The additive manufacturing (AM) industry is rapidly developing and expanding, thereby becoming an important and integral component of the digital revolution in manufacturing practices. While the engineering aspects of AM are under intensive research, there still remain many chances to strengthen the sustainability of additive manufacturing (SAM). Cogently increasing the AM community's attention to SAM is vital for developing the AM industry sustainably from the bottom up. The digital nature of AM provides new opportunities for acquiring, storing, and utilizing data to strengthen SAM through data‐driven approaches. Herein, spotlight on SAM is shone upon and it is placed on a more concrete footing. The corresponding advances in data‐driven methods that can strengthen SAM are featured, such as optimizing designs for AM, reducing material waste, and developing databases. How the AM workforce can be developed and grown as a collaboration between the industry, government, and academia to extensively harness the full potential of AM as well as mitigate its adversarial social impact is discussed. Finally, several critical digital techniques that have the potential to further strengthen SAM in the factory of the future, including hybrid manufacturing, Internet of Things, and machine learning and artificial intelligence, are highlighted.
We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.
more » « less- Award ID(s):
- 1944040
- PAR ID:
- 10495582
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Materials
- Volume:
- 16
- Issue:
- 16
- ISSN:
- 1996-1944
- Page Range / eLocation ID:
- 5680
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ma16165680
