Non-Destructive Testing Using Eddy Current Sensors for Defect Detection in Additively Manufactured Titanium and Stainless-Steel Parts
Bibliographic record
Abstract
In this study, different eddy-current based probe designs (absolute and commercial reflection) are used to detect artificial defects with different sizes and at different depths in parts composed of stainless-steel (316) and titanium (TI-64) made by Laser Additive Manufacturing (LAM). The measured defect signal value using the probes is in the range of (20-200) millivolts. Both probes can detect subsurface defects on stainless-steel samples with average surface roughness of 11.6 µm and titanium samples with average surface roughness of 8.7 µm. It is found the signal reading can be improved by adding a coating layer made of thin paper to the bottom of the probes. The layer will decrease the surface roughness effect and smooth out the detected defect signal from any ripples. The smallest subsurface artificial defect size detected by both probes is an artificially made notch with 0.07 mm width and 25 mm length. In addition, both probes detected subsurface artificial blind holes in the range of 0.17 mm-0.3 mm radius. Results show that the absolute probe is more suitable to detect cracks and incomplete fusion holes, whereas the reflection probe is more suitable to detect small diameter blind holes. The setup can be used for defect detection during the additive manufacturing process once the melt pool is solidified.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".