Influence of low temperatures on mechanical behavior of laser welded dual phase steels
Bibliographic record
Abstract
Low-temperature tensile properties of similar and dissimilar laser-welded joints of dual phase (DP) steels were investigated. DP steels with ultimate tensile strengths of 800 and 1000 MPa were laser welded in similar and dissimilar configurations. The microstructures of the welded joints were characterized, and the welds were tensile tested at temperatures between −40 and 20 °C. Tensile and yield strengths increased as the temperature decreased. However, the DP800-DP1000 dissimilar welded joints exhibited reduced elongation, strength, and absorbed energy when compared to the DP800-DP800 and DP1000-DP1000 similar welded joints throughout the tested temperature range. An in-depth comparison of the deformation mechanisms and failure modes in welds were performed, which showed that the strain gradient for the dissimilar DP800-DP1000 welds is significantly more severe when compared with welds made of similar material combinations (DP800-DP800 and DP1000-DP1000). In addition, the general trend in fracture energy observed in welded similar joints of DP800-DP800 exhibit a decrease with decreasing temperature from 0 to −40 °C, while DP1000-DP1000 joints exhibit an increase in fracture energy as the temperature decreased from 0 to −40 °C. However, the dissimilar DP800-DP1000 joint exhibited relatively consistently lower fracture energy throughout the testing temperatures. The elongation of DP800-DP800-welded joints increased with increasing temperature while the changes in the elongation of welded DP800-DP1000 and DP1000-DP1000 were relatively small. Energy dispersive spectra analysis revealed higher percentages of interstitial atoms, which explains the fluctuating trends seen in the tensile properties of the materials at different deformation temperatures
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 | 0.001 |
| 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".