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A Survey on Data Science Approach to Predict Mechanical Properties of Steel

  • N. SandhyaEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

In any engineering application it is very important to understand the mechanical properties of the material being used. Data science in the arena of material science and engineering helps manufacturers, designers, researchers and students to better understand the selection, discovery, development of materials for different kind of applications. It helps to find out the properties of the material without performing any experiments and makes easy to find out whether the material is suitable or not for the product we want to develop. Stainless steel is most widely used in all industries because it is environment friendly and can be recycled. It is used in all most all applications like construction purposes, household purposes, etc. The principal purpose of this paper is to survey different data science techniques used by the researchers and scholars in the domain of material science and engineering for predicting the mechanical properties of any metals or materials. This deep literature research is aimed to design a method where a comparative study of different data science algorithms will be done and to identify the algorithms with decent prediction accuracy to be integrated with the GUI (Graphical User Interface) so as to deliberate as a tool that is user-friendly and easy to access. The future work aims at a user-friendly GUI proposed to predict the tensile strength and yield point of the steel by specifying some processing parameters of steel using the data science techniques.

Keywords

Data science techniques Material science Mechanical properties of steel 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.CSE DepartmentVNRVJIETHyderabadIndia

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