X-Ray Fractography on Rotating Bending Fatigue Fractured Surfaces.

Fine particle peening (FPP) using hydroxyapatite (HAp) shot particles was performed to improve the fatigue strength and form a HAp transfer layer on a beta titanium alloy (Ti–22V–4Al). The surface microstructures of the FPP-treated specimen were characterized using scanning electron microscopy, micro-Vickers hardness testing, energy dispersive X-ray spectrometry, X-ray diffraction, and electron backscattered diffraction. A HAp transfer layer with a thickness of 5.5 μm was formed on the surface of the Ti–22V–4Al specimen by FPP. In addition, the surface hardness of the Ti–22V–4Al was increased, and high compressive residual stress was generated on the specimen surface by FPP. Rotating bending fatigue tests were performed at room temperature in laboratory air over a wide cycle-life region (103–109 cycles). In the long cycle-life regime, the fatigue strength at 107 cycles of the FPP-treated specimen became higher than that of the untreated specimen. This result is attributed to the formation of a work-hardened layer with high compressive residual stress by FPP. However, the fatigue strength was not improved by FPP in the short cycle-life regime, because fatigue cracks were initiated at surface defects formed during the FPP process. The fatigue fracture mode of the FPP-treated specimens shifted from surface-initiated fracture to subsurface-initiated fracture at a stress amplitude level of 600 MPa.

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On a New Rotating Bending Fatigue Testing Machine

Journal of the Society of Materials Science Japan ◽

10.2472/jsms.15.49 ◽

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

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A FAST ROTATING BENDING FATIGUE TEST MACHINE

10.26678/abcm.cobem2017.cob17-1824 ◽

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

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

Tribology Transactions ◽

10.1080/10402000801918031 ◽

2008 ◽

Vol 51 (2) ◽

pp. 166-172 ◽

Cited By ~ 12

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

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

Materials Science Forum ◽

10.4028/www.scientific.net/msf.610-613.450 ◽

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.

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High Cycle Fatigue Behavior of Thermally Oxidized Ti6Al4V Alloy

Materials Science Forum ◽

10.4028/www.scientific.net/msf.561-565.2179 ◽

2007 ◽

Vol 561-565 ◽

pp. 2179-2182 ◽

Cited By ~ 5

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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