Participatory development of ergonomic design-for-fixture guidelines - A case study
Bibliographic record
Abstract
This paper describes one initiative in a 3 year-collaboration between Research In Motion (RIM) and Ryerson University, the goal of which is to integrate human factors (HF) considerations into the process of designing assembly systems. The RIM-Ryerson steering group suggested this initiative because the engineering group was formalizing their fixture development process with the goal of improving the quality and timeline for fixture design. To incorporate HF into design, research has suggested that the combination of a few specific HF design criteria and active involvement of HF specialists are both critical for positive outcomes. In this initiative, Ergonomists analyzed current assembly fixtures for ergonomics-related concerns. These were shared with nine design engineers in a workshop with a goal of translating the concerns into design guidelines that would prevent the concern. The workshop resulted in 12 design guidelines that are now ergonomic requirements for internal or external vendors. The new fixture development process now includes four process stages where the Ergonomist, working proactively as a design team member, ensures the design meets ergonomics requirements. The stages are: fixture design kick-off meeting to clarify design requirements and initiate the DFMEA (design failure modes effects analysis); the fixture design review; the production tool design sign-off; and lessons learned. The combination of ergonomic design-for-fixture guidelines and the participation of Ergonomists in the fixture design process have the potential for improving assembly ergonomics and quality across thousands of workers.
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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 | 0.043 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| 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".