Redefining the Landscape of Wire Arc Additive Manufacturing: Pioneering Innovations for Residual Stress Mitigation and Process Efficiency
Bibliographic record
Abstract
This study introduces a comprehensive framework for Wire Arc Additive Manufacturing (WAAM) optimization, incorporating five key algorithms: Adaptive Layer Thickness Control, In-Situ Stress Monitoring, Dual-Wire Deposition, Thermal Management Strategies, and Machine Learning for Process Control. The Adaptive Layer Thickness Control Algorithm dynamically adjusts layer thickness based on geometric features and real-time feedback, contributing to stress mitigation. The In-Situ Stress Monitoring Algorithm ensures real-time stress distribution optimization. The Dual-Wire Deposition Algorithm introduces simultaneous deposition of two materials, optimizing multi-material properties. Thermal Management Strategies Algorithm focuses on controlling temperature gradient and adaptive cooling strategies. Machine Learning for Process Control Algorithm integrates input from preceding algorithms to predict optimal parameters, ensuring adaptive layer control and overall process optimization. Evaluation against existing methods demonstrates superior outcomes in residual stress reduction, geometric accuracy improvement, material integrity enhancement, process stability, build rate improvement, and energy efficiency. Visualizations and comparative tables confirm the proposed method’s holistic excellence in WAAM.
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 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".