Open AccessTool Wear Prediction Based on Edge Data Processing and Deep Learning Model
Author(s) -
Yan Dong,
Xinyu Ding,
Shuaiyu Pan,
Haoyang Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1820/1/012165
Subject(s) - enhanced data rates for gsm evolution , computer science , artificial neural network , data pre processing , preprocessor , artificial intelligence , field (mathematics) , edge device , convolutional neural network , deep learning , cloud computing , data processing , pattern recognition (psychology) , data mining , machine learning , database , mathematics , pure mathematics , operating system
In order to improve the accuracy of tool wear prediction and enhance the real-time application in industrial sites, a tool wear prediction method based on edge data processing and CNN-BiGRU neural network is proposed. This method first implements data preprocessing on edge nodes, effectively reducing the amount of data transmission to avoid network link congestion. After that, the CNN-BiGRU neural network was deployed in the cloud for model training. Experimental results show that the tool wear prediction method based on edge data processing and CNN-BiGRU neural network has good real-time performance and high prediction accuracy in simulated industrial field applications.