BRPI1107150A2 - apparatus for estimating the likelihood of limb fatigue fracture, method for estimating the likelihood of limb fatigue fracture, and computer program product - Google Patents

Abstract

Apparatus for estimating the probability of fatigue fracture in the member, method for estimating the probability of fracture of fatigue in the member, and the product of a computer program. The present invention relates to an effective volume V and p of a limb is calculated as an amount of voltage correction or corrected to an effective voltage (amplitude of amplitude) at each position of the limb. such that a variable limb fatigue strength corresponding to a mean stress that varies depending on the limb position is apparently constant at a value when the mean limb stress is 0 (zero) regardless of the limb position and a cumulative fracture probability P ~ tp ~ due to limb fatigue is derived using the effective volume V ~ and p ~ of the limb.

Description

Report of the Invention Patent for "Apparatus for estimating the probability of fatigue fracture in member, method for estimating the probability of fatigue fracture in member, and product of computer program".

BACKGROUND OF THE INVENTION Field of the invention

The present invention relates to an apparatus for estimating the likelihood of limb fatigue fracture, a method for estimating the likelihood of limb fatigue fracture and a computer program product, and particularly those which are preferably used. to estimate the likelihood of fracture due to fatigue of a part of the machine subjected to repeated loading. Description of Related Art

Conventionally, the structure of machine parts (metal parts and the like) subjected to repeated loading is often made to prevent machine fatigue fracture. Specifically, a modified Goodman ratio is created from the fatigue strength and the fatigue limit obtained based on the result of a fatigue test of a material fatigue test piece and a tensile strength of the material. A fatigue limit diagram is created which is modified taking into account effects on fatigue characteristics such as limb size, surface roughness and residual stress based on the modified Goodman ratio. Then, a required fatigue strength is found in consideration of the mean stress at the part and the amplitude of the stress, and it is confirmed that the fatigue limit diagram goes beyond the appropriate safety factor for fatigue strength, according to which is made of the fatigue strength structure of the limb (see, for example, Non-Patent Document 1 for a spring that is one of the typical machine parts).

To deal with variations in material fatigue characteristics, a method of creating the PSN curve and a method of finding a cumulative probability distribution of fatigue strength at a given number of repeated loading times are described in Ja- pan Society of Mechanical Engineers Standard JSME S 002 (Statistical Fatigue Test Method).

Furthermore, in the field of fatigue characteristics of a high strength steel possibly fracturing starting from the inclusion of such spring-loaded steel and the like, the volume of a region where a stress of, for example, 90% of the maximum stress Act is referred to as a risk volume as an indicator of the size of a risk region that is the starting point of the fatigue fracture and the size of the risk volume is assessed (see Non-Patent Document 2).

Non-Patent Document 1 - Spring Technology Association, Third Edition, Maruzen, 1982, pages 379 to 382.

Non-Patent Document 2 - FURUYA Yoshiyuki, MATSUOKA Saburo, Inclusion Inspection Method in Ultrasonic Fatigue Test, CAMP-ISIJ, Vol. 16, 2003, page 578

However, the safety factor described above, the risk volume size and the voltage level as a reference are not based on any theoretical basis, but experimentally determined. Moreover, when a structure is taken into account with a very low probability of fracture, the variations caused by experiments cannot be sufficiently evaluated or many experiments are difficult to conduct in many cases. As a result, it is difficult to accurately estimate the probability of fracture due to machine part fatigue. SUMMARY OF THE INVENTION

The present invention is made in consideration of the problem and its purpose is to make it possible to estimate more accurately than ever before the probability of fatigue fracture that occurs with a low probability in a long life span region of a machine part.

An apparatus for estimating the probability of limb fatigue fracture includes: a processor that performs at least: a first acquisition process to acquire, as first acquisition information, a Weibull coefficient m and a scale parameter au [N / mm2] when a fatigue fracture probability distribution with respect to a stress amplitude of a fatigue test at a given number of times of repeated loading of a fatigue test piece made of a member material is expressed by a distribution Weibull's of two parameters; a second acquisition process to acquire, as a second acquisition information, an aip amplitude [N / mm2] of a maximum principal stress or a corresponding voltage at each limb position and an average aave voltage [N / mm2] being an average of the maximum principal stress or a corresponding stress at each limb position; an effective member volume derivation process of deriving an effective member volume Vep [mm3] from the following Equation (A) and Equation (B); and a limb fracture probability derivation process of deriving a cumulative fracture probability Pfp due to limb fatigue of a following Equation (C); and a reporting unit reporting information related to the likelihood of cumulative Pfp fracture due to limb fatigue.

Here, oap is one is a member fatigue strength [N / mm2] when the fatigue strength at each position is made uniform to a constant value using aip + aCOrr as the amplitude of stress at each member position in a diagram. of fatigue limit representing a relationship between limb fatigue strength and mean limb stress, σΓ is a fatigue strength [N / mm2] at a given position when mean limb stress is Oave mean stress at position acquired in the second acquisition process, in the fatigue limit diagram, max (x) represents a maximum value of x, and Jdv represents the whole member volume integration.

A method for estimating the probability of limb fatigue fracture of the present invention includes: a first acquisition step to acquire as a first acquisition information a Weibull coefficient and a scale parameter or [N / mm2] when a probe distribution Cumulative fracture ability with respect to a stress amplitude of a fatigue test at a given number of times of repeated loading of a fatigue test piece made of a member material is expressed by a Weibull distribution. of two parameters; a second acquisition step to acquire, as a second acquisition information, an oip amplitude [N / mm2] of a maximum main voltage or a corresponding voltage at each limb position and an average Oave voltage [N / mm2] being one. mean maximum head tension or corresponding stress at each limb position; an effective member volume derivation step of deriving an effective member volume Vep [mm3] from the following Equation (A) and Equation (B); and a limb fracture probability derivation step of deriving a cumulative fracture probability Pfp due to limb fatigue of a following Equation (C); and a reporting step related to the probability of cumulative fracture Pfp due to limb fatigue derived by the limb fracture probability derivation step.

Here, oap is a limb fatigue strength [N / mm2] when the fatigue strength at each position is made uniform to a constant value using Ojp + aC0rr as the stress amplitude at each limb position in a boundary diagram. of fatigue representing a relationship between limb fatigue strength and mean limb stress, σΓ is a fatigue strength [N / mm2] at a given position when the mean tension in the limb position is the mean voltage aave acquired at the second acquisition stage in the fatigue limit diagram, max (x) represents a maximum value of x, and idv represents whole-member volume integration.

A computer program product of the present invention causes a computer to perform: the first acquisition step to acquire a Weibull coefficient with a scale parameter or [N / mm2] when a cumulative fracture probability distribution with respect to an amplitude The stress of a fatigue test at a given number of times of repeated loading of a material fatigue test piece made of one-member material is expressed by a two-parameter Weibull distribution; a second acquisition step to acquire an oip amplitude [N / mm2] of a maximum major stress or a corresponding voltage at each limb position and an average Oave voltage [N / mm2] being an average of the maximum principal stress or corresponding tension at each position of the limb; a member effective volume derivation step of deriving an effective member volume Vep [mm3] from the following Equation (A) and Equation (B); and a step of limb fracture probability derivation from deriving a cumulative fracture probability Pfp due to limb fatigue of a following Equation (C); and a reporting step of reporting information regarding the cumulative fracture probability Pfp due to limb fatigue derived by the limb fracture probability derivation step.

Here, aap is a limb fatigue strength [N / mm2] when the fatigue strength at each position is made uniform to a constant value using aip + aCOrr as the amplitude of stress at each limb position in a boundary diagram. of fatigue representing a relationship between limb fatigue strength and mean limb stress, σΓ is a fatigue strength [N / mm2] at a given position when the mean tension in the limb position is The mean Oave voltage acquired at the second acquisition stage, in the fatigue limit diagram, max (x) represents a maximum value of x, and Jdv represents the whole member volume integration. Formula 1

Vep = J {(OiP + aCorr) / max (aip + Occ)} mdV ... (A) ücorr = Oap-Or ... (B)

Pfp = 1-exp [-VeP {max (OiP + Occ) / or} m] ... (C) BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a diagram illustrating an example of the hardware configuration of an apparatus for estimating the probability of limb fatigue fracture;

Figure 2 is a diagram illustrating an example of the functional configuration of the apparatus for estimating the probability of limb fatigue fracture;

Figure 3 is a diagram illustrating examples of the P-S-N curve; Figure 4 is a diagram illustrating an example of Weibull plotting;

Fig. 5 is a diagram illustrating examples of the relationships between a spring wire position and a spring fatigue strength and an acting stress;

Figure 6 is a diagram representing an example of the relationship

modified Goodman's;

Figure 7 is a flow chart explaining an example of the operation of the apparatus for estimating the probability of limb fatigue fracture;

Figure 8 is a diagram illustrating the residual stress distribution of a coil spring in a first example; and

Figure 9 is a diagram illustrating the residual stress distribution of a plate member in a second example. DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

Hardware configuration of an apparatus to estimate the probability of fatigue fracture (part of the machine) in a limb.

Figure 1 is a diagram illustrating an example of the hardware configuration of an apparatus for estimating the probability of limb fatigue fracture 100.

As shown in Figure 1, the apparatus for estimating the probability of limb fatigue fracture 100 has a CPU (Central Process Unit) 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory). 103, a PD (Indication Device) 104, a HD (Hard Disk) 105, a display device 106, a speaker 107, a communication I / F (Interface) 108, and a system bus 109.

CPU 101 is a processor that collectively controls operation on the apparatus for estimating the probability of limb fatigue fracture 100. CPU 101 controls the components (102 to 108) of the apparatus for estimating the probability of limb fatigue fracture. 100 through system bus 109. ROM 102 stores a BIOS (Basic Input / Output System) which is a CPU control program 101, an operating system (OS) program, and a required CPU program. 101 to perform the process according to a flow chart described above and so on.

RAM 103 functions as CPU 101 main memory, workspace, and the like. When the process is executed, CPU 101 loads necessary and similar computer programs from ROM 102 and required and similar information from HD 105 into RAM 103. The CPU 101 then executes the computer programs and the like and the information and the like loaded into RAM 103 to thereby deploy various operations.

The PD 104 is composed of, for example, a mouse, a keyboard and the like. PD 104 is an operating input unit for the operator to perform, when necessary, an operating input to the apparatus for estimating the probability of limb fatigue fracture 100.

HD 105 is a memory that stores various types of information, data and files and the like.

Display device 106 is a display unit that shows various types of information and images based on control of CPU 101.

Speaker 107 is a voice output unit that outputs voice related to various types of information based on control of CPU 101.

Communication I / F 108 communicates various types of information and the like with an external device over a communications network based on control of the CPU 101.

System bus 109 is a bus for connecting CPU 101, ROM 102, RAM 103, PD 104, HD 105, display device 106, communication speaker 107 eol / F 108 in such a way. that they can communicate with each other.

Apparatus for estimating the probability of limb fatigue fracture

Figure 2 is a diagram illustrating an example of the functional configuration of the apparatus for estimating the probability of limb fatigue fracture 100.

In Figure 2, the apparatus for estimating the probability of fracture

100 has a material fatigue characteristic assessment part 201, a member stress analysis part 202, an effective member volume derivation part 203, a cumulative fracture probability drift part member 204, a fracture probability comparison portion 205 and a frame voltage output portion 206.

The material fatigue characteristic evaluation part 201 has a function of evaluating the fatigue characteristic of a material (metal (eg steel material) or the like) or the like which constitutes the probability derivation member. of cumulative fracture. Specifically, the material fatigue characteristic evaluation part 201 in a PSN curve making part 211, a fatigue strength shunt part and Weibull coefficient 212, a test piece stress analysis part 213, a part volume derivative part of test piece 214, and a scaling parameter derivation part 215.

The PSN 211 curve creation portion receives the input of a uniaxial fatigue test result conducted in a state that the average Oave stress at each position of the material fatigue test piece is 0 [N / mm2] (the result of a uniaxial fatigue test). In this embodiment, the uniaxial fatigue test is conducted on a plurality of material fatigue test pieces under a test voltage ot with a constant voltage amplitude and then the uniaxial fatigue test is similarly conducted on the plurality of material fatigue test pieces with voltage range changed. The uniaxial fatigue test here represents a test of repeatedly loading the test voltage ot (tension and compression stress) regularly shifted in one direction on the material fatigue test piece to investigate the number of times to load repeatedly. the test voltage until the material fatigue test piece breaks. The average aave stress at each position of the material fatigue test piece is, for example, the arithmetic mean of the maximum value and the minimum value of the maximum principal stress or the corresponding stress that is found at each position of the fatigue test piece. of material. The test piece of material failure is a test piece of material that constitutes the limb. A test piece that has the same shape and size as those of the test piece that is generally used in the uniaxial fatigue test can be employed as the material fatigue test piece.

In addition, the PSN 211 curve-making part receives input from the result of a torsion fatigue test conducted in a state that the mean stress Tave at each position of the material fatigue test piece is 0 [N / mm2 ]. In this embodiment, the torsion fatigue test is conducted on a plurality of material fatigue test pieces under a Tt test voltage with a constant stress amplitude and then the torsion fatigue test is similarly conducted at plurality of material fatigue test pieces with the voltage amplitude changed. The torsion fatigue test here represents a test of repeatedly loading the test stress Tt (shear stress) regularly changed in a shear direction on the material fatigue test piece to investigate the number of times to repeatedly load the test. test voltage Tt until the material fatigue test piece breaks. The average Tave stress at each position of the material fatigue test piece is, for example, the arithmetic mean of the maximum value and the minimum value of the maximum principal stress or the corresponding stress that is found at each position of the part. material fatigue testing. The material fatigue test piece is a test piece of the material that constitutes the member. A test piece that is the same size and shape as those of the test piece that is generally used in the torsion fatigue test can be employed as the material fatigue test piece.

The curve making part P-S-N 211 can receive input of uniaxial fatigue test and torsion fatigue test results, for example as follows. The P-S-N 211 curve creation portion can acquire information about the results of the uniaxial fatigue test and the twisting fatigue test based on the contents of the user interface operation. The P-S-N 211 curve creation part can read information about the uniaxial fatigue test and the torsion fatigue test stored on a personal computer's hard disk and a transportable memory. The curve making part P-S-N 211 can acquire information about the results of the uniaxial fatigue test and the torsion fatigue test received from outside on the communication network. Note that the voltage-related unit expression will be omitted as needed in the following description.

As described above, both the uniaxial fatigue test result and the torsion fatigue test result can be acquired as the material fatigue characteristics in this mode. In addition, the user can select or use the uniaxial fatigue test result or torsion fatigue test result as the material fatigue characteristics, for example based on the user interface operating contents.

The P-S-N 211 curve creation part creates a P-S-N u-

using the test result according to the user-selected result (the result of the uniaxial fatigue test or the torsion fatigue test). The PSN curve is a curve that indicates the relationship between the number of times the load repeated in the test and the test stress at (stress and compressive stress amplitude) or Tt (shear stress tension amplitude) loaded. in the material fatigue test piece that broke in the number of times repeated loading. Figure 3 is a diagram illustrating examples of the P-S-N curve. Note that Figure 3 illustrates examples of the P-S-N curve when the test voltage (stress amplitude) is the stress and compression stress (stress amplitude) ot. The PSN curve, when the test voltage (voltage amplitude) is the shear voltage (voltage amplitude) Tt, is different only in the shape and value of the curve from those illustrated in figure 3 and then the illustration. of this will be omitted here.

The fatigue resistance shunt part and Wei bull 212 find a fatigue strength distribution function 310 from the values of the PSN curves 301 to 303 in a target number of repeated load times (the target number of times repeated charging is 108 times in this mode). In this embodiment, the Weibull 212 fatigue strength and coefficient derivation part finds the function of fatigue resistance distribution 310 by the method of Japan Society of Mechanical Engineers Standard JSME S 002 (Statistical Fatigue Test Method). ) whose distribution function used in this PE is replaced by the Weibull distribution function. Specifically, in this embodiment, the Weibull 212 fatigue strength and coefficient derivation part creates the Weibull plot that indicates the relationship between the values of the PSN curves curves 301 to 303 at the target number of repeated load times (108 times). (the value found by taking the natural Yogarithm of the test voltage (stress amplitude) σ {or Tt loaded on the material fatigue test piece (= 1 not or 1ηη)) and the value found by taking twice the Natural iogarithm of (1 / (1-F)) where the probability of cumulative fracture of the material fatigue test piece is F (= 1n1n (1 / (1-F)).

Figure 4 is a diagram illustrating an example of Weibull plotting. Note that Figure 4 illustrates the Weibull plot example when the test stress (stress amplitude) is the stress and compression stress (stress amplitude) σ {. Weibull plotting, when test stress (stress amplitude) is the shear stress (stress amplitude) η, is different only in shape and curve value from those illustrated in figure 4, and then the illustration of this will be omitted here. As described above, it is assumed that the probability of cumulative fracture F with respect to the stress amplitude of the fatigue test at a given number of times of repeated loading with the mean stress in the material fatigue test piece being 0 [ N / mm2] is expressed by the Weibull distribution of two parameters in this mode. This probability of cumulative fracture (Weibull distribution of two parameters) F is expressed by the following Equation (1).

Formula 2

F = 1-exp {- (ai / au) m} = 1-exp {-Ves (max (ai) / au) m} ... (1)

In Equation (1), σ is the maximum principal stress or the corresponding stress (stress amplitude) [N / mm2] at each position of the material fatigue test piece. In addition, συ is the scale parameter [N / mm2] when the probability of cumulative fracture with respect to stress amplitude at a given number of times of repeated loading on the assumption that the fatigue test (for example, the fatigue test uniaxial test or torsion fatigue test) by repeated loading with the mean stress in the material being O [N / mm2] was conducted, which is expressed by the Weibull distribution of two parameters. Here, the given number of repeated load times is preferably, but not necessarily, the target number described above repeated load times. Note that the au scale parameter of fracture strength on the assumption that the fatigue test is conducted on the material at the given number of repeated loading times is referred to as a material au scale parameter or an au scale parameter as required in the material. following description. In addition, Ves is the effective volume [mm3] of the material fake test piece. Here, the effective volume represents the size indicator of a region where fatigue fracture occurs.

In addition, m is the Weibull coefficient when the probability of cumulative fracture with respect to stress amplitude at a given number of times of repeated loading, on the assumption that the fatigue test (for example, the uniaxial fatigue test or the torsional fatigue) by repeated loading with the mean stress on the material fatigue test piece being O [N / mm2] is conducted, which is expressed by the Weibull distribution of two parameters. Here, the Weibull coefficient m represents the variations in fatigue strength at the given number of times repeated loading. In addition, the given number of repeated load times may be the target number described above repeated load times or another number of times. Note that despite the Weibull coefficient when cumulative fracture probability in the state that the mean stress in the material fatigue test piece is 0 [N / mm2] is expressed by the Weibull distribution of two parameters being employed here, the mean stress is not. limited to 0 [N / mm2]. In addition, the probability of cumulative fracture with respect to the stress amplitude of the fatigue test at the given number of repeated load times assuming that the repeated load fatigue test with the mean stress in the material fatigue test piece being 0 [N / mm2] is referred to as a Weibull coefficient m of the material fatigue test piece or a Weibull coefficient m as required in the following description.

In addition, max (x) represents the maximum value of χ (this also applies to max (x) in the following equation). Note that the unit expression related to the effective volume will be omitted as needed in the following description. In addition, the maximum principal stress or the corresponding stress (stress amplitude) σ at each position of the material fatigue test piece is referred to as an effective stress (stress amplitude) o at each position of the piece. material fatigue testing as required.

The fatigue strength shunt part and Wei coefficient

bull 212 derives the P-S-N curves 301, 302, 303 indicating the S-N curve at a certain probability of cumulative fracture from plotting the fatigue test result. The Weibull 212 fatigue strength shunt part then derives, as the Weibull coefficient, the slope of a straight line 401 obtained by applying the fatigue strength distribution characteristic at the given number of loading times. of the result for the Weibull distribution 310.

When the use of the uniaxial fatigue test result as the material fatigue characteristic is selected by the user, the fatigue strength bypass portion and Weibull 212 coefficient derives a mean fatigue strength from the fatigue test piece. material from the results of a plurality of uniaxial fatigue tests. The mean fatigue strength of the material fatigue test piece is the expected value (average value) of the fatigue resistance of the material fatigue test piece obtained from the results of the uniaxial fatigue tests conducted. with the mean stress at a plurality of material fatigue test pieces being 0 [N / mm2].

On the other hand, when the use of the torsion fatigue test result as the material fatigue characteristic is selected by the user, the fatigue strength shunt part and Weibull 212 coefficient find an average shear stress (shear amplitude). tension) Tas on the surface of the material fatigue test piece from the results of a plurality of torsion fatigue tests. When the stress main stress (stress amplitude) is used as Oj in Equation (1), the Weibull 212 fatigue strength and coefficient shunt part uses the average shear stress (stress amplitude) as the mean fatigue strength. of the material fatigue test piece. On the other hand, when the corresponding stress (stress amplitude) is used as σ in Equation (1), the Weibull 212 fatigue strength and coefficient derivation part uses the value obtained by multiplying the mean shear stress (ampli - Tude voltage) Tas by a coefficient f (1 Dfa V3) as the mean fatigue strength oas of the material fatigue test piece. The mean fatigue strength of the material fatigue test piece is the expected value (average value) of the fatigue strength of the material fatigue test piece obtained from the results of the conducted torsion fatigue tests. - with the mean stress on the plurality of material fatigue test pieces being O [N / mm2]. Here, ρ coefficient f is a coefficient based on the difference between shear stress and axial force and must be defined in advance in the apparatus to estimate the probability of limb fatigue fracture 100 based on user interface operation. The user may appropriately decide an arbitrary value no less than 1 and no greater than V3 as the coefficient f according to the experimental result or the like. When a value outside this range is selected (entered) as the coefficient f, the apparatus for estimating the probability of limb fatigue fracture 100 does not employ the selected coefficient f but reports the selection of a value within this range. track to the user.

Stress analysis part 213 receives information input about the material fatigue test piece such as the shape of the material fatigue test piece, the load conditions applied to the fatigue test piece. material strength and material strength (eg tensile strength, yield stress, and work hardening characteristic). Stress analysis portion of test piece 213 may receive information input about the material fatigue test piece as follows, for example. The stress analysis part of test piece 213 can get the information about the material fatigue test piece based on the operating contents of the user interface. The stress analysis part of test piece 213 can read information about the material fatigue test piece stored on a personal computer's hard disk and a transportable memory. The stress analysis portion of test piece 213 may acquire information about the material fatigue test piece received from the outside on the communications network.

Stress analysis portion of test piece 213 then derives the effective stress (strain amplitude) Hi at each position of the material fatigue test piece using the information entered about the material fatigue test piece. material. The effective stress (strain amplitude) at each position of the material fatigue test piece can be derived by performing analysis using the FEM (Finite Element Method) or BEM (Limit Element Method) or by performing the I calculate using the method for the strength of the materials. The derivation of the effective stress (stress amplitude) σ at each position of the material fatigue test piece can be implemented by a publicly known method and then the detailed description thereof will be omitted here. The effective volume derivation part of test piece 214

derives the effective volume Ves of the material fatigue test piece from the following Equation (2) using "the Weibull coefficient m of the material fatigue test piece" derived by the shunt resistance part and the Weibull coefficient 212 and "the effective stress (stress amplitude) σ at each position of the material fatigue test piece" derived by the stress analysis part of test piece 213. Note that J in Equation (2) represents the integration of whole material fatigue test piece volume.

Formula 3

Ves = í {Hi / max (Hi)} mdv ... (2)

Scale Parameter Derivation Part 215 derives the scale or material parameter from the following Equation (3) using "the Weibull coefficient m of the material fatigue test piece and the mean fatigue strength oas of material fatigue test piece "derived by fatigue strength derivation part and Weibull coefficient 212 and" effective volume Ves of material fatigue test piece "derived by effective volume derivation part of test piece 214 Note that Γ () in Equation (3) represents the gamma function (this also applies to the expression in the following equations).

Formula 4

au = OasVes1 / m / r (1 + 1 / m) ... (3) The member stress analysis part 202 receives the input

information about the limb and external force such as the shape of a limb, the acting external force acting on the limb (the load applied on the limb), the residual tension of the limb, the resistance to material of the limb (for example, the tensile strength, yield stress and work hardening characteristic), and the tensile strength ob of the material. The member stress analysis part 202 may receive input of the information about the member and external force as follows for example. The member stress analysis part 202 may acquire information about the member and external force based on the contents of the user interface operation. The member stress analysis part 202 can read information about the member and external force stored on the hard disk of a personal computer and a transportable memory. The member stress analysis part 202 may acquire the information about the member and the external force received from the outside about the communications network,

The member stress analysis part 202 then derives the maximum stress or corresponding stress (stress amplitude) ajp at each member position and the mean Oave stress at each member position (for example, the arithmetic mean of the maximum value and the minimum value of the maximum main voltage or the corresponding voltage) using information entered about the limb and external force. They may be derived as follows as an example. The limb stress analysis part 202 first estimates the residual limb stress based on the thermal stress analysis and the residual stress measurement result. The member stress analysis part 202 additionally estimates the stress that occurs within the member with respect to the acting external force by performing analysis using FEM or BEM or calculating using the materials resistance method. The member stress analysis part 202 then superimposes the residual member stress on the stress occurring within the member to estimate the stress state within the member. From the stress state within the limb, the corresponding maximum stress or strain (strain amplitude) oip at each limb position and the average Oave stress at each limb position are derived. Its derivation may be implemented by a publicly known method and then the detailed description thereof will be omitted here. Note that the maximum stress or corresponding stress (stress amplitude) σ, ρ at each limb position is referred to as an effective stress (stress amplitude) σ | ρ at each limb position as required in the following description.

The effective member volume derivation portion 203 derives the effective member volume Vep based on the following Equation (4). Note that í in Equation (4) represents the integer member volume integration. As described above, σ, ρ is the effective stress (strain amplitude) at each limb position. In addition, Ocorr is an amount of stress correction to correct the effect of mean aave stress at each limb position on limb fatigue strength and is expressed by the following Equation (5). In Equation (5), Gap represents a limb fatigue strength when the mean limb stress is 0 (zero), in a fatigue limit diagram representing the relationship between limb fatigue strength and mean limb tension. . In addition, σΓ represents a fatigue strength at a given position when the mean limb stress is "Oave mean stress" at the position derived by the member stress analysis part 202 in the fatigue limit diagram. Note that limb fatigue strength Qap when mean limb stress is O (zero) in the fatigue limit diagram is referred to as a member fatigue strength at a position such as mean stress. 0 as required in the following description. In addition, the fatigue strength σΓ at a given position when the mean stress on the limb is "the mean stress Qave" at the position derived by the member stress analysis part 202 in the fatigue limit diagram is referred to as a stress resistance. limb fatigue in each position as needed.

Formula 5

Vep = J {(oip + acorr) / max (aip + ac0rr)} mdv ... (4)

Occurs = CTap-Or ... (5)

Figure 5 is a diagram illustrating examples of relationships between

between the position of a spring wire and the local fatigue strength 501 and the acting voltage 502 in position. The spring fatigue strength 501 and the acting stress 502 are not constant at the cross-sectional positions of the wire as shown in Figure 5 because of the deformation caused mainly by spring twisting and the compressive residual stress caused by working or riveting or similar spring on the surface of the spring. Generally, the fatigue strength of the limb differs in value depending on the position within the limb because of the effect of mean aave stress on each limb position. Therefore, the amount of current stress correction is added to the effective stress (stress amplitude) aip at each member position so that the member fatigue strength is apparently constant in value when the mean member stress is 0 (zero). regardless of limb position as expressed in Equation (4) in this modality. This makes it possible to properly assess the effective limb Vep volume even using the uniaxial fatigue test result at the same mean stress (here the mean stress 0) (the Weibull coefficient m of the material fatigue test piece, the distribution au scale of the material).

In addition, in this embodiment, the effective limb volume Vep is derived using a modified Goodman ratio as the fatigue limit diagram. Figure 6 is a diagram representing an example of the modified Goodman relationship. As illustrated in Figure 6, a modified Goodman ratio 601 is expressed by a straight line connecting "the tensile strength ab of the material" inserted by the member stress analysis part 202 and "the member aap fatigue strength in the position with the average voltage of 0 "described above. When the modified Goodman ratio is used as the fatigue limit diagram, the limb oap fatigue strength in position with the mean stress 0 and the amount of oCOrr stress correction are expressed by the following Equation (6) and Equation (7 ) respectively. Thus, when the effective volume Vep of the limb is derived using the modified Goodman ratio as the fatigue limit diagram, Equation (4) described above can be rewritten as the following Equation (8).

It is noted that the description in this embodiment is based on the case where the mean stress test strain is 0. This is because the mean stress test method of 0 such as an ultrasonic fatigue test It is considered to be a general method for collecting data from many regions with long lifetimes. However, the use of the modified Goodman ratio makes it possible to estimate similar fatigue fracture probability even based on other mean stresses. Then, in this method, the evaluation as in the case where the mean stress is 0 is possible even if the mean stress in the material fatigue test piece or the member to which the fatigue strength used as the basis for the evaluation is transmitted. is changed to another value. In other words, Figure 6 illustrates the case where the mean stress on the limb that is the basis for the evaluation is 0 with an example. However, for example, the ap may be limb fatigue strength when the fatigue strength at each position is made uniform to a constant value using ajP + acorr as the stress in each limb position in the fatigue limit diagram.

Formula 6

Cfap = auVep "1 / rT (1 + 1 / m) ... (6)

Occur = OapOaVe / Ob ... (7)

Vep = í {(ap + Vep1 / niaur (1 + 1 / m) aave / ab) / max (ap + Vep "1" Tlaur (1 + 1 / m) oave / ab)} m dV ... (8 )

Consequently, the effective member volume derivation portion 203 derives the effective member volume Vep of Equation (8) using "the effective stress (stress amplitude) σ, ρ at each member position, the mean voltage Qave at each position. of the limb and the tensile strength ob of the material "derived by the member stress analysis part 202," the Weibull coefficient m of the material fatigue test piece "derived by the fatigue strength derivation part and the Weibull coefficient 212, and "the scale or material parameter" derived by the scale parameter bypass portion 215 in this embodiment. Note that since the effective volume Vep of the material is described on both right and left sides in Equation (8), the effective member volume derivation part 203 performs the convergence calculation to derive the effective member volume Vep. Note that the convergence calculation can be implemented by a publicly known method and then its detailed description will be omitted here. The effective member volume derivation portion 203 additionally derives the amount of Ocorr stress correction from Equation (6) and Equation (7) described above.

The cumulative limb fracture probability derivation part 204 derives a probability of Pfp fracture due to limb fatigue from Equation (9) using "the effective stress (voltage amplitude) Oip at each limb position" derived by limb stress analysis part 202, "the effective limb volume Vep, the amount of stress correction aCOrr" derived by the effective limb volume derivation part 203 and "the Weibull coefficient m of the material fatigue test "derived by the fatigue strength derivation part and Weibull coefficient 212 and" the scale or material parameter "derived by the scale parameter derivation part 215.

Formula 7

Pfp = 1 -exp [-Vep {max (ajp + oCorr) / σ u} m] ... (9)

The fracture probability comparison part 205 determines whether or not the probability of cumulative fracture Pfp due to limb fatigue derived by the cumulative fracture probability derivation part 204 is equal to or less than the probability of target cumulative fracture defined in advance by the user. When the probability of cumulative fracture Pfp due to limb fatigue is not equal to or less than the probability of target cumulative fracture as a result of the determination, limb stress analysis part 202 requires the user to change the information. the member fence showing a screen (GUI) that requires changing the information about the member fence. The member stress analysis part 202 again derives the effective stress (stress amplitude) Oip at each member position and the mean stress aave at each member position using the information about the member inserted in response to. this requirement. As the information changes, the effective limb shunt part 203 again derives the effective volume Vep of the limb and the cumulative limb probability fracture part 204 again derives the probability of cumulative fracture Pfp due to fatigue. from member. Such a process is performed repeatedly until the probability of cumulative Pfp fracture due to limb fatigue reaches the target cumulative fracture probability or less.

When probability of cumulative fracture Pfp achieves the probability of target cumulative fracture or less in this way, the structure stress output part 206 derives a structure stress in the limb based on information derived from the stress analysis part. 202 at the time when the probability of cumulative fracture Pfp reaches the probability of target cumulative fracture or less. The frame tension output portion 206 then shows the screen (GUI) indicating the frame tension on the member to report the frame tension on the member to the user.

An example of the operation of the device to estimate the probability

The rate of limb fatigue fracture 100 will be described with reference to the flow chart in Figure 7.

First, in step S1, the P-S-N 211 curve creation portion receives input from the uniaxial fatigue test result and the torsion fatigue test result about the material fatigue test piece.

Then, in step S2, the PSN 211 curve creation part creates the PSN curves (see PSN curves 301 to 303 in Figure 3) using the user-selected test result from the uniaxial fake test result. and the result of the torsion fatigue test entered in step S1. Subsequently, in step S3, the resistance derivation part

Fatigue Strength and Weibull 212 creates the Weibull plot (see each plot (o) shown in Figure 4) using the PSN curves curves created in step S2 and derives the Weibull coefficient m from the material fatigue test piece. Weibull plot created. The Weibull 212 fatigue strength and coefficient shunt portion further derives the mean fatigue strength aas from the material fatigue test piece from the uniaxial fatigue test result.

Then, in step S4, the stress analysis portion of test piece 213 receives information input about the material fatigue test piece.

Then, in step S5, s test piece stress analysis part 213 derives the effective stress (stress amplitude) σ at each position of the material fatigue test piece using the information entered about the test fatigue test piece. material fatigue. Note that the process in steps S4 and S5 can be performed

before step S1.

Then, in step S6, the effective volume derivation part of test piece 214 derives the effective volume Vs from the material fatigue test piece of Equation (2) using "the Weibull Coefficient m of the fatigue test piece. of material "derived in step S3 and" the effective stress (voltage amplitude) Oj at each position of the material fatigue test piece "derived in step S5.

Then, in step S7, the scale parameter derivation portion 215 derives the material scale parameter au from Equation (3) using "the Weibull coefficient m of the material fatigue test piece, the resistance to The mean fatigue weight of the material fatigue test piece "derived in step S3 and" the effective volume Ves of the material fatigue test piece "derived in step S6.

Then, in step S8, member stress analysis part 202 receives input of information about member and external force.

Then, in step S9, the member stress analysis part 202 derives the effective stress (stress amplitude) Ojp at each member position and the mean stress Oave at each member position using the information inserted about the member. and external force.

Note that steps S8 and S9 can be performed before

step S7.

Then, in step S10, the effective volume derivation part of

member 203 derives the effective volume Vep from the member of Equation (8) using "the tensile strength ob of the material" entered in step S8, "the effective stress (stress amplitude) o, p at each member position and the mean stress Oave at each member position "derived in step S9," the Weibull coefficient m of the material fatigue test piece "derived in step S3, and" the scale or material parameter "derived in step S7. Effective limb volume derivation portion 203 additionally derives the amount of Ocorr (O) and Equation (7) stress correction correction using "the tensile strength of the material" entered in step S8, " the mean stress Oave at each position of the member "derived in step S9," the Weibull coefficient m of the material fatigue test piece "derived in step S3," the scale or material parameter "derived in step S7, and "the effective volume Vep of the limb" derived in step S10.

Then, in step S11, the cumulative limb fracture probability derivation portion 204 derives the cumulative fracture probability Pfp due to the fatigue of the limb of Equation (9) using "the scale or material parameter" derived in step S7, "the effective stress (stress amplitude) σίρ at each limb position" derived in step S9, "the effective limb volume Vep and the amount of stress correction aCOrr" derived in step S10, and "the Weibull coefficient m Material Fatigue Test Piece "derived in step S3. So in step S12, the probability comparison part

fracture 205 determines whether or not the probability of cumulative fracture Pfp due to limb fatigue derived in step S11 is equal to or less than the probability of target cumulative fracture. When the probability of Pfp cumulative fracture due to limb fatigue is not equal to or less than the target cumulative fracture probability as a result of the determination, the operation returns to step S8 and the member stress analysis part 202 receives the input of information about the member and the external force again. Then steps S8 through S12 are performed repeatedly until the probability of cumulative fracture Pfp due to limb fatigue reaches the probability of target cumulative fracture or less.

Note that when steps S8 and S9 are performed before step S7, the process in steps S8 and S9 is performed after step S12 and then the process in steps S10 to S12 is performed.

When the probability of Pfp cumulative fracture due to limb fatigue reaches the target cumulative fracture probability or less at step S12, the operation proceeds to step S13. Proceeding to step S13, the frame stress output portion 206 derives the frame stress at the limb based on information derived from the member stress analysis part 202 when the probability of cumulative fracture Pfp reaches the probability of target cumulative fracture or less, and shows the screen (GUI) that indicates the structure tension in the limb. Then the process according to the flowchart in figure 6 ends. As described above, in this embodiment, the effective limb volume Vep is calculated as the amount of Ocorr stress correction added to the effective tension (stress amplitude) oip at each limb position so that the resistance of the variable limb that corresponds - where the mean tension that varies depending on the limb position is apparently constant in value when the mean limb tension is O (zero) independent of the limb position. Using the effective member volume Vep, the probability of cumulative Pfp fracture due to limb fatigue is derived. Consequently, it is possible to evaluate the probability of cumulative fracture as a numerical value in probabilistic consideration of the risk of fatigue fracture of the interior of the limb by significantly combining the ideas of the fatigue limit diagram, the Weibull distribution, and the volume. effective member. More specifically, the probability of cumulative Pfp fracture due to limb fatigue having a complex stress distribution can be calculated quantitatively using a relatively simple "fatigue strength distribution of the material fatigue test piece" obtained at from the uniaxial fatigue test result (the result of the fatigue test under the condition of applying a load on a geometrical axis) or from the torsion fatigue test result (the result of the fatigue test under the condition of applying a load torsion). For example, a limb may be designed for fatigue fracture starting from a certain inclusion that is a probabilistic event. In contrast, conventionally, the probabilistic assessment of the distribution of fatigue strength of the material may be made, but the result of this may not be directly used for a different member in a state of material stress. Thus, a limb was designed using the empirically obtained safety factor as described above; it was impossible to sufficiently reflect variations in fatigue characteristics in the limb structure. In particular, the probability of cumulative fracture may not be expected with satisfactory accuracy in a region of low probability of fracture. In this embodiment, an accurate fatigue structure can be made as described above as compared to the conventional fatigue structure determined based on experience such as safety factor or the like.

In addition, the user selects either the uniaxial fatigue test result or the torsion fatigue test result as the material fatigue characteristic in this mode. Consequently, the fatigue characteristic of the material can be selected according to whether the limb that is the object of derivation of the probability of cumulative fracture Pfp due to mainly compression and stress fatigue fracture or mainly fracture by twisting. Consequently, more precise fatigue structures can be made.

Note that PSN curves are created using the uniaxial fatigue test result about the material fatigue test piece and the Weibull plot is created additionally from the PSN curves so that "the Weibull coefficient m of the piece Fatigue Test "is derived from Weibull plotting in this mode. In addition, the mean fatigue strength of the material fatigue test piece is derived from the results of the plurality of uniaxial fatigue tests or the results of the plurality of torsion fatigue tests about the material fatigue test piece. according to the user selection result and the scale or material parameter is derived additionally. However, this is not always necessary. For example, the uniaxial fatigue test result may not be the result of the current test, but an assumed value such as the simulation result assuming that the cumulative fracture probability distribution with respect to the stress amplitude at a given number. times of repeated loading on the assumption that the fatigue test (eg the uniaxial fatigue test) by the repeated loading with the mean stress on the member material being 0 [N / mm2] that is conducted is the distribution of Weibull of two parameters. In addition, the user can define the distribution of the cumulative fracture probability due to material fatigue (two-parameter Weibull distribution) assumed as described above to derive "the Weibull coefficient m from the fatigue test piece. material "and" the scale or material parameter ". In this case, it is unnecessary to create the P-S-N. In addition, the user can directly define "the Weibuli coefficient of the material fatigue test piece" and "the scale or material parameter". In addition, "the scale or material parameter" can be found from the Weibull plot described above.

In addition, the effective stress (strain amplitude) ojp in each limb position and the mean Oave stress in each limb position are derived by the limb stress analysis part 202 in this mode. However, this is not always necessary. For example, these assumed values can be set directly by the user.

Moreover, in this embodiment, the fatigue limit diagram has been described by taking, as an example, the case using the modified Goodman ratio described in "The Society of Materials Science, Japan, Fatigue design handbook, Yokendo, of January 1995, First Edition, page 82 "and the like. However, the fatigue limit diagram is not limited to the modified Goodman ratio. For example, the Gerber diagram discussed in "The Society of Materials Science, Japan, Fatigue Design Handbook, Yokendo, January 20, 1995, First Edition, page 82" and the like, the JSSC Fatigue Design-based diagram Recommendation, or the related equation that estimates the effect of the ratio of or mean stress on fatigue strength such as the stress ratio correction equation discussed in "MURAKAMI Yukitaka, Metal Fatigue: Effect of Small Defects and Inclusives, Yokendo , December 25, 2008, OD edition First Edition, page 110 "may be used as the fatigue limit diagram.

In addition, it is preferred that the user selects either the uniaxial fatigue test result or the torsion fatigue test result as in this mode, but only the result of one of the tests can be entered and used.

(First Example)

In the following, a first example of the present invention will be described. In this example, the estimation of a fatigue fracture load of a coil spring that has a compressive residual stress on its surface will be described. The material constituting the coil spring is a high tensile spring steel with a strength of 1900 [MPa] which corresponds to the SWOSC-V defined in JIS G 3566 and is a steel material in which there is internal fatigue fracture beginning to occur. from inclusion in high-tension spring steel.

First, a material fatigue test piece with a parallel portion length of 20 [mm] and a parallel portion diameter of 4 [mm] is prepared and subjected to the uniaxial fatigue test as described above. The test voltage σ {with a voltage amplitude of 700 [MPa], 750 [Mpa], 800 [MPa], 850 [MPa], 900 [MPa] was repeatedly loaded here into ten fatigue test pieces. materials each. Here, the uniaxial fatigue test was performed so that the mean Oave stress in each portion of the material fatigue test piece was 0 [MPa] as described above.

The results of the uniaxial fatigue tests as described above were entered into the apparatus for estimating the probability of limb fatigue fracture 100. The apparatus for estimating the probability of limb fatigue fracture 100 created the PSN curves from the results of the uniaxial fatigue tests and obtained the probability distribution of cumulative fatigue limit fracture by the Japan Society of Mechanical Engineers Standard method JSME S 002 (statistical fatigue test method) whose distribution function was replaced by the Weibull's distribution of two parameters. As a result, 100 were obtained as the Weibull Coefficient m of the material fatigue test piece of the cumulative probability distribution (Weibull distribution of two parameters) F of the fatigue strength at 106 times repeated loading. . The apparatus for estimating the probability of 100-member fatigue fracture additionally derived the effective volume Ves from the material fatigue test piece using "the Weibull coefficient m from the material fatigue test piece" and "the effective stress (amplitude of stress) σ, at each position of the material fatigue test piece "derived by the apparatus to estimate the probability of 100-member fatigue fracture (see Equation (2)). As a result, the effective Ves volume of a material fatigue test piece was 251 [mm3].

The apparatus for estimating the probability of 100-member fatigue fracture additionally derived the material au scale parameter using "the mean fatigue strength oas of the material fatigue test piece," "the Weibull Coefficient m of the material fatigue test piece. material fatigue, "and" the effective volume Ves of the material fatigue test piece "obtained from the uniaxial fatigue test result of the material fatigue test piece (see Equation (3)). As a result, the material scale parameter Or was 800 [MPa],

In this example, the fatigue fracture load estimation of the spiral spring of such material was made. Here, the estimation of the cumulative fracture probability of the following spiral spring was made. The wire diameter of the spiral spring is 3.3 [mm], The inner diameter of the spiral spring is 18 [mm] and the number of turns of the spiral spring is 6 [turns]. Spiral spring is caused by surface treatment by riveting. The coil spring which has a residual stress distribution of this when created based on the measurement result as shown in figure 8 has been employed. Moreover, in consideration of shear stress and compressive residual stress here, the corresponding stress was employed for the effective stress (stress amplitude) Oi at each position of the material fatigue test piece. For the mean voltage aave at each limb position, the maximum main voltage was employed. In defining the coil spring in a tester, the initial load (the initial value of the current external force entered in the member tension analysis part 202) applied to the coil spring was set to 200 [N] and this value was fixed as minimum load. In addition, the apparatus for estimating the probability of 100-member fatigue fracture was operated on the target cumulative fracture probability that 1 in 50 coil springs would fracture due to fatigue at 1 million times of repeated loading. As a result of this, the load range (the range of the acting external force inserted in the stress analysis part 202) is repeatedly applied to the coil spring when the "probability of cumulative fracture Pfp due to coil spring fatigue". "derivative reaches target cumulative fracture probability was 295 [N],

Therefore, to confirm the certainty of the result of this calculation, the uniaxial fatigue test of repeatedly applying a load in a range of 200 [N] to 495 [N], to 5 [Hz] in 100 coil springs manufactured under the same conditions as those of the spiral spring described above was conducted until the number of repeated charging times reached 1.1 million times. As a result, fatigue fracture occurred in 1 coil spring in each of 0.92 million times, 1.02 million times, 1.05 million times and 1.07 million times, and two coil springs in 1 .9 million times, so that two coil springs were fractured about 1 million times. Consequently, the result for the device to estimate the probability of limb fatigue fracture 100 and the current result roughly matched each other, whereby the efficiency of the estimation of the limb fatigue fracture load by the device to estimate the probability of limb fatigue fracture 100 can be confirmed.

(Second Example)

In the following, a second example of the present invention will be described. In this example, the estimation of the repeated curvature fatigue characteristic of a plate member having a compressive residual stress on its surface will be described. The plate member material is a high tensile steel plate having a strength of 1300 [MPa] which corresponds to the SCM440 defined in JIS G 4105 and is a steel material in which there is internal fatigue fracture beginning from inclusion in high voltage steel.

First, a material fatigue test piece with a parallel portion length of 20 [mm] and a parallel portion diameter of 4 [mm] is prepared and subjected to the uniaxial fatigue test as described above. The test voltage σ {with a voltage amplitude of 450 [MPa], 500 [Mpa], 550 [MPa], 600 [MPa], 650 [MPa] was repeatedly loaded here in ten fatigue test pieces. material in each one. Here, the uniaxial fatigue test was conducted so that the mean Oave stress in each portion of the material fatigue test piece was O [MPa] as described above.

The results of the uniaxial fatigue tests as described above were entered into the apparatus for estimating the probability of limb fatigue fracture 100. The apparatus for estimating the probability of limb fatigue fracture 100 created the PSN curves from the results of the uniaxial fatigue tests and obtained the probability distribution of the cumulative fatigue limit fracture by the Japan Society of Mechanical Engineers Standard JSME S 002 method (statistical fatigue test method) whose distribution function was replaced by the distribution function. Weibull parameter. As a result, 80 was obtained as the Weibull coefficient m of the material fatigue test piece of the cumulative probability distribution (two-parameter Weibull distribution) F of the fatigue strength at 106 times repeated loading. . The apparatus for estimating the probability of 100-member fatigue fracture additionally derived the effective volume Ves from the material fatigue test piece using "the Weibull coefficient m from the material fatigue test piece" and "the effective stress (amplitude of Stress) Ol in each position of the material fatigue test piece "derived by the apparatus to estimate the probability of fatigue fracture in limb 100 (see Equation (2)). As a result, the effective Ves volume of a piece of material fatigue test was 251 [mm3]. The apparatus for estimating the probability of 100-member fatigue fracture additionally derived the scale or material parameter using "the mean fatigue strength of the material fatigue test piece", "the Weibull coefficient m of the material fatigue test piece "and" the effective volume Ves of the material fatigue test piece "obtained from the uniaxial fatigue test result of the material fatigue test piece (see Equation (3 )). As a result, the scale or material parameter was 578 [MPa],

An automatic ultrasonic impact apparatus was also used to perform surface ultrasonic impact treatment on one side of the face of the plate member that is the object of the cumulative fracture probability to thereby apply a compressive residual stress to the surface. on one side of the face of the plate member. The distribution of the compressive residual stress applied to the plate member in this manner is illustrated in Figure 9. In this example, a plate member of length 400 [mm], width 30 [mm] and thickness 20 [mm] was employed. The plate member was defined in a test apparatus with the face subjected to the ultrasonic impact treatment located on the underside such that the plate member was kept at a position of 50 [mm] separate from both sides. end portions in the longitudinal direction of the plate member and a load was applied to the middle of the plate member from the other side of the face (the upper side) that was not subjected to ultrasonic impact treatment by uniformly repeated three-point curvature . Specifically, a load was applied repeatedly to the plate member at 5 [Hz] so that the maximum surface tension and the minimum surface tension on the plate member when the load was repeatedly applied to the plate member having a distribution. The residual voltage A (see the solid line in figure 9) were 900 [MPa], 200 [MPa} respectively. As a result, three out of ten plate members fractured due to fatigue until the number of repeated loading times reached 2 million teams. Therefore, a load was applied repeatedly under the same conditions to the plate member as a residual stress distribution B (see the broken line in figure 9) obtained by changing the conditions of the ultrasonic impact treatment. As a result, all of the ten plate members did not fracture due to fatigue even when the number of repeated loading times reached 2 million times.

To confirm the effect due to the change in residual stress distribution as described above, the probability of cumulative fracture of the plate limb was estimated by the apparatus to estimate the probability of fatigue fracture in limb 100. As the stress that run on the plate member was only the stress substantially in the geometrical axis direction of the member, the maximum main stress was employed for each of the effective stress (stress amplitude) Oj and the mean stress Oave at each position. of the material fatigue test piece. As a result, the probability of cumulative Pfp fracture due to fatigue in 2 million times repeated loading was 26.8 [%] in the plate member having residual stress distribution A, and the probability of fracture cumulative Pfp due to fatigue in the repeated loading times of 2 million times was 1.7 [%] in the plate member that has the residual stress distribution B. Thus, the result by the device to estimate the probability of fracture limb fatigue and the current result roughly matched each other, through this, the effectiveness of estimating the fatigue characteristic of the repeated curvature limb by the apparatus to estimate the likelihood of limb fatigue fracture can be confirmed. .

(Third Example)

In the following, a third example of the present invention will be described. In this example, the case using the torsion fatigue test result will be described.

First, a round bar test piece formed from a carbon steel defined in JIS G4051 S55C and which has a parallel portion with a diameter of 4 [mm] and a length of 10 [mm] is prepared as the sample piece. material fatigue test and subjected to the torsion fatigue test as described above wherein a repeated torsional load is applied to the material fatigue test piece. Here, the Tt test voltage with a voltage amplitude of 280 [MPa] to 360 [MPa] was repeatedly applied in increments of 10 [MPa] on 12 material fatigue test pieces each.

The results of the torsion fatigue tests as described above were entered into the apparatus for estimating the probability of limb fatigue fracture 100. The apparatus for estimating the probability of limb fatigue fracture 100 created the PSN curves from the results. torsion fatigue tests and obtained the probability distribution of cumulative fracture of the fatigue limit by the Japan Society of Mechanical Engineers Standard JSME S 002 method (statistical fatigue test method) whose distribution function was replaced by the Weibull distribution parameters. As a result, 20 was obtained as the Weibull coefficient m of the material fatigue test piece of the cumulative probability distribution (two-parameter Weibull distribution) F of the fatigue strength at 106 times repeated loading. .

The apparatus for estimating the probability of 100-member fatigue fracture additionally derived the effective volume Ves from the material fatigue test piece using "the Weibull coefficient m from the material fatigue test piece" and "the effective stress (amplitude of Oj in each position of the material fatigue test piece "separately derived by the apparatus to estimate the probability of fatigue fracture in limb 100 (see Equation (2)). As a result, the effective Ves volume of a material fatigue test piece was 7.43 [mm3].

The apparatus for estimating the probability of 100-member fatigue fracture additionally derived the scale or material parameter using "Tas mean shear stress of the material fatigue test piece", "the Weibull coefficient m of the test fatigue test piece". material fatigue ", and" the effective volume Ves of the material fatigue test piece "obtained from the torsion fatigue test result of the material fatigue test piece (see Equation (3) ). The average shear stress Tas was 317.5 [MPa]. The coefficient f is 1 to V3. So the scale or material parameter was 412.8 [MPa] to 715.0 [MPa],

Here, 30 test pieces in the same form that have a finer potion length of 10 [mm] and a circular cross-section with a thickness of 10 [mm] were formed using the JIS G4501 S55C material for which the parameter au scale of the material was found as described above. Fatigue tests by (1) repeated torsion, (2) rotational curvature, and (3) repeated axial force were conducted on 10 test pieces each. The maximum surface shear stress of 292 [MPa] in (1), the repeated torsion fatigue test and the maximum stress of 506 [MPa] in (2), the rotational curvature test were defined as the tensile stresses. respectively.

These test stresses were defined by the method described in this embodiment so that the probability of cumulative fracture Pfp was 50 [%] with the scale or material parameter and the Weibull coefficient m defined at the values described above and the coefficient f defined at V3. .

As a result, in (1) the repeated torsion fatigue test, five of ten fractured test pieces before the number of repeated load times reached 106 times. Also, in (2) the rotational curvature test, five of ten fractured test pieces before the number of repeated loading times reached 106 times. In (1) the repeated torsion fatigue test and in (2) the rotational curvature test, the effective volume Ves is identical to 46.5 [mm3]. The test pieces used in these tests are made of material and then are identical in the scale parameter or material and Weibull coefficient m. In addition, considering the Tas mean shear stress employed in (1), the repeated torsional fatigue test and the mean fatigue strength employed in (2), the rotational curvature test takes substantially the respective values. average (although not precisely), the ratio of fTas = oas is established, so that the coefficient f can be calculated based on the ratio. From the above considerations, the definition of f = V3 can be considered to be appropriate in this material.

Therefore, in (3) in the repeated axial force test, the maximum tension was set at 450 [Mpa]. The probability of cumulative Pfp fracture at the 106-fold repeated loading times found by the method described in this embodiment with f = V3 being defined from the result described above was 67 [%]. In (3) in the repeated axial strength test, ten fractured test pieces before the number of repeated load times reached 106 times. As described above, the test result and the estimation result by the method described in this embodiment substantially match each other.

It is noted that the embodiment described above of the present invention may be implemented by a computer running a program. Also, a means for supplying the program to the computer, for example, a computer readable recording medium such as a CD-ROM. or the like having the program recorded in this or a transmission medium transmitting the program is also applicable as the embodiment of the present invention. In addition, a program product such as a computer readable recording medium having the program recorded therein is also applicable as the embodiment of the present invention. The program described above, computer readable recording medium, transmission medium and program product are included within the scope of the present invention.

It should be noted that the above embodiments merely illustrate concrete examples of the implementation of the present invention, and the technical scope of the present invention should not be construed constructively by these embodiments. That is, the present invention may be implemented in a number of ways without departing from the technical spirit or main features thereof.

Conventionally, fatigue strength has been predicted from the material fatigue test result using the empirically obtained safety factor. Thus, conventionally, the probability of cumulative fracture may not be predicted with satisfactory accuracy particularly in a region of low probability of fracture. In contrast, according to the present invention, the cumulative probability distribution of fracture due to limb fatigue can be quantitatively captured.

Claims (15)

1. Apparatus for estimating the probability of limb fatigue fracture comprising: a processor performing at least: a first acquisition process to acquire as a first acquisition information a Weibull coefficient and a scale parameter or [ N / mm2] when a cumulative fracture probability distribution with respect to a stress amplitude of a fatigue test over a given number of times of repeated loading of a material fatigue test piece made of a material that constitutes a member is expressed by a two-parameter Weibull distribution; a second acquisition process to acquire, as a second acquisition information, an Oip amplitude [N / mm2] of a maximum main voltage or a corresponding voltage at each limb position, and an average Oave voltage [N / mm2] being an average the maximum principal stress or the corresponding stress at each position of the limb; an effective limb volume derivation process for deriving an effective limb volume Vep [mm3] from Equations to follow (A) and (B); and a fracture probability derivation process of deriving a cumulative fracture probability Pfp due to limb fatigue from the following Equation (C); and a reporting unit reporting information related to the probability of cumulative Pfp fracture due to limb fatigue, where, where aap is a limb fatigue strength [N / mm2] when the fatigue strength at each position is uniform to a A constant value of aip + oCOrr as the amplitude of tension at each limb position in a fatigue limit diagram representing a relationship between limb fatigue strength and mean limb stress, σΓ is a fatigue strength [N / mm2] at a given position when the mean limb tension is the mean aave tension at the position acquired in the second acquisition process, in the fatigue limit diagram, max (x) represents a maximum value of x, and Jdv represents the integer member volume integration. Formula 1 Vep = /{(aip+acorr)/max(aip+acorr)}mdV...(A) Ocorr = Oap-Or ... (B) Pfp = 1-exp [-Vep {max (aip + current) / au} m] ... (C) Apparatus for estimating the probability of fatigue fracture in a limb according to claim 1, wherein said processor further performs: a third acquisition process to acquire, as third acquisition information, an amplitude Oi [N / mm2] of a maximum principal stress or a corresponding stress at each position of the material fatigue test piece; and an effective volume derivation process of a material fatigue test piece to derive an effective volume Ves [mm3] from the material fatigue test piece from an Equation (D) below, where the Fatigue limit diagram is a modified Goodman relationship, where the first acquisition process additionally acquires, as the first acquisition information, a mean fatigue strength at [N / mm2] by a mean stress fatigue test. O [N / mm2] using a plurality of material fatigue test pieces made from the member material, wherein the first acquisition process derives the scale parameter au [N / mm2] from the resistance to fatigue under the assumption that the fatigue test has been conducted on the material for a certain number of times of repeated loading from an Equation (E) below, where the second acquisition process acquires additionally as the second inform acquisition action, a tensile strength ab [N / mm2] of the member material, wherein the effective member volume derivation process derives the effective member volume Vep from the following (F), and where, where Γ () represents a gamma function, max (x) represents a maximum value of x, and idv in Equation (D) represents a volume integration of the entire material fatigue test piece. Formula 2 Ves = J {ai / max (Hi)} mdv ... (D) Ou = OasVes1 / m / r (1 + 1 / m) ... (E) Vep = í {(apWep "1 ^ our (1 + 1 / m) oave / ob) / max (apWep-1 ^ aur (1 + 1 / m) aave / ab)} m dV ... (F) Apparatus for estimating the likelihood of limb fatigue fracture according to claim 1, wherein the first acquisition process receives an input from a uniaxial fatigue test result, or a presumed value from the result. uniaxial fatigue test, wherein the uniaxial fatigue test repeatedly carries a test voltage at regularly altered [N / mm2] in one direction of the material fatigue test piece in the material fatigue test piece to investigate a number of repeated loading times of stress until the material fatigue test piece breaks, conducted with the average of the maximum principal stress or the corresponding stress on the material fatigue test piece being 0, and the first acquisition information derives using the entered uni- axial fatigue test result or its assumed value. Apparatus for estimating the probability of limb fatigue fracture according to claim 1, wherein the first acquisition process receives input of a result of a torsion fatigue test or an assumed value of the result of the torsion fatigue test, where the torsion fatigue test repeatedly loads a Tt test voltage [N / mm2] regularly changed in a shear direction of the material fatigue test piece in the fatigue test piece. material to investigate a number of times the stress is repeated loading until the material fatigue test piece breaks, conducted with the average of the maximum principal stress or the corresponding stress on the material fatigue test piece being O, and derives the first acquisition information using the entered result of the torsion fatigue test or its assumed value. Apparatus for estimating the probability of limb fatigue fracture according to claim 1, wherein said processor further performs a selection process to select, based on an operation of an input operating unit by a user. , either of: a result of a uniaxial fatigue test or an assumed value thereof, the uniaxial fatigue test that repeatedly carries a regularly altered test voltage ot [N / mm2] in one direction of the material fatigue test piece on the material fatigue test piece to investigate a number of times of repeated stress loading until the material fatigue test piece breaks, and a result of a torsional fatigue test or guess value. and the torsion fatigue test repeatedly carries a regularly altered test voltage η [N / mm2] in a shear direction of the material fatigue test piece on the pedestal. material fatigue test steel to investigate a number of times of repeated stress loading until the material fatigue test piece breaks, where when the uniaxial fatigue test result or the assumed value This is selected by the selection process, the first acquisition process receives the input of the uniaxial fatigue test result or the assumed value of the uniaxial fatigue test result, and the uniaxial fatigue test is conducted with the average of the maximum principal stress or corresponding stress in the material fatigue test piece O, and derives the first acquisition information using the entered uniaxial fatigue test result or the assumed value thereof, and where, when the torsion fatigue test result or its assumed value is selected by the selection process, and the first acquisition process receives the input of the torsion fatigue test result or the assumed value of the torsion fatigue test result, where the torsion fatigue test is conducted with the average of the maximum principal stress or the corresponding stress in the material fatigue test piece being 0, and derives the first acquisition information using the entered result of the torsion fatigue test or the assumed value of it. Apparatus for estimating the probability of limb fatigue fracture according to claim 1, wherein the first acquisition process derives the Weibull coefficient and the scale parameter au [N / mm2] as the first acquisition information assuming that a cumulative fracture probability distribution with respect to a stress amplitude at a given number of times of repeated loading under the assumption that the repeated loading fatigue test that was conducted with the mean stress on the material which constitutes the limb being 0 [N / mm2] is a two-parameter Weibull distribution. Apparatus for estimating the likelihood of limb fatigue fracture according to claim 1, wherein the second acquisition process receives input of a limb shape, of an acting external force acting on the limb, a residual stress. of the member, and a feature of a material that constitutes the member, and uses the entered information to derive the second acquisition information. A method for estimating the probability of limb fatigue fracture comprising: a first acquisition step to acquire, as a first acquisition information, a Weibull coefficient and a scale parameter or [N / mm2] when a cumulative fracture probability distribution with respect to a stress amplitude of a fatigue test over a given number of times of repeated loading of a material fatigue test piece made of a material constituting a member is expressed by a two-parameter Weibull distribution; a second acquisition step to acquire, as a second acquisition information, an amplitude σίρ [N / mm2] of a maximum principal voltage or a corresponding voltage at each member position, and an average voltage aave [N / mm2]. mm2] which is an average of the maximum principal stress or corresponding stress at each member position; an effective limb volume derivation step to derive an effective limb volume Vep [mm3] from the following Equation (A) and Equation (B); and a limb fracture probability derivation step for deriving a cumulative fracture probability Pfp due to limb fatigue from the following Equation (C); and a reporting step for reporting information related to the probability of cumulative fracture Pfp due to limb fatigue derived by said limb fracture probability derivation step, wherein, where p is a resistance to limb fatigue [N / mm2] when the fatigue strength at each position is uniform to a value constant with the use of aip + oCorr as the amplitude of tension at each limb position in a fatigue limit diagram representing a relationship between resistance limb fatigue and mean limb stress, r is a fatigue strength [N / mm2] at a given position when mean limb stress is the mean Oave stress at the position acquired in said second acquisition step in the fatigue limit, max (x) represents a maximum value of x, and | dv represents an integer volume integration. Formula 3 Vep = J {(aip + acorr) / max (aip + aCorr)} mdV ... (A) CTcorr = Oap-Or ... (B) Pfp = 1-exp [-Vep {max (aiP + oCorr) / CTu} m] ... (C) A method of estimating the probability of limb fatigue fracture according to claim 8 further comprising: a third acquisition step for acquiring, as a third acquisition information, an Oi amplitude [N / mm2] of a maximum principal stress or a corresponding stress at each position of the material fatigue test piece; and a step of deriving an effective volume from a material fatigue test piece to derive an effective volume Ves [mm3] from the material fatigue test piece from an Equation (D) below, where the fatigue limit diagram is a modified Goodman relationship, wherein said first acquisition step additionally acquires, as the first acquisition information, a mean fatigue strength at [N / mm2] by a fatigue test with the mean stress being O [N / mm2] using a plurality of material fatigue test pieces made of the member material, wherein said first acquisition step derives the scale parameter au [N / mm2] of fatigue strength on the assumption that the fatigue test was conducted on the material a certain number of times of repeated loading, from a following Equation (E), wherein said second acquisition step further acquires, as the second info acquisition statement, a tensile strength ab [N / mm2] of the member material, wherein said step of deriving an effective member volume derives the effective member volume Vep from an Equation (F) to where Γ () represents a gamma function, max (x) represents a maximum value of x, and jdv in Equation (D) represents a volume integration of the material fatigue test piece all. Formula 4 Ves = l {aj / max (hi)} mdv .. (D) au = aasVes1 / m / r (1 + 1 / m) ... (E) Vep = í {(üp + Vep "1 / maur (1 + 1 / m) aave / Ob) / max (ap + Vep "1 / h1aur (1 + 1 / m) aave / ab)} m dV ... (F) A method for estimating the probability of limb fatigue fracture according to claim 8, wherein said first acquisition step receives input of a uniaxial fatigue test result or a presumed value of the result. uniaxial fatigue test, where the uniaxial fatigue test repeatedly carries a regularly altered test voltage t [N / mm2] in one direction of the material fatigue test piece in the material fatigue test piece to investigate a number of repeated loading times of stress until the material fatigue test piece breaks, conducted with the average of the maximum principal stress or the corresponding stress in the material fatigue test piece being 0, and the first acquisition information using the entered uniaxial fatigue test result or its assumed value. A method for estimating the probability of limb fatigue fracture according to claim 8, wherein said first acquisition step receives input of a result of a torsion fatigue test or an assumed result value. torsion fatigue test, where the torsion fatigue test repeatedly loads a Tt test stress [N / mm2] regularly changed in a shear direction from the material fatigue test piece in the fatigue test piece material to investigate a number of times the stress is repeated loading until the material fatigue test piece breaks, conducted with the average of the maximum principal stress or the corresponding stress on the material fatigue test piece. 0, and derives the first acquisition information using the entered result of the torsion fatigue test or the assumed value of it. A method for estimating the probability of limb fatigue fracture according to claim 8, further comprising: a selection step to select, based on an operation of an input operating unit by a user. , any of: a result of a uniaxial fatigue test or an assumed value thereof, and the uniaxial fatigue test repeatedly carries a regularly altered test voltage t [N / mm2] in one direction of the test piece. material fatigue in the material fatigue test piece to investigate a number of times the repeated loading of the tension until the material fatigue test piece breaks, and a result of a torsion fatigue test or an assumed value. Since the torsion fatigue test repeatedly carries a regularly altered test voltage η [N / mm2] in a shear direction of the material fatigue test piece on the test piece. material fatigue test to investigate a number of times the repeated loading of the stress until the material fatigue test piece breaks, where when the uniaxial fatigue test result or its supposed value is selected by said selection step, said first acquisition step receives an input of the uniaxial fatigue test result or the assumed value of the uniaxial fatigue test result, and the uniaxial fatigue test is conducted with the average of the maximum principal stress or the corresponding stress on the material fatigue test piece being O, and derives the first acquisition information using the entered uniaxial fatigue test result or the assumed value thereof, and when torsion fatigue test result or its assumed value is selected by said selection step, said first acquisition step receives input of the torsion fatigue test result or value of the result of the torsion fatigue test, where the torsion fatigue test is conducted with the average of the maximum principal stress or the corresponding stress in the material fatigue test piece being O, and derives the first information from acquisition using the entered result of the torsion fatigue test or the assumed value of it. A method for estimating the probability of limb fatigue fracture according to claim 8, wherein said first acquisition step derives the Weibull coefficient and the scale parameter or [N / mm2] as the first acquisition information assuming that a probability distribution of cumulative fracture with respect to a stress amplitude at a given number of times of repeated loading on the assumption that the repeated loading stress test has been conducted with the mean stress on the constituent material. the member being 0 [N / mm2] is a two-parameter Weibull distribution. A method for estimating the probability of limb fatigue fracture according to claim 8, wherein said second acquisition step receives input from a limb shape, an acting external force acting on the limb, a residual stress. of the member, and a feature of a material that constitutes the member, and uses the entered information to derive the second acquisition information. 15. Computer program product for causing a computer to perform: a first acquisition step for acquiring a Weibull coefficient with a scale parameter or [N / mm2] when a cumulative fracture probability distribution with respect to an amplitude of stress of a fatigue test at a given number of times of repeated loading of a piece of material fatigue test made of a member material is expressed by a two-parameter Weibull distribution; a second acquisition step to acquire an aip amplitude [N / mm2] of a maximum main voltage or a corresponding voltage at each limb position and an average oave voltage [N / mm2] which is an average of the maximum main voltage or the corresponding tension at each limb position; an effective limb volume derivation step to derive an effective limb volume Vep [mm3] from the following Equation (A) and Equation (B); and a limb fracture probability derivation step for deriving a cumulative fracture probability Pfp due to limb fatigue from the following Equation (C); and a reporting step for reporting information related to the probability of cumulative Pfp fracture due to limb fatigue derived by the limb fracture probability derivation step, where, where oap is a limb fatigue strength [N / mm2] when fatigue strength at each position is uniform at a value constant using σίρ + σ00π- as the amplitude of stress at each limb position in a fatigue limit diagram representing the relationship between fatigue strength limb stress and mean limb stress, σΓ is a fatigue strength [N / mm2] at a given position when mean limb stress is the mean Oave stress at the acquired position in the second acquisition step in the limit diagram fatigue, max (x) represents a maximum value of x, and Jdv represents the volume integration of the entire member. Formula 5 Vep = /{(OíP+aCorr)/max(OiP+Ocorr)}mdV...(A) Ocorr = Oap-Or ... (B) Pfp = 1-exp [-Vep {max (Oip + Occur) / au} m] ... (C)