CN104777218A - Method for determining ferromagnetic material crack generation by metal magnetic memory detection technology - Google Patents
Method for determining ferromagnetic material crack generation by metal magnetic memory detection technology Download PDFInfo
- Publication number
- CN104777218A CN104777218A CN201410018661.7A CN201410018661A CN104777218A CN 104777218 A CN104777218 A CN 104777218A CN 201410018661 A CN201410018661 A CN 201410018661A CN 104777218 A CN104777218 A CN 104777218A
- Authority
- CN
- China
- Prior art keywords
- magnetic memory
- memory signal
- loading
- metal magnetic
- ferromagnetic material
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
- Investigating Or Analyzing Materials By The Use Of Magnetic Means (AREA)
Abstract
The invention discloses a method for determining ferromagnetic material crack generation by a metal magnetic memory detection technology. The method comprises the following steps of testing a sample under fatigue load to detect metal magnetic memory signals of the surface of the sample under the condition of different fatigue cycle frequencies, acquiring a change rule of cycle number and the difference between a wave peak value and a wave trough value of a magnetic memory signal curve by the difference, subtracting magnetic memory signals of initial points from the metal magnetic memory signals of the surface of the sample, and extracting a change rule of the maximum of the magnetic memory signal gradient and cycle number, wherein if the above change curves produce almost linear reduction, the reduction inflection points of the two curves show that ferromagnetic material cracks are produced. Through combination of the metal magnetic memory detection technology, a fatigue load dynamic stress concentration factor and fracture damage mechanics, the method solves the problem that the existing nondestructive test technology cannot forecast crack generation, realizes processing on the magnetic memory signal, effectively eliminates various interferences and has high discrimination accuracy.
Description
Technical field
The invention belongs to ferromagnetic metal material field of non destructive testing, more particularly, that one is concentrated based on metal magnetic memory signal and parameter variation characteristic detection component internal stress thereof, characterize the method for crack propagation process and component damage degree, belong to metal magnetic memory test field in Non-Destructive Testing.
Background technology
Ferromagnetic material, due to its premium properties, has been widely used in the welded structures such as aviation, railway, pipeline, power station, pressure vessel, petroleum engineering, and constantly to maximizing and the future development of high parameter.In the structure of bearing alternate load effect for a long time, fatigue failure is a kind of main failure mode.In fatigue process, stress is concentrated can cause crackle, burn into creep, is the main source causing fatigue break.Fatigue Fracture Process can be divided into the germinating of crackle, the stable expansion of crackle and unstable fracture three processes.Because stress during fatigue break is much smaller than the strength degree under material static load, and rupturing suddenly when there is no obvious plastic yield, often causing catastrophic consequence, therefore the research of crack detection/monitoring is carried out to existing members significant.
Metal magnetic memory detection technology is a kind of new damage detecting method proposed in 1997 by Russian scholar Dubov.The physical basis of metal magnetic memory detecting method is magneto-mechanical effect, be in ferromagnetic component under ground magnetic environment by the effect of operating load, it is directed with irreversible reorientation that its inside can have the magnetic domain tissue of magnetoelastic properties, known by theoretical analysis, at the stray field tangential component H that stress is formed with distortion concentration zones
px () has maximal value, normal component H
p(y) reindexing and there is zero point.Magnetic state irreversible change under this effect of stress is eliminated follow-up continuation of insurance in load and is stayed, thus by stray field normal component H
py the mensuration of (), just deducibility workpiece stress is concentrated and damage location.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, a kind of method utilizing metal magnetic memory detection technology to differentiate ferromagnetic material crack germinating is provided, namely utilizes metal magnetic memory signal and characteristic parameter thereof the breach ferromagnetic material crack under fatigue load effect to be germinated to the method characterized.
Technical purpose of the present invention is achieved by following technical proposals:
Utilize metal magnetic memory detection technology to differentiate a method for ferromagnetic material crack germinating, carry out according to following step:
Step 1, by specimen clamping on fatigue tester, along surface of test piece Measurement channel test samples without load time magnetic memory signal, the initial magnetic memory signal namely recorded when not loading (the normal component H of stray field
p(y));
Step 2, after recording initial magnetic memory signal, setting loading parameters, described loading parameters remains unchanged in whole test process, and such as setting Loaded contact analysis is square wave, and maximum load is 120KN(200MPa), stress ratio is 0.5, and loading frequency is 3Hz;
Step 3, in loading procedure, stops when selecting different CYCLIC LOADING number of times loading, and uses metal magnetic memory testing instrument along magnetic memory signal (the i.e. normal component H of stray field of surface of test piece Measurement channel test samples under different CYCLIC LOADING number of times
p(y)), select until be loaded on sample fracture;
Specifically, stop loading during every CYCLIC LOADING 1000 times when selecting to start, observe sample with low power magnifier and whether occur crackle, with its magnetic memory signal of metal magnetic memory testing instrument on-line measurement, continue to load, after macroscopic cracking occurs, every CYCLIC LOADING stops for 500 times loading, and on-line measurement magnetic memory signal, continue to load, until test specimen fracture.
Step 4, the maximum crest value H in the magnetic memory signal curve measured under different CYCLIC LOADING number of times
p(y)
maxwith minimum crest value H
p(y)
mindiffer from, by maximum crest value and minimum crest value difference H
p(y)
subwith corresponding different CYCLIC LOADING number of times mapping, obtain the first change curve;
Step 5, the initial magnetic memory signal (the initial magnetic memory signal recorded when not loading) recorded when the magnetic memory signal curve measured under different CYCLIC LOADING number of times is deducted respectively and do not load, and the magnetic memory signal maximum of gradients K of the magnetic memory signal measured under extracting above-mentioned different CYCLIC LOADING number of times after treatment
maxmap from different CYCLIC LOADING number of times, obtain the second change curve;
Step 6, when there is linear reduction point according to the first change curve and the second change curve, can judge that crackle has germinated; First change curve and the second change curve incipient stage, maximum crest value and minimum crest value difference H
p(y)
subwith magnetic memory signal maximum of gradients K
maxchange under different CYCLIC LOADING number of times is less, when two change curves start to occur " linear reduce (minimizing) point ", namely maximum crest value and minimum crest value difference H
p(y)
subwith magnetic memory signal maximum of gradients K
max, present negative correlation with the increase of circulation cycle, namely start to occur reducing along with the increase of cycle index, namely now corresponding loaded cycle number of times can be crackle and produces corresponding circulation cycle.
In technical solution of the present invention, the dynamic stress concentration of metal magnetic memory detection technology and fatigue load and fracture damage mechanics are combined, magnetic memory signal method phase component is processed, extract the situation of change of its maxima and minima difference and signal its gradient and cycle index after treatment as the variable of ferromagnetic material under fatigue load effect, set up a kind of characterizing method for crack initiation under band breach ferromagnetic material fatigue load utilizing metal magnetic memory detection technology, compared with prior art, the inventive method has the following advantages: (1) overcomes existing Dynamic Non-Destruction Measurement cannot to the shortcoming of crack initiation prediction, propose the method utilizing metal magnetic memory technique detection/monitoring crack to germinate, (2) magnetic memory signal is processed, effectively remove all kinds of obstacles, differentiate that accuracy rate is high.
Accompanying drawing explanation
Fig. 1 is the ferromagnetic material sample schematic diagram used in the embodiment of the present invention, and wherein following (mm) a of each size marked is 400, b be 30, c be 50, d be 80, e be 60, f is 50.
Fig. 2 is in the embodiment of the present invention, the magnetic memory signal of sample test before not having to record.
The magnetic memory signal curve of sample when Fig. 3 is CYCLIC LOADING 12000 times.
Fig. 4 be maximum crest value and minimum crest value difference H
p(y)
subwith loading number of times change curve, horizontal ordinate is loaded cycle cycle, and ordinate is under respective cycle cycle, peak-to-peak value H in Measurement channel
p(y)
sub.
Fig. 5 is the magnetic memory signal curve of the magnetic memory signal of each point when deducting initial of the magnetic memory signal under each times of fatigue.
Fig. 6 is magnetic memory signal maximum of gradients K
maxwith the change curve of circulation cycle, wherein horizontal ordinate is loaded cycle cycle, and ordinate is the magnetic memory signal maximum of gradients K under respective cycle cycle
max.
Embodiment
Technical scheme of the present invention is further illustrated below in conjunction with specific embodiment.In the present embodiment, with notched ferromagnetic material sample for research object, low cycle fatigue loading is applied to it, stop loading and measuring its surperficial magnetic memory signal under different fatigue number of times.
Use the sample that ferromagnetic material sample as shown in Figure 1 uses as embodiment, wherein (mm): a is 400, b to each size marked as follows be 30, c be 50, d be 80, e be 60, f is 50.
First by specimen clamping on fatigue tester, use TSC-1M-4 type metal magnetic memory testing instrument along surface of test piece Measurement channel test samples without magnetic memory signal when loading, namely at t
0initial magnetic memory signal (the i.e. normal component H of stray field that moment records
p(y)), as shown in Figure 2.
After recording initial magnetic memory signal, setting loading parameters, described loading parameters remains unchanged in whole test process, and setting Loaded contact analysis is square wave, and maximum load is 120KN(200MPa), stress ratio is 0.5, and loading frequency is 3Hz.
In loading procedure, select different CYCLIC LOADING number of times to stop loading, use metal magnetic memory testing instrument along magnetic memory signal (the i.e. normal component H of stray field of surface of test piece Measurement channel test samples at different CYCLIC LOADING number of times
p(y)), magnetic memory signal curve when being CYCLIC LOADING 12000 times as shown in Figure 3, selects until be loaded on sample fracture.
In specific implementation process, after loading, stop during every CYCLIC LOADING 1000 times loading, observe sample with low power magnifier and whether occur crackle, with its magnetic memory signal of metal magnetic memory testing instrument on-line measurement, continue to load, after macroscopic cracking occurs, every CYCLIC LOADING stops for 500 times loading, and on-line measurement magnetic memory signal, continue to load, until test specimen fracture.
The magnetic memory signal recorded under different CYCLIC LOADING number of times shows as the magnetic memory signal curve in corresponding moment, and horizontal ordinate is Measurement channel, i.e. Measurement channel shown in accompanying drawing 1; Ordinate is magnetic memory signal intensity, i.e. the normal component H of stray field
p(y).The magnetic memory signal curve of synchronization shows as the curve waveform with crest, trough, by the maximum crest value H of now this curve waveform
p(y)
maxwith minimum crest value H
p(y)
mindiffer from, namely maximum crest value and minimum crest value difference H
p(y)
sub, that is:
H
p(y)
sub=H
p(y)
max-H
p(y)
min
By maximum crest value and minimum crest value difference H
p(y)
sub, and the loading number of times of corresponding different CYCLIC LOADING is figure, as shown in Figure 4, horizontal ordinate is loaded cycle cycle, and ordinate is under corresponding record circulation cycle, maximum crest value H in Measurement channel
p(y)
maxwith minimum crest value H
p(y)
mindifference, i.e. peak-to-peak value H
p(y)
sub.
The magnetic memory signal recorded when the magnetic memory signal curve measured under different CYCLIC LOADING number of times is deducted respectively and do not loaded is (at t
0the initial magnetic memory signal that moment records), as shown in Figure 5.The magnetic memory signal maximum of gradients K of the magnetic memory signal measured under extracting above-mentioned different CYCLIC LOADING number of times after treatment
maxfrom the Changing Pattern of different CYCLIC LOADING number of times, as shown in Figure 6, wherein magnetic memory signal Grad K:
K=ΔH
p(y)/Δx
In formula: Δ H
py () is H between two check points adjacent on magnetic memory signal detection line
p(y) difference; Δ x is the distance between adjacent two magnetic memory signal check points.
Maximum crest value and minimum crest value difference H
p(y)
subwith change curve, the magnetic memory signal maximum of gradients K of CYCLIC LOADING number of times (namely circulate cycle)
maxwhen all occurring linearly reducing point with the change curve of circulation cycle, can judge that crackle germinates, circulation cycle (being about 16000) corresponding to the position that shown in accompanying drawing 4 and 6, two change curves start to occur " linear reduce (minimizings) point " is the circulation cycle that crackle produces correspondence.
Above to invention has been exemplary description; should be noted that; when not departing from core of the present invention, any simple distortion, amendment or other those skilled in the art can not spend the equivalent replacement of creative work all to fall into protection scope of the present invention.
Claims (5)
1. utilize metal magnetic memory detection technology to differentiate a method for ferromagnetic material crack germinating, it is characterized in that, carry out according to following step:
Step 1, by specimen clamping on fatigue tester, along surface of test piece Measurement channel test samples without load time magnetic memory signal, the initial magnetic memory signal namely recorded when not loading, i.e. the normal component H of stray field
p(y);
Step 2, after recording initial magnetic memory signal, setting loading parameters, described loading parameters remains unchanged in whole test process;
Step 3, in loading procedure, stops when selecting different CYCLIC LOADING number of times loading, and uses metal magnetic memory testing instrument along the magnetic memory signal of surface of test piece Measurement channel test samples under different CYCLIC LOADING number of times, i.e. the normal component H of stray field
p(y);
Step 4, the maximum crest value H in the magnetic memory signal curve measured under different CYCLIC LOADING number of times
p(y)
maxwith minimum crest value H
p(y)
mindiffer from, by maximum crest value and minimum crest value difference H
p(y)
subwith corresponding different CYCLIC LOADING number of times mapping, obtain the first change curve;
Step 5, the initial magnetic memory signal (the initial magnetic memory signal recorded when not loading) recorded when the magnetic memory signal curve measured under different CYCLIC LOADING number of times is deducted respectively and do not load, and the magnetic memory signal maximum of gradients K of the magnetic memory signal measured under extracting above-mentioned different CYCLIC LOADING number of times after treatment
maxmap from different CYCLIC LOADING number of times, obtain the second change curve;
Step 6, when there is linear reduction point according to the first change curve and the second change curve, can judge that crackle germinates.
2. a kind of method utilizing metal magnetic memory detection technology to differentiate ferromagnetic material crack germinating according to claim 1, it is characterized in that, described loading parameters is square wave for setting Loaded contact analysis, and maximum load is 120KN, stress ratio is 0.5, and loading frequency is 3Hz.
3. a kind of method utilizing metal magnetic memory detection technology to differentiate ferromagnetic material crack germinating according to claim 1, is characterized in that, select until be loaded on sample fracture in whole test process.
4. a kind of method utilizing metal magnetic memory detection technology to differentiate ferromagnetic material crack germinating according to claim 1, it is characterized in that, stop loading during every CYCLIC LOADING 1000 times when selecting to start, observe sample with low power magnifier and whether occur crackle, with its magnetic memory signal of metal magnetic memory testing instrument on-line measurement, continue to load, after macroscopic cracking occurs, every CYCLIC LOADING stops for 500 times loading, and on-line measurement magnetic memory signal, continue to load, until test specimen fracture.
5. a kind of method utilizing metal magnetic memory detection technology to differentiate ferromagnetic material crack germinating according to claim 1, is characterized in that, the first change curve and the second change curve incipient stage, maximum crest value and minimum crest value difference H
p(y)
subwith magnetic memory signal maximum of gradients K
maxchange under different CYCLIC LOADING number of times is less, when two change curves start to occur " linear reduce (minimizing) point ", namely maximum crest value and minimum crest value difference H
p(y)
subwith magnetic memory signal maximum of gradients K
max, present negative correlation with the increase of circulation cycle, namely start to occur reducing along with the increase of cycle index, namely now corresponding loaded cycle number of times can be crackle and produces corresponding circulation cycle.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410018661.7A CN104777218A (en) | 2014-01-15 | 2014-01-15 | Method for determining ferromagnetic material crack generation by metal magnetic memory detection technology |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410018661.7A CN104777218A (en) | 2014-01-15 | 2014-01-15 | Method for determining ferromagnetic material crack generation by metal magnetic memory detection technology |
Publications (1)
Publication Number | Publication Date |
---|---|
CN104777218A true CN104777218A (en) | 2015-07-15 |
Family
ID=53618819
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201410018661.7A Pending CN104777218A (en) | 2014-01-15 | 2014-01-15 | Method for determining ferromagnetic material crack generation by metal magnetic memory detection technology |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104777218A (en) |
Cited By (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105353031A (en) * | 2015-12-14 | 2016-02-24 | 中国矿业大学 | Mining vibrating screen magnetic memory detection device and method |
CN108362768A (en) * | 2018-03-01 | 2018-08-03 | 沈阳工业大学 | A kind of non-contact weak magnetic detection method of stress |
CN108362766A (en) * | 2018-03-01 | 2018-08-03 | 沈阳工业大学 | The non-contact weak magnetic detection method of crack initiation area stress |
CN108362769A (en) * | 2018-03-01 | 2018-08-03 | 沈阳工业大学 | A kind of non-contact weak magnetic detection method of crack initiation area stress |
CN108763664A (en) * | 2018-05-11 | 2018-11-06 | 沈阳工业大学 | Based on ultra-soft pseudo potential weld seam magnetic memory signal characteristic detection method |
CN108875135A (en) * | 2018-05-11 | 2018-11-23 | 沈阳工业大学 | A kind of weld seam magnetic memory signal characteristic recognition method |
CN110308043A (en) * | 2019-07-29 | 2019-10-08 | 黑龙江科技大学 | Increasing material manufacturing product earlier damage evaluation method based on metal magnetic memory test |
CN111289608A (en) * | 2020-03-23 | 2020-06-16 | 江苏科技大学 | Method for evaluating welding residual stress |
CN112504833A (en) * | 2020-12-03 | 2021-03-16 | 南昌航空大学 | On-line monitoring method for fatigue crack damage propagation of sintered neodymium iron boron |
CN112507599A (en) * | 2020-11-11 | 2021-03-16 | 南昌航空大学 | Neodymium iron boron magnet crack fracture prediction method based on particle filter algorithm |
CN113281401A (en) * | 2021-04-29 | 2021-08-20 | 同济大学 | Detection method, system and device for hidden diseases of ballastless track |
CN114441624A (en) * | 2022-01-31 | 2022-05-06 | 烟台大学 | Small metal magnetic memory crack detection method |
CN115166024A (en) * | 2022-08-18 | 2022-10-11 | 合肥工业大学 | Method for detecting damage degree of heterogeneous metal coating joint surface through magnetic memory |
CN115219584A (en) * | 2022-07-20 | 2022-10-21 | 江西理工大学 | Metal magnetic memory monitoring and evaluating method for ferromagnetic material |
CN117268961A (en) * | 2023-11-23 | 2023-12-22 | 宁波市特种设备检验研究院 | Fatigue failure early warning method for metal parts |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2000019161A (en) * | 1998-07-06 | 2000-01-21 | Nkk Corp | Fatigue crack progress direction predicting method |
JP2004245815A (en) * | 2002-12-19 | 2004-09-02 | Iwate Univ | Nondestructive measuring method for aged deterioration of strength of austenitic stainless steel |
CN1948961A (en) * | 2005-10-10 | 2007-04-18 | 中国石油天然气集团公司 | Metal magnetic memory detecting method of ferromagnetic material crack |
CN101393167A (en) * | 2008-10-21 | 2009-03-25 | 中国航空工业第一集团公司北京航空材料研究院 | Low-cycle fatigue damage quantitatively characterizing method based on metal magnetic memory detection technology |
CN102841135A (en) * | 2012-09-14 | 2012-12-26 | 天津大学 | Characterization method for welding crack expansion process based on metal magnetic memory detection technology |
-
2014
- 2014-01-15 CN CN201410018661.7A patent/CN104777218A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2000019161A (en) * | 1998-07-06 | 2000-01-21 | Nkk Corp | Fatigue crack progress direction predicting method |
JP2004245815A (en) * | 2002-12-19 | 2004-09-02 | Iwate Univ | Nondestructive measuring method for aged deterioration of strength of austenitic stainless steel |
CN1948961A (en) * | 2005-10-10 | 2007-04-18 | 中国石油天然气集团公司 | Metal magnetic memory detecting method of ferromagnetic material crack |
CN101393167A (en) * | 2008-10-21 | 2009-03-25 | 中国航空工业第一集团公司北京航空材料研究院 | Low-cycle fatigue damage quantitatively characterizing method based on metal magnetic memory detection technology |
CN102841135A (en) * | 2012-09-14 | 2012-12-26 | 天津大学 | Characterization method for welding crack expansion process based on metal magnetic memory detection technology |
Non-Patent Citations (2)
Title |
---|
刘昌奎 等: "构件低周疲劳损伤的金属磁记忆检测试验研究", 《航空材料学报》 * |
王文绚: "铁磁性材料裂纹萌生与扩展的金属磁记忆检测技术研究", 《万方学位论文数据库》 * |
Cited By (26)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105353031B (en) * | 2015-12-14 | 2019-06-04 | 中国矿业大学 | A kind of vibration screen for mineral magnetic memory detecting method |
CN105353031A (en) * | 2015-12-14 | 2016-02-24 | 中国矿业大学 | Mining vibrating screen magnetic memory detection device and method |
CN108362769B (en) * | 2018-03-01 | 2021-10-12 | 沈阳工业大学 | Non-contact weak magnetic detection method for stress of crack initiation region |
CN108362768A (en) * | 2018-03-01 | 2018-08-03 | 沈阳工业大学 | A kind of non-contact weak magnetic detection method of stress |
CN108362766A (en) * | 2018-03-01 | 2018-08-03 | 沈阳工业大学 | The non-contact weak magnetic detection method of crack initiation area stress |
CN108362769A (en) * | 2018-03-01 | 2018-08-03 | 沈阳工业大学 | A kind of non-contact weak magnetic detection method of crack initiation area stress |
CN108362768B (en) * | 2018-03-01 | 2021-10-12 | 沈阳工业大学 | Stress non-contact weak magnetic detection method |
CN108362766B (en) * | 2018-03-01 | 2021-10-12 | 沈阳工业大学 | Non-contact weak magnetic detection method for stress of crack initiation region |
CN108763664A (en) * | 2018-05-11 | 2018-11-06 | 沈阳工业大学 | Based on ultra-soft pseudo potential weld seam magnetic memory signal characteristic detection method |
CN108875135A (en) * | 2018-05-11 | 2018-11-23 | 沈阳工业大学 | A kind of weld seam magnetic memory signal characteristic recognition method |
CN108875135B (en) * | 2018-05-11 | 2022-07-15 | 沈阳工业大学 | Weld magnetic memory signal feature identification method |
CN110308043B (en) * | 2019-07-29 | 2021-11-16 | 黑龙江科技大学 | Additive manufacturing part early damage evaluation method based on metal magnetic memory detection |
CN110308043A (en) * | 2019-07-29 | 2019-10-08 | 黑龙江科技大学 | Increasing material manufacturing product earlier damage evaluation method based on metal magnetic memory test |
CN111289608A (en) * | 2020-03-23 | 2020-06-16 | 江苏科技大学 | Method for evaluating welding residual stress |
CN111289608B (en) * | 2020-03-23 | 2023-03-21 | 江苏科技大学 | Method for evaluating welding residual stress |
CN112507599A (en) * | 2020-11-11 | 2021-03-16 | 南昌航空大学 | Neodymium iron boron magnet crack fracture prediction method based on particle filter algorithm |
CN112504833B (en) * | 2020-12-03 | 2023-04-28 | 南昌航空大学 | Online monitoring method for fatigue crack growth of sintered NdFeB |
CN112504833A (en) * | 2020-12-03 | 2021-03-16 | 南昌航空大学 | On-line monitoring method for fatigue crack damage propagation of sintered neodymium iron boron |
CN113281401A (en) * | 2021-04-29 | 2021-08-20 | 同济大学 | Detection method, system and device for hidden diseases of ballastless track |
CN114441624A (en) * | 2022-01-31 | 2022-05-06 | 烟台大学 | Small metal magnetic memory crack detection method |
CN115219584A (en) * | 2022-07-20 | 2022-10-21 | 江西理工大学 | Metal magnetic memory monitoring and evaluating method for ferromagnetic material |
CN115219584B (en) * | 2022-07-20 | 2023-04-28 | 江西理工大学 | Metal magnetic memory monitoring and evaluating method for ferromagnetic material |
CN115166024A (en) * | 2022-08-18 | 2022-10-11 | 合肥工业大学 | Method for detecting damage degree of heterogeneous metal coating joint surface through magnetic memory |
CN115166024B (en) * | 2022-08-18 | 2024-04-05 | 合肥工业大学 | Method for detecting damage degree of heterogeneous metal coating bonding surface by magnetic memory |
CN117268961A (en) * | 2023-11-23 | 2023-12-22 | 宁波市特种设备检验研究院 | Fatigue failure early warning method for metal parts |
CN117268961B (en) * | 2023-11-23 | 2024-02-20 | 宁波市特种设备检验研究院 | Fatigue failure early warning method for metal parts |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104777218A (en) | Method for determining ferromagnetic material crack generation by metal magnetic memory detection technology | |
CN100573132C (en) | Utilize the method for variated magnetic signal monitoring ferromagnetic material fatigue crack expansion | |
CN104931576B (en) | A kind of characterizing method of weld crack expansion process | |
Mazal et al. | Use of acoustic emission method for identification of fatigue micro-cracks creation | |
CN100573181C (en) | Utilize the method for ferromagnetic materials surface stray magnetic field signal monitoring fatigue damage | |
CN102182933A (en) | Nondestructive detection system and method for pulsed magnetic flux leakage defects and stresses | |
CN107655972B (en) | Ultrasonic evaluation method for residual life of metal material in high-temperature creep | |
CN104713762A (en) | Preparation method of simulation stress corrosion cracks for eddy current testing | |
CN102128879A (en) | Natural magnetizing phenomenon-based austenitic stainless steel plastic damage quantitative detection method | |
CN103926312A (en) | Ultrasonic surface wave nonlinear detection method for roll fatigue hardening layer | |
CN104330474A (en) | Calibration method for rock damage fracture system | |
CN103776895B (en) | Nondestructive examination method for evaluating contact damage of ferromagnetic material | |
CN104515812A (en) | Non-classical nonlinear detection method aiming at microcrack in non-uniform members | |
CN103308602B (en) | A kind of method detecting machine parts or material lifetime based on nondestructive examination | |
CN203455318U (en) | Automatic defect detection dynamic calibration test plate | |
CN102445491B (en) | Method for evaluating stress concentration degree of remanufactured blank by using self-emission magnetic signal | |
Matysik et al. | Assessment of the impact-echo method for monitoring the long-standing frost resistance of ceramic tiles | |
Shen et al. | Investigation on metal magnetic memory signal during loading | |
Bruni et al. | A preliminary analysis about the application of acoustic emission and low frequency vibration methods to the structural health monitoring of railway axles | |
CN110412120A (en) | Pipeline crack detection method and device | |
CN105319268A (en) | Color grade determination method for defect in roller through airborne flaw detector | |
Carboni et al. | A study on the performance of acoustic emission and low frequency vibration methods to the real-time condition monitoring of railway axles | |
CN115219584B (en) | Metal magnetic memory monitoring and evaluating method for ferromagnetic material | |
CN103792280B (en) | Magnetic nondestructive testing method for contact damage inversion of ferromagnetic material | |
Li et al. | An electromagnetic Helmholtz-coil probe for arbitrary orientation crack detection on the surface of pipeline |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
EXSB | Decision made by sipo to initiate substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150715 |
|
RJ01 | Rejection of invention patent application after publication |