A Study on the Al2O3 reinforced Al7075 Metal Matrix Composites Wear behavior using Artificial Neural Networks

2018 ◽  
Vol 5 (5) ◽  
pp. 11376-11385 ◽  
Author(s):  
R. Pramod ◽  
G.B. Veeresh Kumar ◽  
P.S. Shivakumar Gouda ◽  
Arun Tom Mathew
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Wear Behavior ◽  
Matrix Composites ◽  
Artificial Neural

scholarly journals Fatigue Life Predictions of Metal Matrix Composites Using Artificial Neural Networks

2014 ◽  
Vol 59 (1) ◽  
pp. 97-103 ◽  
Author(s):  
I. Uygur ◽  
A. Cicek ◽  
E. Toklu ◽  
R. Kara ◽  
S. Saridemir
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Life ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Volume Fraction ◽  
Matrix Composites ◽  
R Ratio ◽  
Artificial Neural ◽  
Life Predictions

Abstract In this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geometries, and different temperatures have been performed by using artificial neural networks (ANN) approach. Input parameters of the model comprise various materials (M), such as particle size and volume fraction of reinforcement, stress concentration factor (Kt), R ratio (R), peak stress (S), temperatures (T), whereas, output of the ANN model consist of number of failure cycles. ANN controller was trained with Levenberg-Marquardt (LM) learning algorithm. The tested actual data and predicted data were simulated by a computer program developed on MATLAB platform. It is shown that the model provides intimate fatigue life estimations compared with actual tested data.

Modeling the abrasive wear behavior of in-situ synthesized magnesium RZ5/TiB2 metal matrix composites

2021 ◽  
pp. 095440892110655
Author(s):  
Arabinda Meher ◽  
Manas Mohan Mahapatra ◽  
Priyaranjan Samal ◽  
Pandu R. Vundavilli
Keyword(s):  
Abrasive Wear ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Tribological Behavior ◽  
Wear Behavior ◽  
Wear Test ◽  
Regression Equation ◽  
Matrix Composites ◽  
Worn Surface ◽  
Anova Technique

In the present study, the statistical analysis on tribological behavior of RZ5/TiB2 magnesium-based metal matrix composites is carried out using Taguchi design and analysis of variance (ANOVA) technique. Taguchi analysis using signal-to-noise ratio indicates that the sliding distance and wt.% TiB2 are the most significant factors in evaluating weight loss and coefficient of friction, respectively. The regression equation is formulated utilizing the ANOVA technique to study the output responses based on the input abrasive wear test experimental results. The regression equation is validated through a comprehensive study taking a series of abrasive wear tests and indicates the percentage deviation of regression modeling is in the range of ± 10%. The individual and combined effect of wear parameters on tribological behavior are investigated through the main effect plots and response surface plots. The micrograph of the worn surface of RZ5/TiB2 composites is studied using field emission scanning electron microscope (FESEM), indicating the formation of an oxide layer on the worn surface.

Wear, optimization and surface analysis of Al-Al2O3-TiO2 hybrid metal matrix composites

2020 ◽  
Vol 235 (1) ◽  
pp. 93-102
Author(s):  
Naseem Ahamad ◽  
Aas Mohammad ◽  
Kishor Kumar Sadasivuni ◽  
Pallav Gupta
Keyword(s):  
Wear Rate ◽  
Metal Matrix Composites ◽  
Vickers Hardness ◽  
Hybrid Composite ◽  
Metal Matrix ◽  
Wear Behavior ◽  
Equal Proportion ◽  
Matrix Composites ◽  
Hybrid Metal Matrix Composites ◽  
Worn Surface

The aim of the present work is to investigate vickers hardness, wear behavior as well as to perform optimization of wear data for pure Al and Al-Al2O3-TiO2 hybrid metal matrix composites. The hybrid composite (Al-Al2O3-TiO2) was prepared by mechanical stir casting with equal proportion of reinforcement (2.5, 5.0, 7.5 and 10 wt.%). Vickers hardness, wear behavior and its optimization using ANOVA as well as TOPSIS along with the microstructure of the worn surface of prepared sample has been investigated. Vickers hardness increases with an increase in weight percentage of reinforcements. Wear test was carried out under dry sliding condition by pin-on-disc wear machine according to the ASTM G99-95a standard. Wear properties of the sample have been obtained at different percentages of reinforcement. Wear resistance of the hybrid composite increases with the variation of percentage of titanium oxide particles due to its lubricating properties. ANOVA shows that the reinforcements and load have different effect on samples wear rate. TOPSIS analysis shows rank of the sample according to its wear rate. Worn surface morphology was investigated and it showed deep grooves, more debris, delamination and rough surface in pure Al sample as compared to the high percentage of reinforced hybrid metal matrix composites.

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