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Application of machine learning models for failure mechanism classification and fatigue crack detection in aeronautical metal alloys

https://doi.org/10.2139/ssrn.5817138
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11668-011-9503-7openalex-snapshot
Damage Assessment Using Fractography as Failure Surface Evaluation: Applications in Industrial Metalworking Machinery
inferredcites →https://doi.org/10.1016/j.engfailanal.2016.03.003openalex-snapshot
An overview on how failure analysis contributes to flight safety in the Portuguese Air Force
inferredcites →https://doi.org/10.1016/j.engfailanal.2020.104532openalex-snapshot
Deep Learning for fractographic classification in metallic materials
inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-snapshot
Feature Pyramid Networks for Object Detection
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1155/2021/5558668openalex-snapshot
A Hybrid Crack Detection Approach for Scanning Electron Microscope Image Using Deep Learning Method
inferredcites →https://doi.org/10.2196/49023openalex-snapshot
Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial
inferredcites →https://doi.org/10.2472/jsms.69.644openalex-snapshot
Application of Deep-Learning for Classification of Fracture Surface’s SEM Image.
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