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Record W2373609315 · doi:10.15344/2455-2372/2015/117

Reciprocating Wear Behavior of Al Alloys: Effect of Porosity and Normal Load

2015· article· en· W2373609315 on OpenAlexaff
Avijit Sinha, Md. Aminul Islam, Zoheir Farhat

Bibliographic record

VenueInternational Journal of Metallurgical & Materials Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials sciencePorosityPowder metallurgyCompactionMetallurgyReciprocating motionScanning electron microscopeAlloyComposite materialAluminiumTribologyMicrostructureBearing (navigation)

Abstract

fetched live from OpenAlex

Aluminum alloys are attractive for critical applications such as pistons, clutch housings and liners in automotive industry for their high strength to weight ratio, high corrosion resistance and good heat conductivity. These alloys can be fabricated using casting and powder metallurgy techniques in which porosity is a common feature. The presences of pores adversely affect the mechanical properties and wear resistance of these components. Not only the total area percentage of porosity influences the degradation in properties but also size, shape and interconnectivity of pores play an important role. In this study, aluminum alloys were produced using powder metallurgy technique. The amount of porosity was varied by varying compaction pressure and amount of wax added before compaction. Reciprocating wear tests (ball-on-flat configuration) were performed against AISI 52100 bearing steel ball under both low (1.5- 5N) and high (6-20N) loads. Scanning electron microscopy was employed in order to identify possible wear mechanisms. Both detrimental and beneficial effects of porosity under different loading conditions were observed. An attempt has been made to develop a relationship between pore size and distribution and wear behavior of aluminum alloys.

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 score0.028
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.010
GPT teacher head0.233
Teacher spread0.222 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Metallurgical & Materials EngineeringSame topicAluminum Alloys Composites PropertiesFrench-language works237,207