Materials

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Dear Colleagues,

Engineering materials play an important role in many industry sectors, such as machine engineering, construction, etc. Due to the rapid development of industrial branches, as well as the requirements for materials in terms of mechanical properties, the development of new or current production and forming technologies is essential. The improvement of material properties in order to increase the mechanical strength and hardness, while reducing the production costs and the energy consumption, should also be included.

We are pleased to invite you to publish works related to various aspects of technologies and materials. Reviews, original research articles and short communications are welcome.

Detailed topics of interest include, but are not limited to, the following:

- progressive materials for engineering production and methods of technological workability,
- new observations from theory of technological processes of metal forming, welding, surface treatment, machining or plastic processing,
- progressive mechanical engineering technologies,
- experimental, computational and simulation methods in mechanical engineering technologies,
- products quality and production designing, lean manufacturing instruments (LM),
- ecological aspects of engineering technologies,
- application of modern materials and technologies in different industrial areas, especially in aviation and automotive sectors,
- other related topics.

We look forward to receiving your contributions.

Dr. Marta Wójcik
Dr. Tomasz Trzepieciński
Prof. Dr. Ján Slota
Guest Editors

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. Materials is an international peer-reviewed open access semimonthly 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

progressive materials
metals
composites
polymers
metal forming
welding
plastic deformation methods
engineering technologies
computational methods
production designing

Published Papers (1 paper)

Research

22 pages, 10485 KiB

Open AccessArticle

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

by Piotr Myśliwiec, Andrzej Kubit and Paulina Szawara

Materials 2024, 17(7), 1452; https://doi.org/10.3390/ma17071452 - 22 Mar 2024

Viewed by 660

Abstract

This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × 3 factorial design was employed to explore tool rotation speeds (1100 to 1300 rpm) and welding speeds (140 to 180 mm/min). Static tensile tests revealed the joints’ maximum strength at 87% relative to the base material. Hyperparameter optimization was conducted for machine learning (ML) models, including random forest and XGBoost, and multilayer perceptron artificial neural network (MLP-ANN) models, using grid search. Welding parameter optimization and extrapolation were then carried out, with final strength predictions analyzed using response surface methodology (RSM). The ML models achieved over 98% accuracy in parameter regression, demonstrating significant effectiveness in FSW process enhancement. Experimentally validated, optimized parameters resulted in an FSW joint efficiency of 93% relative to the base material. This outcome highlights the critical role of advanced analytical techniques in improving welding quality and efficiency. Full article

(This article belongs to the Special Issue Progressive Technologies and Materials in Mechanical and Materials Engineering)

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