_applied sciences_  
_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**  
**[PERSON] 1,*(c), [PERSON] 1(c), [PERSON] 2(c), [PERSON] 3, [PERSON] 1 and [PERSON] 4**  
1 Department of Mechanical Engineering, Asia Eastern University of Science and Technology, New Taipei 220, Taiwan; [EMAIL_ADDRESS] (S-J.C.); [EMAIL_ADDRESS] (C.-P.F)  
2 Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Daxi District, Taoyuan 335, Taiwan; [EMAIL_ADDRESS]  
3 Department of Mechanical Engineering, National Central University, Chungli District, Taoyuan 320, Taiwan; [EMAIL_ADDRESS]  
4 Department of Mechanical Engineering, Lunghwa University of Science and Technology, Guishan District, Taoyuan 333, Taiwan; dave2 [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; 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 factories. 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 structure 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. 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 potential 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, 2, 3, 4]. Miniature electronic devices are embedded to interact in the physical world and are connected to the network system to make them intelligent and seamlessly 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]. An advanced interconnected network system includes connections between smart machines, storage systems, and production facilities. It can exchange network information independently, trigger actions, and control systems.