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

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.

Cumulative Fatigue Damage under a Symmetrical-Sawtooth Loading Programme

1965 ◽  
Vol 7 (2) ◽  
pp. 138-151 ◽  
Author(s):  
K. J. Marsh
Keyword(s):  
Mild Steel ◽  
Fatigue Limit ◽  
Fatigue Damage ◽  
Bending Fatigue ◽  
Fatigue Damage Accumulation ◽  
Rotating Bending Fatigue ◽  
Rotating Bending ◽  
Stress Cycles ◽  
Cumulative Fatigue Damage ◽  
Stress Block

A description is given of a modified rotating-bending fatigue machine which enables specimens to be submitted to repeated stress cycles modulated by a symmetrical-sawtooth wave, i.e. to a triangular stress block. Results are given of tests on plain and sharply notched mild-steel specimens and are discussed with reference to some existing concepts of fatigue damage accumulation. It is shown that, for mild steel, a hypothesis which neglects stress cycles having an amplitude of less than the fatigue limit of the material gives an optimistic prediction of specimen life but that to neglect cycles of amplitude less than 80 per cent of the fatigue limit gives results which agree closely with experiment.

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.

On a New Rotating Bending Fatigue Testing Machine

1966 ◽  
Vol 15 (148) ◽  
pp. 49-54
Author(s):  
Minoru KAWAMOTO ◽  
Katsumi SUMIHIRO ◽  
Koji KIDA
Keyword(s):  
Fatigue Testing ◽  
Testing Machine ◽  
Bending Fatigue ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

A FAST ROTATING BENDING FATIGUE TEST MACHINE

2017 ◽  
Author(s):  
Marco Antonio Meggiolaro ◽  
Jaime T P Castro ◽  
Rodrigo de Moura Nogueira
Keyword(s):  
Fatigue Test ◽  
Test Machine ◽  
Bending Fatigue ◽  
Bending Fatigue Test ◽  
Rotating Bending Fatigue ◽  
Rotating Bending ◽  
Fast Rotating

A Study onP-S-NCurve for Rotating Bending Fatigue Test for Bearing Steel

2008 ◽  
Vol 51 (2) ◽  
pp. 166-172 ◽  
Author(s):  
Katsuji Tosha ◽  
Daisuke Ueda ◽  
Hirokazu Shimoda ◽  
Shigeo Shimizu
Keyword(s):  
Fatigue Test ◽  
Bearing Steel ◽  
Bending Fatigue ◽  
Bending Fatigue Test ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

Application of ANN Back-Propagation for Fracture Design Parameters in Extra-Low Cycle Rotating Bending Fatigue

2009 ◽  
Vol 610-613 ◽  
pp. 450-453
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Jin Zhang ◽  
Gui Ping He
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Bending Fatigue ◽  
The Neural Network ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

High Cycle Fatigue Behavior of Thermally Oxidized Ti6Al4V Alloy

2007 ◽  
Vol 561-565 ◽  
pp. 2179-2182 ◽  
Author(s):  
Mehmet Cingi ◽  
Onur Meydanoglu ◽  
Hasan Guleryuz ◽  
Murat Baydogan ◽  
Huseyin Cimenoglu ◽  
...  
Keyword(s):  
Fatigue Strength ◽  
Yield Strength ◽  
Thermal Oxidation ◽  
Fatigue Behavior ◽  
Surface Hardness ◽  
Ti6al4v Alloy ◽  
Bending Fatigue ◽  
Rotating Bending Fatigue ◽  
Rotating Bending ◽  
Bending Fatigue Strength

In this study, the effect of thermal oxidation on the high cycle rotating bending fatigue behavior of Ti6Al4V alloy was investigated. Oxidation, which was performed at 600°C for 60 h in air, considerably improved the surface hardness and particularly the yield strength of the alloy without scarifying the tensile ductility. Unfortunately, the rotating bending fatigue strength at 5x106 cycles decreased from about 610 MPa to about 400 MPa upon oxidation. Thus, thermal oxidation leaded a reduction in the fatigue strength of around 34%, while improving the surface hardness (HV0.1) and yield strength 85 % and 36 %, respectively.

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