Defect detection in additively manufactured AlSi10Mg and Ti6Al4V samples using laser ultrasonics and phase shift migration
Bibliographic record
Abstract
Laser ultrasonics (LU) is a non-contact and non-destructive method with a high data acquisition rate, making it a promising candidate for in-situ monitoring of defects in different additive manufacturing (AM) processes, including laser powder bed fusion (LPBF) and directed energy deposition, as well as final part inspection. In order to see the effect of various artificial defect types on an LU sub-surface reconstruction, AlSi10Mg samples with side through-holes, as well as Ti6Al4V samples with bottom blind holes and trapped powder were printed using LPBF, and then ultrasound B-scans of the samples were obtained using an LU system. The resulting scan data was processed using a custom frequency domain phase shift migration (PSM) algorithm, to reconstruct the defects and their locations. Novel ways of pre-processing the B-scan, used as an input to PSM, and taking advantage of its frequency representation, are demonstrated. Newton's method was used to find a stationary phase approximation, used to account in the frequency domain for the fixed offset emitter-receiver arrangement within the PSM calculation. The Newton's method calculation time was reduced by 33%, by using an approximation of the phase function to find an initial guess. The smallest defects that were detected using this method were in the size range between 200 to 300μm for the bottom hole defects, using an 8 ns laser pulse duration. The effect of the laser on the surface of a part being built, and the challenges and further work needed to integrate LU in a LPBF machine for in-situ inspection are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".