Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Mihai Andronie,Mădălina Cocoșatu,Cristian Uță,George Lăzăroiu,Mariana Iatagan,Roxana Ștefănescu

doi:10.3390/electronics10202497

Abstract

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

Highlights

There is an emergent body of literature in relation to how Cyber-Physical Systems (CPS) address the integration of Internet of Things sensing networks, computational applications, and artificial intelligence-based decision-making algorithms with physical devices, being engineered as an interconnection between cyber and physical components
We show that cyber-physical production systems develop by the integrative processes of sustainable industrial big data, artificial intelligence-based decision-making algorithms, and Internet of Things sensing networks in cyber-physical system-based smart factories
This systematic review endeavors to elucidate whether the interoperability between Internet of Things-based real-time production logistics, big data-driven decision support systems, and cyber-physical process monitoring systems can decide upon the progression of operations [36–38] advancing a system to the intended state in Cyber-Physical Production Systems (CPPSs)

Summary

Introduction

There is an emergent body of literature in relation to how Cyber-Physical Systems (CPS) address the integration of Internet of Things sensing networks, computational applications, and artificial intelligence-based decision-making algorithms with physical devices, being engineered as an interconnection between cyber and physical components. This is crucial in the advancement of smart manufacturing by use of cloud computing, social networking, and big data [1–3]. The purpose of this research is to inspect the recently published material on CPPSs and integrate the understandings associated with Internet of Things-based decision support systems, interconnected sensor networks, deep learning-assisted smart process planning, and automatic big data-driven real-time production logistics. Robotic wireless sensor networks, and artificial intelligence data-driven Internet of Things systems are essential in enabling cyber-physical process monitoring systems

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