Alberta Learning Factory for training reconfigurable assembly process value stream mapping
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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