Alberta Learning Factory for training reconfigurable assembly process value stream mapping
Bibliographic record
Abstract
The University of Alberta is currently coping with the training and learning needs of the rapidly increasing number of manufacturing companies across Alberta. The current shift towards industry 4.0 further requires learning with reconfigurable systems. The Alberta learning Factory (AllFactory) is a step towards the creation of an experiential and project-based learning environment, where students are trained in cross-disciplinary project management. Various lean management tools, such as value stream management, line balancing, bottleneck identification, Kanban, shop-floor design, and visual tools are integrated into student group projects. The students are given the task to assemble a Lego-based 3D Printing machine (prototyped in the AllFactory) with different sub-assemblies in a factory simulation environment. The main idea of using Legos is to demonstrate re-configurability as required by industry 4.0. The research in AllFactory is based on Lean tools integrated to the process/product information data from the ERP system, which is connected to the factory shop-floor. Currently, two important research topics in AllFactory are: 1) a Hybrid Lean-ERP systems development; and 2) the development of a generalized value stream mapping system for construction companies. These research topics feed directly to the training modules in the learning factory. This new learning factory will focus initially on re-configurable manufacturing systems, which will be extended to transdisciplinary capstone projects and a training school for industry personnel in the future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.155 | 0.032 |
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".