Optimization of Solution Precursor Plasma Spray Process by Statistical Design of Experiment
Bibliographic record
Abstract
Abstract The solution precursor plasma spray (SPPS) process, in which a solution precursor of the desired resultant material is fed into a plasma jet by atomizing gas or high pressure, was developed in the 1990’s and has been studied extensively since then. Recently, it has been shown that the SPPS process is suitable for deposition of porous electrodes for solid oxide fuel cells (SOFC). High efficiency SOFC requires electrodes with 30%-40% porosity. Because of the complexity of the SPPS process and the large number of processing parameters, it is difficult to investigate the effect of each parameter on the two important properties, i.e. coating porosity and deposition efficiency, separately. Design of experiments can use a small number of experimental runs to analyze the effect of each processing parameter on the properties of the fabricated product, after which the processing parameter combinations for fabricating a target product can be found. In this project, a small central composite design, a second order statistical model, was used to analyze and optimize the SPPS process for Ni-YSZ anode deposition. The processing parameters investigated include: 1) Hydrogen flow rate, which determines arc voltage, 2) Current , 3) Solute flow rate, 4) Solution concentration, 5) Distance between nozzle and gun, and 6) Stand off distance. The effects of the selected processing parameters were analyzed, and the resultant model used to select a combination of processing parameters which produced a coating with the desired characteristics.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".