Analysis of Seeded Defects in Laser Additive Manufactured 300M Steel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it