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A comprehensive metal additive manufacturing platform to transform the marine industry

2024· article· en· W4401926424 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMATEC Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMarine industryBusinessManufacturing engineeringEnvironmental scienceEngineeringEnvironmental resource management

Abstract

fetched live from OpenAlex

Direct Metal Deposition (DMD) is one of the underwater marine additive manufacturing (MAM) technologies known for its capability to build up on semi-finished products. This allows for the creation of complex structures and repair the damaged or worn-out areas. Employing this underwater technology needs a lot of consideration regarding the harsh environment of the ocean. This research endeavours to identify nickel-aluminium bronze’s structural characteristics printed underwater. Simulation studies can help to analyse grain and phase evolution, defects, and melt pool behaviour, enabling the optimization of printing parameters for high-quality marine alloy components. To achieve that a control systems and machine learning algorithms need to developed to enhance precision in the 3D printing process on a moving platform, addressing the challenges of six distinct vessel movements at sea. This integration aims to improve accuracy, contributing to optimal performance in dynamic maritime environments.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.643

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.241
Teacher spread0.216 · 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