Optimization on Tribological Behaviour of Milled Nano-B4C Particles Reinforced with AZ91 Alloy Through Powder Metallurgy Method

Sankar, C.; Gangatharan, K.; Singh, S. Christopher Ezhil; Sharma, R. Krishna; Mayandi, K.

doi:10.1007/s12666-019-01618-y

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…From the above literature, it was found that the magnesium alloy composites with silicon carbide reinforcement of 3% or above exhibited excellent mechanical properties. The influence of control parameters on wear and coefficient of friction for magnesium-based composite is predicted using Taguchi, analysis of variance (ANOVA), and regression analysis [15][16][17][18]. The Artificial Neural Network (ANN) models are used for the prediction of optimization of die casting parameters of AZ91D alloy and also discussed the higher efficiency of ANN model on prediction of significant factors [19].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite

Kishor Kumar,

Raghu Kumar,

Mohan Rao

2023

Applied Engineering Letters

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This paper focuses on modeling the tribological properties of AZ31-SiC composite using an artificial neural network (ANN) fabricated through the stir casting method. The twenty-seven tests were performed with three loads (10 N, 15 N, and 20 N), three sliding speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s), and three sliding distances (500 m, 750 m, and 1000 m) on wear testing machine and are used in the formation of training sets of ANN. Using the wear test data, Taguchi, Analysis of Variance (ANOVA), and regression analysis were carried out to determine the effect of the control parameters on the wear and coefficient of friction (COF). The experimental results demonstrate that the wear rate increases with an increase in load and distance and decreases with an increase in velocity. In addition, an alternative method is proposed to predict the wear and COF using ANN modeling with single and multi-hidden layer techniques. With good training, ANN gives accurate and close results to the experimental results. The results obtained using ANN modeling have a percentage of error of 4.71% and 5.79% for wear and COF respectively, when compared to experimental values. This prediction process saves time and costs for the manufacturer.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite

Kishor Kumar,

Raghu Kumar,

Mohan Rao

2023

Applied Engineering Letters

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“…The microstructures of Mg sample changes with average grain size and weight percentage of the reinforcements added [10]. The reinforcement of nanoparticles to the magnesium composites exhibits superior mechanical properties such as hardness, corrosion resistance and tribological behaviour [11].…”

Section: Introduction mentioning

confidence: 99%

Effect of Nano B4C on the Tribological Behaviour of Magnesium Alloy Prepared Through Powder Metallurgy

Chinthamani¹,

Kannan²,

George³

et al. 2020

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In this present study, the particle size of as received magnesium alloy (AZ91) and B4C powders was reduced through high energy ball mills. The combination of AZ91 (both 10 µm and 60 µm) reinforced with nano B4C particles were fabricated by powder metallurgy technique. The incorporation of nano B4C particles to the Mg matrix was done at various weight % such as 5, 10, 15 and 20. The AZ91 composites were fabricated in a suitable die set assembly and the green compacts were sintered in an electric muffle furnace at 500 °C with argon atmosphere for a dwell time of 1 h. The density of the composites was estimated using Archimedes principle. Micro hardness test was carried out for the prepared specimens and dry sliding wear test was conducted by using pin-on-disc apparatus at room temperature with varying loads and sliding velocities by keeping a constant Sliding Distance (SD). Among the various specimens, the composite with 10 µm size attained a higher Vickers hardness value as well as better wear resistant property. Worn surface analysis of the prepared composites was studied using Scanning Electron Microscope (SEM).

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“…The second order equation developed, and it has shown good correlation between the predicted and experimental values [15]. The Taguchi method has been successfully employed for optimizing the process parameter of milling of mild steel; it provides a systematic and efficient methodology for optimal milling parameters [16]. The CoF have been conceded out in this effort.…”

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confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Jeya Raj,

Lenin Anselm,

Jinnah Sheik Mohamed

et al. 2020

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This research paper discusses the friction and wear behaviour of Al-12Si alloy reinforced with B 4 C prepared through Powder Metallurgy (P/M) method by varying the weight percentage of reinforcement (x = 2, 4, 6, 8, and 10) content. The samples were prepared by using die and punch assembly and the lubricant used to eject the sample from the die was molybdenum disulfide. The compaction was done by using a compression testing machine by applying a pressure of 800 MPa. The dry sliding friction and wear behaviour of the sample was conducted on a Pin-on-Disc machine and the experimental values of friction and wear were calibrated. The Taguchi design experiment was done by applying an L25 orthogonal array for 3 factors at 5 levels for the response parameter Coefficient of Friction (CoF) and wear loss. The SEM images show the shape, size and EDX confirm the existence of Al, Si, B 4 C particles in the composites. Analysis of Variance (ANOVA) for CoF of S/N ratio, shows that the reinforcement having 34.92% influence towards the S/N ratio of CoF, ANOVA for wear loss of S/N ratio shows that the sliding distance having 46.76% influence towards the S/N ratio of wear loss, when compared to that of the other two input parameters. The interaction line plot and the 2Dsurface plot for CoF and wear loss show that the increase in B 4 C content decreases the wear loss and CoF. The worn surface shows that the B 4 C addition will increase the wear resistance.

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Optimization on Tribological Behaviour of Milled Nano-B4C Particles Reinforced with AZ91 Alloy Through Powder Metallurgy Method

Cited by 11 publications

References 18 publications

Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite

Artificial Neural Network Modeling of Tribological Parameters Optimization of AZ31-SiC Metal Matrix Composite

Effect of Nano B4C on the Tribological Behaviour of Magnesium Alloy Prepared Through Powder Metallurgy

Bulletin of the Polish Academy of Sciences: Technical Sciences

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