Materials With the Help of Mechanical Properties for Electrical Vehicle Chassis using Machine Learning Methods

Abstract

Material science is a fast-growing research field where artificial intelligence is applied in a variety of applications to provide accurate solutions to the problem. Due to its generalizability, noise tolerance, and fast computation, machine learning algorithms have emerged in recent years as a potent tool for creating correlations between data, and are finding use in materials science. In this research work electric vehicle chassis material selection is done based on the mechanical properties of the material and this is done using machine learning techniques. Machine learning techniques, like logistic regression, K-Nearest Neighbor, Decision Tree, Random Forest, Naïve Base, XGBoost and AdaBoost techniques are used for the same. The stacking technique is also used which combines a variety of ML algorithms for enhanced performance and is observed that the stacking technique gives better accuracy compared to other classifiers. Binary class, as well as multiclass problems, are taken that will give solutions to the electric vehicle chassis selection material. Accuracy scores of different algorithms are compared and found that stacking works reasonably better compared to others.