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Record W7037707521

Electro-spark Deposition Process for Fine Structures and New Materials Coating

2023· dissertation· en· W7037707521 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

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEngineering
TopicSurface Treatment and Coatings
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInconelCoatingDeposition (geology)Layer (electronics)Substrate (aquarium)ElectrodeElectrical conductorRaw materialScrapProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

The electrospark deposition (ESD) process can be used in industry to apply coatings and repair components. It uses a rod-shaped conductive material as the raw material for the electrode, which is melted by the high temperature of the process of discharging to form an electric spark during contact with the substrate, which is then transferred and solidified to the substrate surface. The speed of this process is also accompanied by the melting of the substrate surface, which fuses with the electrode material to give a mixed layer at the interface between the coating and the substrate. However, the deposition process is extremely fast and thus the heat input is small, which has little to no effect on the properties of the substrate.
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\nBecause the temperature at which electrical sparks are generated is much higher than the melting point of all materials, ESD can make coatings from almost any conductive material. Some materials can also be manufactured into thick buildups with ESD. This process can be treated as an additive manufacturing technique, which can be used to perform the structural repair of damaged parts with fine structures, such as thin wall geometries, thus reducing the cost of replacement parts and reducing the scrap waste of damaged parts. The repair of damaged thin-walled structures with Inconel 718 is described in Chapter 3 of this report, which includes optimization of parameter sets during the manufacturing, and analysis of microstructure, microhardness, and heat-affected zone (HAZ).
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\nThe high temperature in the process of manufacturing a coating with ESD melts the electrode and the substrate, but if there is a conductive interlayer between the two, such as a layer of metal powder, the high temperature of the spark will melt the powder instead, which then fuses and solidifies on both the substrate and electrode so that the transfer of electrode material can be prevented. Therefore, ESD will be used to fabricate the M/HEA coating using powder as the interlayer that will be presented in Chapter 4, as well as the optimization of the manufacturing process, and analysis of microstructure, as well as the wear resistance of M/HEA coating fabricate using electrospark powder deposition (ESPD).
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\nSome application trials with ESD are performed and will be presented in Chapter 5, which describes the repairing of thin walls using manual ESD and automated ESD. The fabrication of high entropy alloy (HEA) coatings with the conventional ESD process will also be presented as a HEA coating trial, along with the discussion of microstructure, microhardness, and the wear resistance of this HEA.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.954

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 integrity0.0000.000
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 head0.212
Teacher spread0.200 · 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