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Record W2549279743 · doi:10.1177/1687814016671620

Tool wear in disk milling grooving of titanium alloy

2016· article· en· W2549279743 on OpenAlexaff
Hongmin Xin, Yaoyao Shi, Liqun Ning

Bibliographic record

VenueAdvances in Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceTool wearDelamination (geology)Enhanced Data Rates for GSM EvolutionTitanium alloyMachiningMetallurgyScanning electron microscopeGrindingAlloyComposite materialComputer science

Abstract

fetched live from OpenAlex

The work efficiency of grooving machining can be improved by the approach of disk milling. However, the problem of tool wear was serious because of big milling force and high milling temperature in manufacture process, by which the tool life and the machined surface quality were influenced. In this study, disk-milling grooving experiments of titanium alloy were designed and conducted. First, milling force and milling temperature were examined, which provided theory basis to tool wear. Then, the tool life, wear patterns, and its corresponding mechanisms were investigated in detail through scanning electron microscope observation, X-ray energy-dispersive spectrometer, and automatic tool analyzer. By analyzing experimental results, it was found that, for an example of five-stage compressor blisk of a certain type aircraft engine, disk cutter can only remove about 50 tunnels’ volume after five times grinding before wear-out failure, the tool life still needs to be greatly enhanced. The damage morphologies were delamination, thermal fatigue crack, plastic deformation, and tipping. Wear mechanisms were the synergistic interaction among adhesion wear, oxidation wear, and diffusion wear. Thermal crack and tipping were easily found for the cutting edges around the keyway. The oxidation degree of major cutting edge was higher than minor cutting edge; rake face was severe compared to flank face. The element W easily diffused into the titanium alloy, the diffusion ability of Co and C were weaker than W, and the element Ti was dead in diffusion process.

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 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 score0.885
Threshold uncertainty score0.522

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 head0.205
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2016
Admission routes1
Has abstractyes

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