Bibliographic record
Abstract
Wear causes loss of materials of moving parts and tools used in many fields. Wear can be reduced by employing appropriate materials and coatings, which requires an understanding of their microstructure, properties and tribological behavior. One of the high wear resistant materials is the Fe-Cr-B-based alloy system. Fe-Cr-B-based alloys have been fabricated using thermal spray, welding and sintering processes; and it has been found that their microstructure, properties and tribological performance vary from process to process. This dissertation focuses on advances made by employing recent processes to fabricate these alloys. The primary aim of this research is to understand the microstructure and tribology of the Fe-Cr-B-based alloys fabricated by the controlled short-circuit metal inert gas (CSC-MIG) welding and spark plasmasintering (SPS) processes.CSC-MIG was used to weld a Fe-28.2Cr-3.8B-1.5Si-1.5Mn (wt.%) cored wire alloy onto 1020 carbon steel substrate. SPS was employed to consolidate a Fe-45Cr-5.9B-2Si-0.1C (wt.%) gas-atomized powder alloy. Solidication behaviors of the gas-atomized powder and weldments were investigated through thermodynamiccalculations. Microstructure characterizations, hardness measurements and tribology testings were performed for these fabricated alloys.Upon cooling, the primary (Cr,Fe)2B phase began to form, followed by eutectic formation of the (Cr,Fe)2B and body-centered cubic (BCC) Fe-based solid solution phases. Because the powder contained a small amount of C, (Cr,Fe)7C3 was precipitated at the end of the solidication. The CSC-MIG weldment was composed of 44 wt.% primary and secondary orthorhombic (Cr,Fe)2B plates embedded in 56 wt.% BCC Fe-based solid solution, containing Fe, Cr, Mn and Si. The SPS specimen contained 65 wt.% (Cr,Fe)2B plates and 1 wt.% (Cr,Fe)7C3 precipitates dispersed in 34 wt.% BCC Fe-based solid solution, containing Fe, Cr and Si. The (Cr,Fe)2B phase was bigger in the weldment than the sintered specimen. The hardness of (Cr,Fe)2B was 24 GPa, independent of the alloy's composition and process parameters. As the B content increased, the fraction of (Cr,Fe)2B increased. As the (Cr,Fe)2B fraction increased, the bulk hardness of the specimens increased linearly. When the specimen's hardness and (Cr,Fe)2B size increased, abrasive wear resistance increased, while sliding wear resistance was independent of hardness but improved as the (Cr,Fe)2B size increased. The abrasive wear mechanism was microcutting, while sliding wear mechanisms were adhesion and mild oxidation.
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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".