Artificial Neural Network in Fibre-Reinforced Polymer Composites using ARAS method

Abstract

Fibre-reinforced polymer composites (FRPCs) have garnered considerable interest across industries owing to their lightweight characteristics, impressive strength-to-weight ratio, and resistance to corrosion. The performance of FRPCs is intricately tied to numerous factors encompassing value. fibre alignment, resin attributes, manufacturing intricacies, and ambient conditions. Accurate prediction of FRPCs’ mechanical properties and behavior is pivotal for their effective design and utilization. Artificial Neural Networks (ANNs) have emerged as robust instruments for predictive modeling within materials science and engineering domains. This paper conducts an exhaustive review of ANNs’ application in forecasting the mechanical attributes and conduct of FRPCs. It delves into the architecture of ANNs, prevalent neural network variants, methodologies for data preprocessing, and training algorithms. Moreover, it scrutinizes diverse research endeavors where ANNs have been harnessed to anticipate properties like tensile strength, flexural modulus, impact resistance, and fatigue endurance of FRPCs. Additionally, the paper underscores the merits and constraints associated with ANNs vis-à-vis conventional analytical and empirical models. Lastly, it outlines future avenues for research and potential advancements in ANNs’ deployment for predictive modelling of FRPCs. The efficacy of ANN models in predicting the mechanical behaviour of FRPCs hinges on several critical factors, including data pre-processing techniques and training algorithms. Data pre-processing involves tasks like normalization, feature scaling, and dimensionality reduction, which enhance the efficiency and accuracy of ANN models by ensuring that input data are appropriately formatted and ranged. Training algorithms, such as backpropagation and gradient descent, iteratively adjust the weights of connections between neurons, minimizing the error between predicted and actual outcomes during the training phase.The ARAS (Analytical Hierarchy Process (AHP) and Remote Sensing) methodology represents an innovative approach that merges the principles of AHP with remote sensing techniques to streamline decision-making processes across diverse domains. This methodology capitalizes on the strengths of both AHP, which furnishes a systematic framework for multi-criteria decision-making, and remote sensing, which furnishes valuable spatial insights from satellite or aerial imagery.Model Robustness Across Datasets, Error Distribution Analysis, Prediction Confidence Intervals, Temporal Stability, Sensitivity to Hyper parameters and Transfer Learning Potential. Prediction Accuracy, Generalization Ability, Computational Efficiency, Robustness to Noise and Uncertainty, Interpretability and Feature Importance Analysis. the Rank in Role of AI in Artificial Neural Network in Fibre-Reinforced Polymer Composites using ARAS Method Transfer Learning Potential is showing the highest value and Temporal Stability is showing the lowest value.