About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Classification of Material Defects in Ni-Base Superalloys Using Deep Learning |
Author(s) |
Yann Niklas Schöbel, Simon Pfingstl, Markus Kolb, Marco Hüller |
On-Site Speaker (Planned) |
Yann Niklas Schöbel |
Abstract Scope |
Rotor discs in aircraft engines are exposed to high stresses and temperatures. The burst of rotor discs is one of the catastrophic failure modes of gas turbine engines. Therefore, these critical components must be carefully inspected to identify material defects before commissioning. One of these inspection methods is the macro etch process, which visualizes anomalies on the surface of the parts. These findings are difficult to distinguish and experience is needed to reliably assign them to a specific defect type. In this approach, optical microscopic images of the detected material defects are classified automatically by a Deep Learning Image Classification model which reaches a test set accuracy of 81% while misclassifying only 2.3% of the most critical findings into the class of harmless defects. Compared to human experts, a similar error rate is achieved while the objectivity of the process is increased and more consistent results are obtained. |
Proceedings Inclusion? |
Undecided |