Strength-toughness synergistic enhancement mechanism in double-wire multilayer GMA-deposited HSLA steel <i>via</i> interlayer dwell time strategy
Bibliographic record
Abstract
High-strength low-alloy (HSLA) steel has been widely applied in engineering machinery, marine engineering, automotive fields, and other sectors. This study developed an interlayer dwell time (IDT) strategy to enhance both strength and toughness in double-wire multilayer GMA-deposited HSLA steel (ER70-G). This method achieves high-efficiency deposition without alloy redesign. Thermal simulation confirmed that IDT regulation effectively alleviated heat accumulation and accelerated cooling rates from 26 °C/s to 48.1 °C/s. This suppressed the formation of grain boundary ferrite (GBF), reducing its content from 15.7% to 5.3%. Additionally, it refined acicular ferrite (AF) grains, decreasing the average size from 9.96 μm to 5.13 μm. Furthermore, it increased the proportion of high-angle grain boundaries (HAGBs) from 52.8% to 68.7%. In 40-mm-thick deposited multilayers, this microstructural engineering simultaneously elevated tensile strength from 692.3 MPa to 805.4 MPa and impact toughness from 135.67 J to 172.67 J, thereby eliminating the conventional strength-toughness trade-off. This strategy provides a cost-effective solution for manufacturing high-performance HSLA steel, requiring neither alloy redesign nor post-deposition heat treatment, demonstrating significant potential for practical applications to not only multilayer welding but also additive manufacturing.
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 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 | 0.000 |
| 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) | 0.001 | 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".