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Record W2330118464 · doi:10.1177/0954405415573851

A thermo-structural manufacturing process using AISI 304L stainless steel

2015· article· en· W2330118464 on OpenAlexaff
Siyavash Towfighi, D.P. Romilly

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2015
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceThermocoupleFinite element methodDeformation (meteorology)TorchMetallurgyYield (engineering)Composite materialStructural engineeringEngineering

Abstract

fetched live from OpenAlex

This article presents a study of high-temperature heating of AISI 304L stainless steel to induce deformations in a manufactured part. Small square samples of AISI 304L were heated to deform a slot using an oxy-acetylene torch. The sample temperature profiles were measured using three thermocouples with maximum temperature values ranging from 760 °C to 1130 °C. Three-dimensional thermo-structural finite element models were created to predict the magnitude of permanent deformation and were validated experimentally. Torch modeling parameters were optimized numerically using a series of finite element simulations. The finite element predictions for deformation were found to be in reasonable agreement with the experimental results. The variation in yield strength of AISI 304L was shown to be an important factor in affecting the magnitude of deformations. Repeated heating experiments also demonstrated additive plastic strain with each heating cycle. The results provide a means to use high temperatures to purposefully alter the dimensions of a slot in a manufactured part but with varying accuracy.

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 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 score0.916
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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