Wear rate optimization of Al/SiCnp/e-glass fibre hybrid metal matrix composites using Taguchi method and genetic algorithm and development of wear model using artificial neural networks

2018 ◽  
Vol 5 (3) ◽  
pp. 035005 ◽  
Author(s):  
Arunkumar M Bongale ◽  
Satish Kumar ◽  
T S Sachit ◽  
Priya Jadhav
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wear Rate ◽  
Taguchi Method ◽  
Glass Fibre ◽  
Metal Matrix ◽  
Matrix Composites ◽  
Wear Model ◽  
Rate Optimization

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.

scholarly journals Optimisation of Drilling Parameters of Metal Matrix Composites using Genetic Algorithm in the Taguchi Method

2021 ◽  
Vol 1126 (1) ◽  
pp. 012035
Author(s):  
C V Subba Rao ◽  
Y Sesha Rao ◽  
P Marimuthu ◽  
R Jeyapaul ◽  
N S Kalyan Chakravarthy ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Matrix Composites ◽  
Drilling Parameters

scholarly journals Mechanical and Microstructural Characterization of Friction Stir Welded SiC and B4C Reinforced Aluminium Alloy AA6061 Metal Matrix Composites

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3110
Author(s):  
Kaveripakkam Suban Ashraff Ali ◽  
Vinayagam Mohanavel ◽  
Subbiah Arungalai Vendan ◽  
Manickam Ravichandran ◽  
Anshul Yadav ◽  
...  
Keyword(s):  
Wear Rate ◽  
Aluminium Alloy ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Weld Zone ◽  
Base Material ◽  
Matrix Composites ◽  
Wear Properties ◽  
Friction Stir ◽  
Behavioural Aspects

This study focuses on the properties and process parameters dictating behavioural aspects of friction stir welded Aluminium Alloy AA6061 metal matrix composites reinforced with varying percentages of SiC and B4C. The joint properties in terms of mechanical strength, microstructural integrity and quality were examined. The weld reveals grain refinement and uniform distribution of reinforced particles in the joint region leading to improved strength compared to other joints of varying base material compositions. The tensile properties of the friction stir welded Al-MMCs improved after reinforcement with SiC and B4C. The maximum ultimate tensile stress was around 172.8 ± 1.9 MPa for composite with 10% SiC and 3% B4C reinforcement. The percentage elongation decreased as the percentage of SiC decreases and B4C increases. The hardness of the Al-MMCs improved considerably by adding reinforcement and subsequent thermal action during the FSW process, indicating an optimal increase as it eliminates brittleness. It was seen that higher SiC content contributes to higher strength, improved wear properties and hardness. The wear rate was as high as 12 ± 0.9 g/s for 10% SiC reinforcement and 30 N load. The wear rate reduced for lower values of load and increased with B4C reinforcement. The microstructural examination at the joints reveals the flow of plasticized metal from advancing to the retreating side. The formation of onion rings in the weld zone was due to the cylindrical FSW rotating tool material impression during the stirring action. Alterations in chemical properties are negligible, thereby retaining the original characteristics of the materials post welding. No major cracks or pores were observed during the non-destructive testing process that established good quality of the weld. The results are indicated improvement in mechanical and microstructural properties of the weld.

scholarly journals Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid System ◽  
Nonlinear Time Series ◽  
Photometric Data ◽  
Nonlinear Time Series Analysis ◽  
Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

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