Advances in Metal Additive Manufacturing: Process Monitoring, Material Characterization, and Computational Modeling

A special issue of Crystals (ISSN 2073-4352). This special issue belongs to the section "Inorganic Crystalline Materials".

Deadline for manuscript submissions: closed (20 April 2021) | Viewed by 2586

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA
Interests: metal additive manufacturing; grain growth; uncertainty quantification; crystal plasticity; metamaterials; bioinspired materials

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Guest Editor
University of Texas at San Antonio, San Antonio, United States
Interests: additive manufacturing; mechanics of materials; uncertainty quantification

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Guest Editor
Southwest Research Institute, San Antonio, TX, USA
Interests: mechanics of materials; additive manufacturing; material characterization

Special Issue Information

Dear Colleagues,

Metal-based additive manufacturing (AM) is considered a promising technology, with many potential applications due to the process's unparalleled design flexibility. AM works by “building up” a part layer by layer, e.g., adding material rather than removing material. As a result, new designs an innovation can be realized that were not possible with traditional manufacturing. This flexibility is achieved in part because the process is highly localized, promising to deliver engineered material systems with precise control of composition and microstructure.

Recent economic reports have shown that the AM industry has experienced rapid annual growth on the order of approximately 30% from 2010 to 2015. In 2018, the AM industry was valued at over $14 billion globally, and it is projected to grow to $23 billion by 2022 and exceed $350 billion by 2035. However, despite the rapid growth of this technology and its transformative potential, the full utility of this material fabrication technology remains unrealized due to the lack of reproducibility and reliability in the process and the uncertainty in their structural properties of fabricated parts. To overcome these challenges, it is essential to establish relationships that integrate process parameters, thermal history, solidification, resultant microstructure, and mechanical behavior of parts fabricated by AM processes. In this view, the objective of this Special Issue is to highlight recent progress in process monitoring, material characterization, and computational modeling methods aimed at advancing the understanding of the processing parameters-structure-property relationships for metal AM materials. Special attention will be given but not limited to the following topics:

  • Process monitoring in Metal Additive Manufacturing
  • Computational models on Metal Additive Manufacturing
  • Melt pool dynamics
  • Microstructure evolution during the AM process
  • Mechanical response of AM components
  • Microstructure and property relations in AM components
  • Uncertainty quantification in Metal Additive Manufacturing
  • Process optimization in Metal Additive Manufacturing

Prof. David Restrepo
Prof. Dr. Harry Millwater
Dr. Carl Popelar
Guest Editors

Manuscript Submission Information

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Keywords

  • Metal Additive Manufacturing
  • Uncertainty Quantification
  • Process Control
  • Process Monitoring
  • Optimization
  • Grain Growth
  • Crystal Plasticity

Published Papers (1 paper)

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Research

7 pages, 3046 KiB  
Communication
Fast Predictive Model of Crystallographic Texture Evolution in Metal Additive Manufacturing
by Yucong Lei, Milad Ghayoor, Somayeh Pasebani and Ali Tabei
Crystals 2021, 11(5), 482; https://doi.org/10.3390/cryst11050482 - 26 Apr 2021
Cited by 1 | Viewed by 2124
Abstract
This communication introduces a fast material- and process-agnostic modeling approach, not reported in the open literature, that is calibrated for predicting the evolution of texture in metal additive manufacturing of stainless steel 304L as a function of a process parameter, namely the laser [...] Read more.
This communication introduces a fast material- and process-agnostic modeling approach, not reported in the open literature, that is calibrated for predicting the evolution of texture in metal additive manufacturing of stainless steel 304L as a function of a process parameter, namely the laser scanning speed. The outputs of the model are compared against independent validation experiments for the same material system and show excellent consistency. The model also predicts a trend in the change of texture intensity as a function of the process parameter. The major novelty and strength of this work is the model’s speed and extremely light computational load. The model’s calibrations and predictions were carried out in 9.2 s on a typical desktop computer. Full article
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