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Record W7061843463

Rework of Robotic Welding Cell Floor Plan at AGI Westfield

2022· report· en· W7061843463 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typereport
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsWeldingDowntimeChangeoverReworkFrame (networking)RobotProcess (computing)Robot welding
DOInot available

Abstract

fetched live from OpenAlex

AGI Westfield is the largest manufacturer of grain augers in the world. To produce the frames for these augers, their Manitoba facility uses three welding cells: two manual and one robotic. The robotic welding cell was added in most recently and was designed to fit in a small space rather than having frame throughput be the more important design goal. The objective of this project was to design a customized robotic welding cell layout that had reduced downtime compared to the current cell for inclusion in a facility expansion that AGI Westfield is planning. Downtime here refers to long changeover times and a layout that causes the robots to be frequently waiting for raw material. The current robotic frame welding cell experiences changeovers that last an average of 90 minutes. This is because there is nowhere to stage the jigs and so only when a batch of frames is completely welded can the forklift remove the cart of finished product and bring in the next jig for the new size of frame to be welded. This is a lengthy process since the jig storage is located approximately 600’ away from the welding cell and requires a forklift to bring them over. This is especially problematic since forklift availability is inconsistent and also the timing for when a cart of finished product needs to be picked up via forklift is inconsistent. After screening several design concepts and using the Single Minute Exchange of Dies (SMED) concept, the design team developed a new layout that will reduce the changeover times to 15-20 minutes. This was done through the addition of an elevated staging area, a mezzanine, specifically made for jigs that sit over the primary staging area. The primary staging area located under the mezzanine is used for raw materials flowing into the cell. The jig storage was also moved closer to the point-of-use. The current robotic welding cell also has back-and-forth material flow and the raw materials and finished goods are placed in the same area. This wastes time since operators perform redundant raw material movement to create space for the finished product. To mitigate this time loss, the team also designed the entire structure of the cell to encourage the flow of materials. This meant moving individual cell elements so that material would flow in a circular path from the inlet to the outlet, eliminating the redundant material movement. The proposed cell makes extensive use of a two-point-of-contact (POC), two degree-of-freedom (DOF) hoist system to quickly and safely move materials and jigs throughout the cell. This is an upgrade from the current single POC, single DOF hoist in terms of safety due to the additional POC, and an upgrade in material movement versatility with the additional DOF. The redesigned robotic welding cell was calculated to have an increase in output of auger frames by 1,494 frames/year (16% increase) while only increasing the floor space used by 400 ft2 (11% increase). This translates to an increase in cell profitability of $977,000 each year. Additionally, the OEE was improved from 80.9% to 91.7%. These values are inflated since it excludes the quality (%) metric as it was out of the scope of the project.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.007

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.

Opus teacher head0.027
GPT teacher head0.225
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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