El análisis del error humano en la manufactura: un elemento clave para mejorar la calidad de la producción
Bibliographic record
Abstract
A pesar del creciente nivel de automatización industrial, el ensamblaje manual continúa desempeñando un rol fundamental en diversos sectores de la manufactura. Sin embargo, las operaciones de tipo manual son susceptibles de errores humanos que ocasionan problemas de calidad y pérdidas económicas. El presente artículo se propone mostrar algunos métodos que permiten identificar diferentes tipos de errores y evaluar la influencia de factores que afectan el desempeño del trabajador. Se muestran, en particular, los métodos SHERPA y HEART. Igualmente se discute sobre la importancia de considerar la complejidad del ensamblaje por su negativo impacto en la carga cognitiva del trabajador lo que puede aumentar la probabilidad de error. En el artículo se emplean conceptos provenientes de la literatura especializada y se realiza una articulación de varias ramas del conocimiento tales como la ergonomía, la ingeniería industrial y la fiabilidad de sistema
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".