Deep learning–enhanced prediction of microstructure and porosity evolution in additive-manufactured membrane coatings for harsh environments
Bibliographic record
Abstract
Abstract This study investigates the capability of additive manufacturing (AM) to produce thick coatings functioning as multifunctional membranes with enhanced barrier, transport, and mechanical properties for harsh operating environments. The primary objective was to evaluate how deposition technique and microstructural optimisation influence porosity, diffusion resistance, corrosion protection, and thermal stability. A combined methodology was implemented, integrating experimental testing of laser cladding, thermal spraying, and direct energy deposition (DED) with mathematical models for permeability, diffusion, and thermal conductivity. Laser cladding demonstrated the densest structures, achieving porosity levels below 2% and reducing gas permeability to 1.2 × 10⁻¹⁵ m², nearly an order of magnitude lower than thermal spraying (1.1 × 10⁻¹⁴ m²). Corrosion testing showed nickel-based cladded coatings reached rates as low as 0.0025 mm/year, representing a 90% reduction compared to uncoated substrates (0.026 mm/year). Thermal barrier evaluation of YSZ coatings indicated a conductivity of 0.95 W/m·K at 1200 °C, corresponding to a 38% reduction in heat flux across 1.2 mm-thick layers. Ultrasonic spray post-treatment reduced surface roughness by up to 55% and biofilm accumulation by nearly half. Error analysis confirmed deviations within ± 6%. These results confirm that AM thick coatings function as functional membranes, offering selective transport regulation, structural durability, and sustainability across the aerospace, energy, and marine sectors.
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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 | 0.001 |
| 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".