E6
Digital Twins 2

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10:40
conference time (CEST, Berlin)
Using Digital Twins to Accelerate Product Development – Beginners Guide
26/10/2021 10:40 conference time (CEST, Berlin)
Room: E
C. Stretton (Dyson Ltd, GBR)
C. Stretton (Dyson Ltd, GBR)
Digital Twins have predominantly been applied to predictive maintenance of high value, low downtime assets. However, realising their full benefit as part of the product development process remains unclear. The main challenge for simulation teams is not creating good models, but the establishment of a new workflow that can make use of connected product data. Indeed, creating a compelling case for a product to be connected in the first place, and provide meaningful data, can be a major hurdle. To overcome these challenges, a proof of concept is initially required to clearly demonstrate benefit to the business, and ultimately the customer. Simulation and product development teams are well placed to deliver this proof of concept and create a compelling business case for the deployment of a connected product, that leverages the full potential of a Digital Twin. This presentation will take a practical look at how a Digital Twin proof of concept is being used to accelerate development of a next generation product and significantly reduce time to market. Aimed at the beginner, we will look at: - How a Digital Twin differs from just a simulation? - Establishing a proof of concept. How to demonstrate the benefits of a Digital Twin Workflow? - What are the challenges and practical tips for getting started? - What additional skills do simulation teams need to build in order to fully leverage a Digital Twin workflow? Digital Twins offer the opportunity for a complete paradigm shift in how businesses interface with and bring products and services to customers. We will explore the vision for how a Digital Twin Workflow can change how businesses interface with customers, differentiate product and service offerings. Overall, this presentation offers a guide to those who wish to realise the full potential of a Digital Twin workflow.
11:00
conference time (CEST, Berlin)
Determination of Digital Twin Maturity Levels Within Value Creation Networks
26/10/2021 11:00 conference time (CEST, Berlin)
Room: E
S. Schulte, R. Stark (Technische Universität Berlin, DEU)
S. Schulte, R. Stark (Technische Universität Berlin, DEU)
Digital Twins offer new solutions within the developments of digitalization that enable a wide range of opportunities. The usage of Digital Twins allows the implementation of innovative business models that have the potential of creating value added not only for the respective company itself but for all actors along the life cycle of the product. Hence, it is essential to have a well founded understanding of the design and the resulting potentials of Digital Twins. In this paper we propose an extension of the “Digital Twin 8-dimension model” introduced by Stark et al. (2019): In addition to Digital Master models, Digital Shadow data needs to be available in adequate quality and granularity. Furthermore, a semantic model is required for the application and evaluation of the models and data. Also, cooperation for data collecting may enhance the functionalities of a Digital Twin. In order to be able to extract the full potential of a Digital Twin a precise description of the target design is required at the beginning of the development process and should be pursued throughout. In this paper a matrix to analyze the maturity levels of different aspects of Digital Twins is described. This concept can be used by companies during the design phase of Digital Twins and the implementation of cost benefit analyses. It is essential to note that a higher degree of maturity does not necessarily imply an enhancement of the Digital Twin. Rather, when defining the maturity levels of the target design, cost benefit analyses should be performed while taking the individual use case into consideration. Additionally, we highlight the potential of Digital Twins to generate value added for all actors involved with the product throughout its life cycle. Digital Twins can be used as the foundation of value creation networks. In this paper the interplay of service streams, data streams, and payment flows between the individual actors of such a value creation network is considered. References: Stark, R., Fresemann, C., & Lindow, K. (2019). Development and operation of Digital Twins for technical systems and services. CIRP Annals - Manufacturing Technology, 68, 129-132.
Design, Digital Twin, Maturity, Value creation network
11:20
conference time (CEST, Berlin)
The Executable Digital Twin: Leveraging Engineering Knowledge at Any Point in the Lifecycle
26/10/2021 11:20 conference time (CEST, Berlin)
Room: E
H. Van der Auweraer, T. Tamarozzi (Siemens Digital Industries Software, BEL); I. McGann, D. Hartmann (Siemens Digital Industries Software, DEU)
H. Van der Auweraer, T. Tamarozzi (Siemens Digital Industries Software, BEL); I. McGann, D. Hartmann (Siemens Digital Industries Software, DEU)
The digital twin has become an intrinsic part of every product creation process. Basically, it is a virtual copy of a real asset, integrating all data, models and other structured digital information of a product, a plant, an infrastructure system, or a production process. The data constituting the digital twin can be generated during design, engineering, manufacturing, commissioning, operation, and/or service. While the digital twin can have multiple appearances, the objective is always to have a digital representation suited to the purpose in terms of level of detail, completeness, accuracy, and execution speed. Consistent, traceable integration of all information is key to leverage existing and create new business opportunities. The true power of the digital twin lies in its relationship with its physical counterpart. The design engineering models should hence be as accurate as possible to represent the as-manufactured and as-built physical asset. Data acquired on the physical asset can validate, update and enrich the digital twin and provide information to improve the design. Powerful IoT solutions are instrumental to this. Inversely, the knowledge contained in the digital representation can be transformed into value for the physical asset itself. To this purpose, specific encapsulations can be made starting from the digital twin to model a specific set of behaviors against a specific product context, thereby delivering a stand-alone executable representation. Such instantiated, self-contained and encapsulated, model can be referred to as the Executable Digital Twin. The key element is that such Executable Digital Twin can be used outside its authoring environment and can be leveraged by anyone at any point of a products lifecycle on any certified device, from edge to cloud, without the need for heavy simulation software. While the integration of model-based functions in operational environments has been done since long, the derivation of such models from the digital twin, keeping full consistency and traceability, is novel and provides a huge leveraging potential. Examples of such usage are as embedded models for virtual sensing, model-based control, performance monitoring or X-in-the-loop hybrid testing applications. But also the use of the Executable Digital Twin as a companion model to accompany the physical asset through its lifecycle for performance assessment, system integration or decision support applications offers new value propositions. Key enabling technologies to create the Executable Digital Twin are fast simulation methods, Model Order Reduction, state estimation and standard model delivery (e.g. as FMU). Machine Learning methods are instrumental to deliver compact models of complex non-linear systems over wide range of operational conditions. In the execution phase, open platforms accepting external models with their execution engines and linking these where needed in a co-simulation environment are key. As the digital twin hereby starts to lead an own life across supply chains and in open -cloud- environments, IP protection is a key challenge. The presentation will address the approach behind the Executable Digital Twin, report the specific developments made on the level of the enabling technologies and illustrate the power of the approach in various examples related to test-based product engineering, the integration with manufacturing control platforms as well as operational system monitoring. Several case studies will be treated including virtual sensing and hybrid (XiL) testing in automotive and aerospace applications and decision support and process monitoring applications in the manufacturing and process industry. The related value propositions include data augmentation, reducing sensor cost, speeding up system validation and optimizing operational performance and asset availability.
Digital Twin, Model Order Reduction, Physical Asset, Virtual Sensing
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