scholarly journals Abrasion and Erosion Resistance of Cermets: A Review

Materials ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 69
Author(s):  
Jakob Kübarsepp ◽  
Kristjan Juhani ◽  
Marek Tarraste

WC-based hardmetals are employed widely as wear-resistant ceramic–metal composites for tools and wear parts. Raw materials supply, environmental concerns and some limitations of hardmetals have directed efforts toward development of alternative wear-resistant composites–cermets. We present a current state of knowledge in the field of ceramic-rich (≥50 vol%) cermets behavior in abrasion and erosion conditions, which are the dominant types of wear in many industrial applications. Distinction is made between two-body and three-body abrasion, solid-particle erosion, and slurry erosion. Cermets, in particular TiC-, Ti(C,N)- and Cr3C2-based composites and hardmetals, are compared for their abrasive and erosive wear performance and mechanism. The review enabled formulation of tribological conditions in which cermets may be comparable or have potential to outperform WC-Co hardmetals. Hardmetals, in general, outperform cermets in abrasion and solid-particle erosion at room and moderate temperatures. However, cermets demonstrate their potential mainly in severe conditions—at elevated temperatures and corrosive (oxidation, electrochemical corrosion) environments.

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaochao Li ◽  
Shusen Chen ◽  
Zhaohui Huang ◽  
Minghao Fang ◽  
Yan’gai Liu ◽  
...  

Solid particle erosion-wear experiments on as-prepared mullite-SiC composite refractories by nitriding reactive sintering were performed at elevated temperatures, using sharp black SiC abrasive particles at an impact speed of 50 m/s and the impact angle of 90° in the air atmosphere. The effects of silicon powder addition and erosion temperature on the erosion-wear resistance of mullite-SiC composite refractories were studied. The test results reveal that Si powders caused nitriding reaction to formβ-sialon whiskers in the matrix of mullite-SiC composite refractories. The erosion-wear resistance of mullite-SiC composite refractories was improved with the increase of silicon powder addition and erosion temperature, and the minimum volume erosion rate was under the condition of 12% silicon added and a temperature of 1400°C. The major erosion-wear mechanisms of mullite-SiC composite refractories were brittle erosion at the erosion temperature from room temperature to 1000°C and then plastic deformation from 1200°C to 1400°C.


Author(s):  
S. G. Sapate ◽  
Manish Roy

Solid particle erosion is an important material degradation mechanism. Although various methods of coating are tried and used for protection against erosion, thermal sprayed coating for such purpose is the most widely used method. In this chapter, evolution of thermal sprayed coating, erosion testing methods, and erosive wear of thermal sprayed coatings are discussed extensively with emphasis on recent developments. It is generally found that erosion of thermal sprayed coatings depends on erosion test conditions, microstructural features, and mechanical properties of the coating materials. Most thermal sprayed coatings respond in brittle manner having maximum erosion rate at oblique impact and velocity exponent in excess of 3.0. Erosion rate is also dependent on thermal spraying techniques and post coating treatment. However, little work is done on dependence of erosion rate on coating techniques and coating conditions. Future direction of work is also reported.


Coatings ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 845
Author(s):  
Yuan Liu ◽  
Ravi Ravichandran ◽  
Kuiying Chen ◽  
Prakash Patnaik

Machine learning (ML) and deep learning (DL) for big data (BD) management are currently viable approaches that can significantly help in high-temperature materials design and development. ML-DL can accumulate knowledge by learning from existing data generated through multi-physics modelling (MPM) and experimental tests (ETs). DL mainly involves analyzing nonlinear correlations and high-dimensional datasets implemented through specifically designed numerical algorithms. DL also makes it possible to learn from new data and modify predictive models over time, identifying anomalies, signatures, and trends in machine performance, develop an understanding of patterns of behaviour, and estimate efficiencies in a machine. Machine learning was implemented to investigate the solid particle erosion of both APS (air plasma spray) and EB-PVD (electron beam physical vapour deposition) TBCs of hot section components. Several ML models and algorithms were used such as neural networks (NNs), gradient boosting regression (GBR), decision tree regression (DTR), and random forest regression (RFR). It was found that the test data are strongly associated with five key factors as identifiers. Following test data collection, the dataset is subjected to sorting, filtering, extracting, and exploratory analysis. The training and testing, and prediction results are analysed. The results suggest that neural networks using the BR model and GBR have better prediction capability.


2010 ◽  
Vol 123-125 ◽  
pp. 213-216 ◽  
Author(s):  
Amar Patnaik ◽  
Ritesh Kaundal ◽  
Alok Satapathy ◽  
Sandhyarani Biswas ◽  
Pradeep Kumar

Fiber reinforced composite materials have been used in main parts of structures; an accurate evaluation of their erosion behavior becomes very important. In this study, short glass fibre reinforced polyester based isotropic polymer composites are fabricated with five different fibre weight-fractions. The effect of various operational variables, material parameters and their interactive influences on erosive wear behavior of these composites has been studied systematically. After systematic analysis of solid particle erosion for all the five composites, 30wt% short glass fiber reinforced polyester based composite shows better erosion resistance. In order to improve the erosion resistance further ceramic silicon carbide particle is reinforced with the 30wt% glass-polyester based hybrid composites. A finite element (FE) model (LS-DYNA) of erosive wear is established for damage assessment and validated by a well designed set of experiments. For this, the design of experiments approach using Taguchi’s orthogonal arrays design is used. It is recognized that there is a good agreement between the computational and experimental results, and that the proposed simulation method is very useful for the evaluation of damage mechanisms.


2012 ◽  
Vol 736 ◽  
pp. 218-222 ◽  
Author(s):  
Hassan Jayaraj Amarendra ◽  
Gajanan P. Chaudhari ◽  
S.K. Nath

In harnessing clean and renewable energy sources water turbines represent a significant portion of the power generation worldwide. Because of erosion, repair and maintenance of hydraulic turbines is a difficult problem. Material removal in hydraulic turbine components may occur either by particle erosion or cavitation erosion or by their combined action. Many ASTM standard and non standard test rigs are aimed at specific tests, like solid particle erosion, cavitation erosion. To simulate the real conditions in a laboratory setup, a novel method is employed to combine the effect of cavitation erosion and slurry erosion in the slurry pot tester. Triangular prismatic cavitation inducers are used in the conventional slurry pot tester. The aluminum test specimens are investigated in the slurry pot tester. A wide variation in material loss was noted under different exposure conditions. The maximum material loss is ascribed to combined effects of solid particle erosion and cavitation erosion.


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