Method and device for predicting fatigue life of material in combination with process influence

Abstract

The present disclosure relates to the technical field of material fatigue life prediction, and particularly relates to a material fatigue life prediction method and device combined with the influence of machining process. The material fatigue life prediction method combined with the influence of machining process comprises: obtaining target machining attribute information and a stress amplitude of a material to be predicted; determining a target surface stress concentration coefficient corresponding to the target machining attribute information based on a mapping relationship between machining attribute information and a surface stress concentration coefficient; wherein the surface stress concentration coefficient is calculated based on surface defect information of a machining sample corresponding to the machining attribute information; and bringing the target machining attribute information, the target surface stress concentration coefficient and the stress amplitude into a fatigue life prediction expression to obtain a predicted life of the material to be predicted. The material fatigue life prediction method combined with the influence of machining process provided by the present disclosure can improve the prediction accuracy of material fatigue life.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of material fatigue life prediction, and particularly relates to a material fatigue life prediction method combining the influence of machining processes and a material fatigue life prediction device combining the influence of machining processes. BACKGROUND

[0002] For spheroidal graphite reinforced materials, spheroidal graphite particles can remain on the surface of a test piece when surface roughness is measured after machining, but spheroidal graphite particles on the surface and near the surface are prone to fall off during a fatigue test, forming surface defects such as pits, which are prone to form crack sources during a fatigue load process, thereby shortening the fatigue life.

[0003] In the prior art, the pits caused by spheroidal graphite particles broken on the surface after the material is machined by different machining processes cannot be completely considered, so that the material fatigue life prediction accuracy is low.

[0004] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present disclosure is to provide a material fatigue life prediction method combining the influence of machining processes and a material fatigue life prediction device combining the influence of machining processes, aiming to improve the material fatigue life prediction accuracy.

[0006] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0007] According to an aspect of an embodiment of the present disclosure, a material fatigue life prediction method combining the influence of machining processes is provided, comprising: obtaining target machining attribute information of a material to be predicted and a stress amplitude; determining a target surface stress concentration coefficient corresponding to the target machining attribute information based on a mapping relationship between the machining attribute information and the surface stress concentration coefficient; wherein the surface stress concentration coefficient is calculated based on surface defect information of a machining sample corresponding to the machining attribute information; and bringing the target machining attribute information, the target surface stress concentration coefficient and the stress amplitude into a fatigue life prediction expression to obtain a predicted life of the material to be predicted.

[0008] According to some embodiments of the present disclosure, based on the foregoing scheme, the method further comprises: constructing a mapping relationship between the machining attribute information and the surface stress concentration coefficient, and the constructing the mapping relationship between the machining process and the surface stress concentration coefficient comprises: configuring a plurality of machining attribute information; referring to GB / T 15248-2008 to design a fatigue test sample size, and performing a fatigue test on a gauge section of a fatigue test sample according to each of the machining attribute information to obtain a machining test sample corresponding to each of the machining attribute information; and calculating a surface stress concentration coefficient corresponding to each of the machining attribute information based on surface defect information of each of the machining test samples to construct the mapping relationship.

[0009] According to some embodiments of the present disclosure, based on the foregoing scheme, the calculating a surface stress concentration coefficient corresponding to each of the machining attribute information based on surface defect information of each of the machining test samples comprises: for a machining attribute information, determining a total defect area and a total profile area of a machining test sample corresponding to the machining attribute information as the surface defect information; calculating a surface stress concentration coefficient based on the total defect area and the total profile area; and traversing the machining attribute information to obtain a surface stress concentration coefficient corresponding to each of the machining attribute information.

[0010] According to some embodiments of the present disclosure, based on the foregoing scheme, the determining a total defect area of a machining test sample corresponding to the machining attribute information comprises: observing a machining surface of the machining test sample to obtain a spherical graphite pit and a spherical graphite powder strip of a defect; measuring a residual particle diameter of the spherical graphite pit to calculate a circular defect area, and measuring a strip area and a strip thickness of the spherical graphite powder strip to calculate a strip defect area; and adding the circular defect area and the strip defect area to obtain the total defect area.

[0011] According to some embodiments of the present disclosure, based on the foregoing scheme, the measuring a strip area and a strip thickness of the spherical graphite powder strip to calculate a strip defect area comprises: calculating a residual particle volume based on the strip area and the strip thickness of the spherical graphite powder strip; converting the spherical graphite powder strip into an equivalent defect spherical graphite according to the residual particle volume to determine a residual particle equivalent radius of the equivalent defect spherical graphite; and calculating the strip defect area based on the residual particle equivalent radius.

[0012] According to some embodiments of the present disclosure, based on the foregoing scheme, the calculating the surface stress concentration coefficient based on the total defect area and the total profile area comprises: calculating an equivalent defect radius based on the total defect area, and calculating a profile proportion coefficient and a defect proportion coefficient based on the total defect area and the total profile area; determining a corrected profile curvature radius according to an initial profile curvature radius, the equivalent defect radius, the profile proportion coefficient and the defect proportion coefficient; and calculating the surface stress concentration coefficient according to the corrected profile curvature radius, a load factor and a profile arithmetic average deviation value.

[0013] According to some embodiments of the present disclosure, based on the foregoing scheme, the method further comprises: determining the fatigue life prediction expression, wherein the determining the fatigue life prediction expression comprises: fitting material parameters in a fatigue life model with the machining attribute information and the surface stress concentration coefficient to obtain a material parameter expression of the material parameters with respect to the machining attribute information and the surface stress concentration coefficient; and bringing the material parameter expression into the fatigue life formula to obtain the fatigue life prediction expression.

[0014] According to a second aspect of the embodiments of the present disclosure, a material fatigue life prediction device combined with machining process influence is provided, comprising: an acquisition module configured to acquire target machining attribute information of a material to be predicted and a stress amplitude value; a determination module configured to determine a target surface stress concentration coefficient corresponding to the target machining attribute information based on a mapping relationship between the machining attribute information and the surface stress concentration coefficient; wherein the surface stress concentration coefficient is calculated based on surface defect information of a machining sample corresponding to the machining attribute information; and a prediction module configured to bring the target machining attribute information, the target surface stress concentration coefficient and the stress amplitude value into a fatigue life prediction expression to obtain a predicted life of the material to be predicted.

[0015] The example embodiments of the present disclosure can have the following partial or all beneficial effects:

[0016] In the technical solutions provided in some embodiments of the present disclosure, the surface stress concentration coefficient is calculated in advance according to the surface defect information of the machining sample corresponding to different machining attribute information, and then the surface stress concentration coefficient caused by the surface topography after machining is supplemented during material life prediction, so as to reduce the influence of the initial defect caused by the loss of surface nodular particles on the fatigue life result and improve the prediction accuracy. On the other hand, the fatigue life prediction expression including the machining attribute information and the surface stress concentration coefficient is designed in advance, and the surface stress concentration coefficient has a mapping relationship with the machining attribute information, so that the machining process of the material can be fully considered for the life prediction of the material, and the prediction accuracy is higher.

[0017] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are incorporated in and form a part of the specification, illustrate one example embodiment of the present disclosure and, together with the description, serve to explain the principles of the disclosure. It is appreciated that the drawings described below are only some embodiments of the present disclosure, and other drawings can be obtained by those of ordinary skill in the art without creative effort on the basis of these drawings. In the drawings:

[0019] Figure 1 A flowchart schematically showing a method for predicting material fatigue life combined with the influence of a machining process in an exemplary embodiment of the present disclosure is shown;

[0020] Figure 2 A schematic diagram of a fatigue test piece size form in an exemplary embodiment of the present disclosure is shown;

[0021] Figure 3 A plan view of a machining surface defect area in an exemplary embodiment of the present disclosure is shown;

[0022] Figure 4 A three-dimensional perspective view of a machining surface defect area in an exemplary embodiment of the present disclosure is shown;

[0023] Figure 5 A three-dimensional perspective view of a machining surface defect in an exemplary embodiment of the present disclosure is shown;

[0024] Figure 6 A schematic diagram of the results of curve fitting in an exemplary embodiment of the present disclosure is shown;

[0025] Figure 7 A comparison between predicted results and test results in an exemplary embodiment of the present disclosure is shown;

[0026] Figure 8 A schematic diagram of the composition of a device for predicting material fatigue life combined with the influence of a machining process in an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0027] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art.

[0028] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the

[0029] The block diagrams shown in the accompanying drawings are merely schematic representations, not necessarily the corresponding physical entities. That is, the functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0030] The flowcharts shown in the accompanying drawings are merely exemplary illustrations, not necessarily including all contents and operations / steps, nor necessarily executed in the described order. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so that the actual execution order can be changed according to actual conditions.

[0031] The implementation details of the technical solutions of the embodiments of the disclosure are described in detail below.

[0032] Figure 1 An exemplary flowchart of a material fatigue life prediction method combined with the influence of a machining process is schematically shown in an exemplary embodiment of the disclosure. As shown in Figure 1 The method includes steps S101 to S103:

[0033] In step S101, target machining attribute information and a stress amplitude of a material to be predicted are obtained.

[0034] In step S102, a target surface stress concentration coefficient corresponding to the target machining attribute information is determined based on a mapping relationship between the machining attribute information and the surface stress concentration coefficient; wherein the surface stress concentration coefficient is calculated based on surface defect information of a machining sample corresponding to the machining attribute information.

[0035] In step S103, the target machining attribute information, the target surface stress concentration coefficient, and the stress amplitude are brought into a fatigue life prediction expression to obtain a predicted life of the material to be predicted.

[0036] In the technical solution provided by some embodiments of the present disclosure, the surface stress concentration coefficient is calculated in advance according to the surface defect information of the machining sample corresponding to different machining attribute information, and then the stress concentration caused by defects on the machining surface due to surface defects is considered in the material life prediction, the surface stress concentration coefficient caused by surface roughness is supplemented, so as to reduce the influence of the initial defects caused by the loss of surface nodular particles on the fatigue life result and improve the prediction accuracy. On the other hand, the fatigue life prediction expression including the machining attribute information and the surface stress concentration coefficient is designed in advance, and the surface stress concentration coefficient has a mapping relationship with the machining attribute information, so that the material life prediction can be performed by fully considering the machining process of the material, and the prediction accuracy is higher.

[0037] In the following, the various steps of the material fatigue life prediction method combined with the influence of the machining process in the present example embodiment will be described in more detail in combination with the accompanying drawings and embodiments.

[0038] In step S101, the target machining attribute information of the material to be predicted and the stress amplitude are obtained.

[0039] In an embodiment of the present disclosure, the material to be predicted can be nodular cast iron, a nodular reinforced material. The nodular particles can remain on the surface of the test piece when the surface roughness is measured after machining, but the nodular particles on the surface and the adjacent surface part are extremely easy to fall off during the fatigue test, forming surface defects such as pits, which are prone to form crack sources during the fatigue load process, thereby shortening the fatigue life.

[0040] The target machining attribute information refers to the machining process of the material to be predicted. The machining attribute information can include information such as feed speed, cutting depth, and spindle speed, for example, feed speed 20 mm / min, cutting depth 0.2 mm, and spindle speed 90 n / min.

[0041] The stress amplitude is S in the S-N (stress-life) fatigue life formula, which reflects the stress level.

[0042] In step S102, the target surface stress concentration coefficient corresponding to the target machining attribute information is determined based on the mapping relationship between the machining attribute information and the surface stress concentration coefficient; wherein the surface stress concentration coefficient is calculated based on the surface defect information of the machining sample corresponding to the machining attribute information.

[0043] In an embodiment of the present disclosure, the mapping relationship between the machining attribute information and the surface stress concentration coefficient is constructed in advance. After obtaining the target machining attribute information of the material to be predicted, the target surface stress concentration coefficient corresponding to the target machining attribute information is queried according to the mapping relationship, denoted as K t .

[0044] In one embodiment of the present disclosure, the method further comprises: constructing a mapping relationship between the machining attribute information and the surface stress concentration coefficient, specifically comprising the following steps:

[0045] Step one, configuring a plurality of machining attribute information;

[0046] Step two, referring to GB / T 15248-2008 to design the fatigue test specimen size, and performing fatigue test on the gauge section of the fatigue test specimen according to each machining attribute information to obtain a machining test specimen corresponding to each machining attribute information;

[0047] Step three, calculating the surface stress concentration coefficient corresponding to each machining attribute information based on the surface defect information of each machining test specimen to construct the mapping relationship.

[0048] Specifically, first in step one, different machining processes need to be considered to configure a plurality of machining attribute information, which can include feed speed, cutting depth, spindle speed, etc.

[0049] Then in step two, referring to GB / T 15248-2008 to design the fatigue test specimen size, and performing standard part fatigue test under different machining processes of the same material.

[0050] Figure 2 The schematic diagram of one fatigue test specimen size form in the exemplary embodiment of the present disclosure is shown schematically, and the designed fatigue test specimen size form is shown in Figure 2 .

[0051] Specifically, a double-sided circular arc fatigue test specimen consistent with the boring machining process can be used, which is closer to the actual machining and working conditions. The gauge section of the fatigue test specimen after different process machining retains the machining marks, and the rest is polished. The fatigue test conditions are stress ratio R=0.1, load is sinusoidal wave, and frequency is 105-110 Hz.

[0052] Finally in step three, the surface defect information is obtained by observing the machining test specimen, and then the surface stress concentration coefficient is calculated according to the surface defect information.

[0053] In one embodiment of the present disclosure, the calculation of the surface stress concentration coefficient corresponding to each machining attribute information based on the surface defect information of each machining test specimen comprises:

[0054] Step one, for a machining attribute information, determining the total defect area and the total contour area of the machining test specimen corresponding to the machining attribute information as the surface defect information;

[0055] Step two, calculating the surface stress concentration coefficient based on the total defect area and the total contour area.

[0056] Step three, traversing the processing attribute information to obtain the surface stress concentration coefficient corresponding to each processing attribute information respectively.

[0057] Specifically, in step one, first, according to the uniform sampling points on the surface after processing, the surface defect information of the processing sample is determined by means of three-dimensional super-depth visual observation equipment, including the total defect area and the total profile area, wherein the total defect area includes the circular defect area and the strip defect area.

[0058] The surface topography test can use a square sample with a size of 10x10x8mm. The sample is processed by wire cutting and other processes and the rest of the surface is polished to remove processing marks. Then, the sample is dried in a dryer after ethanol water and propanol oil removal by ultrasonic waves. The three-dimensional super-depth device is used to observe the surface topography.

[0059] The determination of the total defect area of the processing sample corresponding to the processing attribute information includes: observing the processing surface of the processing sample to obtain the spherulite pits and spherulite powder strips; measuring the residual particle diameter of the spherulite pits to calculate the circular defect area, and measuring the strip area and strip thickness of the spherulite powder strips to calculate the strip defect area; and adding the circular defect area and the strip defect area to obtain the total defect area.

[0060] Figure 3 An overhead view of a processing surface defect area in an exemplary embodiment of the present disclosure is schematically shown; Figure 4 An overhead view of a processing surface defect area in an exemplary embodiment of the present disclosure is schematically shown; Figure 3 And Figure 4 As shown in the figure, when the processing material is graphite cast iron material, pits and spherulite powder strips after spherulite crushing are left after processing. All spherulite pits are counted to calculate the circular defect area, denoted as S 圆形 , and all spherulite powder strips are counted to calculate the strip defect area, denoted as S 条状 , and finally the total defect area is obtained.

[0061] For the circular defect area S 圆形 , there are i spherulite pits. The residual particle diameter of each spherulite pit is measured to calculate the circular defect area, as shown in formula (1):

[0062]

[0063] Where d1, d2…d i-1 , d i are the residual particle diameters of each spherulite pit counted on the processing surface.

[0064] For the area S of the strip-shaped defect 条状 There are j spherical graphite powder strips. The strip area and thickness of each spherical graphite powder strip are measured and converted into equivalent defect spherical graphite. Then, the strip defect area is calculated through the equivalent particle radius of the equivalent defect spherical graphite.

[0065] It should be noted that ductile iron breakage is only one manifestation of surface defects after processing. In practical applications, surface defects also include surface machining marks, material casting defects (exposed on the surface after processing), surface oxidation, and other defects. The circular defect area S of these surface defects can also be calculated using the method described above. 圆形 and the area S of strip-shaped defects 条状 .

[0066] Specifically, the step of measuring the strip area and strip thickness of the ductile iron powder strip to calculate the strip defect area includes: calculating the remaining particle volume based on the strip area and strip thickness of the ductile iron powder strip; converting the ductile iron powder strip into an equivalent defect ductile iron according to the remaining particle volume to determine the remaining particle equivalent radius of the equivalent defect ductile iron; and calculating the strip defect area based on the remaining particle equivalent radius.

[0067] Based on surface morphology observation, the strip area after defects in the ductile iron particles is extracted. The thickness of the strip after defects is distinguished according to the color depth. Combining the experimental observation results with local collection and weighing, the volume of remaining particles corresponding to the ductile iron powder strips is determined, denoted as V. 剩余1 As shown in formula (2):

[0068] V 剩余1 =S j ·d j (2)

[0069] Among them, S j To determine the strip area of ​​the j-th ductile iron powder strip on the surface of the sample, d j The thickness of the ductile iron powder strip is given by the value of the strip.

[0070] Meanwhile, considering the spherical graphite powder strips as equivalent defective spherical graphite, the theoretical residual particle volume corresponding to the spherical graphite powder strips can be derived from the triple integral of the volume of the defective spherical graphite particles in spherical coordinates, denoted as V. 剩余2 .

[0071] Figure 5 A schematic three-dimensional view of a machined surface defect is shown in an exemplary embodiment of this disclosure. (Reference) Figure 5 As shown, the white area represents the missing ductile iron, and the gray area represents the remaining ductile iron. R q h is the radius of the ductile iron particles in the ductile iron material.i V represents the distance from the surface after the defect in the ductile iron particle to the center of the particle. The black area in the figure represents the remaining area after the defect in the ductile iron particle, and the gray area represents the remaining volume V after the defect in the ductile iron particle. 剩余2 The theoretical residual particle volume of the equivalent defective spherical graphite is obtained by simplifying it into a problem of triple integrals in spherical coordinates, thus yielding V. 剩余2 The expression for is shown in formula (3):

[0072]

[0073] in,

[0074] Simplifying formula (3) yields the theoretical remaining particle volume V. 剩余2 As shown in formula (4):

[0075]

[0076] United V 剩余1 =V 剩余2 ,Right now The equivalent defect height h corresponding to the ductile iron powder strip can be obtained. j The expression, h j Solving this problem involves solving a cubic equation in one variable. Depending on the specific numerical values, the solution can have different expressions.

[0077] The equivalent defect height of the j-th ductile iron powder strip is denoted as h. j It can be based on h j Obtain the equivalent radius R of the remaining particles fj As shown in formula (5):

[0078]

[0079] Among them, R q The radius of the spheroidal particles in the equivalent defective spheroidal graphite is denoted as .

[0080] Calculate the equivalent defect area of ​​the spherical ink powder corresponding to each strip to obtain the strip defect area S. 条状 As shown in formula (6):

[0081] S 条状 =π(R) f1 2 +R f2 2 +…+R f(j-1) 2 +R fj 2 (6)

[0082] Among them, R fjThe equivalent defect radius of the remaining particles of the spheroidal graphite powder strip corresponding to the equivalent defect spheroidal graphite.

[0083] The circular defect area S 圆形 and the strip defect area S 条状 are added to obtain the total defect area S 总破碎 .

[0084] S 总破碎 = S 圆形 +S 条状 (7)

[0085] For the total profile area S 总轮廓 , the observation equipment can be used for measurement.

[0086] In step two, the surface stress concentration coefficient is calculated based on the total defect area and the total profile area.

[0087] Specifically, the calculation of the surface stress concentration coefficient based on the total defect area and the total profile area includes: calculating the equivalent defect radius based on the total defect area, and calculating the profile ratio coefficient and the defect ratio coefficient based on the total defect area and the total profile area; determining the corrected profile curvature radius according to the initial profile curvature radius, the equivalent defect radius, the profile ratio coefficient and the defect ratio coefficient; calculating the surface stress concentration coefficient according to the corrected profile curvature radius, the load factor and the profile arithmetic mean deviation value.

[0088] The relationship between the equivalent defect radius and the total defect area is shown in formula (8):

[0089]

[0090] Therefore, after obtaining the total defect area S 总破碎 , the equivalent defect radius r 等效 can be calculated.

[0091] At the same time, the profile ratio coefficient δ1 and the defect ratio coefficient δ2 can be calculated as shown in formula (9) and formula (10) respectively:

[0092]

[0093]

[0094] In the stress concentration coefficient at the micro-notch, the original effective profile valley curvature radius, i.e. the initial profile curvature radius R , is added to the total equivalent defect radius r 等效 of the surface defect to obtain the corrected profile curvature radius ρ 修正 , as shown in formula (11):

[0095]

[0096] wherein, r0 is the initial profile radius of curvature, r 等效 δ1 is the profile ratio coefficient, and δ2 is the defect ratio coefficient.

[0097] Then the surface stress concentration coefficient K corresponding to different machining processes is derived t As shown in formula (12):

[0098]

[0099] wherein, ρ 修正 r is the modified profile radius of curvature; m is the load factor, m = 1 indicates that the shear load is received, and m = 2 indicates that the uniform tensile load is received; and Ra is the profile arithmetic average deviation value, which can be measured by a roughness measuring instrument or the like.

[0100] It should be noted that for each machining attribute information, the surface stress concentration coefficient corresponding to the machining attribute information needs to be calculated, and therefore in step three, all the machining attribute information configured is traversed to obtain the surface stress concentration coefficient corresponding to each machining attribute information, as shown in Table 1.

[0101] Table 1 surface stress concentration coefficients under different machining attribute information

[0102] Processing attribute information Ra (pm) 修正 (μm) ​ Surface stress concentration factor K t ]]> 90 n / min, 20 mm / min, 0.2 mm 2.654 153.164 1.033 120 n / min, 20 mm / min, 0.2 mm 2.552 278.245 1.018 150 n / min, 20 mm / min, 0.2 mm 1.891 259.791 1.014 180 n / min, 20 mm / min, 0.2 mm 2.395 210.850 1.022

[0103] In step S103, the target machining attribute information, the target surface stress concentration coefficient and the stress amplitude are brought into the fatigue life prediction expression to obtain the predicted life of the material to be predicted.

[0104] Specifically, the surface defect rate of the machined surface is considered in the machined surface defect, and the existing fatigue life formula is modified. Therefore, the method further comprises determining the fatigue life prediction expression, specifically comprising: fitting the material parameters in the fatigue life model with the machining attribute information and the surface stress concentration coefficient to obtain a material parameter expression of the material parameters with respect to the machining attribute information and the surface stress concentration coefficient; and bringing the material parameter expression into the fatigue life formula to obtain the fatigue life prediction expression.

[0105] The S-N (stress-life) fatigue life estimation essentially reflects the relationship between the stress amplitude and the fatigue life, and adopts an exponential form of S-N curve expression, as shown in formula (13):

[0106] e mS• N = C (13)

[0107] where S is the stress amplitude and N is the fatigue life.

[0108] The semi-logarithmic linear relationship of S-N obtained by taking logarithm on both sides of the S-N curve expression is shown in equation (14):

[0109] S = A + B lg N (14)

[0110] where S is the stress amplitude, N is the fatigue life, A is the first material parameter, A = lg C / mlge, B is the second material parameter, B = 1 / mlge.

[0111] Considering that different machining processes cause different surface topographies, which in turn cause changes in the surface stress concentration coefficient K t , the material parameters A and B in the above equation (14) are related to the surface stress concentration coefficient K t .

[0112] First, the A and B of the S-N curve fitting of the feed speed 20 mm / min, the cutting depth 0.2 mm, and the spindle speed from 90 n / min, 120 n / min, and 150 n / min are obtained by fitting the test data, and the relationship between the surface stress concentration coefficient K t is obtained, and then the life of the process of 180 n / min is predicted.

[0113] Figure 6 A schematic diagram showing the results of a curve fitting in an exemplary embodiment of the present disclosure is shown in FIG. 2. Figure 6 As shown in FIG. 2, four S-N curves fitted under four machining attribute information corresponding machining processes are shown, and the specific results are shown in Table 2:

[0114] Table 2 S-N fitting curves under different machining attribute information

[0115] Processing attribute information S-N fitting curve 90 n / min, 20 mm / min, 0.2 mm S = 541.027 - 44.821 · log(N) 120 n / min, 20 mm / min, 0.2 mm S = 423.002 - 21.578 · log(N) 150 n / min, 20 mm / min, 0.2 mm S = 496.473 - 35.488 · log(N)

[0116] The A and B in the S = A + B lg N of the S-N fitting curve are fitted with the surface stress concentration coefficient K t , and the material parameter expressions of A and B are shown in equations (15) and (16) respectively:

[0117] A = 2164.33429 - 27.66841 · n · K t + 0.10981 · (n · K t ) 2 (15)

[0118] B = -353.77388 + 5.27549 · n · K t-0.02092 · (n · K t ) 2 (16)

[0119] wherein n is the spindle revolution number in the machining attribute information, in n / min, K t is the surface stress concentration coefficient.

[0120] The A, B expressions of the formula (15) and the formula (16) are brought into the original S-N semi-logarithmic linear relationship S = A + B log N, and the fatigue life prediction expression is obtained, wherein the machining attribute information n and the surface stress concentration coefficient K t are included.

[0121] In the step S103, when the spindle revolution number n of the target machining attribute information of the material to be predicted is determined, and the target surface stress concentration coefficient K t corresponding to the target machining attribute information is obtained, they are brought into the fatigue life prediction expression.

[0122] Taking n = 180 n / min and K t = 1.022 as an example, first, the values of the material parameters A and B are obtained, that is:

[0123] A = 2164.33429 - 27.66841 · n · K t + 0.10981 · (n · K t ) 2

[0124] A = 2164.33429 - 27.66841 × 180 × 1.022 + 0.10981 × (180 × 1.022) 2

[0125] A = 790.565

[0126] B = -353.77388 + 5.27549 · n · K t - 0.02092 · (n · K t ) 2

[0127] B = -353.77388 + 5.27549 × 180 × 1.022 - 0.02092 × (180 × 1.022) 2

[0128] B = -91.254

[0129] Thus, the S-N prediction curve is S = 790.565 - 91.254 · log (N), and the predicted fatigue life is

[0130] The stress amplitude S of the material to be predicted is brought into N1, and the predicted fatigue life of the material to be predicted can be obtained.

[0131] In an embodiment of the present disclosure, in order to verify the accuracy of the predicted fatigue life, the real fatigue life and the predicted fatigue life of the test data fitting are compared.

[0132] According to the test data results, the S-N fitting formula of 180n / min is S=510.687-44.157·log(N), and the fitted actual fatigue life is

[0133] Under the same stress S level, the ratio δ of the predicted fatigue life N1 and the actual fatigue life N2 is calculated, and the result is shown in formula (17):

[0134]

[0135] When the value of δ is within the range of twice the error limit, i.e. 2 or 0.5, the stress levels corresponding to the upper and lower limits of δ are shown in formula (18) and formula (19) respectively:

[0136]

[0137]

[0138] Figure 7 A comparison between the prediction results and the test results in an exemplary embodiment of the present disclosure is schematically shown. Referring to Figure 7 As shown, for the machining process of 180n / min spindle speed (feed speed 20mm / min, cutting depth 0.2mm), the comparison results between the fatigue life prediction model and the test data are as follows, and it is found that when the stress amplitude is in the range of 223-274MPa, the error range is within 2 times the error limit, and the prediction effect is good.

[0139] When the stress amplitude S=250MPa, the predicted fatigue life N1 and the actual fatigue life N2 are shown in formula (20) and formula (21) respectively:

[0140]

[0141]

[0142] Based on the above method, the surface defects caused by the machining surface defects are considered into the surface stress concentration coefficient K of the different process machining surfaces tIn the semi-logarithmic linear relationship S=A+BlgN of the stress life model, two parameters A and B are substituted, and the S-N curve relationship formula obtained by fitting the data obtained by the fatigue test of the three processes is combined with the process n (spindle speed) and the surface stress concentration coefficient K t The fitting relationship of two parameters A and B is obtained, and then the fatigue life under the new process parameters is predicted. The prediction result is within the range of 2 times error limit within the test data fitting curve in a certain stress range (223-274 MPa).

[0143] Therefore, the material fatigue life prediction method considering the influence of machining process provided by the present disclosure considers the influence of roughness in the surface stress concentration coefficient under different machining processes, measures and statistics the influence of surface defects such as spherulite particle loss, and introduces the surface stress concentration coefficient after the influence of surface defects left by machining in the fatigue life model. The existing fatigue life data of the machining process is used to establish a new process parameter locally effective fatigue life prediction model, and the effective prediction of the life in a certain stress range is realized. And it can reduce the number of samples required for fatigue test, effectively improve the utilization rate of existing fatigue data, and achieve a certain prediction effect.

[0144] Figure 8 An exemplary embodiment of the material fatigue life prediction device considering the influence of machining process is schematically shown in the schematic diagram of the exemplary embodiment of the present disclosure, as shown in the schematic diagram of the exemplary embodiment of the present disclosure, the material fatigue life prediction device considering the influence of machining process 800 can include an acquisition module 801, a determination module 802 and a prediction module 803. Wherein: Figure 8

[0145] The acquisition module 801 is used to acquire the target machining attribute information of the material to be predicted and the stress amplitude;

[0146] The determination module 802 is used to determine the target surface stress concentration coefficient corresponding to the target machining attribute information based on the mapping relationship between the machining attribute information and the surface stress concentration coefficient; wherein the surface stress concentration coefficient is calculated based on the surface defect information of the machining sample corresponding to the machining attribute information;

[0147] The prediction module 803 is used to bring the target machining attribute information, the target surface stress concentration coefficient and the stress amplitude into the fatigue life prediction expression to obtain the predicted life of the material to be predicted.

[0148] ​According to the example embodiments of the present disclosure, the material fatigue life prediction device 800 affected by the combined machining process can further comprise a mapping module including a configuration unit, a test unit and a calculation unit; wherein the configuration unit is configured to configure a plurality of machining attribute information; the test unit is configured to refer to GB / T 15248-2008 to design the fatigue test sample size, and perform fatigue test on the gauge section of the fatigue test sample according to each machining attribute information to obtain the machining test sample corresponding to each machining attribute information; and the calculation unit is configured to calculate the surface stress concentration coefficient corresponding to each machining attribute information based on the surface defect information of each machining test sample to construct the mapping relationship.

[0149] According to the example embodiments of the present disclosure, the calculation unit is further configured to, for a machining attribute information, determine the total defect area and the total profile area of the machining test sample corresponding to the machining attribute information as the surface defect information, calculate the surface stress concentration coefficient based on the total defect area and the total profile area, and traverse the machining attribute information to obtain the surface stress concentration coefficient corresponding to each machining attribute information.

[0150] According to the example embodiments of the present disclosure, the calculation unit is further configured to, according to the example embodiments of the present disclosure, observe the machining surface of the machining test sample to obtain the nodule pits and the nodule powder strips of the defects, measure the residual particle diameter of the nodule pits to calculate the circular defect area, and measure the strip area and the strip thickness of the nodule powder strips to calculate the strip defect area, and add the circular defect area and the strip defect area to obtain the total defect area.

[0151] According to the example embodiments of the present disclosure, the calculation unit is further configured to calculate the residual particle volume based on the strip area and the strip thickness of the nodule powder strips, convert the nodule powder strips into an equivalent defect nodule according to the residual particle volume to determine the residual particle equivalent radius of the equivalent defect nodule, and calculate the strip defect area based on the residual particle equivalent radius.

[0152] According to the example embodiments of the present disclosure, the calculation unit is further configured to calculate an equivalent defect radius based on the total defect area, and calculate a profile proportion coefficient and a defect proportion coefficient based on the total defect area and the total profile area, determine a corrected profile curvature radius according to an initial profile curvature radius, the equivalent defect radius, the profile proportion coefficient and the defect proportion coefficient, and calculate the surface stress concentration coefficient according to the corrected profile curvature radius, a load factor and a profile arithmetic average deviation value.

[0153] According to an exemplary embodiment of the present disclosure, the material fatigue life prediction device 800 combined with the influence of machining process can further comprise a calculation module for determining the fatigue life prediction expression, comprising: fitting material parameters in a fatigue life model with the machining attribute information and the surface stress concentration coefficient to obtain a material parameter expression of the material parameters with respect to the machining attribute information and the surface stress concentration coefficient; and bringing the material parameter expression into the fatigue life formula to obtain the fatigue life prediction expression.

[0154] The specific details of each module in the above-mentioned material fatigue life prediction device 800 combined with the influence of machining process have been described in detail in the corresponding material fatigue life prediction method combined with the influence of machining process, and therefore will not be described here.

[0155] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into embodied by multiple modules or units.

[0156] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure following the general principles thereof and including such departures from the present disclosure that come within known, accepted, or customary practice in the art to which the present disclosure pertains.

[0157] It should be understood that the present disclosure is not limited to the precise structures described above and illustrated in the drawings and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for predicting fatigue life of a material in combination with influence of a machining process, characterized by, The method comprises the following steps: obtaining target processing attribute information of a material to be predicted and a stress amplitude; determining a target surface stress concentration coefficient corresponding to the target processing attribute information based on a mapping relationship between the processing attribute information and the surface stress concentration coefficient; wherein the surface stress concentration coefficient is calculated based on surface defect information of a processing sample corresponding to the processing attribute information; obtaining a predicted life of the material to be predicted by bringing the target processing attribute information, the target surface stress concentration coefficient and the stress amplitude into a fatigue life prediction expression; The step of constructing the mapping relationship comprises: configuring a plurality of processing attribute information; designing the size of a fatigue sample according to GB / T 15248-2008, and performing a fatigue test on the gauge section of the fatigue sample according to each of the processing attribute information to obtain a processing sample corresponding to each of the processing attribute information; for a processing attribute information, determining the total defect area and the total profile area of the processing sample corresponding to the processing attribute information as the surface defect information; calculating an equivalent defect radius based on the total defect area, and calculating a profile ratio coefficient and a defect ratio coefficient based on the total defect area and the total profile area; determining a corrected profile curvature radius according to an initial profile curvature radius, the equivalent defect radius, the profile ratio coefficient and the defect ratio coefficient; calculating the surface stress concentration coefficient according to the corrected profile curvature radius, a load factor and a profile arithmetic average deviation value; traversing the processing attribute information to obtain the surface stress concentration coefficient corresponding to each of the processing attribute information to construct the mapping relationship.

2. The method of predicting the fatigue life of a material influenced by a machining process according to claim 1, characterized in that, The step of determining the total defect area of the processing sample corresponding to the processing attribute information comprises: observing the processing surface of the processing sample to obtain spherical graphite pits and spherical graphite powder strips of defects; measuring the residual particle diameter of the spherical graphite pits to calculate the circular defect area, and measuring the strip area and the strip thickness of the spherical graphite powder strips to calculate the strip defect area; adding the circular defect area and the strip defect area to obtain the total defect area.

3. The method for predicting the fatigue life of a material in conjunction with the effects of a machining process according to claim 2, characterized in that, The step of measuring the strip area and the strip thickness of the spherical graphite powder strips to calculate the strip defect area comprises: calculating the residual particle volume based on the strip area and the strip thickness of the spherical graphite powder strips; converting the spherical graphite powder strips into an equivalent defect spherical graphite according to the residual particle volume to determine the residual particle equivalent radius of the equivalent defect spherical graphite; calculating the strip defect area based on the residual particle equivalent radius.

4. The method for predicting the fatigue life of a material in conjunction with the effects of a machining process according to Claim 1, characterized by, The method further comprises determining the fatigue life prediction expression, wherein the step of determining the fatigue life prediction expression comprises: fitting material parameters in a fatigue life model with the processing attribute information and the surface stress concentration coefficient to obtain a material parameter expression of the material parameters with respect to the processing attribute information and the surface stress concentration coefficient; bringing the material parameter expression into the fatigue life formula to obtain the fatigue life prediction expression.

5. A device for predicting fatigue life of a material in combination with influence of a machining process, characterized by, The method comprises the following steps: an obtaining module, configured to obtain target processing attribute information of a material to be predicted and a stress amplitude; determining a target surface stress concentration coefficient corresponding to the target machining attribute information based on a mapping relationship between machining attribute information and surface stress concentration coefficients, wherein the surface stress concentration coefficient is calculated based on surface defect information of a machining sample corresponding to the machining attribute information; predicting a predicted life of the material to be predicted by inputting the target machining attribute information, the target surface stress concentration coefficient, and the stress amplitude into a fatigue life prediction expression; configuring a plurality of machining attribute information; designing fatigue sample sizes according to GB / T 15248-2008, and performing fatigue tests on gauge sections of fatigue samples respectively according to the machining attribute information to obtain machining samples corresponding to each machining attribute information; determining a total defect area and a total profile area of the machining sample corresponding to the machining attribute information as the surface defect information for the machining attribute information; calculating an equivalent defect radius based on the total defect area, and calculating a profile ratio coefficient and a defect ratio coefficient based on the total defect area and the total profile area; determining a corrected profile curvature radius according to an initial profile curvature radius, the equivalent defect radius, the profile ratio coefficient, and the defect ratio coefficient; calculating the surface stress concentration coefficient according to the corrected profile curvature radius, a load factor, and a profile arithmetic average deviation value; and traversing the machining attribute information to obtain surface stress concentration coefficients corresponding to each machining attribute information, so as to construct the mapping relationship.

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