## 2 Related Works  
This section provides a general description of works carried out by other authors to establish the proposed architectures and the technologies and protocols used for the implementation in the industrial sector. For the selection of the articles, the following aspects were considered: (1) methodologies proposed for the implementation of DTs in the industrial area in the last 5 years and (2) combinations in groups of 2 and 3 of the following keywords: [Digital Twin, Industry 4.0, Virtual Reality], [Digital Twin, Industry 4.0, Augmented Reality], [Digital Twin, OPC UA], and [Digital Twin, CPS, MES]. After selection, 18 articles implemented in the industrial field and using any of the technologies or industrial protocols described in the first paragraph were obtained. It was observed that in the collected works the implementations are not governed by a standard for the design of the DT architecture. Also, three papers were selected at the simulation level because, in the proposal, they used industrial protocols with MES and CPS for their proposals which served as a guide for the union of these technologies. The review will be organized according to the methodological approach, technologies used, and applications to analyze the benefits and challenges of developing such technology. This review aims to conduct a synthesis and critical analysis identifying the strengths and limitations of the proposals made by other authors, in addition to identifying current trends, developments, and rapid evolution. In this way, the architectures and technologies used are considered in this research according to the results obtained when applying them to industrial processes. In the first part, articles were selected that showed DT proposals that were implemented in the industry as described below. The authors of [13] present an architecture to perform an adaptive DT; the objective was to show that online optimization and adaptivity can be performed using an early simulation. Within the proposal, there are constrained, dynamic, and autonomous subcomponents that must be considered to associate with any of the state, design, and configuration components and system behavior. The system is implemented in a shoe production process where the results showed that the proposed architecture is suitable for production environments that undergo constant and unpredictable change, whereby obtaining synchronous and asynchronous data obtained from the physical system serves as a basis for such interaction and obtained a continuous production of customized shoes and can continuously reconfigure the design to adapt to new user needs. In [14], they propose a DT to predict the behavior of products in the first phase of manufacturing by creating a set of DT modules that can be reused and recomposed to create variants of DTs. The case study is performed for two cases, the first one a need of a manufacturer and the second one an industrial case of propulsion systems. In both cases, the results showed how the modular platform design has an impact on the cost of physical and digital definition, if commonality and reusability aspects are considered, in addition to improving the cost effectiveness of applying a modular approach to the creation of DTs. In [15] they propose the integration of a digital shadow (DS) simulation model with an MES, thus creating a DT that has two frameworks, one for error state management and the other for triggering disassembly processes; the system was implemented with the Industry 4.0 Lab MES, Matlab 2023 software was used to perform the simulation environment and the sensor data were sent via an OPC UA protocol and then communicate with the MES via TCP/IP. The results showed that by adding a DS to the MES, the system becomes a DT with bidirectional communication and with the possibility of monitoring and decision-making. In [16], they produce a DT with object detection using deep learning (ML).