Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimization

Edgar O Reséndiz-Flores,Armando Salinas-Rodríguez,Antonio E Salas-Reyes,Frank Goodwin,Patricia S Costa,Gerardo Altamirano-Guerrero

doi:10.3390/met11040578

Abstract

This modeling and optimization study applies a non-linear back-propagation artificial neural network, commonly denoted as BPNN, to model the most important mechanical properties such as yield strength (YS), ultimate tensile strength (UTS) and elongation at fracture (EL) during the experimental processing of hot-dip galvanized dual-phase (GDP) steels. Once the non-linear BPNN is properly trained, the most important variables of the continuous galvanizing process, including initial/first cooling rate (CR1), holding time at the galvanizing temperature of 460 °C (tg) and the final/second cooling rate (CR2), are obtained in an optimal way using an evolutionary approach. The experimental development of GDP steels in continuous processing lines with outstanding mechanical properties (550 < YS < 750 MPa, 1100 MPa < UTS and 10% < EL) is possible by using a combined hybrid approach based in BPNN and multi-objective genetic algorithm (GA). The proposed computational method is applied to the specific design of an actual manufacturing process for the first time.

Highlights

The microstructural evolution during the heat treatments was first analyzed on the basis of the continuous cooling transformation (CCT) and time-temperature-transformation (TTT) diagrams theoretically calculated by means of JMatPro (Java-based Materials Properties)
Unlike yield strength (YS) and ultimate tensile strength (UTS), it can be noted that elongation at fracture (EL) directly related to the ductility of steel tended to decrease with increasing the first cooling rate (CR1) and reducing the time taken for the galvanizing process of steel
The prediction error is lower with the common application of artificial intelligence and genetic optimization compared to the biological-inspired optimization algorithm

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In industrial continuous galvanizing lines (CGL), cold rolled steel sheets are processed using a heat treatment that involves intercritical austenitization, controlled rapid cooling at the galvanizing temperature (460 ◦ C), holding at this temperature for a few seconds, and rapid cooling at room temperature The result of this complex thermal cycle should be a ferritic–martensitic microstructure [21,22]. Mechanical properties influenced by microstructural features, transition temperature, heat treatment conditions and alloying elements for high-strength DP steels were studied using artificial intelligence techniques by Krajewski and Nowacki [36]. In this study a non-linear BPNN combined with a MOGA is applied simultaneously to determine the relationship between mechanical properties and continuous galvanizing parameters to define thermal cycle variables in an optimal manner to produce ultra-high-strength GDP steels with specific mechanical properties. The research is structured as follows: experimental methodology and computational procedures are described in Section 2, some numerical optimal results are obtained and discussed, as well as the corresponding experimental validation, in Section 3, followed by some conclusions in the last section

Materials and Computational Method

Artificial Neural Network Modeling and Back-Propagation

Multi-Objective Optimization Problem

Influence of the Heat Treatment Variables

Optimization and Validation

Conclusions