Autonomous exploration of the PBF-LB parameter space: An uncertainty-driven algorithm for automated processing map generation
Bibliographic record
Abstract
Powder bed fusion with laser beam (PBF-LB) is a promising additive manufacturing technique that enables the production of complex geometries with fine resolution and material efficiency, offering significant design freedom and material versatility. However, its broader adoption is limited by the need for extensive parameter tuning, which is often dependent on the specific machine, as well as the material and batch of powder used. In this paper, we introduce a novel algorithm that autonomously identifies melting regimes in an unsupervised manner using optical data acquired from photodiodes — specifically optical emission and reflection. This method eliminates the need for labeled data and achieves an F1 -score of 89.2% across both materials tested: Ti–6Al–4V and 316L. Additionally, we propose an uncertainty-driven iterative strategy designed to efficiently generate processing maps by performing experiments based on uncertainty. This approach enables up to a 67% reduction in the number of required experiments, significantly lowering the associated costs of parameter exploration, while sustaining a maximum performance reduction of only 8.88% compared to traditional full factorial designs . Our results demonstrate the potential of this method to streamline PBF-LB optimization, making it more feasible for industrial applications and paving the way for its broader adoption.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".