Effects of Heat Input and Intertrack Overlap on the Microstructure and Properties of Inconel 686 Weld Overlays
Bibliographic record
Abstract
The objective of this study was to investigate how weld overlays with nickel superalloys are important for the integrity, due the high temperatures and corrosive environments that can be experienced in mineral processing environments, of mining and processing equipment. The Ni-Cr-Mo superalloy Inconel 686 overlays are fabricated through automatic gas metal arc welding with variations in arc voltage and travel speed (i.e., heat input), and they have overlap between adjacent weld tracks for applications in the mining and minerals sector. The impact of variations in the process parameters and the size of the weld overlapping on the dilution, solidification morphology, microsegregation, and microhardness were investigated. Both geometric and chemical composition definitions were used to quantify the extent of the weld dilution. Subsequently, the weld geometry and dilution were correlated with the solidification microstructure and phase transformations. The maximum dilutions were measured to be 13.63% (1/2 overlap, 5.96 kJ·cm−1) and 15.39% (1/3 overlap, 4.77 kJ·cm−1), which shows that less of an overlap increases the dilution level. Scanning electron microscopy and chemical composition analysis revealed that an increase in weld heat input and dilution level led to higher levels of microsegregation for Mo and Cr, as well as the volume fraction of Mo- and Cr-rich phases in the interdendritic/intercellular regions in the overlay layer. Analysis of the weld overlays in the current study revealed strong and unprecedented connections between the weld overlay process conditions, the resultant metallurgy (i.e., dendrite arm spacing, microsegregation, and phase formation), and the hardness of the overlay. It was concluded that the optimal weld overlays in the processing window studied in this investigation were fabricated at mid-level heat inputs (i.e., 4–5 kJ·cm−1) and a 1/2 track overlap.
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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) | 0.000 | 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 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".