Functional Prototyping and Tooling of FDM Additive Manufactured Parts
Bibliographic record
Abstract
In this paper, we present two additive manufacturing applications: (1) vacuum forming tooling using AM; (2) rocket functional prototype using AM for computational fluid dynamics (CFD) and wind-tunnel testing. The first application shows how additive manufacturing (AM) facilitates the manufacture of vacuum formed parts, which allows such parts to be easily produced especially in the manufacturing sector. We show how combining the advantages of the CAD and FDM technology, vacuum forming can be completed quickly, efficiently and cost effectively. The paper shows that using modified build parameters, the tools FDM creates can be inherently porous, which eliminates the time needed for drilling vent holes that are necessary for other vacuum forming tools, while improving part quality with an evenly distributed vacuum draw. Using SolidWorks CAD software, the model of the tool is created. The STL file is exported to the Insight software, and we present how the Tool Paths Custom Group feature is applied to optimize the tool-paths file and then sent to the FDM system that prints the tooling from ABS engineering thermoplastic. The tooling is then used in the Formech 686 manual vacuum forming machine to produce the vacuum formed part. The second application shows how additive manufacturing (AM) has been applied to producing functional model for wind–tunnel testing, as well as providing computational fluid dynamics (CFD) tool for comparing results obtained from the wind-tunnel testing. The present work is focused on applications of FDM technology for manufacturing wind tunnel test models. The CAD model of a rocket was analyzed for its aerodynamic properties and its functional prototype produced using AM for use in wind–tunnel testing so as to verify and tune the aerodynamic properties. Initial wall conditions were defined for the rocket in terms of the air velocity. The flow simulation was carried out and the goals examined are the velocity and pressure fields around the rocket model. The paper examines some practical issues that arise between how the model geometry for CDF process differs from that that of the FDM process. Consequently, we show that AM-based fused deposition modeling (FDM) technology is faster, less expensive and more efficient than traditional manufacturing processes for vacuum forming and for rapid prototyping of function models for wind-tunnel applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".