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

卷积神经网络 计算机科学 GSM演进的增强数据速率 刀具磨损 云计算 人工智能 深度学习 过程(计算) 人工神经网络 信号(编程语言) 边缘设备 刀具 机械加工 机器学习 实时计算 工程类 操作系统 机械工程 程序设计语言
作者
Weidong Li,Xiaoyang Zhang,Sheng Wang,Xin Lü,Zhiwen Huang
出处
期刊:Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture [SAGE Publishing]
卷期号:237 (14): 2203-2213 被引量:2
标识
DOI:10.1177/09544054221148776
摘要

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.
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