The effect of aluminum interlayer on weld strength, microstructure analysis, and welding parameters optimization in resistance spot welding of stainless steel 316L and Ti6Al4V titanium alloy
Bibliographic record
Abstract
Stainless steel (SS) and Titanium alloy (Ti) are the most commonly used materials in many industrial fields such as the automotive and aerospace industry. Stainless steel has good corrosion resistance and titanium alloy has an extremely lightweight characteristic. The combination of both materials has become a tremendous innovation in the industrial sector. Resistance spot welding which has commonly applied in many industrial fields is a good consideration to join these two dissimilar materials due to the high efficiency that could be achieved by using this method. However, the way of joining these dissimilar materials should be carefully considered due to the significant difference in mechanical properties between SS and Ti. In the present study, 3 mm of SS316L and Ti6Al4V sheets were joint under the resistance spot welding method with an aluminum interlayer. The optimized welding parameters were provided under the Taguchi method L9 orthogonal array along with the mechanical properties’ investigation. The optimum welding parameters were 11 kA of weld current, 30 Cycles of welding time, and 5 kN of electrode force which produced 8.83 kN tensile-shear load of the joint. The mechanical structure analysis shows the different morphology between stainless steel and titanium interfaces and the intermetallic compound layer was formed on the SS/Al and Al/Ti interfaces. The EDX analysis shows the atomic diffusion-reaction on the application of aluminum as an interlayer on the SS/Ti joint.
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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) | 0.000 | 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".