Digital Twin for Machining Tool Condition Prediction

Digital twin introduces new opportunities for predictive maintenance of manufacturing machines which can consider the influence of working condition on cutting tool and contribute to the understanding and application of the predicted results. This paper presents a data-driven model for digital twin,...

Full description

Saved in:
Bibliographic Details
Published in:Procedia CIRP 2019, Vol.81, p.1388-1393
Main Authors: Qiao, Qianzhe, Wang, Jinjiang, Ye, Lunkuan, Gao, Robert X.
Format: Article
Language:English
Subjects:
deep learning
digital twin
tool system
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Digital twin introduces new opportunities for predictive maintenance of manufacturing machines which can consider the influence of working condition on cutting tool and contribute to the understanding and application of the predicted results. This paper presents a data-driven model for digital twin, together with a hybrid model prediction method based on deep learning that creates a prediction technique for enhanced machining tool condition prediction. First, a five-dimensional digital twin model is introduced that highlights the performance of the data analytics in model construction. Next, a deep learning technique, termed Deep Stacked GRU (DSGRU), is demonstrated that enables system identification and prediction. Experimental studies using vibration data measured on milling machine tool have shown the effectiveness of the presented digital twin model for tool wear prediction.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2019.04.049