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Record W2091480134 · doi:10.1115/imece2014-39503

Identification and Validation of Marusich’s Constitutive Law for Finite Element Modeling of High Speed Machining

2014· article· en· W2091480134 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueVolume 2A: Advanced Manufacturing · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsÉcole de Technologie Supérieure
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMachiningConstitutive equationFinite element methodChipStructural engineeringChip formationMaterials scienceMechanical engineeringComputer scienceEngineeringTool wear

Abstract

fetched live from OpenAlex

This study aims to identify the coefficients of Marusich’s constitutive equation (MCE) for the aluminum AA7075-T651. Material constants were identified inversely form orthogonal machining tests and from dynamic tests. The proposed material model was successfully implemented in a finite element model (FEM) to simulate the high speed machining of the aluminum AA7075-T6. Deform 2D® software was used. A reasonable agreement between predictions and experiments was obtained. The comparison was based on cutting forces, chip morphology, and tool/chip contact 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.225
Teacher spread0.216 · 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