Skip to main content

Distributed deep learning enabled prediction on cutting tool wear and remaining useful life.

Weidong, L., Xiaoyang, Z., Wang, S., Lu, X. and Huang, Z., 2023. Distributed deep learning enabled prediction on cutting tool wear and remaining useful life. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 1-11.

Full text available as:

IMechEB2022-final (1).pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1177/09544054221148776


To optimise the utilisation cost of cutting tools, it is imperative to develop an online system to efficiently and accurately predict tool wear conditions and remaining useful lives (RULs). With this aim, a novel system is proposed based on deep learning algorithms distributed over an edge-cloud computing architecture. The system is innovative in the following aspects: (i) a lightweight convolutional neural network-random forest (CNN-RF) model is designed to be executed on an edge device to assess tool wear conditions efficiently, which supports severe tool resilience and tool replacement when necessary; (ii) a convolutional neural network-long short-term memory (CNN-LSTM) model is designed to be executed on a cloud to process long-term signals to predict the RUL of the cutting tool, which supports fine-tuning tool parameters dynamically; (iii) a signal compression mechanism is developed to condense the signals of tooling conditions into 2D images so the signal volumes transferred over the network are minimised and signal security is improved. Experiments were performed in a real-world machining workshop for research methodology validation. It showed that the prediction accuracies for tool wear and RUL achieved 90.6% and 93.2%, respectively, and the volume of signals transferred over the network was reduced by 89.0%. The experiments and benchmarks with comparative algorithms demonstrated that the system and its methodology exhibited great potential to reinforce cutting tool optimisation for real-world applications.

Item Type:Article
Additional Information:Funding: This research was sponsored by the National Natural Science Foundation of China (Project No. 51975444). This research was also partially supported by the SABOT project from the Innovative UK funder (UK). Acknowledgments: The authors would acknowledge Dr Jinsheng Wang and Mr Chaowei Hao from the Grindoctor Ltd. for assisting in experimental design.
Uncontrolled Keywords:Cutting tool wear; remaining useful life; edge-cloud computing; deep learning
Group:Faculty of Science & Technology
ID Code:38041
Deposited By: Symplectic RT2
Deposited On:26 Jan 2023 16:53
Last Modified:14 Feb 2023 14:16


Downloads per month over past year

More statistics for this item...
Repository Staff Only -