# Design and Simulation of AI-Enabled Digital Twin Model for Smart Industry 4.0 +
## 1 Introduction  
A digital twin network is a computer simulation model of a communication network, including the environment in which it operates and the application traffic it carries. It uses the Internet of Things (IoT) to enhance decision making in complicated systems by facilitating learning and reasoning. According to a study, adopting DT and IoT technologies is projected to generate economic benefits ranging from USD 5.5 trillion to USD 12.6 trillion worldwide by 2030. The smart manufacturing industry is predicted to expand from USD 214.7 billion in 2020 to USD 384.8 billion in 2025, with a CAGR of 12.4% [1]. Despite smart manufacturing's many advantages, including integrated components and digitization, maintenance remains a significant obstacle. Maintenance is a pivotal determinant that exerts a significant economic impact on the sector, garnering notable emphasis in the era of digitalization. The entire manufacturing cost is projected to include maintenance expenses ranging from around 15% to 40% [2]. Based on the U.S. Department of Energy findings, it has been shown that predictive maintenance offers cost savings of around 8-12% compared to preventive maintenance and can yield savings of up to 40% compared to reactive maintenance [3]. The adoption rate of predictive maintenance experienced a modestincrease from 47% to 51% from 2017 to 2018. This implementation of predictive maintenance strategies reduced equipment failure rates from 61% to 57% [4]. Hence, the maintenance process exerts a direct impact on the economic aspects of the industry. Furthermore, in the contemporary era of Industry 4.0, the utilization of intelligent maintenance techniques incorporating digital twin (DT) technology has the potential to yield substantial advantages compared to existing maintenance methodologies [5]. Therefore, it is imperative to comprehend the concept of a digital twin and the potential of digital twin technology in facilitating an organization's digital transformation. A digital twin refers to a computer-generated model replicating a tangible object's characteristics and behavior or a procedural operation. Digital twins undergo dynamic transformations throughout the life cycles of entities and processes, facilitated by utilizing real-time IoT data. Novel network applications have emerged as a result of society and industry being digitally transformed [6]. The complex requirements for these applications make it difficult for them to be managed by conventional network management techniques like network overprovisioning or admission control. For instance, cutting-edge communication technologies like holographic telepresence and augmented reality/virtual reality demand extremely low deterministic latency, yet modern industrial advancements like vehicular networks demand real-time network topology adaptation. The behavior of current networks is very dynamic and heterogeneous due to the rapid increase in linked devices. Modern communication networks have become so sophisticated and expensive to manage as a result. The DT paradigm has lately been adopted by other industrial sectors to describe complex and dynamic systems [7]. A DT's primary strength is in its ability to accurately replicate a complex system, eliminating the need for costly, time-consuming human interaction. A DT enables Industry 4.0 general network architecture, as shown in Figure 1.  
To examine various situations and forecast decision outcomes, computer simulations are performed. Digital twins vary and are updated frequently to mirror changes in their physical counterparts for timely engagement [8; 9]. Artificial intelligence algorithms and network setups, which are at the heart of modern technologies, are made possible by necessary techniques trained on large volumes of data obtained from numerous connected sensors on physical objects [10]. In addition to raising significant issues for their organizations and procedures, this drives up the expenses for manufacturing businesses.