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.

Perancangan Metode Machine Learning Berbasis Web Untuk Prediksi Sifat Mekanik Aluminium

“…Berbagai macam jenis algoritma machine learning yang digunakan untuk mengetahui sifat mekanik material dari komposisi paduannya dan perlakuan panas tanpa harus merusak spesimen. Penggunaan algoritma machine learning seperti Random Forest, Neural Network, dan Decision Tree dalam memprediksi kekuatan tarik baja memberikan hasil prediksi yang baik [13] selain itu, metode machine learning juga terbukti lebih praktis dalam memprediksi kekuatan lelah material berdasarkan lembar data kelelahan [14] .…”

Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium

“…Data pada penelitian ini diambil pada periode September 2022 dari website matmatch.com yang merupakan sebuah website informasi material bahan terbesar didunia, website ini terdapat menyediakan 70.000 lebih data material dan di portal website ini, dimungkinkan untuk mengakses informasi disediakan oleh ribuan pemasok bahan dari berbagai jenis, termasuk paduan aluminium (Sandhya, N., Sowmya, V., Bandaru, C. R., & Babu, 2019). Website ini merupakan perpustakaan bahan online akses terbuka , yang terdiri dari ribuan entri.…”

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

“…In practice, most of the modelling results in steels and metal alloys presented in the literature concern neural networks with one hidden layer. There are fewer cases of using two [38][39][40][41][42][43] or more hidden layers [30,44].…”

“…Various methods of machine learning, including artificial neural networks, were used, among others, by Geng et al [70] for modelling CCT diagrams of tool steels and Sourmanil and Garcia-Mateo [71] and Rahaman et al [28] for modelling the start temperature of the martensitic phase transformation in steel. Sandhya et al [44] used artificial neural networks and other regression methods to model the tensile strength of corrosion-resistant steels, including Random Forest, Decision Tree, linear regression, and K-Nearest Neighbors.…”