Numerical Investigation of CMT-WAAM: Effects of Substrate Preheating on Molten Pool Dynamics and Thermal History
Bibliographic record
Abstract
Abstract As a Directed Energy Deposition (DED) process, Wire Arc Additive Manufacturing (WAAM) offers high deposition rates and low costs, making it ideal for medium to large metal components. Cold Metal Transfer (CMT) further improves precision by minimizing heat input. However, complex molten pool dynamics in CMT-WAAM can result in irregular bead morphology and discontinuities. This study presents a 3D multiphysics Computational Fluid Dynamics (CFD) model to investigate thermal history, molten pool behavior, and droplet dynamics in the deposition of 17-4 PH stainless steel. The model incorporates the effects of substrate preheating and contact angle variation. The Volume of Fluid (VOF) method was used to track molten pool evolution. Simulation results, validated by experiments, showed that preheating improves pool homogeneity and reduces cooling rates, thereby enhancing deposition stability. Additionally, larger contact angles were found to impair droplet spreading, leading to intermittent bead formation. This work clarifies the interplay between thermal conditions and wetting behavior, offering guidance for optimizing process parameters in WAAM.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".