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Record W7042922881

Reinforced aluminum structure castings for powertrain automotive applications

2009· dissertation· en· W7042922881 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2009
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMicrostructureCoatingIndentation hardnessAlloyCastingImmersion (mathematics)Aluminium
DOInot available

Abstract

fetched live from OpenAlex

The reinforcement of an Al casting with ferrous inserts (hybrid systems) through a joining technique to utilize both Al alloys (lightness) and Fe-based alloys (stiffness) is of interest, especially in the transportation sector. This work focuses on different technological aspects of cast joining of cast iron to an Al alloy using an intermediate material (or coating). The experimental set up consisted of preparing the insert surface followed by coating the insert, and then, immersing it into an Al melt, and allowing the system to cool down to room temperature. The effects of flux treatment, decarburization, and the coating application, as well as the immersion time in the Al melt on the Al-Fe joint quality were investigated. The microstructure evolution of the reaction layer forming at the insert-coating interface was determined as a function of the coating time and the coating composition, and their effects on the joint properties were evaluated. The relationship between the microstructure and microhardness of the joint zone was established. Decarburization, flux treatment, suitable coating, and optimizing the process parameters improved the joint properties. Combination of "McGill 2" coating alloy and 1 min immersion time (in the Al melt) resulted in the formation of an Al-Fe joint with optimized characteristics. The results showed that the cast joining could be used to strengthen the Al castings and improve their performance.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.528
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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.

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
Published2009
Admission routes1
Has abstractyes

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