A Framework for Modeling and Analysis of Human Repetitive Operations in a Production/assembly Line
Bibliographic record
Abstract
<div class="htmlview paragraph">Repetitive movements have been found to affect assembly operations in many ways such as increasing the risk of injuries, increasing the cost of production, and reduction in the quality of products. This has been a big problem for industries. The method adopted by these studies seems to pose more injuries to workers as workers need to perform a task to the extreme level of pain to determine if repetitive injuries will occur or not. The method of modeling and simulation of human operations is a valid technique that is effective, but could be complex. Some of the modeling and simulation software packages make use of such guidelines as NIOSH, Snook and Ciriello, RULA, REBA, and Biomechanics single action analysis. However, various applications of these tools in actual ergonomic studies tend to be very time consuming and trivial due to the lack of a valid framework to guide the process. The objective of this paper is to present a detailed sequence of steps for injury analysis given an existing case study. T his framework can be used in conjunction with Delmia V5 software package to optimize various human operations given an existing assembly line.</div>
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".