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Record W3021114647 · doi:10.5006/c2019-12704

Oil Sands Haul Truck CAT 797 Frame Cracking Finite Element Analysis (FEA)

2019· article· en· W3021114647 on OpenAlexaff
Duane Serate, Chris Semaka, Kevin Suen, Jeff Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsCanadian Natural ResourcesAlpha Technologies (Canada)
Fundersnot available
KeywordsFinite element methodTruckCrackingFrame (networking)Structural engineeringMaterials scienceEngineeringGeologyMechanical engineeringComposite materialAutomotive engineering

Abstract

fetched live from OpenAlex

Abstract A study was performed to understand the mechanism of frame cracking on the mining haul trucks in an oilsands' operating site. A static finite element analysis (FEA) was completed to identify the high stress areas prone to cracking, and recommendations for extending the service life were attained through Fatigue Analysis and Brittle Fracture Assessment. In order to perform this FEA, an accurate 3-dimensional (3D) solid model of the truck frame was built by completing a laser scan of the entire truck frame surfaces. External static loads were applied to this generated 3D solid model for each load case to determine the stresses within the frame. Each load case was then examined to determine its contribution to the total fatigue life consumption, and also determine the critical crack dimensions to prevent brittle fracture. The static FEA results identified opportunities to optimize existing maintenance, inspection, and operating practices. Recommendations are made regarding inspection, repair, and operation of haul trucks based on the ambient temperature, crack depth and length.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.007
GPT teacher head0.216
Teacher spread0.209 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2019
Admission routes1
Has abstractyes

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