Mechanical properties prediction of various graphene reinforced nanocomposites using transfer learning-based deep neural network

Abstract

Nowadays, various machine learning (ML) approaches are widely used in different research areas. However, the need for a large training dataset has restricted the attractiveness of ML techniques for industrial applications, since the preparation of a large dataset is very costly and inefficient. To deal with this limitation, an efficient method is required to fill the gap between industry and research. For this purpose, in this study a transfer learning-based deep neural network (TL-DNN) model was developed to predict the mechanical properties of various graphene reinforced nanocomposites. In this respect, a hybrid multi-layer feedforward DNN was designed, containing one source network and one target network. The source DNN was trained to predict the mechanical properties of graphene/graphene oxide nanocomposites with various matrix types including Al, Cu, PMMA, Si3N4, Al2O3, etc. By transferring the acquired knowledge of the source DNN to the target DNN, the mechanical properties of another material (graphene/epoxy nanocomposite) were estimated with high accuracy level, even with limited number of data samples. It should be mentioned that the optimal values of the network hyperparameters were determined using genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) algorithms.

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

“…Adhesion between the sliding surfaces (Van der Walls, ionic, covalent, and metal bonds) contributes to COF [20]. Friction coefficient d fluctuates according to mechanical properties, including solidity, strength, and modulus, by the micro and macroscopic distortion of severities of the tribosurface.…”

“…It takes a lot of time and effort to set up a battery of tribological testing rigs and prepare samples of widely variable material characteristics that analyze to yield the required data. In order to develop reliable ML representations and have amassed tribological behavior data for Grenhanced AA7075 composites from the existing literature [20]. The developed ML models that can forecast COF and wear rate based on analyses of datasets, including 432 and 390 specimen data points, respectively.…”

Vibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning

“…In particular, ML techniques have shown great potential in material design. Transfer learning-based deep neural networks were developed by Pashmforoush [18] to predict the mechanical properties of various graphene-reinforced nanocomposites, even with limited data samples. A machine-learning model is utilised to estimate the temperature-dependent moduli of neat, thermally reduced graphene and covalently functionalised graphene/epoxy nanocomposites [19].…”

Comparative Review on Machine Learning-Based Predictive Modeling for Mechanical Characterization

“…Mechanical characterization consists of a range of methodologies that are particularly developed to evaluate the physical properties and responses of materials when subject to different stress and strain situations [7]. The fundamental tests comprise three main types: tensile, compressive, and shear evaluation.…”