Correlative Laser Confocal Microscopy Study and Multimodal 2D/3D Registration as Ground Truth for X-ray Inspection of Internal Defects in LPBF Manufacturing
Bibliographic record
Abstract
In a time when engineers working in the additive manufacturing field are interested in the standardized x-ray computed tomography (XCT) image analysis workflow, an insight into a higher resolution imaging and ground truth validation become invaluable. In this work, we propose a repeatable and automated 2D/3D registration protocol between an XCT volume and a laser confocal microscopy image, thus allowing a correlative multiscale validation and comparison study of the flaw detection capabilities and uncertainties of an XCT analysis of additivelymanufactured parts. Once the spatial correlation achieved, a comparison study evaluating the level of confidence of the flaw detection and measurement computed from the XCT volume is presented. To this end, a pore-to-pore comparison between the XCT volume and the laser confocal image, which offers a 4 times higher resolution as well as a better signal to noise ratio, is carried out and various pore morphology metric distributions are compared. The generality of the proposed approach is ensured by the use of printed Ti64 LPBF samples with different levels of the intentionally seeded and controlled porosity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".