CN113392504A - Method for predicting influence of defects on high-cycle and ultrahigh-cycle fatigue strength - Google Patents
Method for predicting influence of defects on high-cycle and ultrahigh-cycle fatigue strength Download PDFInfo
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- 238000009661 fatigue test Methods 0.000 claims description 20
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- 238000005452 bending Methods 0.000 claims description 8
- 230000000376 effect on fatigue Effects 0.000 claims description 6
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Abstract
The invention provides a method for predicting the influence of defects on high-cycle and ultrahigh-cycle fatigue strength. By utilizing the relation, the influence of the defect on the fatigue strength can be represented by the size of the defect, and the influence of the defect on the fatigue strength can be predicted. The method disclosed by the invention is simple in form and convenient to apply, solves the problems of mathematical model description and difficult accurate evaluation of the influence of defects on the fatigue strength of high-cycle and ultrahigh-cycle, and provides a model and technical support for the fatigue performance research and evaluation of materials or engineering parts containing defects.
Description
Technical Field
The invention relates to a method for predicting high-cycle and ultrahigh-cycle fatigue strength of materials or engineering parts, in particular to a method for predicting the influence of defects on the high-cycle and ultrahigh-cycle fatigue strength.
Background
Various types of defects are usually inevitably present in actual engineering parts, such as metallurgical defects during material preparation, possible impact defects during service of the parts, and the like. Under the action of an external load, local stress concentration at the defect part often causes the initiation of fatigue cracks and obviously reduces the fatigue resistance of the material. Therefore, the method for establishing the influence of the defects on the fatigue strength has important scientific significance and engineering application value.
Disclosure of Invention
The invention aims to provide a method for predicting the influence of defects on high-cycle and ultrahigh-cycle fatigue strength of materials or engineering parts.
The technical content of the invention is as follows:
a method of predicting the effect of defects on high and ultra-high cycle fatigue strength, comprising the steps of:
(1) fatigue tests were conducted on the smooth test piece and the test piece containing the defect for prediction to obtain the fatigue strength of the smooth test piece at a certain life for prediction, and the fatigue strength σ of the test piece containing the defectw,1，σw,2，…，σw,nAnd corresponding defect size
Wherein areai(i 1, 2.., n) is the projected area of defect i perpendicular to the primary stress axis;
(2) the fatigue strength at this life and the defect size are assumed to satisfy the following relationship:
namely, it is
Wherein σwIndicates fatigue strength, σw,0Representing the fatigue strength of a smooth sample;
indicating the size of the defect, and area is the projection area of the defect perpendicular to the main stress axis;
critical defect size, below which the defect has no effect on fatigue strength; m and C are parameters relating to material, fatigue life and defect introduction pattern;
(3) and substituting the obtained material parameters m and C into the formula (2) to obtain a model of the influence of the defects on the fatigue strength under the service life.
Furthermore, the fatigue test comprises an axial stress fatigue test, a rotary bending fatigue test, a four-point bending fatigue test and an ultrasonic frequency fatigue test.
Further, the defect size is obtained by a defect preparation method.
It is also possible that the defect size is determined by measurement.
Further, the measurement method is to determine the size of the defect on the fatigue fracture scanning electron microscope picture, and preferably, the measurement method is to determine the size of the defect on the fatigue fracture scanning electron microscope picture through image processing software.
Furthermore, the size of the defect is less than or equal to 1000 mu m.
Further, m is obtained by adopting a least square method for the fatigue strength and the defect size of the defect sample which is lower than the fatigue strength of the smooth sample; preferably, m is calculated by:
further, C is obtained by adopting a least square method to the fatigue strength and the defect size of the defect sample which is lower than the fatigue strength of the smooth sample; preferably, the calculation method of C is:
the method disclosed by the invention determines the high-cycle and ultrahigh-cycle fatigue strength of a smooth sample and a sample containing defects under a certain service life through a fatigue experiment, and then determines the influence relation of the defects on the fatigue strength under the service life through a mathematical model. By utilizing the relation, the influence of the defect on the fatigue strength can be represented by the size of the defect, and the influence of the defect on the fatigue strength can be predicted. The method disclosed by the invention is simple in form and convenient to apply, solves the problems of mathematical model description and difficult accurate evaluation of the influence of defects on the fatigue strength of high-cycle and ultrahigh-cycle, and provides a model and technical support for the fatigue performance research and evaluation of materials or engineering parts containing defects.
Drawings
FIG. 1: a TC17 titanium alloy rotating bending fatigue test sample (unit: mm) is a smooth test sample;
FIG. 2: the TC17 titanium alloy rotating bending fatigue test sample (unit: mm) contains a defect A test sample defect schematic diagram;
FIG. 3: a defect schematic diagram of a TC17 titanium alloy rotating bending fatigue test sample (unit: mm) containing a defect B test sample;
FIG. 4: a defect C sample defect schematic diagram of a TC17 titanium alloy rotating bending fatigue sample (unit: mm);
FIG. 5: a defect D sample defect schematic diagram of a TC17 titanium alloy rotating bending fatigue sample (unit: mm);
FIG. 6: S-N data of a TC17 titanium alloy smooth sample and a defect-containing sample;
FIG. 7: influence model results of the defects on the ultra-high cycle fatigue strength of the TC17 titanium alloy are obtained;
FIG. 8: influence of defects on fatigue limit of EA4T axle steel in literature model results.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
Example 1:
first, the titanium alloy smooth samples and the samples containing defects shown in fig. 1 to 5 were subjected to the rotary bending fatigue test (stress ratio R-1) at different stress ranges, and the fatigue property data thereof were obtained as shown in fig. 6. The defects in fig. 2-4 were obtained by drilling holes in the smallest cross-section of the smooth specimen shown in fig. 1 using a micro-milling machine, and the corresponding defect size was the average size obtained by scanning electron microscopy of fatigue fracture.With defect pair 108The influence of the fatigue strength under the weekly frequency was taken as an example, and based on the experimental result in FIG. 6, 10 was obtained8Fatigue strength of 635MPa for smooth test piece under week and sigma for test piece containing defect Bw,1＝563MPa
Fatigue Strength σ of samples containing Defect Cw,2＝448MPa
Fatigue Strength σ of samples containing Defect Dw,3＝390MPa
Here, 108The fatigue strength at week time was taken as 10 in the experimental data tested8Stress amplitude minimum and history of specimens with fatigue failure before week 108Average value of maximum values of stress amplitude of the test piece in which fatigue failure did not occur in the week.
Then, the samples of the above-mentioned defects B, C and D were set at 108Substituting the fatigue strength and corresponding defect size in the week into the formula (2), and obtaining parameters m and C by a least square method.
And finally, substituting the parameters m and C into the formula (2) to obtain a model result of the influence of the defects on the fatigue strength under the service life. FIG. 7 shows defect pairs 108Effect of fatigue Strength in cycles model results and fatigue Strength of the test specimens containing Defect A of 620MPa
The fit is good. FIG. 7 shows that the model correlates well and predicts the effect of defects on fatigue strength.
Example 2:
FIG. 8 shows the results of a model of the effect of defects on the Fatigue limit (in stress amplitude) of EA4T axle steel in the document Zhang et al int J Fatigue 2020,132: 105379. It can be seen that the model results agree well with the experimental results.
Example 3: a method of predicting the effect of defects on high and ultra-high cycle fatigue strength, comprising the steps of:
(1) carrying out fatigue experiments on the smooth sample and the sample containing the defects for prediction to obtain the fatigue strength of the smooth sample under a certain service life for prediction and the fatigue strength of the sample containing the defects;
(2) determining the influence relation of the defects on the fatigue strength under the service life through a mathematical model;
(3) by utilizing the relation, the influence of the defect on the fatigue strength is represented through the size of the defect, and the influence of the defect on the fatigue strength is predicted.
Example 4:
a method of predicting the effect of defects on high and ultra-high cycle fatigue strength, comprising the steps of:
(1) fatigue tests were conducted on the smooth test piece and the test piece containing the defect for prediction to obtain the fatigue strength of the smooth test piece at a certain life for prediction, and the fatigue strength σ of the test piece containing the defectw,1，σw,2，…，σw,nAnd corresponding defect size
Wherein areai(i 1, 2.., n) is the projected area of defect i perpendicular to the primary stress axis;
(2) the fatigue strength at this life and the defect size are assumed to satisfy the following relationship:
namely, it is
Wherein σwIndicates fatigue strength, σw,0Representing the fatigue strength of a smooth sample;
indicating the size of the defect, and area is the projection area of the defect perpendicular to the main stress axis;
critical defect size, below which the defect has no effect on fatigue strength; m and C are parameters relating to material, fatigue life and defect introduction pattern;
(3) and substituting the obtained material parameters m and C into the formula (2) to obtain a model of the influence of the defects on the fatigue strength under the service life.
Example 5:
a method of predicting the effect of defects on high and ultra-high cycle fatigue strength, comprising the steps of:
(1) fatigue tests were conducted on the smooth test piece and the test piece containing the defect for prediction to obtain the fatigue strength of the smooth test piece at a certain life for prediction, and the fatigue strength σ of the test piece containing the defectw,1，σw,2，…，σw,nAnd corresponding defect size
Wherein areai(i 1, 2.., n) is the projected area of defect i perpendicular to the primary stress axis;
(2) the fatigue strength at this life and the defect size are assumed to satisfy the following relationship:
namely, it is
Wherein σwIndicates fatigue strength, σw,0Representing the fatigue strength of a smooth sample;
indicating the size of the defect, and area is the projection area of the defect perpendicular to the main stress axis;
critical defect size, below which the defect has no effect on fatigue strength; m and C are parameters relating to material, fatigue life and defect introduction pattern;
(3) substituting the obtained material parameters m and C into the formula (2) to obtain a model of the influence of the defects on the fatigue strength under the service life;
the defect size is obtained by a defect preparation method, and the preparation method comprises the prior art of a micro milling machine, electric spark, indentation and the like.
Example 6:
a method of predicting the effect of defects on high and ultra-high cycle fatigue strength, comprising the steps of:
(1) fatigue tests were conducted on the smooth test piece and the test piece containing the defect for prediction to obtain the fatigue strength of the smooth test piece at a certain life for prediction, and the fatigue strength σ of the test piece containing the defectw,1，σw,2，…，σw,nAnd corresponding defect size
Wherein areai(i 1, 2.., n) is the projected area of defect i perpendicular to the primary stress axis;
(2) the fatigue strength at this life and the defect size are assumed to satisfy the following relationship:
namely, it is
Wherein σwIndicates fatigue strength, σw,0Representing the fatigue strength of a smooth sample;
indicating the size of the defect, and area is the projection area of the defect perpendicular to the main stress axis;
critical defect size, below which the defect has no effect on fatigue strength; m and C are parameters relating to material, fatigue life and defect introduction pattern;
(3) substituting the obtained material parameters m and C into the formula (2) to obtain a model of the influence of the defects on the fatigue strength under the service life;
the defect size is determined by measurement; the measurement method is to determine the size of the defect on the fatigue fracture scanning electron microscope picture; the size of the defect on the picture of the fatigue fracture scanning electron microscope is determined by measuring the size of the defect by Image processing software (such as Image-Pro Plus software).
Example 7:
a method of predicting the effect of defects on high and ultra-high cycle fatigue strength, comprising the steps of:
(1) fatigue tests were conducted on the smooth test piece and the test piece containing the defect for prediction to obtain the fatigue strength of the smooth test piece at a certain life for prediction, and the fatigue strength σ of the test piece containing the defectw,1，σw,2，…，σw,nAnd corresponding defect size
Wherein areai(i 1, 2.., n) is the projected area of defect i perpendicular to the primary stress axis;
(2) the fatigue strength at this life and the defect size are assumed to satisfy the following relationship:
namely, it is
Wherein σwIndicates fatigue strength, σw,0Representing the fatigue strength of a smooth sample;
indicating the size of the defect, and area is the projection area of the defect perpendicular to the main stress axis;
critical defect size, below which the defect has no effect on fatigue strength; m and C are parameters relating to material, fatigue life and defect introduction pattern;
(3) substituting the obtained material parameters m and C into the formula (2) to obtain a model of the influence of the defects on the fatigue strength under the service life;
the defect size is determined by measurement; the measurement method is to determine the size of the defect on the fatigue fracture scanning electron microscope picture;
m is obtained by adopting a least square method to the fatigue strength and the defect size of the defect sample which are lower than the fatigue strength of the smooth sample; preferably, m is calculated by:
c, obtaining the fatigue strength and the defect size of the defect sample which are lower than the fatigue strength of the smooth sample by adopting a least square method; preferably, the calculation method of C is:
thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (10)
1. A method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength, comprising the steps of:
(1) carrying out fatigue experiments on the smooth sample and the sample containing the defects for prediction to obtain the fatigue strength of the smooth sample under a certain service life for prediction and the fatigue strength of the sample containing the defects;
(2) determining the influence relation of the defects on the fatigue strength under the service life through a mathematical model;
(3) by utilizing the relation, the influence of the defect on the fatigue strength is represented through the size of the defect, and the influence of the defect on the fatigue strength is predicted.
2. A method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength, comprising the steps of:
(1) fatigue tests were conducted on the smooth test piece and the test piece containing the defect for prediction to obtain the fatigue strength of the smooth test piece at a certain life for prediction, and the fatigue strength σ of the test piece containing the defectw,1，σw,2，…，σw,nAnd corresponding defect size
Wherein areai(i 1, 2.., n) is the projected area of defect i perpendicular to the primary stress axis;
(2) the fatigue strength at this life and the defect size are assumed to satisfy the following relationship:
namely, it is
Wherein σwIndicates fatigue strength, σw,0Representing the fatigue strength of a smooth sample;
indicating the size of the defect, and area is the projection area of the defect perpendicular to the main stress axis;
critical defect size, below which the defect has no effect on fatigue strength; m and C are parameters relating to material, fatigue life and defect introduction pattern;
(3) and substituting the obtained material parameters m and C into the formula (2) to obtain a model of the influence of the defects on the fatigue strength under the service life.
3. The method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength of claim 1, wherein: the fatigue test comprises an axial stress fatigue test, a rotary bending fatigue test, a four-point bending fatigue test and an ultrasonic frequency fatigue test.
4. The method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength of claim 1, wherein: the defect size is obtained by a defect preparation method.
5. The method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength of claim 1, wherein: the defect size is determined by measurement.
6. The method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength of claim 5, wherein: the measurement method is used for determining the size of the defect on the fatigue fracture scanning electron microscope picture.
7. The method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength of claim 6, wherein: and determining the size of the defect on the fatigue fracture scanning electron microscope picture by image processing software.
8. The method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength of claim 1, wherein: the size of the defect is less than or equal to 1000 mu m.
9. The method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength of claim 1, wherein: m is obtained by adopting a least square method to the fatigue strength and the defect size of the defect sample which are lower than the fatigue strength of the smooth sample; preferably, m is calculated by:
10. the method of predicting the effect of defects on high cycle and ultra high cycle fatigue strength of claim 1, wherein: c, obtaining the fatigue strength and the defect size of the defect sample which are lower than the fatigue strength of the smooth sample by adopting a least square method; preferably, the calculation method of C is:
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1500207A (en) * | 2001-03-23 | 2004-05-26 | 株式会社产学连携机构九州 | Long life fatigue strength design method for metallic material |
| US20080015827A1 (en) * | 2006-01-24 | 2008-01-17 | Tryon Robert G Iii | Materials-based failure analysis in design of electronic devices, and prediction of operating life |
| CN108613889A (en) * | 2018-04-27 | 2018-10-02 | 佛山科学技术学院 | A kind of blunt notch fatigue strength loss coefficient appraisal procedure of titanium alloy based on cycle life |
| CN109142529A (en) * | 2018-08-27 | 2019-01-04 | 佛山科学技术学院 | A kind of high-strength titanium alloy electro-beam welding joint super high cycle fatigue life-span prediction method |
| CN109253873A (en) * | 2018-09-19 | 2019-01-22 | 中国科学院金属研究所 | A kind of large-scale moving load component Prediction method for fatigue life determining comprehensive correction factor by analog component |
| CN109614715A (en) * | 2018-12-13 | 2019-04-12 | 电子科技大学 | A kind of lower Field strength method and its application for considering notch effect of multiaxial loading effect |
| CN110609052A (en) * | 2019-08-26 | 2019-12-24 | 武汉钢铁有限公司 | Method and device for predicting fatigue life of cylindrical metal material and electronic equipment |
| CN110763758A (en) * | 2019-09-12 | 2020-02-07 | 中国航发北京航空材料研究院 | Method for determining relation between defects and fatigue performance based on nondestructive testing |
| CN110910972A (en) * | 2019-11-20 | 2020-03-24 | 长沙理工大学 | Fatigue stress concentration coefficient prediction method based on Gaussian process |
| CN110990948A (en) * | 2019-11-27 | 2020-04-10 | 南京航空航天大学 | Method for predicting damage fatigue strength of foreign object of blade of aircraft engine |
| CN111553091A (en) * | 2020-05-09 | 2020-08-18 | 南京航空航天大学 | Fatigue life prediction method considering surface integrity |
-
2021
- 2021-05-18 CN CN202110538157.XA patent/CN113392504B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1500207A (en) * | 2001-03-23 | 2004-05-26 | 株式会社产学连携机构九州 | Long life fatigue strength design method for metallic material |
| US20080015827A1 (en) * | 2006-01-24 | 2008-01-17 | Tryon Robert G Iii | Materials-based failure analysis in design of electronic devices, and prediction of operating life |
| CN108613889A (en) * | 2018-04-27 | 2018-10-02 | 佛山科学技术学院 | A kind of blunt notch fatigue strength loss coefficient appraisal procedure of titanium alloy based on cycle life |
| CN109142529A (en) * | 2018-08-27 | 2019-01-04 | 佛山科学技术学院 | A kind of high-strength titanium alloy electro-beam welding joint super high cycle fatigue life-span prediction method |
| CN109253873A (en) * | 2018-09-19 | 2019-01-22 | 中国科学院金属研究所 | A kind of large-scale moving load component Prediction method for fatigue life determining comprehensive correction factor by analog component |
| CN109614715A (en) * | 2018-12-13 | 2019-04-12 | 电子科技大学 | A kind of lower Field strength method and its application for considering notch effect of multiaxial loading effect |
| CN110609052A (en) * | 2019-08-26 | 2019-12-24 | 武汉钢铁有限公司 | Method and device for predicting fatigue life of cylindrical metal material and electronic equipment |
| CN110763758A (en) * | 2019-09-12 | 2020-02-07 | 中国航发北京航空材料研究院 | Method for determining relation between defects and fatigue performance based on nondestructive testing |
| CN110910972A (en) * | 2019-11-20 | 2020-03-24 | 长沙理工大学 | Fatigue stress concentration coefficient prediction method based on Gaussian process |
| CN110990948A (en) * | 2019-11-27 | 2020-04-10 | 南京航空航天大学 | Method for predicting damage fatigue strength of foreign object of blade of aircraft engine |
| CN111553091A (en) * | 2020-05-09 | 2020-08-18 | 南京航空航天大学 | Fatigue life prediction method considering surface integrity |
Non-Patent Citations (2)
| Title |
|---|
| WEIQIAN CHI等: "Effects of defects on fatigue behavior of TC17 titanium alloy for compressor blades: Crack initiation and modeling of fatigue strength", 《ENGINEERING FRACTURE MECHANICS》, pages 1 - 13 * |
| 周松等: "含缺陷的TB6钛合金疲劳性能研究和强度评估", 《热加工工艺》, vol. 50, no. 12, pages 39 - 43 * |
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