Design of a Semi-Automated Carousel Loading Device
Bibliographic record
Abstract
The following report outlines the design work performed for Vidir Vertical Storage Solutions, on behalf of the University of Manitoba, in developing a semi-automated loading device. This device is to assist Vidir’s customers in loading large, heavy rolled goods into carousels, in order to improve safety, efficiency, and ergonomics. The project definition, including needs, constraints, and limitations are outlined to provide the necessary context for the decisions that were made throughout the project. The preliminary concepts from which Vidir selected the basis of the final design, are also included herein. The final design consists of a carriage which travels along a set of guiding rails and is driven by a set of vertical and horizontal linear actuators. For analysis, the device is broken into its subcomponents. The functionality and design decisions pertaining to the base structure, the carriage, the carousel hooks, and the electrical systems are presented. Subsequently, a thorough stress analysis of each structure is outlined, including loading conditions, stress levels and factors of safety. In addition to the structural explanation and stress analysis, the operating procedure and controls logic for the device are laid out, and a detailed manufacturing plan is provided. A complete drawing package, including a bill of materials, is enclosed. Finally, a cost breakdown of the proposed device is included, with the material cost totalling CA$5,400. The future work to be performed during the next phase of the project, from January to April 2020, includes the manufacturing, construction, and testing of the loading device. Additionally, the PLC programming will be done concurrently with the physical testing.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".