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Record W4214841897 · doi:10.1177/09544054221080033

The influence of tool quality on the machining of additive manufactured and powder metallurgy titanium alloys

2022· article· en· W4214841897 on OpenAlexafffund
Junhui Ma, Tanny Tran, O.A. Gali, Reza A. Riahi

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDrillMetallurgyMaterials scienceDrillingMachiningTitanium alloyTitaniumCarbidePowder metallurgyMicrostructureAlloyTool wear

Abstract

fetched live from OpenAlex

This study was carried out to investigate the impact the quality of the drill bits has on the machining behavior of additive manufacturing (AM) and powder metallurgy (PM) titanium alloys. Therefore, commercially available drill bits which typically reflect two extremes of drill bit quality were selected. The performance of coated carbide twist drills, typically recommended for the drilling of wrought titanium alloys was compared with that of high-speed steel (HSS) drills. The average torque value, specific cutting energy (SCE), and tool wear were used to evaluate the drilling performance of AM and PM titanium alloys. The results of drilling tests revealed the application of the coated carbide drill resulted in lower torque and SCE values, less flank wear, and lower build-up-edge (BUE) compared with the uncoated HSS drill bits for AM fabricated titanium alloys. However, the carbide drill appeared to offer negligible improvement over the uncoated HSS drill when employed with the PM fabricated titanium alloy. In spite of the improvement in the drilling performance offered by the carbide drills for the AM titanium alloy, TiB intermetallic particles (part of the AM titanium microstructure) contributed to the damage of the coated carbide drill which would limit the drill lifetime.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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 designBench or experimental
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

Citations6
Published2022
Admission routes2
Has abstractyes

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