RT journal article
T1 Virtual Sensor for Estimating the Strain-Hardening Rate of Austenitic Stainless Steels Using a Machine Learning Approach
A1 Contreras Fortes, Julia
A1 Rodríguez García, María Inmaculada
A1 Sales Lérida, David
A1 Sánchez Miranda, Rocío
A1 Almagro, Juan F.
A1 Turias Domínguez, Ignacio José
A2 Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica
A2 Ingeniería Informática
K1 austenitic stainless steels
K1 cold working
K1 strain-hardening rate
K1 cold-rolling curves
K1 multiple linear regression
K1 virtual sensor
K1 machine learning
AB This study introduces a Multiple Linear Regression (MLR) model that functions as a virtualsensor for estimating the strain-hardening rate of austenitic stainless steels, represented by the Hardening Rate of Hot rolled and annealed Stainless steel sheet (HRHS) parameter. The model correlatestensile strength (Rm) with cold thickness reduction and chemical composition, evidencing a robustlinear relationship with an R-coefficient above 0.9800 for most samples. Key variables influencing theHRHS value include Cr, Mo, Si, Ni, and Nb, with the MLR model achieving a correlation coefficientof 0.9983. The Leave-One-Out Cross-Validation confirms the model’s generalization for test examples,consistently yielding high R-values and low mean squared errors. Additionally, a simplified HRHSversion is proposed for instances where complete chemical analyses are not feasible, offering apractical alternative with minimal error increase. The research demonstrates the potential of linearregression as a virtual sensor linking cold strain hardening to chemical composition, providing acost-effective tool for assessing strain hardening behaviour across various austenitic grades. TheHRHS parameter significantly aids in the understanding and optimization of steel behaviour duringcold forming, offering valuable insights for the design of new steel grades and processing conditions.
PB MDPI
SN 2076-3417
YR 2024
FD 2024
LK http://hdl.handle.net/10498/35653
UL http://hdl.handle.net/10498/35653
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026