## 2 Literature Review  
Cyber-security configurations, cyber-physical production networks, and smart manufacturing systems are explored in relation to Industry 4.0. Cyber-physical process monitoring systems and environmentally responsible production related to the Internet of Things began with the rapid expansion of digital technology around the turn of the 21 st century [1]. Industry 4.0, often known as smart manufacturing or industrial internet, aims to optimize global production capacities in terms of visual and spatial intelligence, simulation modeling and semantic sensor technologies, and mobile autonomous robots [8]. Industry 4.0-based digitization processes related to cyber-physical production networks develop on autonomous cognitive and digital twin systems, robotic swarm operations, and artificial intelligence technologies [9]. Due to a self-organizing cyber-physical production system, mass production becomes more flexible in terms of quantity and may be customized as a result of process mining techniques, and data modeling and simulation tools [10]. The fundamental components of Industry 4.0 [11] are automation, the incorporation of cutting-edge technology into industrial processes, and data exchange, which require the continual connectivity of machines capable of working without human intervention. This implementation necessitates the deployment of intelligent networked cyber-physical systems and of digital twin technology by integrating computer-generated virtual objects, robotic and sensor devices, and contextual data monitoring tools in smart manufacturing enterprises [12]. The primary objective of cyber-physical smart manufacturing systems [13] is to automate every step of this process, from configuration and production through supply chain and predictive maintenance. The goal of integrating Industry 4.0 is to transform industrial manufacturing by harnessing innovative technologies and digitalization according to remote sensing data, machining process monitoring, and interoperable automation systems. Globalization, which increases data-driven competitiveness, necessitates sufficiently agile production to satisfy market needs [14]. This objective may be accomplished by integrating Industry 4.0 technology and autonomous manufacturing systems into organizational value chains and product design processes [15]. To effectively implement Industry 4.0 and digital twin systems, the integration process requires the transformation of industrial facilities into smart factories, where connectivity and interoperability develop on Internet of Things-enabled monitoring and sensor fusion-based systems, predictive modeling tools, and augmented reality algorithms [16]. Industry 4.0 is a socioeconomic phenomenon as well as a technical one, characterized by the utilization of smart manufacturing big data, real-time advanced analytics, and product decision-making information systems [17]. Technological advances enable the interoperability and interconnectivity of Internet of Things-based real-time production logistics characterizing smart manufacturing facilities, which are the central pillar of Industry 4.0 and artificial intelligence-driven big data analytics. The initial stage in the establishment of smart factories [18] was the manufacturing of a single item by employing production lines comprised of equipment with well-defined functions typified by virtual data analytics, cognitive digital twins, and cloud computing algorithms. With continued expansion, more prospects were explored, such as the broad production of a variety of manufactured items developed on modeling and simulation tools, mobile robotic devices, and interactive data visualization technologies. Innovative computer numerical control devices now make flexible manufacturing possible [19].