Citation: Hsu, C.-H.; Cheng, S.-J.;
Chang, T.-J.; Huang, Y.-M.; Fung,
C.-P.; Chen, S.-F. Low-Cost and
High-Efficiency Electromechanical
Integration for Smart Factories of IoT
with CNN and FOPID Controller
Design under the Impact of COVID-19.
Appl. Sci. 2022, 12, 3231. https://
doi.org/10.3390/app12073231
Academic Editors: Arkadiusz Gola,
Izabela Nielsen and Patrik Grznár
Received: 8 January 2022
Accepted: 9 March 2022
Published: 22 March 2022
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Article
Low-Cost and High-Efficiency Electromechanical Integration
for Smart Factories of IoT with CNN and FOPID Controller
Design under the Impact of COVID-19
Chang-Hung Hsu
1,
* , Shan-Jen Cheng
1
, Te-Jen Chang
2
, Yi-Mei Huang
3
, Chin-Ping Fung
1
and Shih-Feng Chen
4
1
Department of Mechanical Engineering, Asia Eastern University of Science and Technology,
New Taipei 220, Taiwan; cheng5721@gmail.com (S.-J.C.); cpfung@mail.aeust.edu.tw (C.-P.F.)
2
Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense
University, Daxi District, Taoyuan 335, Taiwan; karl591218@gmail.com
3
Department of Mechanical Engineering, National Central University, Chungli District, Taoyuan 320, Taiwan;
t330005@cc.ncu.edu.tw
4
Department of Mechanical Engineering, Lunghwa University of Science and Technology, Guishan District,
Taoyuan 333, Taiwan; dave2esc@gm.lhu.edu.tw
* Correspondence: chshiu@mail.aeust.edu.tw; Tel.: +886-2-77-388-000 (ext. 3133); Fax: +886-2-77-380-145
Abstract:
This study proposes a design for unmanned chemical factories and implementation based
on ultra-low-cost Internet of Things technology, to combat the impact of COVID-19 on industrial fac-
tories. A safety and private blockchain network architecture was established, including a three-layer
network structure comprising edge, fog, and cloud calculators. Edge computing uses a programmable
logic controller and a single-chip microcomputer to transmit and control the motion path of a four-axis
robotic arm motor. The fog computing architecture is implemented using Python software. The struc-
ture is integrated and applied using a convolutional neural network (CNN) and a fractional-order
proportional-integral-derivative controller (FOPID). In addition, edge computing and fog computing
signals are transmitted through the blockchain, and can be directly uploaded to the cloud computing
controller for signal integration. The integrated application of the production line sensor and image
recognition based on the network layer was addressed. We verified the image recognition of the
CNN and the robot motor signal control of the FOPID. This study proposes that a CNN + FOPID
method can improve the efficiency of the factory by more than 50% compared with traditional manual
operators. The low-cost, high-efficiency equipment of the new method has substantial contribution
and application potential.
Keywords: smart factory; IoT; machine learning; robot control; COVID-19
1. Introduction
The COVID-19 crisis has had a substantial effect on the economy of many countries
with increased risk to the lives of many people. The Internet of Things (IoT) has the poten-
tial to improve the transformation of manufacturing technology, and it has also attracted
attention from academia and industry. The IoT envisages the seamless integration of the
physical world and cyberspace by being ubiquitous. Devices are widely deployed with
embedded identification (ID), sensing, and driving functions, and can be extended to the
physical field of the IoT [
1
–
4
]. Miniature electronic devices are embedded to interact in the
physical world and are connected to the network system to make them intelligent and seam-
lessly integrated into the final network infrastructure. Therefore, the IoT can be extended
to include manufacturing resources/capabilities in different stages of the manufacturing
and business planning processes. In addition, it enables vertical integration at different
hierarchical system levels. This mechanism can provide existing or new manufacturing
services and applications with unprecedented opportunities [5,6].
Appl. Sci. 2022, 12, 3231. https://doi.org/10.3390/app12073231 https://www.mdpi.com/journal/applsci
