Online Tool Wear Monitoring via Long Short-Term Memory (LSTM) Improved Particle Filtering and Gaussian Process Regression
Accurate prediction of tool wear plays a vital role in improving machining quality in intelligent manufacturing. However, traditional Gaussian Process Regression (GPR) models are constrained by linear assumptions, while conventional filtering algorithms struggle in noisy environments with low signal...
Saved in:
| Main Authors: | Hui Xu, Hui Xie, Guangxian Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Journal of Manufacturing and Materials Processing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4494/9/5/163 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP_ADABOOST ALGORITHM
by: ZHU Xiang, et al.
Published: (2019-01-01) -
Enhancing Fault Detection in Stochastic Environments Using Interval-Valued KPCA: A Cement Rotary Kiln Case Study
by: Abdelhalim Louifi, et al.
Published: (2025-01-01) -
Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory
by: Lan Cao, et al.
Published: (2024-12-01) -
Radiation Mapping: A Gaussian Multi-Kernel Weighting Method for Source Investigation in Disaster Scenarios
by: Songbai Zhang, et al.
Published: (2025-07-01) -
Milling Cutter Wear Prediction Based on Feature Extraction and LongShort-term Memory Neural Networks
by: ZHOU Chengpeng, et al.
Published: (2021-01-01)