## 1 Introduction  
Advances in manufacturing technologies and digital transformation are changing the industrial sector worldwide, implementing new concepts and cutting-edge technologies such as the Internet of Things (IoT), Big Data (BD), Cyber-Physical Systems (CPS), Cloud Computing (CC), and Artificial Intelligence (AI) [1]. These technologies have greatly stimulated the development of smart manufacturing, also called Industry 4.0, where there is greater data management that allows factories to save time, improve productivity, customize products, and respond flexibly to current requirements. Due to disruptionscaused by the COVID-19 pandemic, most industries are considering a foray into Industry 4.0, taking advantage of its benefits to simulate working conditions in real-time, operate production systems remotely, and maintain sustainable operations [2]. Cyber-physical systems are a promising tool to transform many application fields positively. However, they are also a great challenge because there is much to be explored and discovered regarding cyber-physical integration for achieving smart manufacturing [3]. Currently, digital models used for process simulation are stored as cloud services. Due to updates or latency, these models may not reflect the actual state of the physical system, resulting in inaccurate simulation and control [4]. The industrial revolution has given rise to an emerging concept called DT that has the potential to break the barrier between physical and cyberspace in smart manufacturing [5; 6]. It integrates CPS information and fuses heterogeneous data from multiple sources, facilitating the connection between virtual and actual data [7; 8]. This technology can improve the integrity and feasibility of process planning, through training artificial intelligence and 3D computer-aided (CAD) models, and then build an evaluation and optimization twin for the transformation of theoretical processes into practical operations, improving the viability and effectiveness to optimize processes, and providing a friendly interface. DT is one of the main concepts associated with Industry 4.0. However, in the scientific literature, there is no single definition for this concept [9; 10], no complete understanding, and no unifying model [11]. Studies show that DT still needs to form an integrally connected technology and is still in its early stage of development [12]. So far, very little research has been conducted in the industry because it is difficult to analyze and estimate the structural and environmental parameters in real-time considering dynamic changes, and also due to the high complexity of representing and modeling the physical processes involved in the production stage [13]. One of the main challenges is the need to monitor and interact with the physical entity to concentrate the functionalities of a highly reliable system that bridges and integrates the physical and virtual worlds [14]. In the industrial field, AR is a complementary technique that helps operators perform tasks such as assembly, assistance, visualization, and interaction. This technology also allows operators and technical personnel to manage industrial variables in a more dynamic and less risky way; it is also used for the maintenance and programming of industrial machinery. Augmented and virtual reality, also known as \"extended reality\" (XR), offer a new set of 3D applications that may revolutionize the factories of the future, enabling the development of novel virtual applications that included improved forms of collaboration, efficient training processes, and improved productivity [15]. AR allows the improvement of the visualization and interactivity effects, where the digital twin receives data from sensors and controllers. Its main features include real-time monitoring, reprogramming, offline simulation, virtual test bench for optimization, and operator training, improving traditional methods because more accurate data and timely responses to dynamic process events can be obtained.