International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019 235 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: C3952098319/19©BEIESP DOI:10.35940/ijrte.C3952.098319 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. Index Terms: Data Science algorithms, Material Science, Mechanical properties of steel, process parameters of steel, Statistical measures of accuracy. I. INTRODUCTION The designers, manufacturers, researchers of material sciences and engineering have habitually contingent on results obtained from the experiments that are conducted in testing laboratory to identify the mechanical properties of any material. So, to obtain the desired properties for a material they need to customize composition of material and process parameters of the material prior of conducting the experiment. But these procedures demand massive expenditure and time to figure out the properties of materials. Different types of materials in material science and engineering includes ceramics, Biomaterials, Composites, Concretes, Electronic and Optical materials, Glasses and Metal alloys. This paper on the whole focuses on Steel which is a metal alloy and prominent material used in wide variety of applications like construction, infrastructure, automobiles, machine appliances, ships, cars, weapons, vessels, household purposes, surgical instruments, etc. Steel is predominantly accustomed in many applications due to its elevated tensile strength and recyclability without Revised Manuscript Received on September 15, 2019 Dr. N. Sandhya, Department of computer science and engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India. V. Sowmya, Department of computer science and engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India. Dr. Chennakesava Rao Bandaru, Department of Mechanical Engineering, Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India. Dr. G. Raghu Babu, Department of Mechanical Engineering, Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India. dropping down the mechanical property values. To meet the modern market requirements and to withstand the competitiveness many industries emphasized to produce high precision and good quality steel products. But using the traditional approach of conducting tensile tests using UTM (universal tensile testing machine) and some other tests are not effective in terms of cost and time consumed for tests for finding out mechanical properties of steel. There is a lot of progress in the recent years by using Data Science techniques in material sciences and engineering for discovering the design, structure, physical and mechanical properties of any material. The main goal of data science is to diminish cost and reduce time mechanical properties prediction to help the manufacturers and designers of advanced material science and engineering. Accurate prediction of material mechanical properties and its behavior based on existing data has been the persistent effort of many material researchers. Data Science has succeeded in adding value to business models with the help of statistics, machine learning and deep learning. The main aim of the Data Science is to develop novel approaches, algorithms, tools, methods and the associated infrastructure to extract the high value information based on the available data and resources. The Data Science techniques are widely classified into machine learning, regression, logistic regression, pattern recognition, Feature selection, Text mining, Attribute modelling, k-means clustering, Association analysis, Anomaly detection, Social network analysis, collaborative filtering, Time series forecasting, Model fitting, cross validation, LTV and RFM Analysis, etc. This paper aims to implement data science techniques to predict tensile strength of Stainless steels. II. RELATED WORK Titus Thankachan.et.al [1] proposed method predicts tensile strength, yield strength of hydrogen charged aluminum alloys using artificial neural network by considering composition of alloy and various processing parameters as an input to the model. GH Senussi.et.al [2] presented an approach for predicting stainless steel micro hardness using artificial neural network, where micro hardness of six different types of stainless steel is predicted based on the distance from the base surface. Raghuram Karthik Desu.et.al [3] predicted Tensile strength, yield strength of Austenitic stainless steel 304L and 316L using feed forward back prorogation neural network. The composition of steels is kept constant while predicting the properties at varying strain rates and at elevated temperatures. Yang Weng.et.al [4] implemented Single Index model that predicts Tensile strength, Yield strength of hot rolled strips of C-Mn steels concerning its chemical composition and processing parameters. Vandana Prediction of Mechanical Properties of Steel using Data Science Techniques N. Sandhya, Valluripally Sowmya, Chennakesava Rao Bandaru, G. Raghu Babu