CAD/SOFTWARE FEATURE In the modern world, the design job isn’t finished once the product has been
shipped. Today, it is possible for the designer to get feedback when the product is in use, track how it is being used, and then support, maintain and improve the product in the future. This is where digital twins that are in sync with the real product – even
when it’s out in the field – come in, as Zvi Feuer, senior vice president, manufacturing engineering solutions at Siemens and Zvika Weissman, industries & production management director at Siemens Industry, explain
In sync with product
development W
hereas in the past product development used to be more of a trial and error
approach with the creation of prototypes to experiment on, today the designer needs to come up with a fully specified design, that behaves as it should, in the conditions it will face, long before the physical product is created. Many new products are a combination of
mechanical and electronic systems, so a 3D model isn’t enough to represent that design. Instead it needs to be a mock-up that represents the product as a system, including the electronics, functional behaviour, control logic and software on top of the mechanical parts, and it has to have enough detail to use for simulation, testing and verification. For more complex products, the representation will need to model many different types of physics. The design process may include systems
simulations, finite element analyses of components and assemblies to understand stress, dynamics and failure, computational fluid dynamics to analyse fluids and thermal properties, multibody dynamics to represent motion behaviour, and test-based methods that augment the user’s simulations. Predictive engineering analytics – to combine data from simulations, benchmarks, prototype tests and even usage data from existing products – can be of benefit here to help predict the performance of the design. Increasingly, there is a need to explore the whole design space, changing tens or even hundreds of parameters at a time and visualising the right combinations of those parameters to experiment with. This means a digital representation is needed that can predict the ways a product will perform, at each step of development and on into actual usage – a digital twin of the physical product.
AN ACCURATE REPRESENTATION As the digital twin evolves, it will be necessary to correlate the data measured with that predicted by the model in the simulation – over several cycles to make sure the two are converging and that the model is an accurate representation of the product being designed and built.
Most manufacturing companies use a
managed environment because they need to work with 3D CAD models and track changes made to the models through the product lifecycle. But to respond to trends like the shift from mechanical devices to ones that combine electrical and mechanical features, a more integrated approach to product engineering is needed that combines the data and models required. PLM tools will need to go beyond tracking requirements and CAD data to cover simulation, engineering and verification in the same system, enabling the integration of test, performance and sensor data from products at different stages of their lifecycle. Tracking the evolution of the digital twin
through design and manufacturing, alongside the critical parameters and the performance data, means it will be possible to go back and look at what might have led to an improvement or a problem.
TRACKING PRODUCT BEHAVIOUR In the most sophisticated systems the digital twin can help answer questions about how the product behaves in the real world. Not only do a growing number of products feature sensors collecting information, but they are increasingly being controlled by software that can be improved and updated when the product is in use. This provides an opportunity to improve customer satisfaction or even to avoid the cost of product recalls. Between 2013 and 2014, the number of automotive recalls in the US almost tripled – from 22 million to 63 million, according to the National Highway
Traffic Association, at a cost of around $100 per vehicle. In the same year, more than 550 different consumer products were recalled, all of them adding up to a hit to company reputations as well as their bottom line. With a digital twin, if you need to handle
complaints about vibration in a tractor, for example, your model will be able to understand that once you’ve used a product for 10,000 hours the performance can be affected by anything from minor cracks in the structure to the properties of lubricant. Sensor data can show environmental conditions like temperature, as well as how long it had been in use that day and what the operator was doing when the vibration started. The digital twin needs to represent the
true behaviour of your product, including how product usage and current operating conditions are affecting performance. You can apply the real working conditions to the simulation model for the tractor and perform experiments, like changing the lubricant; or improving the software to control the engine more precisely; or altering the material used in specific locations in the vehicle. Virtual experiments can improve the
component out in the field. It might be possible to find a fix for the next version, or solve the problem in time to warn the customer to update the vehicle software or change their operating procedures before there’s a catastrophic failure. In such cases, a digital twin will have a real impact on the product.
Siemens PLM
www.plm.automation.siemens.com
DESIGN SOLUTIONS | SEPTEMBER 2019 27
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