- Tytuł:
- Advancing Chipboard Milling Process Monitoring through Spectrogram-Based Time Series Analysis with Convolutional Neural Network using Pretrained Networks
- Autorzy:
-
Kurek, Jarosław
Szymanowski, Karol
Chmielewski, Leszek
Orłowski, Arkadiusz - Powiązania:
- https://bibliotekanauki.pl/articles/27323142.pdf
- Data publikacji:
- 2023
- Wydawca:
- Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
- Tematy:
-
convolutional neural networks
CNN
vgg16
vgg19
resnet34
tool state monitoring
chipboard milling - Opis:
- This paper presents a novel approach to enhance chipboard milling process monitoring in the furniture manufacturing sector using Convolutional Neural Networks (CNNs) with pretrained architectures like VGG16, VGG19, and RESNET34. The study leverages spectrogram representations of time-series data obtained during the milling process, providing a unique perspective on tool condition monitoring. The efficiency of the CNN models in accurately classifying tool conditions into distinct states (‘Green’, ‘Yellow’, and ‘Red’) based on wear levels is thoroughly evaluated. Experimental results demonstrate that VGG16 and VGG19 achieve high accuracy, however with longer training times, while RESNET34 offers faster training at the cost of reduced precision. This research not only highlights the potential of pretrained CNNs in industrial applications but also opens new avenues for predictive maintenance and quality control in manufacturing, underscoring the broader applicability of AI in industrial automation and monitoring systems.
- Źródło:
-
Machine Graphics & Vision; 2023, 32, 2; 89--108
1230-0535
2720-250X - Pojawia się w:
- Machine Graphics & Vision
- Dostawca treści:
- Biblioteka Nauki
Artykuł