Bibliographic record
Abstract
This project addresses several key barriers to wide-spread adoption of additive manufacturing (AM) technology as applied to solid state lighting luminaires. The solution will utilize cutting edge AM approaches for integrating structure with thermal management solutions, electronic functionality, and optics. The research team (Eaton, Lighting Research Center (LRC) at Rensselaer Polytechnic Institute, Xerox Research Centre of Canada (XRCC)) utilize their AM and lighting expertise to investigate breakthrough manufacturing approaches that will significantly reduce cost, eliminate manufacturing process waste, and improve luminaire efficacy. The team has identified critical areas of research and proposed novel technical approaches to achieve these goals. Key areas of focus in Budget Period 1 (BP1) of the project quantified the impact of applying AM methodologies to the main, discrete subsystem components (Heat Sink, Housing, Optics, Electronics). Budget Period 2 (BP2) research explored similar impact on a fully integrated, AM modular luminaire concept. Final Achievement of the Target Metrics for the project are as follows: Material Reduction: achieved > 57.45% (target is 50%) Manufacturing Process: achieved > 51% reduction (target is 50%) Application Efficacy: achieved 126 lm/W (target is 130 lm/W) First Cost vs Baseline: demonstrated 49% improvement in project timing, 59% improvement in man hour savings and 89% worse BOM costs (due to deficiencies in current “state of the art” equipment). The BOM costs improve to 53% savings if state of the art processes and equipment could have been used.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".