Surface Residual Stress Analysis in GMAW and LBW of the Dissimilar TRIP-DP Steels Joint: An Experimental Approach
Bibliographic record
Abstract
A transformation-induced plasticity (TRIP) steel and a dual-phase (DP) steel were paired together by employing gas metal arc welding (GMAW) and laser beam welding (LBW) processes. The post-weld microstructure, the hardness profile, and the uniaxial tensile behavior of the welded steels have been analyzed in detail. The experimental surface residual stress distribution across the weldment was measured through the X-ray diffraction sin2Ψ technique. The results indicate that although a harder microstructure composed of predominant martensite was observed along the weldment, the uniaxial tensile behavior resulted in better elongation properties and a higher UTS in the LBW specimen as compared to the GMAW specimen. The resultant residual stress distribution in the heat-affected zone (HAZ) had an increase to a maximum value, followed by a steady decrease up to the base metal following the trend: upper-critical UC-HAZ (maximum) → inter-critical IC-HAZ (moderated) → subcritical SC-HAZ (lowered), which was particularly more evident on the GMAW specimen. Overall, the resultant residual stresses along the weldment were lower on the LBW specimen (172 MPa maximum) which clearly contrasts to the GMAW specimen (421 MPa maximum). Finally, the tensile residual stresses in both the GMAW or LBW did not influence the overall tensile properties of the weldments.
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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 | 0.000 |
| 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".