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Executive Summaries

Tuesday, December 11, 2018

Evolving from Digital Prototypes to Physics-Based Digital Twins

With the growth of the Industrial Internet of Things and millions of smart, connected systems, the concepts of digital twin and digital thread are gaining rapid acceptance across many industry segments and promise to revolutionize the way that companies design, build, and maintain tomorrow’s complex cyber-physical systems and interconnected “systems of systems.” The physics-based digital twin is enabled by digital modeling and simulation technologies supporting closed-loop lifecycle performance engineering and will be key to realizing the promised business benefits of digital transformation.

Key Takeaway 1

Increased complexity of cyber-physical products with ever expanding embedded software and electronics along with Internet of Things-enabled operating environments demand thinking beyond the traditional engineering design silos and associated stage-gate oriented engineering approaches. Model-based engineering methods require a holistic, agile, and closed-loop lifecycle approach to successfully achieve the promised business benefits of digital transformation. A physics-based digital twin plays a key role in making this business transition.

Key Takeaway 2

Being competitive in the modern fast-paced business environment requires applying industry best practices in organizational culture, product development processes, and digital technology to the business—continuously evolving to higher maturity levels than the competition. Market segment leaders are already working to achieve higher maturity levels of sustainable innovation for their businesses. This involves creating and enabling a lifecycle digital twin strategy that evolves over time and is sustainable across programs based on investment in virtual modeling and simulation technology, best practice simulation processes, people skills, and domain expertise.

Key Takeaway 3

Evolving from today’s digital and physical prototypes towards a lifecycle digital twin approach requires high levels of digital model accuracy that spans a wide range of multi-physics domains as well as varying levels of model scale and fidelity—dependent on lifecycle applications. High levels of confidence in using models to make engineering decisions can only be achieved within an integrated environment that closely ties together the digital and physical worlds.

Key Takeaway 4

Siemens PLM Software (Siemens), with its Teamcenter lifecycle collaboration platform of MindSphere, their Internet of Things (IoT) operating system, and an integrated Simcenter portfolio for simulation, test, and design exploration; is a leader in providing model-based engineering solutions (see Figure 1) that innovative industrial companies use to realize first mover benefits of implementing a physics-based digital twin approach across product ideation, realization, and utilization. Business benefits include optimizing total lifecycle ROI, accelerating time to market, enabling innovation through enhanced engineering insights, and reducing total product development costs across the entire product lifecycle including associated manufacturing and in-service support costs.

Siemens Figure 1

Figure 1—Siemens “Digital Twin” Portfolio for Closed-loop Performance Engineering
(Courtesy of Siemens)

Introduction to Model-Based Engineering Terminology

The primary terms used in this paper are defined below.

Model-Based Systems Engineering

INCOSE defines MBSE as the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later lifecycle phases.”

System Modeling and Simulation (SMS)

SMS is the use of interdisciplinary functional, architectural, and behavioral models (with physical, mathematical, and logical representations) in support of MBSE to specify, conceptualize, design, analyze, verify, and validate an organized set of components, subsystems, systems, and processes.

Internet of Things (IoT) and Big Data

IoT is a worldwide network of intelligent computers, devices, and objects that collect and share huge amounts of data (often referred to as Big Data). The collected data for an organization is typically sent to a central cloud-based service where it is aggregated with other data and then shared with end users in a way that meets their unique needs and objectives. IoT increases automation in homes, schools, stores, and in many industries.

Industrial Internet of Things (IIoT)

IIoT is a subset of the larger scope of IoT focused on “smart” manufacturing systems. It revolutionizes manufacturing by enabling the acquisition and accessibility of far greater amounts of real world product data far more efficiently than ever before. A number of innovative companies have started to implement IIoT by leveraging intelligent, connected devices (such as machine tools) in their factories. IIoT and Industry 4.0 are often used to refer to the same industry trends although there are not exactly one and the same.

Industry 4.0

Industry 4.0 is a name given to the current trend of automation and data exchange technologies as applied to cyber-physical “smart” systems used in product manufacturing. As such, it leverages new technologies such as the IoT, cloud computing, big data analytics, artificial intelligence, machine learning, and cognitive computing. Industry 4.0 is commonly referred to as the Fourth Industrial Revolution.

Product Innovation Platform

A product innovation platform is an integrated set of evolving functional domains—processes, lifecycle stages, and technical disciplines such as system ideation, profitability management, and quality and compliance. These are orchestrated by the platform with a “system of systems” approach that, in essence, makes a product innovation platform the enabler of the next generation of PLM-enabling solutions.

Digital Thread

The digital thread is a dynamic collaboration framework that connects virtual surrogates of physical assets (i.e., digital twins) and their associated lifecycle data. The digital thread enables product and process innovation across the entire product lifecycle and across organizational silos by leveraging digital twins to deliver the right information to the right decision makers in a closed-loop, real-time environment.

Digital Twin

Digital twin refers to a digital surrogate that is a dynamic, physics-based description of physical assets, processes, and/or systems that can be used for various purposes. The twin accompanies its real-world companion throughout its lifecycle—being changed in tandem with the physical version.

Evolving from Digital Prototypes to Physics-Based Digital Twins: The Key Role of Modeling & Simulation in Enabling Closed-Loop Lifecycle Performance Engineering is a 26-page white paper.

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