Optimization of process parameters in wire arc additive manufacturing for strengthening cracked steel plates: A thermo-mechanical study
Bibliographic record
Abstract
Wire Arc Additive Manufacturing (WAAM) also known as wire arc-based directed energy deposition (WA-DED) has emerged as a promising technology for repairing and strengthening steel structures. This study investigates the optimization of key process parameters in the WAAM process to improve residual stress (RS) distribution and structural integrity of cracked steel plates. A thermo-mechanical finite element model is developed and validated to predict temperature evolution and RS profiles. Following determination of the optimal dwell time type, the effects of travel speed, dwell time, and substrate preheating temperature on thermal history, residual stress distribution at the WAAM/plate interface, and maximum stress under external loading are systematically analyzed. The results reveal that increasing travel speed effectively reduces tensile residual stresses, while extended dwell times lead to higher residual and maximum stresses due to increased thermal gradients. Additionally, substrate preheating significantly lowers tensile residual stresses but also reduces compressive stresses near the crack tip, which may influence crack arrest effectiveness. By optimizing the combination of process parameters, reductions of up to 40% in both maximum residual stress and maximum stress under external loading were achieved. These findings provide valuable insights for enhancing the fatigue performance and structural reliability of WAAM-strengthened steel components. • Thermo-mechanical FEM simulates WAAM strengthening of cracked steel plates • Model validated for temperature evolution and residual stress profiles • Travel speed, dwell time, and preheating effects are systematically studied • Optimal process parameters reduce maximum residual stresses by up to 40% • Preheating lowers tensile stress but may reduce crack arrest potential
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".