Low Alloy Steel Tensile Properties Prediction Using Machine Learning
Deepraj Chetan Patil
Student, Dr. D. Y. Patil Institute of Engineering, Management & Research, Akurdi, Pune, India
Darshan Devendra Kawade
Student, Dr. D. Y. Patil Institute of Engineering, Management & Research, Akurdi, Pune, India
ABSTRACT:
In the current version of industrialization i.e. industry 4.0, implementation of AIML (Artificial Intelligence & Machine Learning) and Automation and Robotics are at the centre of the industries. Here in this project, we are implementing the concepts of ML (Machine Learning) to determine the tensile properties of low alloy steel which is one of the most used materials in the manufacturing industries. Generally, for determining and evaluating the properties of any type of steel traditional method of using UTM (Universal Testing Machine) is preferred, UTM itself is a piece of very costly equipment and it is a destructive type of testing method which are time consuming, labor- intensive and causes wastages of material. So to overcome these cons of UTM methods here we are using data science and machine learning to predict the properties of steel depending upon the composition of its alloying elements. With the help of available standardized data, we are predicting four properties which are Tensile Strength (MPa), 0.2% proof stress (MPa), Elongation (%), and Reduction in Area (%).
Published in: International Journal of Research in Engineering, Science and Management (Volume 7, Issue 11, November 2024)
Page(s): 13-17
Date of Publication: 12/11/2024
Publisher: IJRESM
Cite as: Deepraj Chetan Patil, Darshan Devendra Kawade, “Low Alloy Steel Tensile Properties Prediction Using Machine Learning,” in International Journal of Research in Engineering, Science and Management, vol. 7, no. 11, pp. 13-17, November 2024.