## 1 Introduction
AR and DT are tools that facilitate the fusion of the virtual and the physical and have shown a growing interest in the area of research and industry under the wave of the human-centered concept such as Industry 5.0, being one of the enabling technologies that can positively influence the man-machine relationship [16]. Several authors have conducted research in these areas to show their potential and have presented various methodologies, such as in [17], which presents a dynamic system for operators of \"virtual eyes and hands\" in the physical system; each system produces and consumes services to have interoperability, that is, it has heterogeneous and remotely accessible web services, and also a middleware to allow interaction with existing legacy systems (SCADA, ERP). Reference [18] presents an implementation of an architecture that covers four production stages, namely: design, operation, optimization, and validation, and implementation where a model is created to simulate the behavior of the physical system, and later bidirectional communication between the real world (PLC) and the virtual model is established. In [19], they propose a semi-automatic methodology to generate a DT of an industrial process that consists of extracting information from the diagrams and converting the information to a graphical format to generate a simulation model, then configure and parameterize the simulation model according to process data to obtain a digital twin. The results of several works implemented in the industry have shown an improvement in the productivity of the processes and quality standards, in addition to having the capacity for simulation, adaptive automation, and evaluation of new technologies without disturbing the production of the plant using industrial protocols such as OPC-UA. The DT concept, introduced with the Industry 4.0 revolution, will continue to change many areas of our lives [20]. Several enabling technologies contribute to creating the Digital Twin of a physical process by collecting different data sets to develop updated digital simulation models based on the generated knowledge, implement optimization strategies, and evaluate the behavior of the physical environment [21]. By 2025, the global market is expected to reach USD 26.07 billion, with an annual growth rate of 38.2% [22], which will bring significant changes in many areas of our lives. Several pioneering companies in developing products for industrial automation are working to create Digital Twins--for example, General Electric, Hirotec Corporation, Rockwell Automation, ABB, Siemens, etc. [23]. The development of DTs supports the growing and accelerating demand for industrial evolution, increasing functionality, reliability, self-prediction, and autonomy to react to unexpected events [24]. Although DTs are very important, a methodology has yet to be defined to create and use them in smart manufacturing [25]. It is evident that there are several works carried out; however, there are no established methodologies or protocols to implement DT in the industry, for which the present work seeks to contribute with new proposals combining industrial hardware and software with embedded systems and immersion technologies. This research shows the development of a Digital Twin applied to an industrial process. A methodology was used to obtain the physical process information, create the digital environment, communicate the physical and real environment, apply simulation models in the digital environment, and parameterize the simulation environment with the physical process in real time to obtain the Digital Twin. The article has several sections; Section 2 shows the key concepts for the development of DT; Section 3 shows the design and implementation of the DT; Section 4 shows the results and discussion; and Section 5 shows the conclusions.