Material Selection Using Mechanical Properties (Decision Tree Using Gini Index)

Abstract

In this research streamlines the material selection process based on mechanical qualities by using decision trees, specifically by using the Gini index. In mechanical engineering, material selection is important since the best options affect component performance. Conventional approaches frequently depend on subjective assessment, which results in discrepancies. This project uses a Python software to recommend the optimal material based on user-defined requirements by objectively evaluating key attributes like tensile strength and hardness using a decision tree model. The method guarantees a consistent, data-driven material selection process, reduces human error, and increases the precision of decision-making.