Digital Human Modeling for Long, and Low-Volume Assembly Processes: Gas Turbine Assembly Case Study
Bibliographic record
Abstract
Musculoskeletal disorders (MSDs) are prevalent among workers in industrialized countries, reducing productivity, affecting business profitability, and causing personal discomfort. Poor workplace design is a key contributing factor to the phenomenon. The Digital Human Modeling (DHM) approach, initially developed and primarily used for large-scale, repetitive production environments, facilitates the early identification of potential risks associated with tasks in design situations. This paper aims to investigate the extent to which existing Digital Human Modeling (DHM) tools, such as Jack 7.1, can accurately assess ergonomic risks in long, low-volume assembly processes. To this end, a single case involving the work of tightening in a position above the heart, as part of the assembly of gas turbines in a restricted space, was analyzed using Jack 7.1. The work system was simulated in JACK 7.1 and the Predetermined Time Standard tool based on MTM-1, included in the software, was used for time calculations. The rest allowances were calculated in accordance with the guidelines set forth by the International Labour Office and Kanawaty (1996). The MSD risk assessment for over-the-heart work was performed in two phases: initially, a simulation and RULA ergonomic analysis in JACK 7.1, followed by additional ergonomic evaluations using the OCRA Index and KIM-MHO. This paper concludes that JACK 7.1 can be used to analyze ergonomic risks in long, and low-volume assembly processes. However, the analysis needs to be completed using other methods not available in the software, such as the OCRA Index and KIM-MHO.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".