CN115901502A - Method for predicting relation between welding repair defects and fatigue life of TC17 titanium alloy blade - Google Patents
Method for predicting relation between welding repair defects and fatigue life of TC17 titanium alloy blade Download PDFInfo
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- 230000007547 defect Effects 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000003466 welding Methods 0.000 title claims abstract description 19
- 230000008439 repair process Effects 0.000 title claims abstract description 17
- 229910001069 Ti alloy Inorganic materials 0.000 title claims abstract description 14
- 238000005452 bending Methods 0.000 claims abstract description 45
- 238000012360 testing method Methods 0.000 claims abstract description 26
- 238000010438 heat treatment Methods 0.000 claims abstract description 10
- 238000009661 fatigue test Methods 0.000 claims description 13
- 238000000151 deposition Methods 0.000 claims description 11
- 230000008021 deposition Effects 0.000 claims description 10
- 238000009826 distribution Methods 0.000 claims description 8
- XKRFYHLGVUSROY-UHFFFAOYSA-N Argon Chemical compound [Ar] XKRFYHLGVUSROY-UHFFFAOYSA-N 0.000 claims description 6
- 239000000463 material Substances 0.000 claims description 6
- 238000011056 performance test Methods 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 4
- 238000009825 accumulation Methods 0.000 claims description 3
- 229910052786 argon Inorganic materials 0.000 claims description 3
- 238000001816 cooling Methods 0.000 claims description 3
- 238000005520 cutting process Methods 0.000 claims description 3
- 238000005242 forging Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 238000000227 grinding Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 2
- 230000008571 general function Effects 0.000 claims 1
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 239000000956 alloy Substances 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
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- 238000010586 diagram Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
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- 238000012163 sequencing technique Methods 0.000 description 1
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a method for predicting the relation between welding repair defects and fatigue life of a TC17 titanium alloy blade, which comprises the following steps: establishing a fatigue life prediction model related to defect repair; step two: preparing a rotary bending fatigue sample; step three: carrying out heat treatment on a surfacing sample; step four: processing a rotary bending fatigue sample; step five: testing rotary bending fatigue; step six: fracture analysis and defect statistics; step seven: and establishing a rotary bending fatigue life prediction model. The invention selects the arc fuse repaired test block to test the rotary bending fatigue performance, observes and counts the appearance, size, position and the like of the defect at the fracture, analyzes the corresponding relation between the defect and the fatigue life, establishes a fatigue life prediction model related to the repaired defect, and can determine the influence of the air hole defect on the fatigue life of the repaired blade through the model.
Description
Technical Field
The invention relates to the technical field of manufacturing and repairing of titanium alloy blades of aero-engines, in particular to a method for predicting the relation between welding repair defects and fatigue life of a TC17 titanium alloy blade.
Background
The titanium alloy compressor blade and blisk part of the aircraft engine is made of TC17, the problems of abrasion, foreign Object Damage (FOD), cracks, corrosion, fatigue damage and the like can exist after the blade and the blisk part is used, and in order to reduce the manufacturing cost and prolong the service life of the blade, the blade is usually repaired by adopting a welding or laser deposition method. Because the part needs to bear higher rotating speed and centrifugal load during the use process, the requirement on the fatigue life of materials and structures is high.
The titanium alloy is very active in chemical properties, and the capability of the titanium alloy to absorb oxygen, nitrogen and hydrogen is obviously improved along with the increase of temperature. Therefore, after the titanium alloy material blade and the blisk are welded or repaired by laser deposition, the defects of air holes with different degrees inevitably exist in the repair body, and the existence of the defects can obviously influence the fatigue performance of the blade.
Disclosure of Invention
In order to solve the technical problems, a method for predicting the relation between the welding repair defects and the fatigue life of the TC17 titanium alloy blade is provided, and the specific technical scheme is as follows:
a method for predicting the relation between welding repair defects and fatigue life of TC17 titanium alloy blades is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: establishing a fatigue life prediction model related to repairing defects
Selecting a test block for repairing an arc fuse, testing the rotary bending fatigue performance of the test block, observing and counting the appearance, size, position and the like of a defect at a fracture, analyzing the corresponding relation between the defect and the fatigue life, and establishing a fatigue life prediction model related to repairing the defect;
step two: preparation of rotary bending fatigue sample
Selecting a TC17 forging as a test plate base, and performing arc deposition in the test plate by adopting an automatic argon arc welding machine, wherein the welding wire is TC17 in mark and 1.6mm in diameter;
step three: heat treatment of build-up welding sample
Carrying out vacuum heat treatment on the test plate subjected to arc deposition, wherein the heat treatment system comprises the following steps: preserving heat for 4 hours at 550 ℃, and cooling along with the furnace;
step four: a rotation bending fatigue sample is taken by adopting a linear cutting method in the direction, and the rotation bending fatigue sample meeting the standard requirement is processed by adopting a turning and grinding method, so that the interface between the matrix and the accumulation area is ensured to be in the center of the sample;
step five: rotary bending fatigue test
(1) Performing a rotary bending fatigue performance test in a gradient loading mode, wherein the samples are divided into 3 groups, and the initial loading stress is 350MPa, 450MPa and 500MPa respectively;
(2) Setting the number of samples in each group, and performing rotary bending fatigue performance test at stress levels of 350MPa, 470MPa and 500MPa;
step six: fracture analysis and defect statistics
Observing the fracture of the bending fatigue test sample, and performing statistical analysis on the shape, size and position of the defect;
step seven: building a rotary bending fatigue life prediction model
According to the dependence relationship of the yield strength of the material and the parameters such as components, the following general functional form of the fatigue life is selected: sigma N o ＝C
Wherein σ is fatigue stress, and a and C are constants;
considering that the fatigue stress is closely related to the repair of the defect, the shape, position, size, distribution and the like of the defect all affect the service life; considering fatigue stress as a function of flaw size and location only: σ = σ (D, S)
Wherein D and S are the defect position and size respectively;
according to experimental data, under the limitation of nominal fatigue stress and sample macroscopic size, an expression for obtaining the fatigue life by adopting supervised machine learning is as follows:
the life predicted by the model and the actual life, wherein the upper and lower 10% confidence intervals are marked by a dotted line; from the numerical model, a general model of the spin bending fatigue life can be written as follows:
wherein, N and σ o The fatigue life and nominal fatigue stress are respectively, D is the perpendicular distance of the defect from the surface, R is the sample radius, S is the defect size, and the remainder are constants.
The invention has the beneficial effects that:
the invention selects the arc fuse repaired test block to test the rotary bending fatigue performance, observes and counts the appearance, size, position and the like of the defect at the fracture, analyzes the corresponding relation between the defect and the fatigue life, establishes a fatigue life prediction model related to the repaired defect, and can determine the influence of the air hole defect on the fatigue life of the repaired blade through the model. The method is directly applied to welding and laser deposition repair of TC17 material blades and blisk parts, guides the establishment of welding and laser deposition repair quality acceptance standards, and ensures that the performance of the repaired blades meets the fatigue performance requirements. Thereby prolonging the service life of the parts and reducing the replacement and repair cost. The method can be expanded to the fatigue life prediction of the welded and repaired rotor parts made of other materials, and has wide application space and economic benefit.
Drawings
FIG. 1 is a schematic view of an arc deposition sample;
FIG. 2 is a schematic view of a spin-bend specimen;
FIG. 3 is a schematic diagram of a bending fatigue life prediction model and experimental values.
Detailed Description
The invention will be described in more detail below with reference to the embodiments of fig. 1-3.
Example 1
A method for predicting the relation between welding repair defects and fatigue life of a TC17 titanium alloy blade is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: establishing a fatigue life prediction model related to repairing defects
Selecting a test block for repairing an arc fuse, testing the rotary bending fatigue performance of the test block, observing and counting the appearance, size, position and the like of a defect at a fracture, analyzing the corresponding relation between the defect and the fatigue life, and establishing a fatigue life prediction model related to repairing the defect;
step two: preparation of rotary bending fatigue sample
Selecting a TC17 forging as a test plate base, and performing arc deposition in the test plate by adopting an automatic argon arc welding machine, wherein the welding wire is TC17 in mark and has the diameter of 1.2-1.6 mm;
step three: heat treatment of weld deposit specimen
Carrying out vacuum heat treatment on the test plate subjected to arc deposition, wherein the heat treatment system is as follows: keeping the temperature at 550 +/-10 ℃ for 4-4 hours for 10 minutes, and cooling along with the furnace;
step four: a rotation bending fatigue sample is taken by adopting a linear cutting method in the direction, and the rotation bending fatigue sample meeting the standard requirement is processed by adopting a turning and grinding method, so that the interface between the matrix and the accumulation area is ensured to be in the center of the sample;
step five: rotary bending fatigue test
(1) Performing a rotary bending fatigue performance test in a gradient loading mode, wherein the samples are divided into 3 groups, each group of 6 samples have primary loading stresses of 350MPa, 450MPa and 500MPa, and if the samples are not broken for 100 ten thousand times, the stress level is increased to 500MPa for secondary loading;
(2) Performing a rotary bending fatigue performance test by adopting a single stress loading mode, wherein the samples are divided into 2 groups, the loading stress is 470MPa, 500MPa and 470MPa, 6 samples in each group are respectively loaded, and 16 samples in each group is 500MPa;
step six: results of the bending fatigue test
(1) The test results of the gradient loading mode are shown in table 1, the service lives of 6 samples under the 350MPa stress condition reach 100 ten thousand cycles, the service lives of 2 samples under the 450MPa stress condition are less than 100 ten thousand cycles, the service lives of 4 samples under the 500MPa stress condition are less than 100 ten thousand cycles, and the probability of fatigue failure fracture is improved along with the improvement of the stress level;
TABLE 1 gradient loading Room temperature rotational bending fatigue test results
(2) The single stress loading mode result is shown in table 2, the service life of 2 samples under the condition of 470MPa stress is less than 100 ten thousand times, similar to the condition that the initial stress in the gradient loading experiment is 450MPa, and no fracture occurs after 3 samples are circulated to 1000 ten thousand times; only 1 sample is not fractured under the stress condition of 500MPa, the fatigue life of the fractured sample is discretely distributed from about 2 ten thousand to 400 ten thousand, and the volatility is obvious;
TABLE 2 Single stress Loading Room temperature rotational bending fatigue test results
Step seven: fracture analysis and defect statistics
And summarizing the results of the rotary bending fatigue test and the fracture analysis, and counting the shape, the size and the distribution condition of the defects, which are shown in tables 3 and 4. When the defects are positioned on the surface, the fatigue life is obviously lower and generally does not exceed 10 ten thousand weeks; when the defects are located internally, fatigue life is affected by the defect size and distribution.
TABLE 3 gradient loading spin-bending fatigue fracture Defect statistics
TABLE 4 statistics of defects in 500MPa single stress loading rotary bending fatigue fracture
Observing fractures of the gradient loading rotary bending fatigue test sample with the initial stress of 350MPa, and sequencing fatigue life from low to high under 550 MPa; the fractures can be seen as circular air holes and are crack sources, when defects exist in the test sample, cracks can be initiated by requiring the stress level to reach a certain degree, and when the stress is lower than a certain level, namely lower than the fatigue limit, even if certain defects exist, the test sample can be regarded as safe; statistics on the size and the distribution position of the air holes shows that the distribution of the air holes has obvious influence on the fatigue life, the fatigue life of 3 samples with the air holes on the surface is less than 10 ten thousand cycles, and the farther the air holes are from the surface, the larger the size is, the fatigue life of more than 100 ten thousand cycles can be achieved;
the samples which are fractured under 450MPa are all found to have defects on the surfaces of the samples, 3 samples which are fractured under 550MPa have little difference in service life, but the defects are different in distribution, the 450-3# defect is close to the surface but has the size of only about 13 mu m, no defect is found at the 450-4# crack source, the 450-5# air hole has the larger size but exceeds 500 mu m from the surface, so that the defect size is smaller, and the spin bending fatigue life is longer under the condition of being far away from the surface;
most of the gradient loading fatigue test sample with the initial stress of 500MPa breaks under 500MPa, so that the fracture of the gradient loading fatigue test sample with the initial stress of 500MPa and the fracture of the fatigue test sample with the single stress of 500MPa are classified as the same type for analysis; when the air holes are formed on the surface of the sample, the fatigue life is obviously shorter, and the fatigue life is gradually increased along with the reduction of the size of the air holes and the distance from the surface of the sample;
step eight: building a rotary bending fatigue life prediction model
According to the dependence relationship of the yield strength of the material and the parameters such as components, the following general functional form of the fatigue life is selected: sigma N o ＝C
Wherein σ is fatigue stress, a and C are constants;
considering that the fatigue stress is closely related to the repair of the defect, the shape, position, size, distribution and the like of the defect all affect the service life; considering fatigue stress as a function of flaw size and location only: σ = σ (D, S)
Wherein D and S are the defect position and size respectively;
according to experimental data, under the limitation of nominal fatigue stress and sample macroscopic size, the expression of obtaining the fatigue life by adopting supervised machine learning is as follows:
the life predicted by the model and the actual life, wherein the upper and lower 10% confidence intervals are marked by a dotted line; from the numerical model, a general model of the spin bending fatigue life can be written as follows:
wherein, N and σ o Fatigue life and nominal fatigue stress, respectively, D is the perpendicular distance of the defect from the surface, R is the sample radius, S is the defect size, and the remainder are constants.
Claims (2)
1. A method for predicting the relation between welding repair defects and fatigue life of a TC17 titanium alloy blade is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: establishing a fatigue life prediction model related to repairing defects
Selecting a test block for repairing an arc fuse, testing the rotary bending fatigue performance of the test block, observing and counting the appearance, size, position and the like of a defect at a fracture, analyzing the corresponding relation between the defect and the fatigue life, and establishing a fatigue life prediction model related to repairing the defect;
step two: preparation of rotary bending fatigue sample
Selecting a TC17 forging as a test plate base, and performing arc deposition in the test plate by adopting an automatic argon arc welding machine, wherein the welding wire is TC17 in mark and 1.6mm in diameter;
step three: heat treatment of build-up welding sample
Carrying out vacuum heat treatment on the test plate subjected to arc deposition, wherein the heat treatment system is as follows: preserving heat for 4 hours at 550 ℃, and cooling along with the furnace;
step four: a rotation bending fatigue sample is taken by adopting a linear cutting method in the direction, and the rotation bending fatigue sample meeting the standard requirement is processed by adopting a turning and grinding method, so that the interface between the matrix and the accumulation area is ensured to be in the center of the sample;
step five: rotary bending fatigue test
(1) Performing a rotary bending fatigue performance test in a gradient loading mode, wherein the samples are divided into 3 groups, and the initial loading stress is 350MPa, 450MPa and 500MPa respectively;
(2) Setting the number of samples, and performing rotary bending fatigue performance test at stress levels of 350MPa, 470MPa and 500MPa;
step six: fracture analysis and defect statistics
Observing the fracture of the bending fatigue test sample, and performing statistical analysis on the shape, size and position of the defect;
step seven: building a rotary bending fatigue life prediction model
According to experimental data, an expression of the fatigue life is obtained by adopting supervised machine learning in terms of nominal fatigue stress and sample macroscopic size, and a general model of the rotary bending fatigue life is established according to a numerical model.
2. The TC17 titanium alloy blade of claim 1, wherein the weld repair defect is associated with fatigue lifeA method for predicting a system, comprising: and seventhly, selecting the following general function form of the fatigue life according to the dependence of the yield strength of the material and the parameters such as components and the like:
wherein σ is fatigue stress, and a and C are constants;
considering that the fatigue stress is closely related to the repair of the defect, the shape, position, size, distribution and the like of the defect all affect the service life; considering fatigue stress as a function of flaw size and location only: σ = σ (D, S)
Wherein D and S are the defect position and size respectively;
according to experimental data, under the limitation of nominal fatigue stress and sample macroscopic size, the expression of obtaining the fatigue life by adopting supervised machine learning is as follows:
LogV＝10.0214-1.33389Log[1-D/R]-1.09829Log[S]
wherein R is the radius of the rotary-bent sample, D is the distance between the defect and the surface of the sample, and S is the diameter of the defect;
the life predicted by the model and the actual life, wherein the dotted line marks the upper and lower 10% confidence intervals; from the numerical model, a general model of the spin bending fatigue life can be written as follows:
wherein, N and σ o Fatigue life and nominal fatigue stress, respectively, D is the perpendicular distance of the defect from the surface, R is the sample radius, S is the defect size, and the remainder are constants.
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| CN117373580A (en) * | 2023-12-05 | 2024-01-09 | 宝鸡富士特钛业（集团）有限公司 | Performance analysis method and system for realizing titanium alloy product based on time sequence network |
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| CN117373580A (en) * | 2023-12-05 | 2024-01-09 | 宝鸡富士特钛业（集团）有限公司 | Performance analysis method and system for realizing titanium alloy product based on time sequence network |
| CN117373580B (en) * | 2023-12-05 | 2024-03-08 | 宝鸡富士特钛业（集团）有限公司 | Performance analysis method and system for realizing titanium alloy product based on time sequence network |
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