Advances in Additive Manufacturing: Horizons of Novel Processes and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 383
Special Issue Editor
Interests: phase transitions; reactive diffusion; heat treating of metallic alloys; ceramic materials; composite materials; powders technologies; additive manufacturing; advanced materials characterization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Additive manufacturing (AM), being one of the most common technological methods worldwide nowadays, is a modern promising approach, for which scientific and technological boundaries are still not understood clearly due to the extremely fast development that has occurred in recent years. AM is highly attractive to scientists, as well as to mechanical, materials, chemical, and biomedical engineers and technologists, and it involves not only different important issues of physics and chemistry, but also machinery, software, machine learning, process organization, control, optimization and repeatability, economics, materials characterization, failure analysis and quality assurance, etc. The main reason for its popularity is the unique possibility of combining AM with different 3D printing technologies relevant to a wide spectrum of materials (metals, ceramics, composites, polymers, organics, etc.) with processes digitalization and artificial intelligence.
This Special Issue provides a forum for researchers, engineers, and practitioners to report their latest achievements and to highlight critical issues and challenges for future investigations of the design and applications of 3D printing and additive manufacturing.
Dr. Alexander Katz-Demyanetz
Guest Editor
Manuscript Submission Information
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Keywords
- additive manufacturing
- powdered raw materials
- process parametrization
- as-built product
- post-treatment
- product’s final density
- mechanical properties
- process simulation
- machine learning
