Prediction of mechanical properties of steel using data science techniques

Abstract

Stainless steel is most extensively utilized material in all engineering applications, house hold products, constructions, because it is environment friendly and can be recycled. The principal purpose of this paper is to implement different data science algorithms for predicting stainless steel mechanical properties. Integrating Data science techniques in material science and engineering helps manufacturers, designers, researchers and students in understanding the selection, discovery and development of materials used for various engineering applications. Data science algorithms help to find out the properties of the material without performing any experiments. The Data Science techniques such as Random Forest, Neural Network, Linear regression, K-Nearest Neighbor, Support vector Machine, Decision Tree, and Ensemble methods are used for predicting Tensile Strength by specifying processing parameters of stainless steel like carbon content, sectional size, temperature, manufacturing process. The research here is developed as part of AICTE grant sanctioned under RPS scheme [19] and it aims to implement different data science algorithms for predicting Tensile strength of steel and identifying the algorithm with decent prediction accuracy.