Microstructure and Mechanical Properties Assessments of 304 Austenitic Stainless Steel and Monel 400 Dissimilar GTAW Weldment
Bibliographic record
Abstract
Monel 400 nickel alloy and AISI 304 austenitic stainless steel dissimilar fusion welding influenced in very important fields like oil, nuclear, space industries and petrochemical where high temperatures and corrosive environments are involved with weldments.Also, this dissimilar joint extremely important with environments demand high heat resistance, corrosion resistance, resistance thermal cycles consequences, creep resistance and good mechanical properties.One of the most important advantages of dissimilar welds is saving of novel and expensive materials cost.Dissimilar welding joints in this research produced with gas tungsten arc welding techniques (GTAW) and ENiCrFe-2 filler.Welding joint configuration simulated welding joint design in real working site to achieved best results and asses real welding site up.SEM/EDS analysis, optical microstructure examination, Vickers microhardness test, tensile test and V-notch impact test employed to study and understand welding microstructure details and properties and its impacts in weldment mechanical properties.Research results reveals formation of partial melting zone (PMZ), unmixed zone (UZ) and second phase in HAZ while dendritic solidification with heat flow direction, epitaxial growth, cellular epitaxial solidification and migrated grain boundaries (MGBs) observed in welding zone microstructure, this research deeply analyses formation of these phenomena, and its effects on weldments mechanical properties were discussed.This research results are very important to welding technologist and engineers to understand and prediction resultant welding zone and HAZ microstructures and understanding its impacts on required weldments design criteria when establish welding procedure.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".