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Record W4399430613 · doi:10.3390/jmmp8030120

Theoretical Assessment of the Environmental Impact of the Preheating Stage in Thermoplastic Composite Processing: A Step toward Sustainable Manufacturing

2024· article· en· W4399430613 on OpenAlexaff
Seyed Abbas Hosseini

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLife-cycle assessmentProcess engineeringEnvironmental impact assessmentProcess (computing)Energy consumptionComposite numberManufacturing processManufacturing engineeringThermoplasticProduction (economics)Parametric statisticsThermoplastic compositesEnvironmental scienceMaterials scienceComputer scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Manufacturing processes have always played a pivotal role in the life cycle assessment of products, necessitating focused efforts to minimize their impact on the environment. Thermoplastic composite manufacturing is no exception to this concern. Within thermoplastic composite manufacturing, the preheating process stands out as one of the most energy-intensive stages, significantly affecting the environment. In this study, a theoretical analysis is conducted to compare three modes of preheating: conductive, radiative, and convective modes, considering their energy consumption and environmental impact. The analysis reveals the potential for substantial energy savings and emissions reduction through the selection of a proper preheating mode. Since the analysis used in this study is theoretical, it facilitates a parametric study of different modes of preheating to assess how process parameters impact the environment. Moreover, this study includes a comparison between emissions from material production and the preheating process, highlighting the substantial contribution of the preheating process to the overall product life cycle assessment.

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 score0.321
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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
Published2024
Admission routes1
Has abstractyes

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