# Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes
## 1 Introduction  
Digital transformation has been driven by various technologies, such as cyber-physical systems, big data, Internet of Things (IoT), Industrial Internet of Things (IIoT), mobile devices, cloud computing, robotics, artificial intelligence (AI), blockchain, mixed reality, DTs, and collaborative and social tools [1]. Smart manufacturing, also known as Industry 4.0, aims to enhance productivity, quality, and efficiency in production by integrating technologies to automate and optimize various tasks, including supply chain management, quality control, and production planning [2]. These emerging digital technologies, such as industrial interconnection and DTs, have changed methods, production models, industrial organizations, and international patterns, and the digital economy is transforming and enhancing the manufacturing industry [3]. Industry 4.0 has revolutionized manufacturing systems through several technologies that have proven to be highly valuable. The main ones are virtualization through CPS, IoT, and DTs; these technologies combine data collection and analysis to facilitate interaction between physical and virtual spaces with bidirectional communication [4, 5], enabling the connection and visualization of all elements. Moreover, they provide information concisely and seamlessly to facilitate intelligent decision-making, thus accelerating industrial modernization. A cyber-physical systemcombines computational, networking, and physical elements to control and monitor the physical environment in real time [6]. Digital manufacturing and CPS have contributed to enhancing DTs through real-time production and operation data [7]. The main reasons for using these technologies in the industry are analysis, diagnosis, and predictions about future performances in the processes [8]. DTs are 2D or 3D computer-based digital replicas that simulate and reflect a physical entity's life to extract data from the real world and process information in digital models to monitor, diagnose, optimize, predict, and support real-time decision-making [9; 10]. They are considered a fundamental technology for smart manufacturing, so they have been extensively researched in academic and industrial domains. Several authors have proposed different methodologies and architectures for their implementation in the industry. However, only some studies show a standardized approach for developing DTs in industrial applications [11]. One of the most significant challenges is to create a well-defined 3D digital structure to connect them with the actual model. This indicates that some barriers in the industry must be overcome so that all the benefits of DTs can be fully leveraged in an industrial context [12]. Another challenge is to create reliable models with acceptable computational costs, low latency, and analysis using artificial intelligence techniques. However, the development has gained strength in recent years with the advancement of Information and Communication Technologies (ICTs), as sensors and technological infrastructure have become more cost-effective and reliable [13]. Several studies have shown that the implementation of DTs in industries provides the opportunity for seamless information transfer, process replication, diagnosis, increased productivity, the development of intelligent systems, and predictive analysis [14]. As the integration between the real and the cyber world progresses, virtual interfaces for visualization between the operator and the virtual environment must evolve in parallel. For this reason, immersion technologies, such as virtual and augmented reality, allow for the immersive and interactive visualization of DTs [4]. The integration between DTs and AR in the industry provides valuable information about the design and function of a product, in addition to optimizing processes and reducing operator training costs [15].