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Record W2987332392 · doi:10.1002/mawe.201800139

Effects of defects in hybrid sheet moulding compound – Evaluation of defects and the impact on mechanical properties

2019· article· en· W2987332392 on OpenAlexaff
Marielouise Schäferling, Benjamin Häfner, Gisela Lanza, Anna Trauth, Kay André Weidenmann, Michael R. Thompson

Bibliographic record

VenueMaterialwissenschaft und Werkstofftechnik · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSheet moulding compoundMaterials scienceComposite materialInjection mouldingThermosetting polymerUltimate tensile strengthGlass fiber

Abstract

fetched live from OpenAlex

Abstract Sheet moulding compound is a widely used fibre‐reinforced material. Generally, it consists of discontinuous glass fibres in a thermoset matrix system. Due to the finite fibre length, mechanical properties of structural components are limited. To overcome this drawback, sheet moulding compound is locally reinforced with a unidirectional carbon fibre sheet moulding compound material in the approach presented in this contribution. The manufacturing of this hybrid material consisting of discontinuous glass fibre sheet moulding compound and continuous carbon fibre sheet moulding compound can result in different defects, such as folds or fibre misalignments. These defects may affect mechanical properties of the hybrid material. Consequently, this article deals with the investigation and analysis of defective hybrid sheet moulding compound components, which were examined by means of tensile tests. Results point out that investigated defects have different effects on mechanical properties. However, independent from the type of defect, mechanical properties were reduced. With a reduction of 68.86 %, folds have one of the greatest influences on tensile strength. In addition, depending on the angle deviation, even greater reductions can occur. Furthermore, the reduction of the mechanical properties can be identified clearly with increasing angle deviation.

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 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 score0.024
Threshold uncertainty score0.912

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 communication0.0000.000
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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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