Mesoscale Simulations for AM Alloys
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Additive Manufacturing".
Deadline for manuscript submissions: closed (1 April 2022) | Viewed by 3636
Special Issue Editor
Interests: simulations; processing-structure-property; additive manufacturing; high-performance computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The special issue will focus on advances in computational approaches, data-driven approaches, and the use of high-performance computing platforms to address the challenges involved in simulating processing-microstructure-property linkages in AM alloys. Specifically, this topic will focus on the following topics: (1) simulating the solidification microstructures and solid-state phase transformations that occur under thermal conditions typical of AM processes for structural, multi-component alloys using either laser or e-beam sources using realistic AM process thermal boundary conditions, (2) post-process heat treatments to optimize structure for meeting specific property requirements, and (3) mesoscale simulations of mechanical response of AM microstructures using crystal plasticity based approaches. The overall goal is to quantify how changes in AM process parameters and/or changes in local cross-sections in an AM component can affect the local microstructure and the local mechanical properties. The emphasis is to highlight how realistic modeling of AM processes can be handled utilizing emerging high performance computational platforms and recent developments in computational approaches including the utilization of GPUs to allow handling of statistically significant computational domain sizes with the spatial resolution required to handle the local structural features and their mechanical response. Finally, the special issue will also include data-driven approaches including machine-learning concepts to develop reduced order models for processing-structure and structure-property relationships for AM-processed structural alloys.
Dr. Balasubramaniam Radhakrishnan
Guest Editor
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Keywords
- high performance computing
- data-driven approaches
- machine learning
- processing
- structure properties
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