Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion

Tool wear prediction is of critical importance to maintain the desired part quality and improve productivity. Inspired by the successful application of deep learning in many condition monitoring tasks. In this article, a novel modeling framework is presented, which includes multiple stacked sparse a...

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Vydané v:IEEE transactions on industrial informatics Ročník 16; číslo 8; s. 5150 - 5159
Hlavní autori: Shi, Chengming, Luo, Bo, He, Songping, Li, Kai, Liu, Hongqi, Li, Bin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Condition monitoring
Deep learning
Feature extraction
feature fusion
Force
Ground penetrating radar
Informatics
Machine learning
Modelling
Prediction models
Predictive models
stacked sparse autoencoders
Tool wear
tool wear prediction
Vibrations
ISSN:1551-3203, 1941-0050
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Shrnutí:Tool wear prediction is of critical importance to maintain the desired part quality and improve productivity. Inspired by the successful application of deep learning in many condition monitoring tasks. In this article, a novel modeling framework is presented, which includes multiple stacked sparse autoencoders and a nonlinear regression function for tool wear prediction. Multiple stacked sparse autoencoders consists of two main structures. One model is designed with multidimensional stacked sparse autoencoders, which can learn more features from different feature domains in the raw vibration signal, and another single-dimensional stacked sparse autoencoders is used for feature fusion and deeper features learning. And a modified loss function is applied that improves the learning ability. In addition, due to the good properties of tool wear process in nonstationarity and complex nonlinear, a nonlinear regression function is utilized to enhance the progressive tool wear prediction tasks. A dataset from a real manufacturing process is used to evaluate the performance of the proposed modeling framework. Experimental results show that tool wear can be predicted accurately and stably by the proposed tool wear predictive model, which outperforms the already developed methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2949355