Citation: Maru, V.; Nannapaneni, S.;
Krishnan, K.; Arishi, A. Deep-
Learning-Based Cyber-Physical
System Framework for Real-Time
Industrial Operations. Machines 2022,
10, 1001. https://doi.org/10.3390/
machines10111001
Academic Editors: Shuai Li,
Dechao Chen, Mohammed
Aquil Mirza, Vasilios N. Katsikis,
Dunhui Xiao and Predrag
S. Stanimirovic
Received: 20 September 2022
Accepted: 23 October 2022
Published: 31 October 2022
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Article
Deep-Learning-Based Cyber-Physical System Framework for
Real-Time Industrial Operations
Vatsal Maru * , Saideep Nannapaneni, Krishna Krishnan and Ali Arishi
Department of Industrial, Systems and Manufacturing Engineering, Wichita State University,
120 Engineering Building, 1845 Fairmount St., Box 35, Wichita, KS 67260-0035, USA
* Correspondence: vkmaru@shockers.wichita.edu
Abstract:
Automation in the industry can improve production efficiency and human safety when
performing complex and hazardous tasks. This paper presented an intelligent cyber-physical system
framework incorporating image processing and deep-learning techniques to facilitate real-time
operations. A convolutional neural network (CNN) is one of the most widely used deep-learning
techniques for image processing and object detection analysis. This paper used a variant of a CNN
known as the faster R-CNN (R stands for the region proposals) for improved efficiency in object
detection and real-time control analysis. The control action related to the detected object is exchanged
with the actuation system within the cyber-physical system using a real-time data exchange (RTDE)
protocol. We demonstrated the proposed intelligent CPS framework to perform object detection-
based pick-and-place operations in real time as they are one of the most widely performed operations
in quality control and industrial systems. The CPS consists of a camera system that is used for
object detection, and the results are transmitted to a universal robot (UR5), which then picks the
object and places it in the right location. Latency in communication is an important factor that can
impact the quality of real-time operations. This paper discussed a Bayesian approach for uncertainty
quantification of latency through the sampling–resampling approach, which can later be used to
design a reliable communication framework for real-time operations.
Keywords: cyber-physical system; deep learning; robotics; real time; industrial operations
1. Introduction
The fourth industrial revolution (Industry 4.0), which refers to the increased usage
of Internet-based applications and its digitization of processes in the industry [
1
], has
reorganized the control of the product life cycle [
2
]. This Internet-based digitization has
provided the opportunity for these applications to be implemented in real time [
3
] and also
to be self-learning [
4
]. The real-time and self-learning applications can connect different
fragments of the industry and improve overall functionalities in various stages of a product
life cycle [
1
]. In addition to increasing the efficiency in industrial operations, it is also
important to reduce wastage leading to sustainable operations. Sustainability is recognized
as a core factor for businesses by the United Nations (UN) Sustainability 2030 agenda [5].
For these reasons, the techniques emerging from Industry 4.0, such as the Internet of
Things (IoT) and cyber-physical systems (CPSs), offer key contributions to the sustainability
of businesses [
6
]. Cyber-physical systems refer to the integration of computing and physical
systems to satisfy desired functional operations. The Internet of Things refers to the
interconnection of several entities (both cyber and physical) over the Web that facilitates
the transmission of information across those entities, thereby enabling automation. Ref. [
7
]
developed an evaluation method to understand the impacts of Industry 4.0 on sustainability
and concluded that Industry 4.0 improved the sustainability dynamics of the industry. They
used UN sustainable development goals as metrics to derive a conclusion in their study.
The IoT and CPS combined have a promise for introducing improved operations
(both in terms of efficiency and sustainability) in a variety of domains such as smart
Machines 2022, 10, 1001. https://doi.org/10.3390/machines10111001 https://www.mdpi.com/journal/machines
