The concept of the Digital Twin is still increasing but it is powerful and self-evident enough that most manufacturers believe that they need it. Leading organizations anticipate that digital twins will help them deliver better products, services, and experiences to their customers, at lower costs than are currently possible. As digital replicas of physical (or cyber-physical) products, digital twins should act as crystal balls allowing engineering teams to understand how their products will operate and respond to real-world use and abuse, long before that information is needed. 
 
As manufacturers move from selling products to increasingly offering product-based services, the lifespan and total lifecycle costs of their devices become more important. If you can produce less expensively, then naturally your profit margins are higher. And here is the key to using a digital twin to improve your competitive position.
 
Remember you have time and resources to devote to this. Just before moving to choose a digital twin methodology in your company, it’s a good idea to ensure that you have a complete understanding of what it might look like.
 
At its heart, a digital twin a mathematical model, and the “digital” part of the name means that this mathematical model is represented in binary form, which can be calculated and manipulated by computers. But a model for what purpose? There are many kinds of models, and most people think of 3D models first. A 3D geometric model is a good start and an important foundation for the digital twin.
 
But to be truly useful, the digital twin should express other aspects of the physical artifact—like behavior. Digital twins could include models of materials, coatings, embedded software, embedded control systems, power sources, internal chemical reactions, reactions to environmental conditions (such as temperature, electrical fields, weather, etc.), and a lot more. All of these aspects of behavior bundle to recreate the real-world behavior of the physical device.
 
There is one more wrinkle that digital twins (can) consider: the singularity of each physical instance. The old wives’ tale claims that you should not buy a car built on a Monday or a Friday due to manufacturing workers produce poor quality results at the beginning and end of a week. Although I am confident that it's not true, it is a reminder that serial number 1 and serial number 100 of a product are built in a different way, perhaps by different people under different conditions.
 
Digital twins incorporate not merely virtual modeling of the theoretical performance of any particular serial number but come with the instance-specific details for individual physical products in the series. There is a distinctive digital twin for every serial number that rolls off the manufacturing line. Because of this, all of the data collected during manufacturing (temperature was low on the paint-baking machine that day), and all of the data collected by the device during its use (IoT sensors) combine to enhance the picture we have of that particular instance of the product.
 
Each single digital twin consists of the relevant details of that specific physical instance and can estimate its unique behavior responding to changing environmental and user-driven conditions in the future. That assumes that we have put the perfect capabilities in place to capture, track and manage this information.
 
Many of the capabilities mentioned so far exist today. You will discover software tools for 3D modeling, control system modeling, static/dynamic analysis, chemical reaction modeling, fatigue analysis, IoT data capture, manufacturing execution (IIoT) data capture, and much more. But what is the system that brings all of that data together in a significant way, so that you can ask questions and run scenarios? That system is what does not exist. Let’s give some thought to for a moment how we might build this digital twin system.
 
If we start out with systems modeling, 3D modeling and some basic finite-element analysis models, we can create a Virtual Twin that is a good foundation for digital twins. To connect, configure, control and manage all of these models, we need a system like PLM (Product Lifecycle Management). To dive much closer into your virtual twin’s capabilities, you might add things like control system modeling with Hardware-in-the-Loop test capabilities, MDAO (Multi-Discipline Analysis & Optimization) tools, and FMI (Functional Mockup Interface) capabilities.
 
Somewhere along the route, you will likely choose to maximize your product definition data and come to be more of a Model Based Enterprise (MBE). This activity may lag a bit, but proceed in parallel with a PLM system deployment.
 
Integration with ERP/MRP/MES is another important step to establish a bridge from the virtual to the physical. Presuming you actually have a modern, robust MES system in place, integrating this data back through ERP/MRP and into your virtual models to deliver enhanced analysis results is a great phase. At this point, your first digital twins will start rolling off the line at the same time as their physical counterparts.
 
Many companies today are hectic learning how they will increase IoT sensors, what data they will capture, and how they will use that data for understanding how their products behave during real-world usage. Assuming an IoT project like that has already been ongoing and proceeding in parallel with all of the activities above, your initial digital twins can be enhanced to track and update along with your products out in the field.
 
If your business is hoping to pin into selling products-as-a-service, or you just understand the long view of serviceability (and profitability), then it’s time to begin laying the foundation of your digital twin factory—today.
 
This article is originally posted on tronserve.com