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Record W1542627695

Powder Die Fill Study for Powder Metallurgy Applications Using New Experimental Approaches

2010· dissertation· en· W1542627695 on OpenAlexaboutno aff
Dhanashree Aole

Bibliographic record

VenueMacSphere (McMaster University) · 2010
Typedissertation
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
Fundersnot available
KeywordsPowder metallurgyDie (integrated circuit)Materials scienceMetallurgyNanotechnologySintering
DOInot available

Abstract

fetched live from OpenAlex

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Times; color: #2d2d2d} span.s1 {color: #4c4c4c} span.s2 {font: 11.0px Helvetica} p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Times} span.s1 {font: 11.0px Helvetica} span.s2 {font: 10.0px Verdana} span.Apple-tab-span {white-space:pre} The aim of this research is to enhance performance and durability of the final PM component by improving spatial density homogeneity at die filling stage of powder metallurgy process. In this research, powder die filling processes has been studied using a novel laboratory experimental set-up, with a ring-shaped die, for assessing die fill characteristics of thin, high precision components of interest to Gates Canada. In this investigation, usefulness of incorporating a perforated plate at the bottom of the feed shoe to improve the density uniformity in the filling condition is assessed. With this arrangement, flow and distribution of powder during the delivery stage is monitored. The powder flow pattern observed through the transparent window is utilized for obtaining full-field displacement data using an optical measurement technique. In addition to the above, several powder flow characteristics during die filling have been studied through a series of high-speed camera recordings. The role of shoe speed, and powder properties in the development of density gradient have been experimentally assessed. A series of full-scale experiments with coloured salt as a powder medium to mimic the iron powder flow have been conducted to understand flow patterns, and segregation of powder during the filling process. Further, a novel method of density measurement of the part in die-fill condition by heating iron powder with a polymeric resin is explored. Qualitative density data obtained from the die filling experiments, and subsequent porosity data from powder sintering in the die have been compared, to understand the reasons for density gradient in the die-cavity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.226
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

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