CN107315910A - The lossless detection method of iron copper series alloy heat ageing state estimation - Google Patents
The lossless detection method of iron copper series alloy heat ageing state estimation Download PDFInfo
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- CN107315910A CN107315910A CN201710464912.8A CN201710464912A CN107315910A CN 107315910 A CN107315910 A CN 107315910A CN 201710464912 A CN201710464912 A CN 201710464912A CN 107315910 A CN107315910 A CN 107315910A
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- series alloy
- iron copper
- copper series
- heat ageing
- detection method
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/022—Environment of the test
- G01N2203/0222—Temperature
- G01N2203/0226—High temperature; Heating means
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/06—Indicating or recording means; Sensing means
- G01N2203/067—Parameter measured for estimating the property
- G01N2203/0676—Force, weight, load, energy, speed or acceleration
Abstract
The invention discloses a kind of lossless detection method of iron copper series alloy heat ageing state estimation, it comprises the following steps：Test and record the barkhausen noise of iron copper series alloy part exemplar each active time point under the high temperature conditions（Magnetic Barkhausen Noise abbreviations MBN）Signal value and mechanical properties value, MBN signal values and the algorithm model and active time and the algorithm model of mechanical property of active time are set up respectively, the MBN signals of the iron copper series alloy then set up and the MBN signal values of mechanical performance index algorithm model last test part to be measured, substituted into the algorithm model set up in previous step step, calculation obtains the assessment foundation of material heat ageing state.This method can in operational outfit surface direct measurement, with it is quick, lossless and efficient the characteristics of, while detection process equipment can significantly reduce cost without shutting down, improve economy.The other technology can also realize the on-line monitoring of equipment state, so that it is guaranteed that security.
Description
Technical field
The present invention relates to a kind of field of metal material detection, more particularly to a kind of iron copper series alloy heat ageing state estimation
Lossless detection method.
Background technology
Iron copper (Fe-Cu) is that alloy has excellent mechanical property and good formability, is a kind of reliable structural wood
Material, is widely used in nuclear industry, auto industry and chemical pipeline field.However, solid solubility is extremely low in Fe matrixes due to Cu,
According to Fe-Cu binary phase diagramls, Cu atoms are substantially insoluble in α-Fe at room temperature, and at 660 DEG C and 370 DEG C, solid solubility is respectively
0.46% and 0.016%.Therefore, Fe-Cu systems alloy component longtime running under the conditions of high temperature (>=300 DEG C), it may occur that rich Cu
The precipitation of phase, so as to cause material aging, is mainly shown as hardness increase, toughness declines, the possibility of brittle fracture in performance
Increase, so safety to equipment and life-span cause to have a strong impact on.
However, the precipitation of richness Cu phases belong to material microstructure in terms of change rather than gross imperfection, therefore, it is existing it is ripe,
General nondestructiving detecting means are (for example：Ray, ultrasound, vortex etc.) can not be effectively to its examinations.Often need to carry out
Pipe cutting performance test, or even the method for laboratory simulation obtain its ageing state.Equipment needs to shut down during above-mentioned test implementation
Maintenance, time between overhauls(TBO) length, cost are high；Simultaneously often there is certain deviation in laboratory simulation mode testing result and reality.Therefore,
It is necessary to develop a kind of lossless detection method suitable for Fe-Cu systems alloy heat ageing state estimation.
The content of the invention
It is an object of the invention to provide a kind of lossless detection method of iron copper series alloy heat ageing state estimation.
In order to solve the above technical problems, the present invention is adopted the following technical scheme that：A kind of iron copper series alloy heat ageing state is commented
The lossless detection method estimated, it comprises the following steps：
A. test and record the barkhausen noise of iron copper series alloy part exemplar each active time point under the high temperature conditions
Signal value, then sets up barkhausen noise signal value and the algorithm model of active time；
B. test and record iron copper series alloy part exemplar under the high temperature conditions with each active time point identical in step a
The mechanical properties value at time point, then sets up the algorithm model of active time and mechanical property；
C. according to the barkhausen noise signal and mechanical performance index algorithm mould of step a and b the iron copper series alloy set up
Type；
D. the barkhausen noise signal value of iron copper series alloy part part to be measured is tested and recorded, is substituted into step c
In the algorithm model of foundation, calculation obtains the mechanical performance index under sample corresponding temperature and active time and old as material heat
The assessment foundation of change state.
Optimization, the order of step a, b is in no particular order.
Optimization, the barkhausen noise signal of institute's test sample part and part to be measured is electric signal in step a, d, using spy
The mode of value indicative is characterized to it.
Further, the barkhausen noise signal of institute's test sample part and part to be measured uses for electric signal in step a, d
Root mean square is used as barkhausen noise signal characteristic valueV in formulaRMSFor MBN signal RMS values, Vi is sampling
The signal value of point.
Optimization, mechanical property can choose corresponding index according to focal point in step c, mainly include：Hardness, surrender
Intensity, ballistic work.
Optimization, step a, b, d are carried out at a temperature of being not less than 70 DEG C.
Optimization, the active time point in step a, b, d is respectively 0,0.1h, 0.2h, 1h, 2h, 10h, 50h, 120h,
312h。
The beneficial effects of the present invention are：By Barkhausen noise, (Magnetic Barkhausen Noise are referred to as
MBN) the foundation of signal and material mechanical performance model, is alloy examination to the iron copper (Fe-Cu) for needing to carry out ageing state assessment
Sample only needs the measurement of MBN values, and the value surveyed is substituted into the model that step d is set up, you can rapidly obtain corresponding mechanical property
Energy index, so that the assessment for material heat ageing state provides foundation.Compared to existing pipe cutting performance test, laboratory simulation etc.
Technology above method can in operational outfit surface direct measurement, with it is quick, lossless and efficient the characteristics of, while detection process is set
It is standby to significantly reduce cost without shutting down, improve economy.The other technology can also realize the on-line monitoring of equipment state,
So that it is guaranteed that security.
Brief description of the drawings
Accompanying drawing 1 is VRMSWith thermal aging time change curve；
Accompanying drawing 2 is change curves of the HV with thermal aging time；
Accompanying drawing 3 is HV~VRMS matched curves.
Embodiment
The lossless detection method of iron copper series alloy heat ageing state estimation, it comprises the following steps：
A. test and record the barkhausen noise of iron copper series alloy part exemplar each active time point under the high temperature conditions
Signal value, then sets up barkhausen noise signal value and the algorithm model of active time；
B. test and record iron copper series alloy part exemplar under the high temperature conditions with each active time point identical in step a
The mechanical properties value at time point, then sets up the algorithm model of active time and mechanical property；
C. according to the barkhausen noise signal and mechanical performance index algorithm mould of step a and b the iron copper series alloy set up
Type；
D. the barkhausen noise signal value of iron copper series alloy part part to be measured is tested and recorded, is substituted into step c
In the algorithm model of foundation, calculation obtains the mechanical performance index under sample corresponding temperature and active time and old as material heat
The assessment foundation of change state.
The order of step a, b is in no particular order.The barkhausen noise of institute's test sample part and part to be measured in step a, d
Signal is electric signal, and it is characterized by the way of characteristic value.The Bark of institute's test sample part and part to be measured in step a, d
The gloomy noise signal of person of outstanding talent is special as barkhausen noise signal using root mean square (Root Meam Square abbreviation RMS) for electric signal
Value indicativeV in formulaRMSFor MBN signal RMS values, Vi is the signal value of sampled point.
Mechanical property can choose corresponding index according to focal point in step c, mainly include：Hardness, yield strength, punching
Hit work(.
Step a, b, d are carried out at a temperature of being not less than 70 DEG C.Active time point in step a, b, d is respectively 0,
0.1h, 0.2h, 1h, 2h, 10h, 50h, 120h, 312h.
Shown embodiment is described in detail below to the present invention below in conjunction with the accompanying drawings：
Heat ageing state of the Fe-1.0wt%Cu alloys under 550 DEG C of hot conditions is estimated in the present embodiment, walked
It is rapid as follows：
1) it is respectively 0,0.1h, 0.2h to choose the heat ageing period, and 1h, 2h, 10h, 50h, 120h, 312h alloy sample enters
Row MBN signal detections, set up the relation curve of MBN signal characteristics value and thermal aging time, as shown in Figure 1.
2) Mechanics Performance Testing is carried out to above-mentioned alloy sample, Vickers hardness (HV) is chosen herein and is characterized as mechanical property
Parameter, sets up HV and the relation curve of thermal aging time, as shown in Figure 2.
1) and 2) 3) according to MBN signals and HV measured in, MBN signal values and HV algorithm models are set up, such as Fig. 3 institutes
Show.It can thus be appreciated that HV~VRMS relations are as follows：
HV1=355.16-2226.45VRMS(t≤2h) (3-1)
HV2=553.33-4551.65VRMS(t ＞ 2h) (3-2)
4) carry out MBN signal characteristics value to required beta alloy sample to test, and 3) correspondence is substituted into according to thermal aging time
Computation model be the mechanical property HV value sizes that can obtain measured sample, and for material heat ageing state assessment provide according to
According to.
For example:Thermal aging time measures VRMS=0.0866V for 1.5h alloy sample, and its applicable computation model is 3-
1, then HV1=355.16-2226.48 × 0.0866=162；Thermal aging time measures VRMS=for 100h alloy sample
0.0948V, its applicable computation model is 3-2, then HV2=553.33-4551.65 × 0.0948=122.
The above embodiments merely illustrate the technical concept and features of the present invention, and its object is to allow person skilled in the art
Scholar can understand present disclosure and implement according to this, and it is not intended to limit the scope of the present invention.It is all according to the present invention
The equivalent change or modification that spirit is made, should all be included within the scope of the present invention.
Claims (7)
1. a kind of lossless detection method of iron copper series alloy heat ageing state estimation, it is characterised in that it comprises the following steps：
A. test and record the barkhausen noise signal of iron copper series alloy part exemplar each active time point under the high temperature conditions
Value, then sets up barkhausen noise signal value and the algorithm model of active time；
B. test and record iron copper series alloy part exemplar under the high temperature conditions with each active time point identical time in step a
The mechanical properties value of point, then sets up the algorithm model of active time and mechanical property；
C. according to the barkhausen noise signal and mechanical performance index algorithm model of step a and b the iron copper series alloy set up；
D. the barkhausen noise signal value of iron copper series alloy part part to be measured is tested and recorded, is substituted into step c and set up
Algorithm model in, calculation obtains mechanical performance index under sample corresponding temperature and active time and as material heat ageing shape
The assessment foundation of state.
2. the lossless detection method of iron copper series alloy heat ageing state estimation according to claim 1, it is characterised in that：Institute
State the order of step a, b in no particular order.
3. the lossless detection method of iron copper series alloy heat ageing state estimation according to claim 1, it is characterised in that：Institute
The barkhausen noise signal for stating institute's test sample part and part to be measured in step a, d is electric signal, and it is entered by the way of characteristic value
Row is characterized.
4. the lossless detection method of iron copper series alloy heat ageing state estimation according to claim 3, it is characterised in that：Institute
The barkhausen noise signal of institute's test sample part and part to be measured in step a, d is stated to make an uproar as Barkhausen using root mean square for electric signal
Message characteristic valueV in formulaRMSFor MBN signal RMS values, Vi is the signal value of sampled point.
5. the lossless detection method of iron copper series alloy heat ageing state estimation according to claim 1, it is characterised in that：Step
Mechanical property can choose corresponding index according to focal point in rapid c, mainly include：Hardness, yield strength, ballistic work.
6. the lossless detection method of iron copper series alloy heat ageing state estimation according to claim 1, it is characterised in that：Step
Rapid a, b, d are carried out at a temperature of being not less than 70 DEG C.
7. the lossless detection method of iron copper series alloy heat ageing state estimation according to claim 1, it is characterised in that：Step
Active time point in rapid a, b, d is respectively 0,0.1h, 0.2h, 1h, 2h, 10h, 50h, 120h, 312h.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108959175A (en) * | 2018-05-30 | 2018-12-07 | 南京航空航天大学 | A kind of ferrimagnet yield strength successive Regression estimation method based on MBN |
| CN109409271A (en) * | 2018-10-16 | 2019-03-01 | 北京工业大学 | Testing of Ferromagnetic Material Hardness prediction algorithm based on BP neural network innovatory algorithm |
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| CN1194376A (en) * | 1997-01-10 | 1998-09-30 | 新日本制铁株式会社 | Diagnostic method for steel structure fatigue life and iron and steel parts with life diagnostic function |
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| CN103323304A (en) * | 2013-05-23 | 2013-09-25 | 中国航空工业集团公司北京航空材料研究院 | Making method of standard samples for verifying heat injury Barkhausen detection sensitivity |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108959175A (en) * | 2018-05-30 | 2018-12-07 | 南京航空航天大学 | A kind of ferrimagnet yield strength successive Regression estimation method based on MBN |
| CN109409271A (en) * | 2018-10-16 | 2019-03-01 | 北京工业大学 | Testing of Ferromagnetic Material Hardness prediction algorithm based on BP neural network innovatory algorithm |
| CN109409271B (en) * | 2018-10-16 | 2022-03-11 | 北京工业大学 | Ferromagnetic material hardness prediction algorithm based on BP neural network improved algorithm |
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