Development of digital twin model for mechanical application

Abstract

Abstract The notion of digital twin technology is one that is just starting to gain traction in industry or business and more recently, academia. A virtual depiction of a real process or thing is called a “digital twin.” that may gather data from the real environment in order to represent, validate and simulate the physical twins. For artificial intelligence, the internet of things (IoT), and digital twins, the problems, applications, and enabling technologies are discussed. Particularly in the industrial sector, the development of Industry 4.0 principles has aided in its expansion. In order to adapt the idea to production. It is simulation technology that, by making full use of physical models, sensor data, and operation histories, blends interdisciplinary, multi-physical quantity, multiscale, and multi-probability. Utilizing cutting-edge analytical, monitoring, and predictive capabilities to test digital twin processes and services. It eliminates the expensive failure of physical items. It is a crucial facilitator of object lifecycle management, product validation and simulation, complex systems monitoring, and data-driven decision-making. Using a machine learning technique, this paper examines fault detection and develops a digital replica of the conveyor system of quality analysis of gear.