Real Time Monitoring in L-PBF Using a Machine Learning Approach
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
Laser powder bed fusion (L-PBF) is an additive manufacturing process whereby a heat source (laser) is used to consolidate material in powder form to build three-dimensional parts. This paper uses real-time monitoring in L-PBF for quality control. Acoustic Emission (AE) is used to detect various defects like pores and cracks during the powder bed selective laser melting process via the machine learning approach. Data collection is performed under various process parameters, using an AE sensor. Several time and frequency-domain features are extracted from the AE signals during data mining. K-means clustering is employed during the unsupervised learning, and a neural network approach is employed for the supervised machine learning on the dataset. Data labelling is conducted for different laser powers, clustering results and signal time durations. The results show the potential of real-time quality monitoring using AE during the L-PBF process.
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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) | 0.000 | 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 it