Soft Computing Tools To Predict Varied Weight Components, Material and Tribological Properties of Al2219-B4C-Gr

TL;DR

This paper demonstrates the use of soft computing tools like fuzzy logic, decision trees, and genetic algorithms to accurately predict material compositions and properties of Al2219 composites with B4C and graphite, reducing experimental efforts.

Contribution

It introduces the application of multiple soft computing techniques for predicting and optimizing the properties of Al2219-B4C-Gr composites, showing comparable accuracy to neural networks and experiments.

Findings

B4C significantly improves mechanical and wear properties.

Soft computing predictions align with experimental and neural network results.

Multi-objective optimization effectively predicts optimal material compositions.

Abstract

Soft computing tools emerged as most reliable alternatives of traditional regression and statistical methods. In recent times, these tools can predict the optimum material compositions, mechanical and tribological properties of composite materials accurately without much experiment or even without experiment. In the present study, soft computing tools like fuzzy logic, Decision tree, genetic algorithms are employed to predict the reinforcement weight percentage of B4C(Boron Carbide) and Graphite(Gr) along with Aluminum (matrix material) weight percentage for Al2219 with B4C and graphite. The optimized material and tribological properties of Al2219 were also predicted using NSGA II genetic algorithms for multi-objective optimization. It is found that the predictions are at par with earlier ANN (artificial neural network) studies and experimental findings. It can be inferred that inclusion B4C has more impact on enhancement of mechanical properties as well as wear strength compared to Gr.