Tool Wear Predicting Based on Multisensory Raw Signals Fusion by Reshaped Time Series Convolutional Neural Network in Manufacturing

Tool Wear Predicting Based on Multisensory Raw Signals Fusion by Reshaped Time Series Convolutional Neural Network in Manufacturing

Tool wear monitoring is a typical multi-sensor information fusion task. The handcrafted features may be a suboptimal choice that will lower the monitoring accuracy and require significant computational costs that hinder the real-time applications. In order to solve these problems, this paper propose...

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Bibliographic Details
Main Authors: Zhiwen Huang, Jianmin Zhu, Jingtao Lei, Xiaoru Li, Fengqing Tian
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Tool wear predicting
multi-sensor
raw signals
convolutional neural network
reshaped time series
manufacturing
Online Access:https://ieeexplore.ieee.org/document/8928491/

Internet

https://ieeexplore.ieee.org/document/8928491/

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