Advances in Modelling for Additive Manufacturing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Materials Science and Engineering".
Deadline for manuscript submissions: closed (1 April 2021) | Viewed by 9550
Special Issue Editors
Interests: computational intelligence; human-level machine intelligence; artificial intelligence; interpretable machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: Simulation of Manufacturing and Materials (PRISM2); microstructure; manufacturing process; engineering alloys. ICME frameworks; simulation of materials behaviour relevant to manufacturing and in-service conditions
Special Issue Information
Dear Colleagues,
The topic of Additive Manufacturing has seen a dramatic increase in research activity in recent years, with significant attempts to better understand processes at a fundamental level, develop hardware and software technologies that will enable better process monitoring, control and optimisation, as well as develop computational frameworks to ensure robust manufacture. At the core of many research efforts is the non-trivial task of development and use of mathematical models to capture and understand process-powder-part behaviours. A range of mathematical modelling methods are used, such as physics-driven analytical and numerical methods as well as data-driven methods relying on data-science and machine learning attempts. In this special issue, we aim to exemplify state-of-the-art modelling approaches with application to additive manufacturing, towards demonstrations of better understanding underlying AM physics and processes, as well as demonstrations of standalone models or integrated model-based systems that aim to monitor, control or optimise an AM process. Case studies describing real-life applications within laboratory or industrial environments are also very welcome.
Prof. Dr. George Panoutsos
Prof. Dr. Hector Basoalto
Guest Editor
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Keywords
- Additive Manufacturing
- Modelling in Additive Manufacturing
- Numerical Modelling
- Data-Driven Modelling
- Machine Learning
- Process Optimisation
- Process Control
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