MétaCan
Menu
Back to cohort
Record W3095898384 · doi:10.1115/pvp2020-21287

Plasma Arc Welding for Code Compliance Nuclear Applications

2020· article· en· W3095898384 on OpenAlexaff
Dongmei Sun, Rob Pistor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsTD Bank Group
Fundersnot available
KeywordsWeldingOverlayPlasma arc weldingArc weldingWelding power supplyMechanical engineeringPlasmaMaterials scienceComputer scienceEngineeringFiller metalOperating systemPhysics

Abstract

fetched live from OpenAlex

Abstract Plasma Arc Welding (PAW) has been used for many critical applications due to its flexibility, reliability and high weld quality. In this paper, two code compliance plasma arc welding applications in the nuclear industry are discussed. The first application is an innovative welding process using PAW with specially designed dual hot wire feeding system, namely Dual Hot Wire Gas Metal Plasma Arc Welding (GMPAW). The GMPAW process offers unique advantages for high deposition and low dilution weld overlay application. The second application is a remote weld overlay repair from pipe inside diameter (ID) for a highly radiated nuclear component using PAW process with remote machining and NDE capability. In this paper, the benefits and advantages are provided for the aforementioned PAW applications. The versatility of plasma arc welding system configuration, as well as high quality and productivity can make plasma arc welding a good candidate for many critical code compliance applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.055
GPT teacher head0.265
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 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicWelding Techniques and Residual StressesFrench-language works237,207