By: Toni Boger, Marketing Coordinator and Thought Leadership Content Strategist, and Jim Rusk, Chief Technology Officer
Others will define a digital twin as digital replicas of different assets, processes and systems in your business which can be used a number of ways. This generic definition is basically correct. However, a Siemens digital twin is more accurately described as an integrated set of digital replicas or models driven by a rich information model called a digital thread.
A true digital twin is not a single model of the asset, even though it’s referred to as a singular “twin.” It consists of many mathematical models and virtual representations that comprehend the asset’s entire lifecycle – all the way from ideation, through realization and utilization – and all its constituent technologies, including electronics, software, mechanical, manufacturing and in-service performance.
We refer to a set of three digital twins: Product, Production and Performance. Each of these digital twins consists of multiple virtual models appropriate for the given product and production system.
Accurate digital twins comprehend the interactions between all aspects of the product and production system. The Siemens PLM Digital Enterprise Software, which was built on our Innovation platform, enables the creation of the most accurate, comprehensive digital twins possible. No other company offers the breadth or depth that we do, particularly with electronics, software and factory automation.
Product digital twin. A Siemens digital twin for products is typically created using our Systems Driven Product Development (SDPD) methodology, which drives the creation of intelligent 3D models. Technologies like Synchronous, Convergent Modeling, Generative Design and Predictive Analytics are the basis for these intelligent models.
Key enablers for SDPD include our comprehensive, semantic digital thread data model in Teamcenter, as well as a complete set of integrated tools which include LMS Amesim, Simulink, HEEDS and the Architecture Modeler. These tools help you create model-based system representations and accurate digital twins of the product, and these digital twins are able to comprehend the impact of design changes on the production system.
A product digital twin will typically include electronics and software simulations, finite element structural, flow and heat transfer models and motion simulations.
Product digital twins also rely on predictive engineering analytics, which applies multidisciplinary engineering simulation and tests with intelligent reporting and data analytics. These capabilities lead to digital twins that can predict the product’s real-world behavior throughout its lifecycle. Predictive engineering analytics includes tools that manufacturers leverage to expand traditional design verification and validation into a predictive role that supports SDPD. The ultimate goal of implementing a predictive engineering analytics strategy is delivering innovation for complex products faster and with greater confidence.
Production digital twin. The Siemens digital twin for production uses many of the same tools and techniques to create a twin of the production system. Production digital twins, like product digital twins, can include 3D models and predictive analytical models. They also include production engineering specific models that use tools like the Mechatronics Concept Designer for the conceptual design and virtual commissioning of smaller Production Systems and Machine Tools, Line Designer, Automation Designer and Process Simulate can also be used to engineer and virtually commission large production lines. These digital twins could also include the PLC code for driving factory work stations and Electronic Work Instructions.
Performance digital twin. The Siemens digital twin for performance is based on our Mindsphere open IoT solution, which includes Big Data analytics from our Product Intelligence Mind App. These tools enable Big Data insight discovery, analysis and monitoring from in-service products and production systems. Performance Analytics quickly identifies product issues disrupting the supply chain, manufacturing process or customer experience.
The performance digital twin may also include data analysis to discover hidden product issues before they occur; graphical displays to clearly identify potentially problematic configurations; and automated data monitoring and parametric data analysis to fine-tune operations and provide insight for improving your products.
Stay tuned for more from Jim to learn more about the product, production and performance digital twin capabilities Siemens offers.