Pioneering ML-driven framework for in-situ vertical surface roughness prediction in LPBF
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
Real-time prediction of vertical surface roughness in laser powder bed fusion (LPBF) is essential for process control and quality assurance, yet it remains largely unexplored due to view-blocking by loose powder in the machine bed. This study introduces the first integrated framework that combines in-situ photodiode monitoring with machine learning (ML) to predict sidewall roughness during fabrication. A high-speed photodiode sensor captures melt pool intensity signals near vertical surfaces, which are processed into time- and frequency-domain features. These features, together with process parameters, serve as inputs to ML models, while post-process surface roughness measurements (Sa), obtained via laser scanning confocal microscopy, are used as outputs during training. Once trained, the model can then be applied in real-time to predict roughness directly from photodiode signals acquired during printing, enabling side-specific monitoring without additional measurement steps. Among the five models evaluated, Random Forest (RF) and eXtreme Gradient Boosting (XGB) achieved the highest predictive accuracy, with RF improving from R2 = 0.35 (parameters only) to R2 = 0.78 when in-situ features were included. This framework demonstrates that photodiode-based monitoring, coupled with ML, enables reliable, side-specific, real-time prediction of vertical surface roughness in LPBF, offering a pathway towards adaptive quality control and reduced post-processing.
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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