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Electrolyte jet tomography: Three-dimensional microstructure mapping with an electrochemical machine tool and an optical microscope

2024· article· en· W4401876711 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

VenueJournal of Materials Processing Technology · 2024
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of British Columbia
FundersEngineering and Physical Sciences Research Council
KeywordsMicrostructureMaterials scienceOptical microscopeMicroscopeElectrolyteTomographyElectrochemistryJet (fluid)Biomedical engineeringOpticsNanotechnologyComposite materialScanning electron microscopeElectrodeChemistryMedicinePhysicsMechanics

Abstract

fetched live from OpenAlex

There is a general separation between the manufacturing processes that add value to materials on the factory floor and the techniques engineers use in the laboratory to evaluate the microstructures and the surface integrity that results. These techniques are often destructive or require a vacuum and are incompatible with production lines. However, this information has intrinsic value and could be exploited to inform production decisions during manufacture. In this study, a novel approach to acquire this information is presented that is underpinned by electrolyte jet machine tool coupled with optical microscopy, which can allow the extraction of both grain-wise partial orientation and morphological information, and crystallographic macro textures in three dimensions. Here, iterative sections are precisely machined into the near surface of a commercially pure titanium alloy using an electrochemical jet and subsequently imaged, allowing the reconstruction of high-fidelity microstructure models rapidly and under ambient conditions. In doing so, new insights into the specific orientation-dependent dissolution mechanisms are offered, and the acquisition of appropriate conditions that result in nanoscale roughness surfaces (avoiding the dominance of pitting and preferential grain removal) is firstly explored. Building on prior work, a piecewise approach is presented to analyse the acquired image stacks to map partial crystal orientations, while different approaches are proposed to account for jet-specific surface artefacts and waviness. This is repeated over 20 layers in an individual specimen and layer-wise orientation maps are used to construct volumetric models of the specimen. These data sets are then explored from the perspective of materials/manufacturing engineers, who may use to this information to effect advancements to materials processing technologies. • An electrolyte jet can be used as a rapid, ambient, and low-cost serial sectioning tool to map microstructures in 3D. • Surface generation mechanisms explored in Ti CP 1, allowing insight into stable process parameters for surface imaging. • Process stability was demonstrated by 20 repeated sections at a layer resolution of 1.8 μm allowing grain reconstruction. • Partial orientations were extracted from polarized light images, allowing observation of grain interfaces from 3D datasets.

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)0.001
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 score0.061
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.008
GPT teacher head0.260
Teacher spread0.253 · 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