Overcoming Distortion and Residual Stress Challenges in Wire Arc Additive Manufacturing through Advanced Process Control
Bibliographic record
Abstract
Wire Arc Additive Manufacturing (WAAM) has emerged as a transformative technology in the field of additive manufacturing, offering the potential for rapid, cost-effective, and large-scale production. This paper presents a comprehensive framework for optimizing and controlling various aspects of WAAM, addressing challenges such as distortion and residual stress. The proposed methodology comprises five algorithms, each focusing on specific stages of the additive manufacturing process. Algorithm 1, Optimization of Deposition Parameters, initiates the iterative optimization process for achieving enhanced material deposition, minimizing thermal gradients. Algorithm 2, Real-Time Thermal Monitoring, dynamically adjusts temperature distribution in real-time, crucial for maintaining uniform layering. Building upon this, Algorithm 3, Microstructure Control, manages cooling rates to minimize distortion and enhance material properties. The performance evaluation, presented through detailed tables and visualizations, demonstrates the consistent superiority of the proposed method over existing approaches. The proposed method excels in dimensional accuracy, stress reduction, microstructural control, layer uniformity, thermal gradient management, and process stability.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".