# Conceiving a Digital Twin for a Flexible Manufacturing System
## 1 Introduction
### A Brief Literary Overview of Digital Twins for Manufacturing Systems
[43] presented a data-driven model for DT, along with a model prediction method based on deep learning that uses a technique to predict the condition of a machining tool. Studies using vibration data measured on milling tools showed the effectiveness of their DT model for predicting tool wear. [PERSON] et al. [44] described a machining DT on a large-scale CNC machine tool with adaptive control of machining operations integrating real-time model-based simulations with online feedback for residual stress control, chatter prediction, and adaptive feed rate. The feedback was able to update the spindle speed and feed rate in real-time to prevent chatter before it occurred. This method showed that vibration could be predicted and avoided, thus protecting the tool, the workpiece, and the machine. [PERSON] et al. [45] developed a DT to track the life cycle of a cutting tool. They developed a virtual cutting tool testing platform with physical and virtual tool wear data to ensure the continuous improvement of the process and tool. [PERSON] et al. [46] proposed a method based on DT to monitor in real-time the wear of 264 cutting tools for turning operations. According to the authors, their DT model did not consider tool nose eccentricity and room temperature variation, nor the tool life cycle, which includes product design, manufacturing, and service phases. More DT models need to be developed which seek to improve the accuracy of tool life prediction algorithms. [PERSON] et al. [47] presented a DT of a single-edge micro-cutting ma chine tool in a collaborative cloud-based product lifecycle management (PLM) platform. The DT was designed to simulate machine behavior under different cutting process conditions. The authors also sought to integrate models created by different computing applications into a single collaborative PLM platform and obtain an interoperable model-based DT. According to the authors, the integration of existing models is complex and requires a significant amount of rework. Additionally, the integration of the kinematic and dynamic models required redefining many of the constraints and data included in both models. [PERSON] et al. [3] described a general DT visualization architecture for flexible manufacturing systems (FMS) in order to address the life cycle functionality of human-cyber-physical manufacturing systems. The authors showed how CP DT modeling of heterogeneous information can be described and how to explore human-machine interaction visualized in 3D with DT scenario information. For this, the authors propose a modeling concept called GHOST (geometric information, historical samples, object attribute, snapshot collection and topology constraint) for the development of prototypes. [PERSON] et al. [48] presented a multi-layer architecture that provides the infrastructure required for a DT within the cyber-physical production systems (CPPS) paradigm. Even though the paper specifically considers a manufacturing case study, the architecture is independent of the application-specific details and facilitates wider application. The architecture provides a local data layer, an IoT (Internet of Things) gateway layer that relays information between the physical world and cyberspace, a layer with cloud-based data repositories, and, finally, a layer with emulation and simulation software. In that study, a robotic gripper assembly DT was developed. The building process of a high-fidelity DT model is divided into two parts. The mechanical equipment's transmission part is decomposed according to the meta-action theory, while the static part is decomposed according to its sub-function. The machine is decomposed into transmission chains according to FMA (function--motion--action) decomposition, and the chain is decomposed into transmission units. Each transmission unit has inputs and outputs, and has a corresponding data interface.