Analysis of Seeded Defects in Laser Additive Manufactured 300M Steel
Bibliographic record
Abstract
Abstract This research activity was initiated to better assess the capacity for traditional nondestructive testing (NDT) approaches to ascertain the defects inherent to materials fabricated through a directed energy laser additive manufacturing (LAM) process. A methodology was developed to intentionally seed defects in 300M steel specimens through intermittent modification of fabrication parameters. Several 300M steel specimens were fabricated and the concentration of defects or bulk density was characterized using optical microscopy and variations of the Archimedes’ principle. Specimens were then evaluated using NDT (radiographic testing, ultrasonic testing). Results show that by using n-hexane as the displacement liquid, the Archimedes’ principle was found to have repeatability in density values of 0.1 ± 0.1 %. The results reveal the unique defects produced through the LAM process and the limitations for conventional NDT techniques to adequately detect defects in LAM materials. Ultrasonic testing was found to be a promising tool for assessing the LAM defect distribution. Future work will focus on LAM alloys with higher densities and relate microstructure and defects to overall material performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".