Adaptive Spatiotemporal Thermal Model for Real-Time Temperature Prediction in Directed Energy Deposition
Bibliographic record
Abstract
Directed Energy Deposition (DED) is an advanced metal additive manufacturing process involving complex thermal dynamics that significantly impact the quality and structural integrity of fabricated components. Effective thermal management in DED requires accurate and real-time temperature predictions throughout the workpiece. However, traditional simulation methods often lack the computational efficiency necessary for real-time control applications. In this study, we introduce a novel physics-based, data-driven, and control-oriented spatiotemporal thermal model featuring adaptive meshing to enhance real-time temperature prediction accuracy and control performance. The proposed model dynamically refines the computational mesh in regions with steep thermal gradients and progressively coarsens it in areas experiencing relatively stable thermal conditions as additional deposition layers are built, substantially reducing computational requirements. Model validation against high-fidelity finite difference (FD) simulations shows mean absolute percentage errors ranging from 5.56% to 8.30% across multiple deposition layers of the entire component, and from 3.77% to 6.25% in regions near the melt pool characterized by steep thermal gradients. Remarkably, the model completed temperature predictions for five deposition layers in just 33.7 seconds, significantly faster than the 545.9 seconds required by FD simulations and well within the total printing time of 101.4 seconds, demonstrating suitability for real-time control applications. The developed framework offers a robust, scalable, and computationally efficient solution for thermal management in DED, potentially enhancing closed-loop control capabilities and promoting broader industrial adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".