Bibliographic record
Abstract
ABSTRACT Technical Presentation: Over the last 20 years, 3D printing has evolved from a technology that was once used primarily by hobbyists into a mainstream prototyping and production tool. Spurred on by the “maker movement” of the 2010's, 3D printing technology enjoyed high levels of adoption in the education sector. As graduates have entered the workforce, 3D printing technology has now made its way into advanced manufacturing facilities for prototype iteration, jigs and fixtures, as well as low piece count production. While many different types of 3D printing technologies exist, currently none is more prevalent than Fused Deposition Modeling (FDM). This presentation will cover FDM technology use cases, materials, limitations, and equipment considerations, and highlight prototype iteration as one of the technology's main advantages in today's advanced manufacturing sector. Biography: Tim Repetski has been working with Centennial College within the Mechanical Engineering, Automation & Robotics, and Aerospace Manufacturing Engineering Technology programs since 2012. Tim currently works as Professor and Program Coordinator for the Aerospace Manufacturing Engineering Technician and Technology Programs that operate out of the Bombardier Centre for Aerospace and Aviation at Downsview Campus in Toronto, Canada. Prior to joining the post-secondary sector, Tim's career has been focused on advanced manufacturing in Ontario, specializing in design and build of production tooling, automation lines, robotic welding, as well as CNC programming and machining. Tim is currently completing a Master of Education at University of Toronto, holds a red seal trade certificate as a licensed Tool and Die Maker, and is a member of Ontario College of Teachers. Files Available to Download: • Slides (PDF) On Demand Webinar
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".