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Record W2076039428 · doi:10.1243/095440605x16910

Experimental study of the deformation behaviour of compacted metal powders used in a new rapid tooling process

2005· article· en· W2076039428 on OpenAlexaff
Xiaoping Jiang, Xingyang Liu, Chao Zhang

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2005
Typearticle
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsWestern UniversityNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceDeformation (meteorology)Composite materialMixing (physics)Compressive strengthCompression (physics)Metal powderMetalStress (linguistics)ExtrusionMetallurgy

Abstract

fetched live from OpenAlex

To examine the feasibility of a newly proposed rapid tooling process based on the backing of a metal shell with metal powders, the compressive deformation behaviour of selected metal powders and metal shells was investigated. It was found that the mechanical properties of the powder material and the mixing ratio of powders of different sizes had a significant effect on the compressive deformation behaviour of the compacted powders. Softer metal powders with the same compressive stress tended to produce greater plastic deformation and formed a block under a stress of 138 MPa. Mixing two or more powders of different sizes could significantly increase the deformation resistance of the compacted powders. Minimum plastic deformation was achieved when the fraction of the coarse powder in a binary powder mixture reached 0.77. Initial loading at a higher stress could minimize or eliminate the plastic deformation in subsequent loading cycles. This feature could be used advantageously in powder packing for the proposed new rapid tooling process. Some elastic properties of the compacted metal powders were also determined from the compression experimental data.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.019
GPT teacher head0.256
Teacher spread0.236 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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