The Effect of Abrasive Particle Size on the Wear Behaviour of Metal Matrix Composites

Şahin, Yusuf; Özdin, Kamil

doi:10.1063/1.1766548

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…Together, these materials make up a class of materials known as high-heat and electrical stability ceramics. Tese composites were studied for their mechanical qualities like hardness, ultimate tensile strength, and ultimate compressive strength, as well as for their metallurgical characteristics, for instance, microstructure with varying weight % of strengthening in the in-stir composites [10][11][12][13]. In addition, the abrasive and erosion behaviours were examined under several testing settings, as well as the result fndings were optimized using the Taguchi method, ANOVA [14,15].…”

Section: Introductionmentioning

confidence: 99%

Study of Wear Behavior on AA6061 Reinforced with Hybrid Ceramic Composites through Optimization

Shabbir

Alagarsamy²,

Krishnamurthy³

et al. 2023

Advances in Materials Science and Engineering

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This present work uses the three main components for alloying, silver nitrate, aluminium nitride, and titanium diboride. Stir casting was used to make AA 6061 composites with varying weights of 0, 3, 6, 9, and 12%. Microhardness, tensile strength, and compressive strength of produced composites were evaluated. Several wear tests were performed on the composites to measure their resistance to abrasion and erosion. The mass loss was calculated because of wear testing. Conventional and unconventional methods like the Taguchi technique and analysis of variance were employed to study and analyze the abrasive and erosion wear test outcomes to obtain better wear resistance for different weight % of AA 6061 compounds and to investigate the most important input and output characteristics employing the said optimal strategies. Reinforcement percentages were raised to improve mechanical quality.

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Section: Introductionmentioning

confidence: 99%

Study of Wear Behavior on AA6061 Reinforced with Hybrid Ceramic Composites through Optimization

Shabbir

Alagarsamy²,

Krishnamurthy³

et al. 2023

Advances in Materials Science and Engineering

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“…Sahin et al [7] studied the effect of abrasive particle size on the wear behavior of metal-based composite materials. They found that the hardness of metal-based composite materials increases with the size of silicon carbide particles.…”

Section: Introductionmentioning

confidence: 99%

Granularity reconstruction and characterization of silicon carbide micronized powders

Wang

Liu

Song

et al. 2023

J. Phys.: Conf. Ser.

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Water glass and silica-alumina sol were used as adhesives, uniformly mixed with silicon carbide powder. The effects of binder, binder concentration, sintering temperature, and sintering process on the granularity reconstruction of SiC powders were studied in detail. The results show that when water glass is used as the binding agent, vacuum sintering can effectively inhibit the oxidation of silicon carbide, and suitable granulation and sintering samples are obtained at a concentration of 5%. When silica-aluminum sol is used as the binding agent, it is discovered that the increase in engagement can effectively increase the particle size, and the best effect is achieved at a concentration of 10%. Both systems of binding agents can successfully bind silicon carbide micronized powder, enlarge silicon carbide particles, and accomplish the goal of Granularity reconstruction.

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“…Wear performance of materials are considered to be very important for tribological systems such as pistons, cylinder heads and others .Therefore various studies have been carried out on the wear behavior of SiC p reinforced MMCs based on experimental and theoretical work on abrasive wear of MMCs [4][5][6][7]. From the literature survey, it was found that some of their reports [8][9] highlighted that the size of the abrasive played a significant role in governing the wear resistance of the specimens .However ,it is contradictory to the report for example, Modi et al [10] showed that the effect of applied load on the wear rate of 10 weight percentage of Al 2 O 3 composite was more severe as compared to that of abrasive size at different loads ,the contradictions in the reported works had led to an opportunity to fabricate aluminium MMC by powder metallurgy technique with SiC particulates as reinforcement and Al2219 alloy as matrix. It could be noticed that most of the studies were focused on experimental work and a few mathematical models were reported.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network (ANN) for Predicting the Wear Behaviour of Al 2219-SiC<sub>p</sub> Composite

Radhakrishnan

Chandrasekaran

Ramesh

et al. 2016

AMM

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In the present study, artificial neural network (ANN) model was developed to predict the wear characteristics of Al 2219-SiCp composite. In the development of predictive model, weight fraction of the reinforcement, sintering temperature, applied normal load on the pin and disc speed were considered as model variables. Full factorial experiments were carried out and observed wear characteristics were taken as input for the model. A feed forward back propagation hierarchical neural network was considered in the study. Out of 81 datasets, 49 sets of data were used for training, 16 sets of data for validation and 16 sets of data for testing. The results exhibit good prediction accuracy of about 85% on average of all wear characteristics and percentage error was within the acceptable limits.

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The Effect of Abrasive Particle Size on the Wear Behaviour of Metal Matrix Composites

Cited by 10 publications

References 8 publications

Study of Wear Behavior on AA6061 Reinforced with Hybrid Ceramic Composites through Optimization

Study of Wear Behavior on AA6061 Reinforced with Hybrid Ceramic Composites through Optimization

Granularity reconstruction and characterization of silicon carbide micronized powders

Application of Artificial Neural Network (ANN) for Predicting the Wear Behaviour of Al 2219-SiC<sub>p</sub> Composite

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