Real-time multivariable control of directed energy deposition via adaptive model predictive control
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
Additive manufacturing processes such as directed energy deposition (DED) enable precise material deposition and customization, but ensuring consistent material properties remains a challenge due to the complex interplay of process parameters. This research presents a novel adaptive model predictive control (AMPC) algorithm for real-time multivariable control in DED, integrating an adaptive one-dimensional thermal model for accurate prediction of both temperature distribution and spatial cooling rate. The model was experimentally validated in single-track deposition tests across four different materials, achieving temperature predictions within ±1% of infrared camera measurements and spatial cooling rate errors below 2.73%. The validated model was embedded within the control framework and evaluated in five-layer wall experiments under open-loop, single-input single-output (SISO), and multi-input multi-output (MIMO) closed-loop control configurations. Results demonstrate that the AMPC algorithm effectively stabilized melt pool dynamics through simultaneous control of laser power and traveling speed, leading to consistent layer heights and improved material uniformity. This work introduces a scalable, adaptive, physics-based framework for real-time thermal prediction and multivariable control in advanced manufacturing processes that use concentrated energy sources, improving melt pool stability, material consistency, and overall part quality.
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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