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Record W2536311096

Investigating the Effects of Controllable Parameters on Cold-wire Gas Tungsten Arc Weld Bead Geometry

2012· dissertation· en· W2536311096 on OpenAlexfundno aff
Vladimir Stefanovski

Bibliographic record

VenueUWSpace (University of Waterloo) · 2012
Typedissertation
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
FundersOntario Centres of Excellence
KeywordsTungstenMaterials scienceArc (geometry)GeometryBeadWeldingMechanical engineeringStructural engineeringMetallurgyEngineeringComposite materialMathematics
DOInot available

Abstract

fetched live from OpenAlex

Multi-layer Gas Tungsten Arc Welding (GTAW) is widely used for refurbishing plastic injection molds. This welding process can provide the high quality weld that is required for molds that will be subject to high temperatures and pressures during production. This refurbishing weld process is currently performed manually, which exposes the welders to poor working conditions. GTA welding is a tedious and time consuming process when compared to other welding processes. The advantages of automating this process are two-fold: removing workers from arduous working conditions, and decreasing production cycle time by increasing the deposition rate through robotic control. The purpose of this study was to investigate the e ects of controllable welding parameters on weld bead geometry for standard welding operating ranges when refurbishing plastic injection molds. Another aim was to determine and predict the sensitivity and response, respectively, of \nbase metal orientation on bead geometry. Remanufactured molds require the removal of tool steel, which can be achieved with current CNC machining technology. However, automating the addition tool steel is di cult achieve due to the inherent complexity of the GTA welding process. The work presented in this thesis is an integral component for simplifying the mold remanufacture process by eliminating the guesswork in choosing optimal welding parameters. Through the means of experimental design and statistical analysis, relationships were developed between welding parameters and their e ects on \nbead height, bead width, penetration, heat a ected zone (HAZ) depth and HAZ width. The regression models were capable of predicting responses in bead geometry within 1 \nmillimeter in variation. The resulting regression models can be extended to cold wire GTA welding applications that require high deposition rates and the capabilities of predicting bead geometry for various base metal orientations.

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)0.002
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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
Published2012
Admission routes1
Has abstractyes

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