FDM - 3D printing of thermoplastic composites with high energetic solids content designed for gun propellants
Bibliographic record
Abstract
This study represents an important step forward in the domain of additive manufacturing of energetic materials. It presents the successful formulation and fabrication by 3D printing of gun propellants using Fused Deposition Modeling (FDM) technology, highlighting the immense potential of this innovative approach. The use of FDM additive manufacturing technology to print gun propellants is a significant advancement due to its novel application in this field, which has not been previously reported. Through this study, the potential of FDM 3D-printing in the production of high-performance energetic composites is demonstrated, and also a new standard for manufacturability in this field can be established. The thermoplastic composites developed in this study are characterized by a notably high energetic solids content, comprising 70% hexogen (RDX) and 10% nitrocellulose (NC), which surpasses the conventional limit of 60% energetic solids typically achieved in stereolithography and light-curing 3D printing methods. The primary objective of the study was to optimize the formulation, enhance performance, and establish an equilibrium between printability and propellant efficacy. Among the three energetic formulations developed for 3D printing feedstock, only two were suitable for printing via the FDM technique. Notably, the formulation consisting of 70% RDX, 10% NC, and 20% polycaprolactone (PCL) emerged as the most advantageous option for gun propellants, owing to its exceptional processability, ease of printability, and high energetic performance. • FDM-3D-printed gun propellant formulations with high energetic solids content, while ensuring the printability of the energetic thermoplastic composites, were obtained. • The 3D-printed energetic composites surpass the typical 60 wt.% energetic solids loading limit in gun propellants obtained via stereolithography or light curing 3D printing. • The use of FDM additive manufacturing technology to print gun propellants is a significant advancement due to its novel application in this field, which was not previously reported. • The FDM printing strategy for gun propellants offers both performance enhancement and scalability of the fabrication with practical implications for the defense and security sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".