Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network
In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal. However, the power signal is not very sensitive to tool wear conditions. The present work addresses this issue by propos...
Main Authors: | Weihang Dong, Xianqing Xiong, Ying Ma, Xinyi Yue |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-09-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/19/9026 |
Similar Items
-
Training Artificial Neural Network Using Back-Propagation & Particle Swarm Optimization for Image Skin Diseases
by: Hanan A. R. Akkar, et al.
Published: (2011-09-01) -
Improved Salp Swarm Algorithm for Tool Wear Prediction
by: Yu Wei, et al.
Published: (2023-02-01) -
A Novel Error-Correcting Particle Swarm Optimization Back Propagation Fault Diagnosis Method for Microgrid
by: Lijing Wang, et al.
Published: (2023-06-01) -
Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension
by: Yan Yan, et al.
Published: (2022-12-01) -
Experimental Investigation into Wear and Tool Life of Milling Cutter PVD Coated Carbide Inserts While Armox 500 Steel Hard Milling
by: Jozef Majerík, et al.
Published: (2018-01-01)