A variability taxonomy to support automation decision-making for manufacturing processes
Why this work is in the frame
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Bibliographic record
Abstract
Although many manual operations have been replaced by automation in the manufacturing domain, in\nvarious industries skilled operators still carry out critical manual tasks such as final assembly. The\nbusiness case for automation in these areas is difficult to justify due to increased complexity and costs\narising out of process variabilities associated with those tasks. The lack of understanding of process\nvariability in automation design means that industrial automation often does not realise the full benefits\nat the first attempt, resulting in the need to spend additional resource and time, to fully realise the\npotential. This article describes a taxonomy of variability when considering automation of\nmanufacturing processes. Three industrial case studies were analysed to develop the proposed\ntaxonomy. The results obtained from the taxonomy are discussed with a further case study to\ndemonstrate its value in supporting automation decision-making.
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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 | 0.000 | 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 | 0.000 |
| 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 it