Vickers hardness prediction from machine learning methods

Abstract

The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com.

First-principles study of stability and electronic properties of B2 X–Ru alloys for high-temperature structural applications

“…Furthermore, while the Vickers hardness procedure is used frequently for testing metallic systems and other hard materials, we note that its primary design targets softer materials like plastics and assesses their resistance to deformation under constant stress. However, it was found to be reliable in predicting the hardness of various metallic systems with sufficient accuracy compared to experimental data, 52,53 such as that considered in the current study.…”

“…Open metallic systems with sufficient accuracy compared to experimental data [52,53], such as the one being considered in the current study.…”

Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments

“…Dovale et al used a gradient boosting regressor (GBR) to predict hardness based on mechanical properties such as bulk modulus (B), shear modulus (G), Young's modulus (Y), and Poisson's ratio (ν). They also implemented the classification model (GBC) to predict the best relationship for calculating hardness with these input variables [14]. Jeon et al employed support vector regression (SVR), k-nearest neighbors (kNN), random forest regression (RFR), and artificial neural networks (ANN) to predict the hardness of low-alloy steels under various tempering conditions such as temperature, holding time, and alloy composition.…”