Optimization of Metal Additive Manufacturing Processes (2nd Edition)
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Additive Manufacturing".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 2952
Special Issue Editor
Interests: additive manufacturing of metals, especially powder bed melting processes including leaser melting processes and electron beam melting processes; computational and experimental approaches for the optimization of powder bed processes
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Additive manufacturing (AM), also known as 3D printing, utilizes advanced computer algorithms and sophisticated machines to deposit materials layer by layer to form a part. The AM technique is a disruptive technology that has revolutionized manufacturing due to its many advantages, such as its low-cost and rapid prototyping, reduced waste of materials, lack of geometric limitations, freedom in design, and ability to fabricate complex and customized parts, improved product performance, and enhanced material efficiency. However, achieving high product quality and the desired properties and geometries of additively manufactured components is dependent on many different parameters, such as process parameters (i.e., alloy composition, process parameters, and geometry), and is still the common topic of research papers.
This Special Issue aims to present the state-of-the-art achievements in the field of additive manufacturing and its related topics. Papers on experimental work, numerical simulation, or a combination of both are welcome. The specific scopes of interest include but are not limited to:
- Process optimization for reducing defects;
- Approaches in reducing the residual stresses in parts made using AM;
- Design optimization and concurrent design;
- Microstructural manipulation and optimization;
- In situ monitoring of the process including measurements of temperatures, stress, defects, geometry, etc.;
- Use of machine learning and artificial intelligence in process and use of computational and experimental learning approaches;
- New materials and processes;
- Alloy development for AM;
- New AM technologies;
- Microstructural/mechanical characterization techniques;
- Tribology, tribocorrosion, oxidation, and corrosion properties;
- Simulation and modeling of AM processes;
- New applications.
Prof. Dr. Jafar Razmi
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
- additive manufacturing (AM)
- alloy development
- processing
- advanced materials
- characterization
- machine learning
- artificial intelligence
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