Experimental study and prediction using ANN on mass loss of hybrid composites

Abstract

Purpose -The purpose of this paper is to produce Al6061 metal matrix composites reinforced with silicon carbide (SiC) and graphite particulates and study their wear behavior and also to develop artificial neural network model to predict the mass loss of hybrid composites. Design/methodology/approach -The hybrid composites were produced by using stir casting process. The experiments were conducted based on the central composite rotatable design matrix using pin-on-disc wear testing machine. The set of data collected from the experimental values were used to train a back propagation (BP) learning algorithm with one hidden layer network. In artificial neural network (ANN) training module, four input vectors were used in the construction of proposed network namely, weight percentage of SiC particles, weight percentage of graphite particles, applied load and sliding distance. Mass loss was the output to be obtained from the proposed network. After training process, the test data collected from the experimental values were used to check the accuracy of proposed ANN model. Findings -The results show that the well trained one hidden layer network have smaller training errors and much better generalization performance and can be successfully used for the prediction of mass loss of hybrid aluminium metal matrix composites. Originality/value -In this paper the ANN method was adopted to predict the mass loss of hybrid composites. It was found that artificial neural network can be successfully used for prediction of mass loss of composites.

Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network

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“…Recently artificial neural network (ANN) has been utilized as an effective tool to predict an objective variable from multiple factors influencing the variable. Predictions of mechanical properties using ANN models have been reported in metals and alloys, 6–13 ceramics, 14 and metal‐ceramic composites 15–17 . In these studies, mechanical properties as objective variables were regression‐predicted from features such as material compositions, processing parameters, and so forth.…”

Artificial Neural Network Modeling of Abrasion Loss and Surface Roughness of Crab Carapace Impregnated Coir Vinyl Ester Composites

“…e grounded particles were used as filler for coir vinyl ester composites. e major compositions of crab carapace were carbon 37.77%, oxygen 29.86%, and calcium 32.37% [23].…”

Prediction of friction coefficient of su-8 and its composite coatings using machine learning techniques

“…Ray et al 29 used ANN, 4-7-1 architecture, to predict wear loss of glass/polymer composite to obtain a percentage error of less than 5%. Velmurugan et al 30 presented a deep learning network capable of predicting mass loss for composite materials due to wear at an error of 6%. Many such studies in literature have shown promising performance of soft computing, Artificial neural networks (ANN), for predicting wear rate and other tribological properties for different composite materials including metal alloy metal composites, polymeric hybrid composites, powdered chip reinforcement based composites etc.. [31][32][33][34] By taking the advantage of different machine learning (ML) techniques, different multi-variable factors could be reasonably co-related to predict the surface properties of interest ML techniques have been used in the recent years by material and tribology experts to generate complex co-relations between factors such as material constituents, mechanical/tribological properties.…”