# Design and Simulation of AI-Enabled Digital Twin Model for Smart Industry 4.0 +
## 1 Introduction
Because of this, it is expected that AI-driven DT technology will be able to successfully assist decision making in multi-objective issues by adapting traditional model-based methodologies to shifting boundary circumstances and providing a demand-oriented, real-time assessment foundation. Many studies have previously provided descriptions and definitions of DTs from the standpoint of broad ideas and technological frameworks [11]. Not only that, but product design, simulation, and modeling would not be able to take advantage of their own unique enabler, artificial intelligence diagnostics and prognostics for faults [12]. The industrial technology revolution has brought attention to manufacturing concepts like  
Figure 1: Digital twin general network architecture [2]. personalized and distributed manufacturing. These new manufacturing paradigms and the Industrial Internet of Things (IIoT) make connected microsmart factories in factory-as-a-service systems inefficient in cost and production [13]. A digital twin, which employs a digital version of a process with identical manufacturing elements, synchronized information, and functional units, was created to tackle these issues. The digital twin leverages up-to-date information from the Internet to collect data from IIoT devices and operates in many applications. It also generates the components of a detailed digital twin application design and defines procedures. This study could help managers organize the benefits of digital twin utilization through a hierarchy by providing real-time monitoring, tracking information, and operational decision-making support [14]. The proposed application also effectively mitigates cost and production inefficiencies, leading to the optimal functioning of a manufacturing system. We explore the problem in several phases:  
Technical complexity: from functional requirement selection and architecture planning to the integration and verification of the final (digital) models. Data Incompatibility: we address how physical components exchange real-time information with DTs, as well as experimental platforms, to build DTs (including protocols and standards). Security Risks: interoperability between different systems and devices can increase the risk of security breaches, as it creates more opportunities for hackers to exploit vulnerabilities. In this paper, we aim to design a digital twin model for smart industries that are AI-enabled. The paper's primary contributions include the following:  
1. We focus on the construction of a DT network model. 2. More specifically, we focus on determining (methodologically) how to design, create, and connect physical objects with their virtual counterparts, which will improve interoperability, resilience, and security in smart manufacturing systems. 3. We implement an Access Control List (ACL) and an authentication, authorization, and accounting (AAA) system to judiciously manage access to computer resources, enforce rules, audit usage, and deliver the data required to charge for services. 4. The proposed digital twin network model incorporates remote access capabilities. The remainder of the article is organized as follows: Section 2 introduces the digital twin frameworks for development. Section 3 discusses the simulation result. Finally, Section 4 summarizes the main findings of this work.