Modifying a Manufacturing Task for Teamwork between Humans and AI; Initial Data Collection to Guide Requirements Specifications
Bibliographic record
Abstract
Recent advances in AI, above all machine and deep learning, have brought about unprecedented possibilities in automation, prediction and problem solving with impact on operators and their way of working and interacting with automation on the shop floor.While the expected effects are focusing on increasing the efficiency, flexibility, and productivity of operations in the industrial and service sector, there is justified scepticism towards its implementation due also to the challenge of integrating AI into operator's current way of working and practices in a way that actually supports also the human in the loop.Therefore, it is now time to consider the user's side from an employees' point of view in order to foster AI in a human-technology relationship.The present paper is exploring the preliminary steps taken in this direction while trying to identify a problem definition and its suitable solutions for, firstly, improving the human automation interaction and, secondly, reduce the time variability and improve efficiency in a milling process for large metal metal components of a wind turbine at a manufacturing facility.To complement this description, a data analysis of the manufacturing process status is provided.The analysed data sets contain general information of relevant parameters of the manufacturing system as well as the required inputs from the operators.The purpose of this report is to establish the basis on which a thorough operational description of the overall man-automation process is defined and the usefulness of including a better integration for the manual tasks in it.The operational description of the tasks is a key ingredient to achieve better requirements specifications and how we can enhance the human performance of the operators by increasing their situational awareness on the shop floor.Moreover this task mapping can account of a lot of missing information regarding variability of execution time in the process and to support scheduling of manual activities for the operator to perform while the automated task may not need direct supervision.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".