Reinforcement Learning-Based Feedforward Control for Solidification Cooling Rate Regulation in Directed Energy Deposition
Bibliographic record
Abstract
Directed Energy Deposition (DED) is a widely used additive manufacturing for fabricating metal components with complex geometry and mechanical properties. A critical challenge in DED is the regulation of the melt pool's solidification cooling rate (SCR), which directly influences the microstructure and mechanical properties of the fabricated part. This paper proposes reinforcement learning (RL)-based feedforward control for the SCR regulation in DED. An RL control is developed to optimize process parameters, namely, laser power, traverse speed, and inter-layer dwell time, on a layer-by-layer basis. The proposed method leverages offline finite difference simulations to iteratively learn an optimal control policy that minimizes SCR deviations from a reference value while reducing overall production time. The effectiveness of the RL controller is evaluated for different references, demonstrating superior regulation performance and production time compared to control with constant process parameters.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".