Ketahanan terhadap Retak-Tegangan-Korosi Sambungan Las Dissimilar Baja ASTM A36 dengan Baja Stainless 316L Pada Berbagai Variasi Suhu
Bibliographic record
Abstract
On many occasions, the Stress Corrosion Cracking (SCC) phenomenon was neglected, which may have caused catastrophic failures such as the blowout of three pipelines in Argentina, explosions in the pipelines transporting natural gas from central Australia to Sydney, and the Trans-Canada pipeline that ruptured in Winnipeg. According to research, the root cause of those accidents was SCC aspects. For structures with welding joints, SCC considerations should be applied. In this study, we welded ASTM A36 steel and 316L Stainless Steel using Capacitive Discharge Welding (CDW). The welding parameters, such as the gap between metals before welding, pressure, welding energy, and surface parameters of those two metals, were kept constant. While the specimens were exposed to the SCC load, the concentration of the corrosive solution of HCl was kept at 0.5 M. The independent variable, i.e. temperature, was maintained at 30°C, 40°C, and 50°C, respectively, while the concentration of the corrosive solution was maintained at 0.5 M. The Stress Corrosion Cracking resilience was evaluated based on the time to fail of the joint when exposed to specific dead loads and corrosive media. For the same load, the higher temperature decreases CDW joint failure time. This increases the joint's susceptibility to SCC load. The experiment shows that 10°C increases the SCC threshold by 40%. The macro photos confirm the results of SCC susceptible measurement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".