A case study of serial‐flow car disassembly: Ergonomics, productivity and potential system performance
Bibliographic record
Abstract
Abstract A recent European Union (EU) directive increases demands on car recycling. Thus, present craft‐type disassembly systems need reconfiguration in order to be more efficient. A line‐based system tested in the Netherlands was investigated regarding system performance and ergonomics. The system had reduced performance compared to the design specifications due to such factors as system losses, operator inexperience, and teamwork deficiencies. Operators' peak low back loads were lower than in Swedish craft‐type systems. Direct, value‐adding work comprised 30% of the workday, compared to about 70% in the Swedish manufacturing industry. Alternative system configurations were simulated and discussed using a novel combination of flow and human simulations. For example, a smaller variation in cycle time implied higher output in number of cars per week and larger operator cumulative loading on the low back. In all models the cumulative load was high compared to the loads previously recorded in assembly work. © 2007 Wiley Periodicals, Inc. Hum Factors Man 17: 331–351, 2007.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".