creation unit 102 creates an equipment model, a bent model, and a smooth model based on the structural information. Then, the model creation unit 102 sends the equipment model to the vibration analysis unit 103. In addition, the model creation unit 102 sends the bent model and the smooth model to the coefficient calculation unit 105.

[0073] In step S3, the vibration analysis unit 103 of the prediction device 1 receives the equipment model from the model creation unit 102. Next, the vibration analysis unit 103 performs vibration analysis using the equipment model. Then, the vibration analysis unit 103 sends the vibration analysis result to the distribution calculation unit 104.

[0074] Specifically, the vibration analysis unit 103 first generates a vibration waveform. In this embodiment, the vibration waveform is information indicating the relationship between frequency and acceleration power spectrum density (hereinafter also referred to as "acceleration PSD"). Next, the vibration analysis unit 103 sets a damping ratio of the vibration waveform and calculates the stress distribution of the structure. Then, the vibration analysis unit 103 outputs the stress distribution as a vibration analysis result.

[0075] In step S4, the distribution calculation unit 104 of the prediction device 1 receives the vibration analysis result from the vibration analysis unit 103. Next, the distribution calculation unit 104 calculates the stress PSD based on the vibration analysis result. Then, the distribution calculation unit 104 sends the stress PSD to the life prediction unit .

[0076] In step S5, the coefficient calculation unit 105 of the prediction device 1 receives the bent model and the smooth model from the model creation unit 102. Next, the coefficient calculation unit 105 calculates a stress concentration coefficient using the bent model and the smooth model. Then, the coefficient calculation unit 105 sends the stress concentration coefficient to the strength estimation unit 106.

[0077] Specifically, the coefficient calculation unit 105 applies an equal load (e.g., 1.8 N) to each of the bent model and the smooth model, and calculates the bending stress of each. Next, the coefficient calculation unit 105 divides the stress value of the bent model by the stress value of the smooth model. For example, if the stress value of the bent model is 547 MPa and the stress value of the smooth model is 200 MPa, the stress concentration coefficient is 2.7 (=547÷200).

[0078] In step S6, the strength estimation unit 106 of the prediction device 1 receives the stress concentration coefficient from the coefficient calculation unit 105. Next, the strength estimation unit 106 reads out a first relational expression from the relational expression storage unit 100. Then, the strength estimation unit 106 estimates a fatigue strength coefficient corresponding to the stress concentration coefficient by using the first relational expression.

[0079] In step S7, the strength estimation unit 106 of the prediction device 1 reads out the second relational equation from the relational equation storage unit 100. Next, the strength estimation unit 106 estimates a fatigue strength index corresponding to the stress concentration factor by using the second relational equation. Then, the strength estimation unit 106 sends the fatigue strength coefficient estimated in step S6 and the fatigue strength index estimated in step S7 to the curve estimation unit 107.

[0080] In step S8, the curve estimation unit 107 of the prediction device 1 receives the fatigue strength coefficient and the fatigue strength index from the strength estimation unit 106. Next, the curve estimation unit 107 generates a fatigue life curve using the fatigue strength coefficient and the fatigue strength index. Then, the curve estimation unit 107 sends information indicating the fatigue life curve to the life prediction unit 108.

[0081] In step S9, the life prediction unit 108 of the prediction device 1 receives the stress PSD from the distribution calculation unit 104. The life prediction unit 108 also receives information indicating the fatigue life curve from the curve estimation unit 107. Next, the life prediction unit 108 predicts a fatigue life based on the stress PSD and the fatigue life curve. Then, the life prediction unit 108 sends the prediction result of the fatigue life to the result output unit 109.

[0082] Specifically, the life prediction unit 108 predicts the fatigue life based on the cumulative damage law. In this embodiment, the life prediction unit 108 predicts the fatigue life using the Dirlik method. The life prediction unit 108 first calculates a damage value D per unit time using equation (3). Next, the life prediction unit 108 assumes that damage will occur when the damage value D becomes 1.0 or more, and calculates the fatigue life T by T=1 / D.

[0083]

number

[0084] where E(P) is the number of times the peak of the stress amplitude occurs per unit time, s is the stress range that occurs under random vibration, p(s) is the probability density function, and C,k is σ f =C 1 / k,b=-1 / k. E(P) and p(s) can be calculated based on the stress PSD. C and k can be calculated based on the fatigue life curve.

[0085] The number of peak occurrences E(P) can be calculated using equation (4).

[0086]

number

[0087] where m2 is the second moment (variance) of the PSD, and m4 is the fourth moment (kurtosis) of the PSD. The nth moment of the PSD can be calculated using equation (5). The 0th to 4th moments of the PSD can be calculated using equations (6) to (9).

[0088]

number

[0089] where f is the frequency, G(f) is the PSD function, and m1 is the first moment (standard deviation) of the PSD.

[0090] The probability density function p(s) can be calculated using equations (10) to (17).

[0091]

number

[0092] In step S10, the result output unit 109 of the prediction device 1 receives the fatigue life prediction result from the life prediction unit 108. Next, the result output unit 109 transmits the fatigue life prediction result to the terminal device 2.

[0093] In the terminal device 2, the result display unit 202 receives the fatigue life prediction result. Then, the result display unit 202 outputs the fatigue life prediction result to the display device 506.

[0094] <Effects> The prediction device 1 in this embodiment estimates a fatigue life curve based on the shape of a stress concentration part of a structure, and predicts the fatigue life of the structure based on the distribution of stress amplitudes generated at the stress concentration part by a fluctuating load applied to the structure and the fatigue life curve. In the conventional technology, the prediction accuracy of the fatigue life may decrease when local stress occurs in the structure. Therefore, according to this embodiment, the fatigue life of the structure can be predicted with high accuracy.

[0095] The prediction device 1 in this embodiment may estimate a fatigue life curve based on a stress concentration coefficient calculated based on the shape of the stress concentration portion. The prediction device 1 may generate a fatigue life curve using a fatigue strength coefficient and a fatigue strength index estimated based on the stress concentration coefficient. In the conventional technology, it is necessary to obtain a fatigue life curve for each shape of a structure, and it takes a huge amount of cost and time to predict the fatigue life of a structure having a new shape. Therefore, according to this embodiment, it is possible to reduce the man-hours required to predict the fatigue life of a structure having a new shape.

[0096] The prediction device 1 in this embodiment may estimate the fatigue strength coefficient and the fatigue strength index based on a predetermined relational expression. The relational expression may be determined based on data obtained by observing fatigue lives of a plurality of structures having stress concentration portions with different shapes. Therefore, according to this embodiment, it is possible to accurately estimate the fatigue life curve of a structure having a new shape.

[0097] The fatigue life in this embodiment may be the number of repeated fluctuating loads at which damage occurs at the stress concentration portion. The stress amplitude distribution in this embodiment may be the power spectrum density of the stress amplitude. Therefore, according to this embodiment, the fatigue life can be accurately predicted in the frequency domain.

[0098] The fluctuating load in this embodiment may be applied to the structure by vibrating the structure. The structure in this embodiment may be an electronic component, and the stress concentration portion may be a bent portion of a lead terminal of the electronic component. Therefore, according to this embodiment, it is possible to accurately predict the fatigue life of an electronic component having a lead terminal including a bent portion when a fluctuating load due to vibration is applied to the electronic component.

[0099] [Variations] In the above embodiment, a configuration for predicting a fatigue life using the Dirlik method has been described as an example of a method for predicting a fatigue life in the frequency domain. Since the prediction device 1 in the embodiment can accurately estimate a fatigue life curve according to the shape of a structure, the fatigue life can also be predicted by a method for predicting a fatigue life other than the frequency domain as long as the method uses a fatigue life curve.

[0100] For example, the rainflow method is known as an example of a method for predicting fatigue life in the time domain. In the rainflow method, a waveform showing the time change of stress amplitude due to a fluctuating load is identified, and fatigue life is predicted based on the cumulative damage law. In the embodiment, fatigue life is predicted using a stress PSD showing the distribution of stress amplitude in the frequency domain, but the rainflow method can predict fatigue life using the time change of stress amplitude (in other words, the distribution of stress amplitude in the time domain).

[0101] In this modification, the distribution calculation unit 104 calculates a waveform showing a time change of the stress amplitude, and the life prediction unit 108 predicts the fatigue life in the time domain. The life prediction unit 108 may predict the fatigue life using, for example, the rainflow method. The curve estimation unit 107 in this modification may estimate the fatigue life curve based on the shape of the structure to be predicted, as in the embodiment.

[0102] <Effects> The prediction device 1 in this modification calculates a waveform showing a time change of the stress amplitude, and predicts a fatigue life based on the fatigue life curve and the calculated waveform. Therefore, according to this modification, it is possible to predict a fatigue life in the time domain with high accuracy.

[0103] [supplement] Each function of the above-described embodiments can be realized by one or more processing circuits. Here, the term "processing circuit" in this specification includes a processor programmed to execute each function by software, such as a processor implemented by an electronic circuit, and an ASIC (Application Specific Integrated Circuit), DSP (Digital Signal Processor), FPGA (Field Programmable Gate Array), conventional circuit module, and other devices designed to execute each function described above.

[0104] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of the gist of the present invention described in the claims. [Explanation of symbols]

[0105] 1000: Fatigue life prediction system 1: Prediction device 2: Terminal device 100: Relational equation storage section 101: Structure information reception department 102: Model Creation Department 103: Vibration analysis department 104:Distribution calculation part 105: Coefficient calculation section 106: Strength estimation part 107:Curve estimation part 108: Life Prediction Department 109: Result output section 201: Structure information input section 202:Result display section

Claims

1. A curve estimator configured to estimate a fatigue life curve based on a shape of a stress concentration portion of the structure; a distribution calculation unit configured to calculate a distribution of stress amplitudes generated at the stress concentration portion due to a fluctuating load applied to the structure; and a life prediction unit configured to predict a fatigue life of the structure based on the distribution of the stress amplitude and the fatigue life curve; A prediction device comprising:

2. The prediction device according to claim 1 , the curve estimator is configured to estimate the fatigue life curve based on a stress concentration factor calculated based on a shape of the stress concentration portion. Prediction device.

3. The prediction device according to claim 2, The curve estimator is configured to generate the fatigue life curve by using a fatigue strength coefficient and a fatigue strength index estimated based on the stress concentration factor. Prediction device.

4. The prediction device according to claim 3, The curve estimation unit is configured to estimate the fatigue strength coefficient and the fatigue strength index based on a predetermined relational expression. Prediction device.

5. The prediction device according to claim 4, The relational expression is determined based on data obtained by observing the fatigue life of a plurality of the structures having different shapes of the stress concentration portion. Prediction device.

6. The prediction device according to any one of claims 1 to 5, The fatigue life is the number of repeated fluctuating loads at which damage occurs at the stress concentration portion. Prediction device.

7. The prediction device according to any one of claims 1 to 5, The distribution is a power spectral density of the stress amplitude. Prediction device.

8. The prediction device according to any one of claims 1 to 5, The distribution is a waveform showing a change in the stress amplitude over time. Prediction device.

9. The prediction device according to any one of claims 1 to 5, The fluctuating load is applied to the structure by the structure vibrating. Prediction device.

10. The prediction device according to any one of claims 1 to 5, the structure is an electronic component, the stress concentration portion is a bent portion of a lead terminal of the electronic component, Prediction device.

11. The computer A step of estimating a fatigue life curve based on a shape of a stress concentration portion of a structure; A procedure for calculating a distribution of stress amplitudes generated at the stress concentration portion due to a fluctuating load applied to the structure; predicting a fatigue life of the structure based on the stress amplitude distribution and the fatigue life curve; A forecasting method to perform.

12. On the computer, A step of estimating a fatigue life curve based on a shape of a stress concentration portion of a structure; A procedure for calculating a distribution of stress amplitudes generated at the stress concentration portion due to a fluctuating load applied to the structure; predicting a fatigue life of the structure based on the stress amplitude distribution and the fatigue life curve; A program for executing the above.

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