MétaCan
Menu
Back to cohort
Record W4365456233 · doi:10.21741/9781644902479-135

Machining of PAM green Y-TZP: Influence of build and in-plane directions on cutting forces and surface topography

2023· article· en· W4365456233 on OpenAlexfundno aff
Laurent Spitaels

Bibliographic record

VenueMaterials research proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
FundersBeef Cattle Research Council
KeywordsCeramicSurface roughnessMachiningSurface (topology)Surface finishMaterials sciencePlane (geometry)Mechanical engineeringProcess (computing)GeometryComposite materialComputer scienceEngineeringMathematicsMetallurgy

Abstract

fetched live from OpenAlex

Abstract. The combination of the pellet additive manufacturing (PAM) process and green ceramic machining within the same hybrid machine is a very promising route to obtain green ceramic parts with complex shapes, smooth surface topography and tight tolerances. However, there is still a lack of data due to the novelty of this manufacturing route. This article studies the possible influence of the build and in-plane directions on the cutting forces and surface topography during the milling of Y-TZP green ceramic parts obtained by the PAM process. The RMS cutting forces, arithmetic and total roughness (Ra and Rt, respectively) were measured. The in-plane direction (aligned with one of the horizontal part edges) did not have a significant influence neither on the cutting forces nor on the surface topography. Conversely, the build direction has a significant effect on the cutting forces recorded. The layers deposited the furthest from the build platform required 57.5% less force to be milled than those in contact with it. The surface topography was not significantly modified across the build direction, all values of Ra were within the 0.8 µm Ra class while all Rt values were < 5 µm.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.301
Teacher spread0.273 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMaterials research proceedingsSame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207