Numerical modelling, prediction of Cu–W nano powder composite in dry sliding wear condition using response surface methodology

Vettivel, S.C.; Selvakumar, N.; Narayanasamy, R.; Leema, N.

doi:10.1016/j.matdes.2013.03.072

Cited by 101 publications

(48 citation statements)

References 32 publications

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“…RSM is a mathematical and statistical method used for modeling and identifying clear relationships between the parameters and response with a minimum number of experiments [11]. This method is used here to study the relationship between the parameters (load, velocity and sliding distance) and the response (dry sliding wear characteristics).…”

Section: Response Surface Methodologymentioning

confidence: 99%

Dry Sliding Wear Behaviour of Aluminium Al–Si12Cu/TiB2 Metal Matrix Composite Using Response Surface Methodology

Radhika

Raghu

2015

Tribol Lett

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An aluminium Al-Si12Cu/TiB 2 metal matrix composite was fabricated using the liquid metallurgy route, and its dry sliding wear characteristics were investigated under various sliding parameters. The titanium diboride (TiB 2 ) particles (10 wt%, average size 50-60 lm) were incorporated into the matrix and its microstructural characteristic was examined. A five-level central composite design experiment was developed using response surface methodology; parameters such as load, velocity and sliding distance were varied in the range of 10-50 N, 1-5 m/s and 500-2500 m, respectively. Dry sliding wear tests were performed as per the experimental design using a pin-ondisc tribometer at room temperature. Significance tests, analyses of variance and confirmatory tests were performed to validate the developed model. Study of the microstructural characteristics revealed uniform dispersion of the reinforcement particles throughout the composite. The regression result showed that the developed model performed well in relating the wear process parameters with the response and predicting the wear behaviour of the composite. The surface plot showed that wear rate increased with increasing load at all velocities and distances, and decreased with increasing sliding distance. In the case of velocity, the wear rate decreased initially, increasing after the transition velocity had been reached. Scanning electron microscopy analysis revealed severe wear at a high load due to a higher level of deformation of the composite surface.

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Section: Response Surface Methodologymentioning

confidence: 99%

Dry Sliding Wear Behaviour of Aluminium Al–Si12Cu/TiB2 Metal Matrix Composite Using Response Surface Methodology

Radhika

Raghu

2015

Tribol Lett

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“…The primary processing followed by hot extrusion led to reduction in the porosity of composites and enhanced the interfacial bonding between the matrix and the reinforcement [10]. An addition of about 1% nano-sized titanium particles to magnesium improved the tensile and compressive strengths of composites [11][12][13]. Metal Matrix Composites (MMCs) exhibiting high wear resistance are ready to be used in the automobile and aerospace industries.…”

Section: Introductionmentioning

confidence: 99%

Fabrication and Tribological Behaviour of Mg-TiO2 Composites

Pillai¹,

Jinu²,

Govindan³

2018

TFAMENA

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SummaryIn the present study pure magnesium was taken as a base metal which is reinforced with various weight percentages of TiO 2 (2.5, 5, 7.5, 10 % wt). This is done through a vacuum stir casting route with argon as a shielding gas to prevent oxidation. The prepared samples were machined using the Electric Discharge Machining (EDM) to achieve accurate dimensions for the study of tribological properties. A computerized pin-on-disc machine is used to study the dry sliding wear behaviour of the Mg-based composites. Wear losses were calculated using a variety of load parameters (10N, 15N, 20N, and 25N), sliding velocities (1 m/s, 1.5 m/s, and 2.0 m/s) and the weight percentage of the reinforcement. The results proved that the wear loss increased with an increase in the load, and decreased with an increase in the sliding velocity. The wear resistance is improved with an increase in the percentage of reinforcements in the matrix. The morphology and distribution of reinforcements were analysed by means of Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray (EDX) spectroscopy, respectively. The results revealed a uniform distribution of TiO 2 particles throughout the magnesium matrix in the Mg-TiO 2 composites.

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“…From our previous paper [32] it was confirmed that no oxides were formed during sintering because of inert gas circulation. Fig.…”

Section: Energy Dispersive Spectroscopy Analysismentioning

confidence: 86%

“…RBFNNs have been applied in the area of ANN, and use as a placement of the sigmoidal unit in Back Propagation NN to reduce the error [30]. Wear is a system-dependent property and therefore, the friction and wear properties depend on the operating environment, load, sliding speed, material properties, grain size and hardness [31][32]. The main objective of this present work is to estimate the size of the pore using different sintering atmospheres, and pore size reduction, during the extrusion process of Finally, the authors have developed a model to predict the mechanical and tribological behavior of Cu-W nano composite material.…”

Section: Introductionmentioning

confidence: 99%

Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network

Leema

Radha²,

Vettivel³

et al. 2015

Materials & Design

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Numerical modelling, prediction of Cu–W nano powder composite in dry sliding wear condition using response surface methodology

Cited by 101 publications

References 32 publications

Dry Sliding Wear Behaviour of Aluminium Al–Si12Cu/TiB2 Metal Matrix Composite Using Response Surface Methodology

Dry Sliding Wear Behaviour of Aluminium Al–Si12Cu/TiB2 Metal Matrix Composite Using Response Surface Methodology

Fabrication and Tribological Behaviour of Mg-TiO2 Composites

Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network

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