Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.6
Journals
Metals
Special Issues
Advances in Quench and Tempered Steels
share announcement

Advances in Quench and Tempered Steels

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Casting, Forming and Heat Treatment".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2456

Special Issue Editor

Dr. Sumit Ghosh

E-Mail Website1 Website2
Guest Editor
Materials and Mechanical Engineering, Centre of Advanced Steels Research, University of Oulu, 90014 Oulu, Finland
Interests: additive manufacturing; surface engineering; structural characterization; thermomechanical simulation; structure–property correlation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Different generations of advanced high-strength steels have been developed over the years, with varying combinations of strength and toughness. In this context, quenching and partitioning (Q&P) and quenching and tempering (Q&T) processes have received significant attention from both academic and industry sectors due to their potential to improve both the uniform and total elongations as well as work hardening-behavior. They have emerged as a promising route for developing ultra-high-strength steels.

When the Q&P process is preceded by thermomechanical rolling comprising controlled rolling in the recrystallization regime, followed by controlled rolling in the no-recrystallization regime, it results in enhanced dislocation density and finer packets and laths of martensite in the transformed microstructure. This leads to enhanced strengthening. To meet the growing challenges, a new optimal Q&P design approach with suitable chemical compositions must be continuously developed. Moreover, a detailed understanding of the physical metallurgy (microstructure, texture), mechanical properties (strength, ductility, toughness, fatigue strength, delayed fracture strength, and wear properties) of different-grade steels obtained through different Q&P/Q&T process conditions will be greatly helpful to steel engineers.

This Special Issue of “Metals” is dedicated to the alloy design, microstructure, and mechanical properties optimization through a novel process design approach of different-grade high-strength steels such as low-alloy TRIP-aided steels, quenching and partitioning steels, carbide-free bainitic steels, medium Mn steels, etc. Review and research articles on the recent progress on advance rapid tempering process and associated micromechanisms are also highly welcome for this issue.

Dr. Sumit Ghosh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quenching and partitioning
  • retained austenite
  • thermomechanical processing/plastic deformation
  • phase transformation
  • mechanical property
  • carbides
  • fatigue/fracture

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 4010 KiB  
Article
Controlled Cooling Temperature Prediction of Hot-Rolled Steel Plate Based on Multi-Scale Convolutional Neural Network
by Xiao Hu, Daheng Zhang, Ruijun Tan and Qian Xie
Metals 2022, 12(9), 1455; https://doi.org/10.3390/met12091455 - 30 Aug 2022
Cited by 4 | Viewed by 2182
Abstract
Controlled cooling technology is widely used in hot-rolled steel plate production lines. The final cooling temperature directly affects the microstructure and properties of steel plates, but cooling and heat transfer constitutes a nonlinear process, which is difficult to be accurately described using a [...] Read more.
Controlled cooling technology is widely used in hot-rolled steel plate production lines. The final cooling temperature directly affects the microstructure and properties of steel plates, but cooling and heat transfer constitutes a nonlinear process, which is difficult to be accurately described using a mathematical model. In order to improve the accuracy of the controlled cooling temperature, a multi-scale convolutional neural network is used to predict the final cooling temperature. Convolution kernels with different sizes are introduced in the layer of a multi-scale convolutional neural network. This structure can simultaneously extract the feature information of different sizes and improve the perceptual power of the network model. The measured steel plate thickness, speed, header flow, and other variables are taken as input. The final cooling temperature is taken as the output and predicted using a multi-scale convolutional neural network. The results show that the multi-scale convolution neural network prediction model has strong generalization and nonlinear fitting ability. Compared with the traditionally structured BP neural network and convolution neural network (CNN), the mean square error (MSE) of the multi-scale convolutional neural network decreased by 24.7% and 12.2%, the mean absolute error (MAE) decreased by 19.6% and 7.97%, and the coefficient of determination (R2) improved by 4.26% and 2.65%, respectively. The final cooling temperature traditional structure by the multi-scale CNN agreed with the actual temperature within ±10% error bands. As the prediction accuracy improved, the multi-scale CNN can be effectively applied to hot-rolled steel plate production. Full article
(This article belongs to the Special Issue Advances in Quench and Tempered Steels)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop