arXiv (All)
open access
Sparse data limit what a mechanistic corrosion model can predict
Mechanistic corrosion models are routinely fitted with four to six parameters to a few measurements and extrapolated across service lifetimes, yet whether the data determine them is rarely tested. We identify a reduced point defect model for the duplex oxide on Nb-stabilized AISI 347 in simulated bo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Machine-Learning Modeling of Organic Corrosion Inhibitors#461090
- Rolling Contact Fatigue Life Prediction of Traction Drives Based on Observation of Surface Crack Growth#69791
- AI-Augmented Surface Metrology for Machining : An Integrated Approach to On-Machine Surface Quality Assessment#1133926
- Stacking faults from a different angle – Overcoming the edge-on limit in high-resolution defect analysis / Nicolas Karpstein, Lukas Müller, Andreas Bezold, Michael J. Mills, Steffen Neumeier, Erdmann Spiecker#983076
- Machine learning-enhanced reflective microscopy in corrosion research#312025
Record · ID 999549
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive