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Record W2079396643 · doi:10.1115/imece2008-67991

Effect of Preheating on the Delamination and Crack Formation in Laser Solid Freeform Fabrication Process

2008· article· en· W2079396643 on OpenAlexaff
Masoud Alimardani, Ehsan Toyserkani, Jan P. Huissoon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFabricationMaterials scienceDelamination (geology)Composite materialProcess (computing)ThermalComputer simulationSubstrate (aquarium)Stress (linguistics)MechanicsComputer science

Abstract

fetched live from OpenAlex

This paper presents a numerical-experimental investigation on the effects of preheating the substrate on the potential delamination and crack formation across the parts fabricated using the Laser Solid Freeform Fabrication (LSFF) process. For this purpose, the temperature distributions and stress fields induced during the multilayer LSFF process, and their correlation with the delamination and crack formation are studied throughout the numerical analysis and the experimental fabrication of a four-layer thin wall of SS304L. A 3D time-dependent numerical approach is used to simulate the LSFF process, and also interpret the experimental results in terms of the temperature distribution and the thermal stress fields. The numerical results show that by preheating the substrate prior to the fabrication process, the thermal stresses throughout the process domain substantially reduce. Accordingly, this can result in the reduction of potential micro-cracks formation across the fabricated part. Preheating also decreases the transient time for the development of a proper melt pool which is an important factor to prevent poor bonding between deposited layers. The experimental results are used to verify the numerical findings as well as the feasibility of preheating on the reduction of the micro-cracks formed throughout the fabrication process.

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 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 score0.365
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
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.

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
Published2008
Admission routes1
Has abstractyes

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