Journal Article

Solving the part identification problem using their STL models

Pechenina, Ekaterina, Pechenin, Vadim

2022

Overview

The article is aimed at solving the problem of aerospace parts identification. A neural network model for part identification was developed. The proposed model consists of three modules: object detection using the YOLO3 model, preprocessing of the selected fragment, and classification of the processed fragment using the VGG19 model. A distinctive feature of the developed model is the use of STL objects for training the VGG19 neural network. To increase the reliability of the classification for each object we used photos made from three angles. The developed model has been tested on the parts of the rotor of a small gas turbine engine. The test was conducted on 100 test cases including 300 photos of parts. To train the neural network, 13,200 images were simulated using STL models. The loss function (categorical cross-entropy) for the training sample was 0.0004, and the classification accuracy was 100%. The accuracy of identification of real photos using the developed model was 97%.

• Publisher: IOP Publishing
• Subject: Accuracy / Classification / Gas turbine engines / Model testing / Network reliability / Neural networks / Object recognition / Part identification / Physics / Training