In-process imaging of morphology and temperature for laser welding and selective laser melting (Conference Presentation)
Bibliographic record
Abstract
Directly measuring morphology and temperature changes during laser processing (such as in keyhole welding and selective laser melting) can help us to understand, optimize, and control on-the-fly the manufacturing process. Even with such great potential, the technical requirements for such an in situ metrology are high due to the fast nature of the highly localized dynamics, all the while in the presence of bright backscatter and blackbody radiation, and possible obstructions such as molten ejecta and plumes. We have demonstrated that by exploiting coherent imaging through a single-mode fiber inline with the processing lens, we can image morphology at line rates up to 312 kHz, with sufficient robustness to achieve closed loop control of the manufacturing process. Applied to metal additive manufacturing, inline coherent imaging can directly measure powder layer thickness and uniformity, and formed track roughness including the onset of balling. Inline coherent imaging measures morphology dynamics but that is only part of the story. Temperature is also key to final part quality. Standard thermal imaging exploits blackbody radiation but are plagued by the highly variable emissivity of the region of interest, making quantitative measurement challenging. We were able to exploit the same apparatus used for coherent imaging to collect surface temperature profiles. Since we spectrally resolve a wide signature, we have overcome the emissivity problem to measure absolute temperature on the micron scale during laser processing.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".