Improving Quality of 3D Printed Components for RPAS using Curved Layer Filament Fabrication
Bibliographic record
Abstract
The work in this thesis, explores methods of Additive Manufacturing applied to Remotely Piloted Aircraft Systems.The objective was to implement and test an alternate method of Fused Filament Fabrication using non-planar/curved layers.This was met by adapting a desktop 3D printer with an adapted nozzle and using a software offering non-planar layers.Both planar and non-planar prints were made to compare the strength using tensile and bend specimens.By completing flexural testing, it was determined that including non-planar layers did not provide a benefit to flexural strength with four or six non-planar top layers.Through printing different types of samples and angles, it was determined that using thicker layers and at low angles, non-planar printing provided improved surface quality.Recommendations for future work includes testing samples with different parameters, and improvements of printing hardware such as a custom printing nozzle or software.iii This thesis is dedicated in loving memory of my father Vladimir Kanevski, who would have been proud to have seen my accomplishments.Thank you to the students of the MC3041 office for their moral support.I couldn't have asked for a better group of hard working people to have as friends and role models.A special thank you to Brendan Ooi for helping with the 3D printer and teaching me how to fix and run it myself, as well as always being a helping hand.Thank you to Olivia Chamberland for helping out when I was out of town and to always being there for me.Thank you to Anthony Dewar in assisting with printing samples needed for testing and for sharing a lot knowledge about 3D printing.Thank you to Steve Truttmann for assistance in mechanical testing, endless knowledge, helpful insight, and moral support.Thank you to David Raude for
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".