Influence of TiO2 Additives on Cavitation Erosion Resistance of Al-Mg Alloy Micro-Arc Oxidation Coating

Four ceramic coatings are fabricated on 6061 aluminum alloy substrates with a micro-arc oxidation technique in silicate electrolytes with different TiO2 nano-additive concentrations. To explore the cavitation erosion resistance of the micro-arc oxidation (MAO) coating, cavitation tests are performed using a vibratory test rig. After cavitation tests lasting 10 min, the mass losses, surface morphologies, and chemical compositions of the samples after cavitation tests are examined using a digital balance, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS), respectively. The results indicate that, in contrast to the aluminum alloy, the MAO coatings, by adjusting TiO2 nano-additive concentration, can decrease the mean depth of erosion rate (MDER) due to the cavitation damage, and lead to an excellent cavitation erosion resistance. The results also show that: In contrast to aluminum alloy, MAO coatings can decrease the MDER due to the cavitation damage in a short period of time by adjusting TiO2 nano-additive concentration. With the increase of TiO2 nano-additive concentration, the compactness and the surface hardness of MAO coatings decrease, which can easily lead to larger erosion pits.

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Cavitation Erosion Resistance of Silicified Graphite by Liquid Silicon Penetration Technique

Journal of Tribology ◽

10.1115/1.4035869 ◽

2017 ◽

Vol 139 (6) ◽

Cited By ~ 1

Author(s):

Hongqin Ding ◽

Shuyun Jiang

Keyword(s):

Cavitation Erosion ◽

Erosion Resistance ◽

Test System ◽

Surface Microstructure ◽

Chemical Compositions ◽

X Ray Diffraction ◽

X Ray ◽

Sic Ceramic ◽

Cavitation Erosion Resistance ◽

Sem Morphology

This technical brief studied the cavitation erosion behavior of the silicified graphite. The phase constituents, surface microstructure, and chemical compositions of silicified graphite were examined by using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS), respectively. Cavitation experiments were carried out by using an ultrasonic vibration test system. The experimental results show that the silicified graphite exhibits an excellent cavitation erosion resistance; this can be attributed to the fact that the silicified graphite has the characteristics of both the silicon carbide and the graphite. The SEM morphology studies of the erosion surfaces indicated that the inherent brittleness of SiC ceramic material results in the formation of erosion pits on the surface of silicified graphite.

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Cavitation erosion resistance of 316L stainless steel fabricated using selective laser melting

Friction ◽

10.1007/s40544-020-0443-7 ◽

2020 ◽

Author(s):

Hongqin Ding ◽

Qing Tang ◽

Yi Zhu ◽

Chao Zhang ◽

Huayong Yang

Keyword(s):

Stainless Steel ◽

Selective Laser Melting ◽

Laser Power ◽

Cavitation Erosion ◽

316L Stainless Steel ◽

Erosion Resistance ◽

Scanning Speed ◽

Laser Melting ◽

Cavitation Damage ◽

Cavitation Erosion Resistance

AbstractCavitation erosion degrades the performance and reliability of hydraulic machinery. Selective laser melting (SLM) is a type of metal additive manufacturing technology that can fabricate metal parts directly and provide lightweight design in various industrial applications. However, the cavitation erosion behaviors of SLM-fabricated parts have rarely been studied. In this study, SLM 316L stainless steel samples were fabricated via SLM technology considering the scanning strategy, scanning speed, laser power, and build orientation. The effect of the process parameters on the cavitation erosion resistance of the SLM-fabricated 316L stainless steel samples was illustrated using an ultrasonic vibratory cavitation system. The mass loss and surface topography were employed to evaluate the surface cavitation damage of the SLM-fabricated 316L stainless steel samples after the cavitation test. The cavitation damage mechanism of the SLM-fabricated samples was discussed. The results show that the degree of cavitation damage of the sample fabricated via SLM with a few defects, anisotropic build direction, and columnar microstructure is significantly decreased. Defects such as pores, which are attributed to low laser power and high scanning speed, may severely aggravate the cavitation damage of the SLM-fabricated samples. The sample fabricated via SLM with a low laser power and exposure time exhibited the highest porosity and poor cavitation erosion resistance. The cellular structures are more prone to cavitation damage compared with the columnar structures. A sample with a high density of grain boundaries will severely suffer cavitation damage.

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Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3-13 wt% TiO2 Coatings

Processes ◽

10.3390/pr8121544 ◽

2020 ◽

Vol 8 (12) ◽

pp. 1544

Author(s):

Mirosław Szala ◽

Leszek Łatka ◽

Michał Awtoniuk ◽

Marcin Winnicki ◽

Monika Michalak

Keyword(s):

Thermal Spray ◽

Process Parameters ◽

Cavitation Erosion ◽

Erosion Resistance ◽

Ceramic Coatings ◽

Thermal Spray Process ◽

Spray Process ◽

Tio2 Coatings ◽

Cavitation Erosion Resistance ◽

Selection Of

The study aims to elaborate a neural model and algorithm for optimizing hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13 wt% TiO2 ceramic coatings were deposited onto 316L stainless steel by atmospheric plasma spray (ASP). The coatings were prepared with different values of two spray process parameters: the stand-off distance and torch velocity. Microstructure, porosity and microhardness of the coatings were examined. Cavitation erosion tests were conducted in compliance with the ASTM G32 standard. Artificial neural networks (ANN) were employed to elaborate the model, and the multi-objectives genetic algorithm (MOGA) was used to optimize both properties and cavitation erosion resistance of the coatings. Results were analyzed with MATLAB software by Neural Network Toolbox and Global Optimization Toolbox. The fusion of artificial intelligence methods (ANN + MOGA) is essential for future selection of thermal spray process parameters, especially for the design of ceramic coatings with specified functional properties. Selection of these parameters is a multicriteria decision problem. The proposed method made it possible to find a Pareto front, i.e., trade-offs between several conflicting objectives—maximizing the hardness and cavitation erosion resistance of Al2O3-13 wt% TiO2 coatings and, at the same time, minimizing their porosity.

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Neural Modelling of APS Thermal Spray Process Parameters for Optimising the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3 -13% TiO2 Coatings

10.20944/preprints202011.0200.v1 ◽

2020 ◽

Author(s):

Mirosław Szala ◽

Leszek Łatka ◽

Michał Awtoniuk ◽

Marcin Winnicki ◽

Monika Michalak

Keyword(s):

Thermal Spray ◽

Process Parameters ◽

Cavitation Erosion ◽

Erosion Resistance ◽

Ceramic Coatings ◽

Thermal Spray Process ◽

Spray Process ◽

Conflicting Objectives ◽

Cavitation Erosion Resistance ◽

Selection Of

The study aims to elaborate a neural model and algorithm for optimising hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13wt.%TiO2 ceramic coatings were deposited onto 316L stainless steel by atmospheric plasma spray (ASP). The coatings were prepared with different values of two spray process parameters: the stand-off distance and torch velocity. Microstructure, porosity and microhardness of the coatings were examined. Cavitation erosion tests were conducted in compliance with the ASTM G32 standard. Artificial neural networks (ANN) were employed to elaborate the model, and the multi-objectives genetic algorithm (MOGA) was used to optimise both properties and cavitation erosion resistance of the coatings. Results were analysed with Matlab software by Neural Network Toolbox and Global Optimization Toolbox. The fusion of artificial intelligence methods (ANN+MOGA) is essential for future selection of thermal spray process parameters, especially for the design of ceramic coatings with specified functional properties. Selection of these parameters is a multicriteria decision problem. The proposed method made it possible to find a Pareto front, i.e. trade-offs between several conflicting objectives – maximising the hardness and cavitation erosion resistance of Al2O3-13%TiO2 coatings and, at the same time, minimizing their porosity.

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