The recent advent of IOT and Industry 4. One such term is Digital twin. After reading this article you will be in a position to answer following questions. The digital twin and digital thread are not similar concepts. Both these concepts help in understanding the actual working of the physics-based models by reflecting the exact operating conditions like performance and failure modes, but their application and functioning is different from one another.
The main goal of a digital twin is to create, build, and test your equipment in a virtual environment. Digital twin comprises of several elements like manufacturing simulations, 3D CAD models, and real-time data feeds from sensors that are incorporated into the physical operating environment. Digital twin can benefit all aspects of manufacturing, right from design to real-time data feed. The aim of creating a digital twin is not just to cut prototyping or construction costs, but also to predict failures more easily and accurately and effectively, thereby reducing both maintenance costs and downtime.
For example, the digital twin created for a wind farm helps in informing the manufacturer about the configuration of each wind turbine prior to procurement and construction. The digital thread concept helps in delivering the right information at the right time and at the right place. Digital thread provides the ability of accessing, integrating, analyzing, and transforming data from disparate systems into actionable information throughout the product lifecycle.
By developing digital thread, product engineers can collaborate with manufacturing engineers for creating a 3D model linked to visuals for production process instructions. The actual implementation of a digital twin depends on the intended business outcome and sophistication of business logic. Different integration scenarios for Digital Twin Implementation include. The digital twin implementation will largely be managed from the cloud to facilitate the network-centric engagement models described above.
However, most scenarios will distribute actual data and algorithms between an edge or gateway implementation located on or near the physical object and the cloud in a distributed architecture. Learning and model development are primary functions in the cloud, however, not all data is relevant to be transmitted.
