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Jiduo Zhang
Mr
Doctor of Philosophy
,
Department of Mechanical, Aerospace & Civil Engineering
https://orcid.org/0000-0001-5788-5995
Overview
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Research output
(3)
Similar Profiles
(1)
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Dive into the research topics where Jiduo Zhang is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
1
Similar Profiles
Deep Learning Method
Engineering
100%
Cutting Parameter
Engineering
88%
Convolutional Neural Network
Engineering
76%
Carbon Fibre Reinforced Polymer
Engineering
66%
Support Vector Machine
Engineering
66%
Fiber-Reinforced Polymer
Engineering
44%
Learning Approach
Engineering
33%
Classification Performance
Engineering
33%
Research output
3
Article
Research output per year
Research output per year
Minimum sufficient signal condition of identifying process incidence in stacked drilling through deep learning
Zhang, J.
,
Heinemann, R.
,
Bakker, O. J.
, Li, S., Xiao, X. & Ding, Y.,
15 Apr 2025
,
In:
Mechanical Systems and Signal Processing.
229
, 112499.
Research output
:
Contribution to journal
›
Article
›
peer-review
Open Access
Deep Learning Method
100%
Machining
100%
Drilling
50%
Machining Operation
14%
Signal Acquisition
14%
In-process tool incidence identification based on temporal pyramid pooling and convolutional neural network
Zhang, J.
,
Heinemann, R.
,
Bakker, O. J.
& Zhu, M.,
2024
,
In:
Procedia CIRP.
126
Research output
:
Contribution to journal
›
Article
›
peer-review
Carbon Fibre Reinforced Polymer
100%
Cutting Parameter
100%
Convolutional Neural Network
100%
Learning Approach
50%
Classification Performance
50%
Process incidence monitoring in material identification during drilling stacked structures using support vector machine
Zhang, J.
,
Heinemann, R.
&
Bakker, O. J.
,
10 Dec 2024
,
In:
The International Journal of Advanced Manufacturing Technology.
136
,
p. 827–840
Research output
:
Contribution to journal
›
Article
›
peer-review
Open Access
Support Vector Machine
100%
Fiber-Reinforced Polymer
66%
Cutting Parameter
33%
Cutting Speed
33%
Classification Accuracy
33%