Monitoring Respirable Dust Exposure in Fettling Work Environment of a Foundry: A Proposed Design Intervention
Bibliographic record
Abstract
Foundry workers are exposed to numerous health hazards, which includes respirable dust exposure as a prominent health issue. Apart from metallic dust, respirable crystalline silica (RCS) is generated during the fettling operation, which is very hazardous and a leading cause for silicosis. Evaluation and control of such hazards deserve particular attention in order to reduce the dust exposure level upto the permissible limits. In present study, an attempt was made to propose a low-cost design control intervention utilizing sensor based intelligence (ATmega328P microcontroller and an optical dust-sensor, SHARP GP2Y1010AU0F) for monitoring the dust exposure, followed by virtual ergonomics approach in CATIAV5R20. The proposed device was tested under several operating conditions. The results indicated that the prototype was sensitive to dust, having higher average dust density values under the dust haze weather condition (arithmetic mean (AM): 0.2590 mg/m3, geometric mean (GM): 0.2350 mg/m3) and fettling work-section (AM: 0.2550 mg/m3, GM: 0.2164 mg/m3) respectively, as compared to other conditions. Also, biomechanics analysis yielded the spine compression values lesser than the recommended limits, validating that the proposed fettling booth models could be considered as suggestive engineering control measures in conjunction with the designed prototype, so as to minimize the respirable dust exposure levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".