Cargando…
Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force si...
Autores principales: | Zhang, Yaping, Qi, Xiaozhi, Wang, Tao, He, Yuanhang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221429/ https://www.ncbi.nlm.nih.gov/pubmed/37430508 http://dx.doi.org/10.3390/s23104595 |
Ejemplares similares
-
Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning
por: Yuan, Jun, et al.
Publicado: (2020) -
Micro-Milling Tool Wear Monitoring via Nonlinear Cutting Force Model
por: Liu, Tongshun, et al.
Publicado: (2022) -
Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels
por: González, D., et al.
Publicado: (2022) -
A deep learning-based ensemble method for helmet-wearing detection
por: Fan, Zheming, et al.
Publicado: (2020) -
A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
por: Ou, Jiayu, et al.
Publicado: (2020)