Machine learning approach for predicting yield strength of nitrogen-doped CoCrFeMnNi high entropy alloys at selective thermomechanical processing conditions

The yield strength property is important to consider while designing high entropy alloys (HEAs). In order to obtain the desired yield strength of HEAs through the experimental method it is a difficult, expensive, and time-consuming process due to the broad compo-sition space available. The room temperature yield strength property of nitrogen-doped (CoCrFeMnNi)100-x-Nx HEAs at preferred thermomechanical conditions was predicted using the machine learning (ML) technique based on the linear regression model in the present investigation. The yield strength prediction result of 2% nitrogen-doped CoCr-FeMnNi HEA subsequently cold-rolled 91 (%) and annealed at 850 & DEG;C temperature consisting of 563.6 MPa is consistent with the experimental value of 556 MPa. It implies that the yield strength predictions of (CoCrFeMnNi)100-x-Nx HEAs are accurate. As a result, selecting suitable models and material parameters to design a wide range of materials with superior properties attributed to various compositions of HEAs through ML technology could be a potential approach.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

The yield strength property is important in the design of high entropy alloys (HEAs). However, experimental determination of the desired yield strength of HEAs is difficult, expensive, and time-consuming due to the vast composition space. This study utilized machine learning (ML) techniques to predict the room temperature yield strength of nitrogen-doped (CoCrFeMnNi)100-x-Nx HEAs under preferred thermomechanical conditions, achieving accurate predictions and demonstrating the potential of ML for designing HEAs with superior properties.