Cold spray characteristics of mixed 316L stainless steel and commercial purity Fe powders
Bibliographic record
Abstract
Some of the current trends in cold spray include the production of metal-metal composite coatings. Among the various strategies to prepare the composite feedstock, e.g. agglomerate-sintering, mechanically milling and coated/cladded feedstock, mixing metal powders (either by premixing or using dual feeder) is a straightforward method and is convenient to vary the mixture composition. However, it has been found to be difficult to predict the cold spray characteristics of mixed powders (e.g. deposition efficiency (DE), porosity and compositional yield) from those of the single component powders, which means that any new mixtures need to undergo extensive preliminary cold spray trials in order to optimize the process parameters. Thus, fundamentally, analyzing the cold spray characteristics of two metal powders will lead to accurate modeling of the process and minimize the cold spray trials required to optimize the process. This thesis aims to develop a better understanding of the effects of mixing powders on the cold spray characteristics of 316L/Fe mixed powders. Single component 316L and Fe powders were deposited by cold spray, as well as the mixed 316L/Fe powders with a dual feeder setup. The as-sprayed coatings were also polished and single 316L and Fe particles were deposited on the as-polished coatings to form splats. To understand the splat deposition behavior of different impact scenarios (i.e. 316L on 316L, 316L on Fe, Fe on 316L, Fe on Fe), metrics of splat deformation, substrate deformation, splat adhesion strength/energy, and splat rebound were characterized. With a better understanding of the splat deposition behavior, efforts were made to explain the cold spray characteristics of bimodal size 316L/Fe powder mixtures. Since the finding suggests that the feedstock particle size could also be influential of the mixed powders deposition characteristics, different size combinations of 316L/Fe mixtures were deposited and studied. Results show that the splat deposition behavior onto as-polished coatings is indicative of the feedstock deposition behavior during composite coating formation. In particular, the 316L on Fe impact scenario generates strong rebound/poor deposition; whereas the reverse scenario Fe on 316L leads to excellent deposition. The distinct deposition behaviors of different impact sequences between 316L and Fe were explained through the relative particle/substrate hardness, surface oxide thickness, and particle morphology. Besides, results also show the cold spray characteristics of bimodal size 316L/Fe mixtures exhibit unexpected variations with increasing feedstock Fe fractions, which reveals the contributions of various mechanisms during mixed powders deposition: mixed 316L/Fe impact interfaces, particle size difference, and tamping. Finally, the DEs of different sizes of 316L/Fe mixtures show different variations with increasing feedstock 316L fractions, which is due to particle-particle interactions (i.e. tamping and retention) only occur in small size feedstocks during cold spray
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".