The University of Manchester
United Kingdom of Great Britain and Northern Ireland
→
Jiduo Zhang
Education
The University of Manchester
United Kingdom of Great Britain and Northern Ireland
→
Northwestern Polytechnical University
China
→
Northwestern Polytechnical University
China
→
RWTH Aachen
Germany
→
Publications
EBPC: a deep learning cloud computing framework for hybrid stack drilling monitoring
Journal of Intelligent Manufacturing
journal-article
Knot-TPP: A Unified Deep Learning Model for Process Incidence and Tool Wear Monitoring in Stacked Drilling
Journal of Manufacturing and Materials Processing
journal-article
Knot-TPP: A Unified Deep Learning Model for Process Incidence and Tool Wear Monitoring in Stacked Drilling
Journal of Manufacturing and Materials Processing
journal-article
Knot-TPP: A Unified Deep Learning Model for Process Incidence and Tool Wear Monitoring in Stacked Drilling
Journal of Manufacturing and Materials Processing
journal-article
Minimum sufficient signal condition of identifying process incidence in stacked drilling through deep learning
Mechanical Systems and Signal Processing
journal-article
Minimum sufficient signal condition of identifying process incidence in stacked drilling through deep learning
Mechanical Systems and Signal Processing
journal-article
Intelligent machining of CFRP composites via data-driven prediction and optimization: Advances, challenges and future prospects
preprint
Process incidence monitoring in material identification during drilling stacked structures using support vector machine
The International Journal of Advanced Manufacturing Technology
journal-article
Process incidence monitoring in material identification during drilling stacked structures using support vector machine
International Journal of Advanced Manufacturing Technology
journal-article
Process incidence monitoring in material identification during drilling stacked structures using support vector machine
The International Journal of Advanced Manufacturing Technology
journal-article
In-process tool incidence identification based on temporal pyramid pooling and convolutional neural network
Procedia CIRP
conference-paper
In-process tool incidence identification based on temporal pyramid pooling and convolutional neural network
Procedia CIRP
journal-article
Minimum Sufficient Signal Condition of Identifying Process Incidence in Stacked Drilling Through Deep Learning
SSRN
other
IN-PROCESS PROCESS INCIDENCE IDENTIFICATION IN DRILLING OF AEROSPACE STACKS
Proceedings of International Conference on Computers and Industrial Engineering, CIE
conference-paper
Enhancing cutting tool sustainability based on remaining useful life prediction
Journal of Cleaner Production
journal-article
In-process tool condition forecasting based on a deep learning method
Robotics and Computer-Integrated Manufacturing
journal-article
In-process tool condition monitoring based on convolution neural network,基于卷积神经网络的刀具磨损在线监测
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
journal-article
Non-linear Wiener process–based cutting tool remaining useful life prediction considering measurement variability
International Journal of Advanced Manufacturing Technology
journal-article
Digital twin model for cutting tools in machining process,面向切削过程的刀具数字孪生模型
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
journal-article