Effect of titanium alloy powder reinforcement on the mechanical properties and microstructural evolution of GMAW mild steel butt joints
Bibliographic record
Abstract
Despite its well-reported application in a few welding processes, the use of reinforcing powders in weld joints to improve weld integrity has not garnered ample research attention for Gas Metal Arc Welding (GMAW) process. In this study, the adoption of Titanium alloy powders as metallic reinforcement for mild steel butt welds was investigated. By adopting Taguchi’s L4 orthogonal array, process optimisation for titanium-reinforced mild steel butt welds were first carried out. In the second phase of welding, the optimum parameters were used to create and compare two sets of weldments; one set was reinforced with titanium alloy powder and the other set left unreinforced. It was observed that in the Weld Metal (WM) region, the titanium-reinforced samples had higher micro-hardness values than their unreinforced counterparts with an average of 285.62 HV and 211.6 HV respectively. However, there was no substantial improvement in the ultimate tensile strength of the mild steel butt welds due to titanium powder reinforcements. Interestingly, the formation of acicular ferrite microstructure was more prevalent in the titanium-reinforced weldments and this was attributed to the presence of titanium inclusions in the weld metal. This prevalence of acicular ferrite suggests improved toughness properties in the weld joint region. While the higher hardness values in the Weld Metal of the reinforced sample indicates improved wear resistance.
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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 | 0.001 |
| 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".