Research on the fatigue failure behavior of 1Cr17Ni2 blades ground by abrasive belt with passivation treatment

Huang, Yun; Li, Shaochuan; Xiao, Guijian; Chen, Benqiang; He, Yan; Song, Kangkang

doi:10.1016/j.engfailanal.2021.105670

Cited by 16 publications

(5 citation statements)

References 29 publications

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“…During the service of aero-engine blades, they are always subjected to loads with different frequencies and amplitudes due to the change of the running state of the aircraft, and the first-order resonance frequency is the most harmful to the blade (Huang et al, 2021b). So, the vibration fatigue test is carried out at the first-order resonance frequency.…”

Section: Methodsmentioning

confidence: 99%

Fatigue failure mechanism analysis of 1Cr17Ni2 stainless steel blades ground by an abrasive belt

Zhang¹,

Li²,

Wang

et al. 2023

Front. Mater.

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Fatigue failure, as the main failure form of aero-engine blades, has a direct impact on the reliability and service life of aviation equipment. In order to improve the service performance of machined blades, it is necessary to understand the failure process and failure mechanism of blades and then optimize the grinding process. This paper takes abrasive belt grinding of an 1Cr17Ni2 stainless steel blade as the research object and analyzes the fatigue failure mechanism by characterizing the surface morphology, cross-sectional microstructure, and cross-sectional characteristics of the fatigue failure blade. The results show that cracks are prone to propagate in carbon-rich areas with poor mechanical properties inside the material, and the accumulation of large-size carbon-rich areas leads to continuous cracks easily and accelerates crack growth. The grinding process promotes the migration and consumption of surface carbon elements and forms a carbon consumption layer on the surface of the material, which can inhibit the initiation of fatigue cracks. The point-like pits on the ground surface have an adverse effect on the fatigue life and play a role in the initiation of fatigue crack enhancement. The direction of material research and development to homogenize the structure of the material and the direction of anti-fatigue grinding to increase the thickness of the carbon consumption layer on the ground surface and avoid the damage of micro-pits are proposed. The research has important guiding significance for anti-fatigue machining of key components.

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Section: Methodsmentioning

confidence: 99%

Fatigue failure mechanism analysis of 1Cr17Ni2 stainless steel blades ground by an abrasive belt

Zhang¹,

Li²,

Wang

et al. 2023

Front. Mater.

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“…In the process of grinding simulation, most scholars at home and abroad use single particle-abrasive grain to simplify the processing. Combined with the characteristics that the shape of abrasive grains is mostly positive polygon and negative rake angle [22][23] , and considering the phenomenon of abrasive grain passivation caused by grinding wheel pre-grinding [24][25] , this paper defines the CBN single abrasive grain model with square cone and truncated square cone. The three-dimensional geometric model was established by Abaqus software.…”

Section: Simulation Design and Analysis 21 Grinding Model Of Single A...mentioning

confidence: 99%

Study on grinding surface quality of superalloy GH4145

zhao,

Gao,

Wang

et al. 2024

Preprint

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Nickl-based Superalloy GH4145 is widely used in aero-engine springs, blades, and other parts due to its excellent mechanical properties. It is a high-hardness and difficult-to-machine material. Improving the surface quality after processing has great significance for the long-term stable service of aero-engines. In this paper, Simulation models with different rake angles were established to predict the damage forms on the machined surface and subsurface on Abaqus. The effects of different process parameters on the surface quality of GH4145 were studied by using CBN grinding wheel. The influence of experimental parameters on the surface quality of GH4145 was analyzed and the related grinding mechanism was expounded. Under the scanning electron microscope (SEM) detection, the crack propagation process and the phenomenon of serious damage to the surface quality such as white layer were analyzed. The results show that the single particle prediction models with different rake angles can reflect the actual grinding situation to a large extent, and the model with the rake angle of -15° is more consistent with the detection phenomenon observed in this experiment. The surface damage forms are mainly pits, burrs, and boundary bosses caused by plastic deformation under simulation and experimental detection. The detection of the subsurface shows that in addition to the above phenomena, there are cracks and debris adhesion. The linear velocity of the grinding wheel is the main factor affecting the surface quality of the machined surface, the cutting depth is the second, and the feed rate has the least influence. The maximum roughness in the grinding experiment reaches 1.992 µm. Considering the thermal softening phenomenon of the material, the linear velocity range should be selected from 30 m/s to 35 m/s, and the feed rate and grinding depth should be selected relatively small values.

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“…In fact, the strength design and life prediction of a machined part are significantly affected by the surface finishing, because the local microscopic stress and strain concentration at the surface defects are major factors for crack initiation and propagation, also generally that fatigue cracks initiate from the free surface. Therefore, the surface topography is considered a major factor in the fatigue behavior of the product 7–10 …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the surface topography is considered a major factor in the fatigue behavior of the product. [7][8][9][10] The surface roughness provides a global view of the surface topography, and it is generally accounted for in many fatigue life models. A great deal of previous research into fatigue life has focused on the effect of surface roughness on fatigue strength.…”

Section: Introductionmentioning

confidence: 99%

Feature selection of surface topography parameters for fatigue‐damage detection using Pearson correlation method and neural network analysis

Alqahtani

Ray

2023

Fatigue Fract Eng Mat Struct

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The global objective of this study was to investigate the best features of the surface topography for fatigue‐damage detection and classification. The presence of the stress concentration in valleys of the surface topography causes a grain slip and a crack initiation at the surface of the machined structure and finally leads to fatigue failures. Therefore, the surface topography has a major influence on the fatigue strength of the machined structure. An optical confocal measurement system (Alicona) was applied to measure six surface topography parameters. In this paper, feature selection using the Pearson correlation method was adopted to select the best surface textures that provide best the neural network (NN) model performance. The NN model is capable of detecting and classifying the damage with an accuracy of up to ∼ 94.4%.

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Research on the fatigue failure behavior of 1Cr17Ni2 blades ground by abrasive belt with passivation treatment

Cited by 16 publications

References 29 publications

Fatigue failure mechanism analysis of 1Cr17Ni2 stainless steel blades ground by an abrasive belt

Fatigue failure mechanism analysis of 1Cr17Ni2 stainless steel blades ground by an abrasive belt

Study on grinding surface quality of superalloy GH4145

Feature selection of surface topography parameters for fatigue‐damage detection using Pearson correlation method and neural network analysis

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