ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction

ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction

In order to improve the accuracy of tool wear prediction, an attention-based composite neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed. Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional convolutional neural netw...

Full description

Bibliographic Details
Main Authors: Renwang Li, Xiaolei Ye, Fangqing Yang, Ke-Lin Du
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Machines
Subjects:
tool wear prediction
feature extraction
attention
LSTM
metrology
Online Access:https://www.mdpi.com/2075-1702/11/2/297

Internet

https://www.mdpi.com/2075-1702/11/2/297

Similar Items