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Record W2983202842 · doi:10.1002/pamm.201900124

Motivating the development of a virtual process chain for sheet molding compound composites

2019· article· en· W2983202842 on OpenAlexaff
Johannes Görthofer, Nils Meyer, Tarkes Dora Pallicity, Ludwig Schöttl, Anna Trauth, Malte Schemmann, Martin Hohberg, Pascal Pinter, Peter Elsner, Frank Henning, Andrew N. Hrymak, Thomas Seelig, Kay André Weidenmann, Luise Kärger, Thomas Böhlke

Bibliographic record

VenuePAMM · 2019
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsWestern University
FundersDeutsche Forschungsgemeinschaft
KeywordsMolding (decorative)Compression moldingProcess (computing)Chain (unit)Materials scienceSheet moulding compoundKey (lock)Boundary (topology)Computer scienceComposite materialEngineering drawingMechanical engineeringEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract This contribution presents a physical process chain and the corresponding virtual process chain for sheet molding compound (SMC) composites. Here, focus lies on the physical process chain as a motivation for the virtual process chain as discussed in the authors' publication [1]. The key steps of the virtual process chain are the identification of initial and boundary conditions, the compression molding simulation, the mapping of data and the structural simulation. The so established virtual process chain is validated via experimental investigations on a demonstrator structure. Both, the predicted results of the compression molding simulation, as well as the results of the structural simulation are in good accordance with the experiments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2019
Admission routes1
Has abstractyes

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Same venuePAMMSame topicComposite Material MechanicsFrench-language works237,207