Posted on : Thursday 18th June 2020 09:46 AM
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 perceive how their products will act 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 crucial. If you can produce less expensively, then of course 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. Prior to moving to implement a digital twin methodology in your company, it’s a good idea to make certain 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 indicates that this mathematical model is represented in binary form, which can be calculated and altered 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 undoubtedly useful, the digital twin should express other aspects of the physical artifact—like behavior. Digital twins might 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 incorporate to replicate the real-world behavior of the physical device.
There's one more wrinkle that digital twins (can) consider: the uniqueness of each physical instance. The old wives’ tale claims that you should not buy a car built on a Monday or a Friday since manufacturing workers produce low quality results at the beginning and end of a week. Although I am positive that this is not true, it is a reminder that serial number 1 and serial number 100 of a product are built in different ways, perhaps by different people under different conditions.
Digital twins incorporate not only virtual modeling of the theoretical performance of any particular serial number but consist of 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. For that reason, all of the data accumulated 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 maximize the picture we have of that particular instance of the product.
Every digital twin consists of the relevant details of that specific physical instance and can forecast its unique behavior in reaction to changing environmental and user-driven conditions in the future. That assumes that we have put the right capabilities in place to capture, track and manage this information.
Quite a lot of the capabilities mentioned so far exist today. There're 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 delivers 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 consider for a moment how we might build this digital twin system.
If we get started with systems modeling, 3D modeling and some basic finite-element analysis models, we can easily 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 deeper 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 enhance your product definition data and grow 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 develop 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 occupied learning the way they will improve 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 is already started 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 turn into selling products-as-a-service, or you just understand the long view of serviceability (and profitability), then it’s time to start laying the foundation of your digital twin factory—today.
This article is originally posted on manufacturing.net