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Record W2135719853 · doi:10.1115/imece2014-37348

Influence of Hard Turning on Microstructure Evolution in the Subsurface Layers of Inconel 718

2014· article· en· W2135719853 on OpenAlex
Heithem Touazine, M. Jahazi, P. Bocher

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
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au QuébecMcGill University
KeywordsInconelNanoindentationMaterials scienceMachiningMicrostructureSurface integrityResidual stressSurface roughnessCarbideMetallurgySurface finishSofteningComposite material

Abstract

fetched live from OpenAlex

This study investigated the effects of semi finish, finish and critical finish machining parameters on the microstructural evolution of subsurface layers in Inconel 718. In order to assess the microstructural evolution in the subsurface layer following machining, advanced characterization methods including opto-digital microscopy, X-ray diffraction and nanoindentation were used. Results showed that friction between the tool and the workpiece during machining lead to microstructural changes such as hardness enhancement on the surface, and softening on the subsurface. It was also observed that damage in the machined surface is related to the presence of defects such as cracks, cavities and carbide detachment from the surface. Finally, residual stress measurements revealed that, within the investigated parameters, the cutting speed has the most significant effect on surface integrity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.711

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 head0.003
GPT teacher head0.197
Teacher spread0.194 · 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