Electrolyte jet tomography: Three-dimensional microstructure mapping with an electrochemical machine tool and an optical microscope
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".