GMAW weldability of HPVDC structural alloy: impact of surface and core characteristics
Bibliographic record
Abstract
High-integrity die castings are often welded into complex structures. Compared to conventional die castings, high-vacuum can significantly reduce weld porosity after gas-metal arc or laser welding. However, achieving robust welding remains an industrial challenge due to the large number of variables involved. Surface characteristics, in addition to core material, are known to play a significant role in weldability. This paper investigates the impacts of die lubricant type, location on the part, cleaning method, and welding process. Critical factors contributing to robust joining capability were identified using a combination of advanced characterization and machine learning models. A 3-mm thick flat die insert with edge features was designed to generate detrimental filling patterns within the available envelope. Specimens were cast with the Aural™-2 alloy and welded in the F temper. Before welding, specimens underwent non-destructive testing by immersed ultrasound, with millimeter-scale traceability. Surface characteristics were also investigated after surface treatment using infrared spectroscopy to quantify oxides and remaining traces of die lubricants. Specimens were then welded in a bead-on-plate configuration with GMAW or autogenous laser processes. Given the potential shot-to-shot variations, five specimens were tested for each case. Post-weld quality was evaluated with 2D X-ray inspection and segmented along each weld. Finally, since inputs and outputs were traceable along the specimens and weld lines, quality was correlated with casting and welding parameters using visual interpretation methods and statistical analyses. This work highlights the impact of surface characteristics on post-weld quality to identify favourable process windows for robust joining procedures.
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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 | 0.001 | 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) | 0.001 | 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".