Industry 4.0 brought in significant changes to the product development process. Industry 4.0 provided the framework for designing products that were connected, at times 3D printed. The framework for computing moved from HPC to Cluster almost unlocking the potential in terms of simulations. The transformation was relevant to several industrial verticals, catering to product conceptualization, product design, product validation, and manufacturing. This brought in a change for businesses as well as in getting set to bring about Digital Structure and Culture. Analysis and simulation that could be run using digital twins is emerging as a key part of simulation driven engineering. iOT is proving to be game changer as the communication channel for digital twins.

A digital twin, also called a virtual twin, or simulated twin, are the virtual form of a real-world asset or process. They are built to exist along with the life of the product, and help to optimize the real physical asset. Data is exchanged from the physical to the virtual twin through IoT. It is a two-way communication channel, where data travels through the physical and numerical worlds back and forth as needed. It is a digital duplicate that represents a physical object or process, however it is not intended to replace a physical object; but merely to act as a digital counterpart. The concept of digital twins as a means to analyze, record and test data has been around for a while, butwith the explosion of technological data and manipulation that is growing in IoT, the potential of digital twins and what can be achieved through them has increased exponentially, and this is why we hear of the term being used so much nowadays.

Digital twins is in an exciting space right now. With the increasing developments in the world of IoT, the volume and complexity of data that can be captured is increasing exponentially, and the softwares capability to handle, analyseand manipulate that data is struggling to keep up. With additional software and data analytics, digital twins can often optimize systems or processes designed to detect, prevent, predict, and optimize through real time analytics to deliver business value. A digital twin can be extremely useful along the entire product cycle of a product, right from design to deployment, to its end. A digital twin can be a model of a component, a system of components, or a system of systems – such as pumps, engines, power plants, manufacturing lines, or a fleet of vehicles. It can even be the model of a process, which needs that level of analysis and real time feedback. Nearly all industries are looking at adopting digital twins as a technology in some form or the other.

However, even with all the hype around the concept, it is still extremely complex to navigate around, and mass adoption is still a few years away. The biggest pull is still in industries where parameters of Operational Optimization, Predictive maintenance, Anomaly detection have the biggest impact on business or lives. Deployment costs, testing costs and even simulation costs are a factor to account for, not to mention the predictability of the setup over a span of years is still nascent, as external and environmental factors vary over time.

CAE plays a very important role when simulation using digital twin is necessary. CAE builds the numerical model to correlate to actual model, and with the use of digital twins, it has evolved from static input conditions to connected real time input, resulting in better accuracy for development or maintenance of products.

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Assume the CAD model is built, the real word data like load conditions, which can be gathered for a machine outside, could be pumped in live to the software, and it can be part of the CAD model. You can also carry it forward to the FE model, and can analyse the FE model with real loads. And thussince your FE model is live, you can communicate directly and have an efficient model suited to real- words conditions.

Changes can be done in the most optimum way in this scenario. Sometimes FE models could be too heavy, and it could be easier to work with data from Reduced Order Models, like 1 D or 3 D models as well. DEP MeshWorks parametric CAE modeling methodology can be used to create these optimized and parametric FE models, or Response Surface Models, order models, which can be used for analysis.

Views expressed in this article are the personal opinion of Shirin Hameed, Chief Marketing Officer (CMO), Detroit Engineered Products (DEP).