Cargando…
Application of Siamese Networks to the Recognition of the Drill Wear State Based on Images of Drilled Holes
In this article, a Siamese network is applied to the drill wear classification problem. For furniture companies, one of the main problems that occurs during the production process is finding the exact moment when the drill should be replaced. When the drill is not sharp enough, it can result in a po...
Autores principales: | Kurek, Jarosław, Antoniuk, Izabella, Świderski, Bartosz, Jegorowa, Albina, Bukowski, Michał |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729912/ https://www.ncbi.nlm.nih.gov/pubmed/33291345 http://dx.doi.org/10.3390/s20236978 |
Ejemplares similares
-
Advanced Feature Extraction Methods from Images of Drillings in Melamine Faced Chipboard for Automatic Diagnosis of Drill Wear
por: Antoniuk, Izabella, et al.
Publicado: (2023) -
Improved Drill State Recognition during Milling Process Using Artificial Intelligence
por: Kurek, Jarosław, et al.
Publicado: (2023) -
Decision Confidence Assessment in Multi-Class Classification
por: Bukowski, Michał, et al.
Publicado: (2021) -
Effect of Nitrogen Ion Implantation on the Tool Life Used in Particleboard CNC Drilling
por: Wilkowski, Jacek, et al.
Publicado: (2022) -
Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard
por: Wieczorek, Grzegorz, et al.
Publicado: (2021)