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Additive manufacturing (AM) has emerged as a promising approach to achieve energetic materials (EMs) with intricate geometries and controlled microstructures, which are crucial for safety and performance optimization. However, current AM methods still face limitations such as limited densities and inadequate solids loading. To overcome these limitations, we have developed a pressure‐assisted binder jet (PBJ) process that has the potential to allow for the fabrication of intricate EMs while preserving their desired properties. This study aims to investigate the effects of printing parameters on the microstructures and properties of EMs, including density, solids loading, mechanical properties, and heterogeneity. Our results demonstrate that the PBJ process achieves exceptional properties in EMs, including densities up to 83.4 % and solids loading up to 95.4 %, surpassing those achieved by existing AM processes. Furthermore, the mechanical properties of the fabricated EMs are comparable to those achieved using conventional fabrication techniques, including a compressive strength of 3.32 MPa, a Young's modulus of 16.68 MPa, a Poisson's ratio of 0.45, a shear modulus of 5.73 MPa, and a bulk modulus of 21.01 GPa. Various test cases were printed to showcase the ability of the PBJ process to create EMs with complex structures and exceptional properties. Micro‐computed tomography was employed to analyze the influence of printing parameters on the internal composition and microstructures of the printed specimens.
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Tuning Young’s modulus in multiphoton lithography
Since Multiphoton Lithography (MPL) is applied as an additive manufacturing technique for the fabrication of operational microsystems, the need to predict the mechanical response of the fabricated structures emerges. This work focuses on determining the Young's Modulus of structures fabricated via MPL. With this objective in mind, two series of experiments were designed and conducted: the first one for the determination of the factors whose impact is significant, and the second one for generating a dataset used in the training of a machine learning tool that will define the suitable set of fabrication parameters for the fabrication of a structure with desired Young's Modulus.
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- Award ID(s):
- 2124826
- PAR ID:
- 10542975
- Editor(s):
- Kabashin, Andrei V; Farsari, Maria; Mahjouri-Samani, Masoud
- Publisher / Repository:
- SPIE
- Date Published:
- ISBN:
- 9781510670082
- Page Range / eLocation ID:
- 12
- Format(s):
- Medium: X
- Location:
- San Francisco, United States
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1117/12.3005653
