Prediction of Mechanical Properties of Steel using Data Science Techniques
N. Sandhya1, Valluripally Sowmya2, Chennakesava Rao Bandaru3, G. Raghu Babu4
1Dr. N. Sandhya, Department of computer science and engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India.
2V. Sowmya, Department of computer science and engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India.
3Dr. Chennakesava Rao Bandaru, Department of Mechanical Engineering, Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India.
4Dr. G. Raghu Babu, Department of Mechanical Engineering, Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India.
Manuscript received on 4 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 235-241 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3952098319/19©BEIESP | DOI: 10.35940/ijrte.C3952.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Scope of the Article: Bio-Science and Bio-Technology