Melt Pool Area Control in Directed Energy Deposition Using Iterative Learning Control and Substrate Pre-heating<sup>*</sup>
Bibliographic record
Abstract
This paper proposes an Iterative Learning Control (ILC) algorithm to determine the laser power input during the deposition of each layer, in order to regulate the m0065lt pool area (MPA) in the Directed Energy Deposition (DED) metal additive manufacturing process. Based on the laser power applied and the corresponding MPA error obtained experimentally, the ILC algorithm updates the laser power so that the MPA error is reduced in the next iteration. It turns out that, even with the well-tuned ILC algorithm, the MPA error persists in lower layers due to the heat dissipation properties of the cold substrate. As a remedy of this issue, the substrate pre-heating strategy is introduced. The combination of the ILC algorithm with the substrate pre-heating shows the potential in minimizing the MPA error across all the layers of the deposited part, thereby enhancing the quality in DED process.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".