Jiduo Zhang

Jiduo Zhang
The University of Manchester · School of Mechanical, Aerospace and Civil Engineering

Master of Engineering
PhD student in The University of Manchester

About

7
Publications
977
Reads
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221
Citations
Introduction
Deep learning application in remaining useful life of tool
Education
January 2021 - January 2025
The University of Manchester
Field of study
  • Adaptive drilling
September 2017 - April 2020
Northwestern Polytechnical University
Field of study
  • Aeronautical and Astronautical Manufacturing Engineering
October 2016 - April 2017
RWTH Aachen University
Field of study
  • mechanical engineering

Publications

Publications (7)
Article
Full-text available
Drilling of stacks comprising carbon fibre-reinforced polymers (CFRP) and aluminium in a single shot is a typical operation in the assembly of aircraft. This paper proposes a novel approach to identify incidences in CFRP/Al stack drilling with 94 % classification accuracy based on signal features and support vector machine (SVM). This enables the a...
Conference Paper
Full-text available
Adaptive drilling enables the optimisation of cutting parameters to suit the specific requirements of multi-material stacks, for example hybrid stacks comprising carbon fibre reinforced polymer (CFRP) and aluminium which are commonly used in the aerospace industry. This work proposed a deep learning approach to identify process incidences from diff...
Article
It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is propose...
Article
Full-text available
Remaining useful life prediction is essential for cutting tool utilization evaluation and replacement decision-making. However, it is very difficult to build a mechanism model for the time-varying and non-linear cutting tool wear and life decreasing process. Based on big samples, artificial intelligence–based models have weak interpretability and u...
Article
Full-text available
As a critical part of machining, cutting tools are of great importance to sustainability enhancement. Normally, they are underused, resulting in huge waste. However, the lack of reliable support leads to a high risk on improving the cutting tool utilization. Aiming at this problem, this paper proposes an approach to enhance the cutting tool sustain...

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