In Service Detection of 45 Steel’s Rotary Bending Fatigue Damage Based on Metal Magnetic Memory Technique

2010 ◽  
Vol 97-101 ◽  
pp. 4301-4304 ◽  
Author(s):  
Ming Xiu Xu ◽  
Min Qiang Xu ◽  
Jian Wei Li ◽  
Jian Cheng Leng ◽  
Shu Ai Zhao
Keyword(s):  
Fatigue Damage ◽  
Mechanical Effect ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Metal Fatigue ◽  
Bending Fatigue ◽  
Fatigue Process ◽  
Rotating Bending Fatigue ◽  
On Line ◽  
Memory Signals

In order to study the relation between metal fatigue damage and the associated magnetic memory signals, the detection theory was studied based on magneto mechanical effect, and the rotating bending fatigue experiments on 45 steel were carried out combined with on-line data collecting system. The magnetic signals along axis at prefab defects in the fatigue process were studied. Experimental results show that the characteristics of the magnetic signals are different at every different stage of the fatigue process, in particular the magnetic signal changing faster in the late fatigue process, which indicates that it is feasible to detect fatigue damage using metal magnetic memory technique. Accordingly, the method is proposed to detect the rotation-bending fatigue damage for specimens in service by magnetic signals.

The Metal Magnetic Memory Detection of Rotating Bending Fatigue Damage for 45QT Steel

2011 ◽  
Vol 4 (4) ◽  
pp. 1627-1632
Author(s):  
Mingxiu Xu ◽  
Dabo Wu ◽  
Minqiang Xu ◽  
Jiancheng Leng ◽  
Jianwei Li
Keyword(s):  
Fatigue Damage ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Bending Fatigue ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

Effect of tensile stress on metal magnetic memory signals during on-line measurement in ferromagnetic steel

2021 ◽  
Vol 117 ◽  
pp. 102378
Author(s):  
Huipeng Wang ◽  
Lihong Dong ◽  
Haidou Wang ◽  
Guozheng Ma ◽  
Binshi Xu ◽  
...  
Keyword(s):  
Tensile Stress ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Line Measurement ◽  
On Line ◽  
Memory Signals ◽  
Ferromagnetic Steel

Characterization of Fatigue Damage of Crankshaft Remanufacturing Core by Two-Dimensional Magnetic Memory Signal

2012 ◽  
Vol 538-541 ◽  
pp. 1588-1593 ◽  
Author(s):  
Nan Xue ◽  
Li Hong Dong ◽  
Bin Shi Xu ◽  
Cheng Chen ◽  
Shi Yun Dong
Keyword(s):  
Fatigue Test ◽  
Fatigue Damage ◽  
Normal Component ◽  
Stray Field ◽  
Magnetic Memory ◽  
Memory Testing ◽  
Two Dimensional ◽  
Metal Magnetic Memory ◽  
Bending Fatigue ◽  
Metal Magnetic Memory Testing

Fatigue damage degree of crankshaft remanufacturing core was studied based on Metal Magnetic Memory Testing. Bending fatigue test of crankshaft remanufacturing core was conducted on the resonant fatigue test rig and variations of two-dimensional magnetic memory signal distribution in crankshaft pin fillets were studied at different bending fatigue cycle. Experimental research shows that distributions of Hp(x) signals, namely, tangential component of spontaneous stray field and Hp(y) signals, namely, normal component of spontaneous stray field in crankshaft pin fillets have no obvious change with loading cycle when no crack initiation and propagation occur in crankshaft pin fillets. Characteristics of Hp(x) and Hp(y) signal both show dynamic variations when crack in crankshaft pin fillets initiates and extends at medium rate or high rate. Metal Magnetic Memory Testing is a dynamic method for monitoring fatigue crack propagation in crankshaft.

Metal Magnetic Memory Testing Technique for Typical Defects in Pressure Vessels and Pipelines

2015 ◽  
Author(s):  
Weihe Guan ◽  
Pengju Guo ◽  
Chen Xuedong
Keyword(s):  
High Efficiency ◽  
Low Cost ◽  
Orientation Angle ◽  
High Sensitivity ◽  
Pressure Vessels ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Time Saving ◽  
Hydrogen Induced Cracking ◽  
Memory Signals

Metal magnetic memory technique has been extensively applied in different fields due to its unique advantages of time-saving, low cost, and high efficiency. However, very limited research has been carried out on studying the characteristics of metal magnetic memory signals of different defects except crack, and also the effect of orientation angle between testing direction and defect on magnetic memory signals. To promote study in this area, the magnetic memory signals of typical defects (such as crack, slag inclusion) are investigated as well as hydrogen-induced cracking. In addition, the characteristics of magnetic memory signals when measured with different angle between testing direction and defects were obtained. The results indicate that the metal magnetic memory technique is a promising method to detect typical defects of welding and also hydrogen-induced cracking. Moreover, the technique has high sensitivity on defects no matter the angle between testing direction and defect. However, further research is needed because it can only find the possible location of defects but cannot quantitatively describe the defect.

scholarly journals Diagnosis Model of Pipeline Cracks According to Metal Magnetic Memory Signals Based on Adaptive Genetic Algorithm and Support Vector Machine

2015 ◽  
Vol 9 (1) ◽  
pp. 1076-1080 ◽  
Author(s):  
Lihong Gong ◽  
Zhuxin Li ◽  
Zhen Zhang
Keyword(s):  
Support Vector Machine ◽  
Kernel Functions ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Stress Concentrations ◽  
Rbf Kernel ◽  
Diagnosis Model ◽  
Memory Signals

Metal magnetic memory (MMM) signals can reflect stress concentration and cracks on the surface of ferromagnetic components, but the traditional criteria used to distinguish the locations of these stress concentrations and cracks are not sufficiently accurate. In this study, 22 indices were extracted from the original MMM signals, and the diagnosis results of 4 kernel functions of support vector machine (SVM) were compared. Of these 4, the radial basis function (RBF) kernel performed the best in the simulations, with a diagnostic accuracy of 94.03%. Using the principles of adaptive genetic algorithms (AGA), a combined AGA-SVM diagnosis model was created, resulting in an improvement in accuracy to 95.52%, using the same training and test sets as those used in the simulation of SVM with an RBF kernel. The results show that AGA-SVM can accurately distinguish stress concentrations and cracks from normal points, enabling them to be located more accurately.

Sign in / Sign up

Export Citation Format

Share Document