An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy

Accurately predicting tool wear in real time is crucial to enhance the tool prognostics and health monitoring system in computerized numerical control (CNC) machining. This paper proposed a novel integrated deep learning model for predicting the wear of milling tools by fusing multi-sensor features....

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Sensors (Basel, Switzerland) Ročník 25; číslo 8; s. 2391
Hlavní autori:	He, Zhaopeng, Shi, Tielin, Chen, Xu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 09.04.2025 MDPI
Predmet:	Accuracy Acoustics Advanced manufacturing technologies Artificial intelligence autoencoder Big Data Comparative analysis Data compression Decomposition Deep learning ensemble learning Industry 4.0 Information technology Machine learning Maintenance and repair Manufacturing Measurement Mechanical wear Methods milling Monitoring systems multi-sensor fusion Neural networks Sensors Signal processing Support vector machines Testing tool wear prediction Vibration China ensemble learning deep learning tool wear prediction autoencoder milling multi-sensor fusion
ISSN:	1424-8220, 1424-8220
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!