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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".