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Metals
Special Issues
Welding and Joining of Advanced High-Strength Steels (2nd Edition)
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Welding and Joining of Advanced High-Strength Steels (2nd Edition)

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Welding and Joining".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1120

Special Issue Editor

Prof. Dr. Víctor H. Baltazar-Hernández

E-Mail Website
Guest Editor
Materials Science and Engineering Program, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico
Interests: Advanced microstructural characterization (XRD, SEM, TEM, nanoindentation); welding metallurgy; welding of ferrous and non-ferrous metals; mechanical properties and formability of AHSS; welding processes (RSW, FSW, arc and laser); hardfacing and coating technology; wear
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays advanced high strength steels (AHSS) are being predominantly employed by the automotive industry. Among the main advantages, AHSS provide improved fuel efficiency (due to weight reduction by downgauging), and accomplish passenger safety requirements (enhanced crashworthiness behavior), without compromising the overall properties. The AHSS family (Dual Phase, DP; Complex-Phase, CP; Ferritic-Bainitic, FB; Martensitic, MS; Transformation-Induced Plasticity, TRIP; Hot-Formed, HF; Twinning-Induced Plasticity, TWIP; boron-based Press Hardened Steels, PHS; and Quenching & Partitioning, Q&P), possess sophisticated and unique multiphase microstructures that provides them with extraordinary strength, ductility, toughness, fatigue, and/or a combination of such properties.

Welding and joining of AHSS is challenging; in particular, when choosing the adequate technique according to established requirements e.g., portability, cost, heat input, welding speed, joint and design restrictions, etc. There is an ample number of available technologies utilized for joining AHSS such as resistance spot welding (RSW), laser welding, friction stir spot welding (FSSW), arc welding processes (GMAW, TIG, Plasma), arc stud welding, high frequency induction welding (HFIW), magnetic pulse welding (MPW), brazing procedures (GMA, plasma, laser), adhesive bonding, hybrid welding, mechanical joining, etc.

This Special Issue on welding and joining of AHSS aims to cover various topics of interest (but not limited): microstructure-property relationships, welding metallurgy (weld pool solidification, phase transformations, etc.), performance and properties (strength, impact, fatigue), dissimilar metal joining (i.e., AHSS-others), forming and manufacturing of tailored welded blanks, weldability of AHSS, progress in related welding processes, welding and joining process simulation, neural network applications, industrial applications, weld inspection and repair.

In this Special Issue, you are invited to submit original research articles and reviews.

I look forward to receiving your contributions.

Prof. Dr. Víctor H. Baltazar-Hernández
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

  • Advanced High Strength Steels (AHSS)
  • welding and joining of AHSS
  • microstructure-property relationship of welded AHSS
  • welding manufacturing of AHSS
  • applications and performance of welded AHSS

Published Papers (1 paper)

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Research

17 pages, 3429 KiB  
Article
Modeling Yield Strength of Austenitic Stainless Steel Welds Using Multiple Regression Analysis and Machine Learning
by Sukil Park, Myeonghwan Choi, Dongyoon Kim, Cheolhee Kim and Namhyun Kang
Metals 2023, 13(9), 1625; https://doi.org/10.3390/met13091625 - 20 Sep 2023
Viewed by 874
Abstract
Designing welding filler metals with low cracking susceptibility and high strength is essential in welding low-temperature base metals, such as austenitic stainless steel, which is widely utilized for various applications. A strength model for weld metals using austenitic stainless steel consumables has not [...] Read more.
Designing welding filler metals with low cracking susceptibility and high strength is essential in welding low-temperature base metals, such as austenitic stainless steel, which is widely utilized for various applications. A strength model for weld metals using austenitic stainless steel consumables has not yet been developed. In this study, such a model was successfully developed. Two types of models were developed and analyzed: conventional multiple regression and machine-learning-based models. The input variables for these models were the chemical composition and heat input per unit length. Multiple regression analysis utilized five statistically significant input variables at a significance level of 0.05. Among the prediction models using machine learning, the stepwise linear regression model showed the highest coefficient of determination (R2) value and demonstrated practical advantages despite having a slightly higher mean absolute percentage error (MAPE) than the Gaussian process regression models. The conventional multiple regression model exhibited a higher R2 (0.8642) and lower MAPE (3.75%) than the machine-learning-based predictive models. Consequently, the models developed in this study effectively predicted the variation in the yield strength resulting from dilution during the welding of high-manganese steel with stainless-steel-based welding consumables. Furthermore, these models can be instrumental in developing new welding consumables, thereby ensuring the desired yield strength levels. Full article
(This article belongs to the Special Issue Welding and Joining of Advanced High-Strength Steels (2nd Edition))
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