Data-driven parameter optimization for bead geometry in wire arc additive manufacturing of 17-4 PH stainless steel
Bibliographic record
Abstract
Due to its high strength, corrosion resistance, and toughness, 17-4 Precipitation Hardening (PH) stainless steel is widely used in aerospace, petrochemical, and marine industries. Additive manufacturing (AM) technologies enable the fabrication of complex and/or customized components while offering superior material efficiency and shorter lead times. Because of its high deposition rate, Wire Arc Additive Manufacturing (WAAM) can produce large metal structures. However, consistent bead profiles remain challenging because the process is highly sensitive to variations in thermal input and deposition conditions. Achieving uniform bead geometry during additive manufacturing is essential to avoid defects such as humming, spattering, and distortion, which can compromise the structural integrity of 3D components. To achieve a uniform bead profile in WAAM, in this study, a full-factorial design of experiments is implemented to optimize the process parameters such as Wire Feed Rate (WFR), Torch Travel Speed (TTS), and Gas Flow Rate (GFR) for 17-4PH stainless steel. A backpropagation neural network (BPNN) is trained to model a non-linear relationship between these process parameters and bead geometry. Moreover, a genetic algorithm (GA) optimizes for bead uniformity and deposition efficiency. With a Pearson Correlation Coefficient (PCC) of 0.85, the optimized parameters exhibited significantly improved uniformity and higher deposition efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".