Recent Progress in Materials  (ISSN 2689-5846) is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. This periodical is devoted to publishing high-quality papers that describe the most significant and cutting-edge research in all areas of Materials. Its aim is to provide timely, authoritative introductions to current thinking, developments and research in carefully selected topics. Also, it aims to enhance the international exchange of scientific activities in materials science and technology.
Recent Progress in Materials publishes original high quality experimental and theoretical papers and reviews on basic and applied research in the field of materials science and engineering, with focus on synthesis, processing, constitution, and properties of all classes of materials. Particular emphasis is placed on microstructural design, phase relations, computational thermodynamics, and kinetics at the nano to macro scale. Contributions may also focus on progress in advanced characterization techniques.          

Main research areas include (but are not limited to):
Characterization & evaluation of materials
Metallic materials 
Inorganic nonmetallic materials 
Composite materials
Polymer materials
Biomaterials
Sustainable materials and technologies
Special types of materials
Macro-, micro- and nano structure of materials
Environmental interactions, process modeling
Novel applications of materials

Publication Speed (median values for papers published in 2023): Submission to First Decision: 5.3 weeks; Submission to Acceptance: 12.6 weeks; Acceptance to Publication: 7.5 days (1-2 days of FREE language polishing included)

Current Issue: 2024  Archive: 2023 2022 2021 2020 2019

Special Issue

Additive Manufacturing Technology in Construction

Submission Deadline: May 31, 2025 (Open) Submit Now

Guest Editor

Seyed Ghaffar, BEng (Hons.), PhD, CEng, FICE, FHEA, MICT

Associate Professor in Civil Engineering

Director of Additive Manufacturing Technology in Construction (AMTC) Research Group 

Department of Civil & Environmental Engineering, Brunel University London, UK

Website | E-Mail

Research Interests: additive manufacturing of cementitious composites; concrete 3D printing; civil engineering materials; advanced functional materials; low-carbon concrete

About This Topic

Construction is an important economic engine, but also one of the largest consumers of resources and energy. The modern construction industry is undergoing a period of dramatic policy shift and priority change from a profit-charged business machine to a socio-economic and environmentally driven sector. Progress in the construction industry to implement additive manufacturing (AM) as an eco-innovative solution will be the focus of this Special issue with reference to essential parameters and the practical changes needed to realize a holistic solution that will potentially bring many benefits to the construction industry. Additive manufacturing technology has the potential to help the construction industry transition into a responsive and technically advanced sector.

Manuscript Submission Information

Manuscripts should be submitted through the LIDSEN Submission System. Detailed information on manuscript preparation and submission is available in the Instructions for Authors. All submitted articles will be thoroughly refereed through a single-blind peer-review process and will be processed following the Editorial Process and Quality Control policy. Upon acceptance, the article will be immediately published in a regular issue of the journal and will be listed together on the special issue website, with a label that the article belongs to the Special Issue. LIDSEN distributes articles under the Creative Commons Attribution (CC BY 4.0) License in an open-access model. The authors own the copyright to the article, and the article can be free to access, distribute, and reuse provided that the original work is correctly cited.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). Research articles and review articles are highly invited. Authors are encouraged to send the tentative title and abstract of the planned paper to the Editorial Office (rpm@lidsen.com) for record. If you have any questions, please do not hesitate to contact the Editorial Office.

Welcome your submission!

Publication

Open Access Original Research

Influence of Thermal Environment Conditions on Mechanical Properties of MEX Printed 17-4 PH Stainless Steel

Received: 18 February 2024;  Published: 22 August 2024;  doi: 10.21926/rpm.2403020

Abstract

This study investigates the influence of thermal environmental conditions during printing and their effects on the mechanical properties of material extrusion (MEX) 3D-printed 17-4 PH stainless steel. Various ambient temperatures and cooling behaviors were used during the printing process of the tensile specimens. Following DIN EN ISO 50125 [...]
Open Access Original Research

Detection of Anomalies in Additively Manufactured Metal Parts Using CNN and LSTM Networks

Received: 10 January 2023;  Published: 26 July 2023;  doi: 10.21926/rpm.2303028

Abstract

The process of metal additive manufacturing (AM) involves creating strong, complex components by using fine metal powders. Extensive use of AM methods is expected in near future for the production of small and medium-sized batches of end-use products and tools. The ability to detect loads and defects would enable AM components to be used in [...]
Open Access Original Research

Automated Quality and Process Control for Additive Manufacturing using Deep Convolutional Neural Networks

Received: 24 November 2021;  Published: 28 February 2022;  doi: 10.21926/rpm.2201005

Abstract

Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained [...]
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