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Record W2896795896 · doi:10.2351/1.5061039

A coupled time-dependent numerical simulation on temperature and stress fields in laser solid freeform fabrication process

2007· article· en· W2896795896 on OpenAlexaff
Masoud Alimardani, Ehsan Toyserkani, Jan P. Huissoon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceFabricationClampingDeposition (geology)Stress (linguistics)ThermalSubstrate (aquarium)Process (computing)Layer (electronics)Stress fieldComposite materialMechanical engineeringFinite element methodStructural engineeringComputer scienceThermodynamics

Abstract

fetched live from OpenAlex

This paper presents a coupled 3D time-dependent numerical approach for modeling the laser solid freeform fabrication (LSFF) process by which the geometry of the deposited materials, temperature distribution, and stress field can be predicted throughout the process. In the proposed method, coupled thermal and stress distributions are numerically obtained assuming the interaction between the laser beam and the powder stream is decoupled. Main process parameters affected by a multilayer deposition due to the formation of non-planar surfaces such as powder catchment efficiency are incorporated into the modeling. Fabrication of a four-layer thin wall of AISI 304L steel is modeled using the proposed algorithm. The geometry of the wall, the temperature, and the stress fields across the modeling domain are studied throughout the fabrication process. The model is then used to investigate the effects of preheating, and clamping the substrate to the workstation. Results show that preheating improves the process by reducing the thermal stresses as well as the settling time for the formation of a steady-state melt pool in the first layer. The results also indicate that clamping the substrate decreases thermal stresses at its critical locations (i.e. deposition region). The reliability and the accuracy of the model are experimentally verified.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.364

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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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 designSimulation or modeling
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
Published2007
Admission routes1
Has abstractyes

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