A digital twin is a digital replica of a living or non-living physical entity. By bridging the physical and the virtual world, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical entity. Now, enterprises are incorporating the multi-billion-dollar digital twin use cases in their Industry 4.0 initiatives.

Digital manifestations of physical environments have different definitions based on the context and use. A digital twin is an environment of linked data and models that reproduce an accurate, virtual representation of real-world entities and processes. Cutting-edge technology allows for synchronization of the virtual data environment with real-world conditions in order to facilitate holistic understanding, optimal decision-making, and effective action throughout the lifecycle of those entities.

Of Digital Models, Digital Shadows and Digital Twins – Jeff Winter

According to IoT Analytics, the market for digital twins expanded by 71% between 2020 and 2022. 63% of manufacturers are currently developing a digital twin or have plans to develop a digital twin.

According to IEEE, the digital twin concept consists of three distinct parts: the physical product, the digital/virtual product, and the connections between the two products.

The connections between the physical product and the digital/virtual product is data that flows from the physical product to the digital/virtual product and information that is available from the digital/virtual product to the physical environment.

This idea can be divided into three subcategories according to the different integration level, namely the different degree of data and information flow that may occur between the physical part and the digital copy: 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐌𝐨𝐝𝐞𝐥 (𝐃𝐌), 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐒𝐡𝐚𝐝𝐨𝐰 (𝐃𝐒) and 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧 (𝐃𝐓).

𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐌𝐨𝐝𝐞𝐥: A digital version of a preexisting or planned physical object, to correctly define a digital model there is to be no automatic data exchange between the physical model and digital model. Examples of a digital model could be, but not limited to, plans for buildings, product designs and development. The important defining feature is there is no form of automatic data exchange between the physical system and digital model. This means once the digital model is created a change made to the physical object has no impact on the digital model either way.

𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐒𝐡𝐚𝐝𝐨𝐰: A digital representation of an object that has a one-way flow between the physical and digital object. A change in the state of the physical object leads to a change in the digital object and not vice versus.

𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: If the data flows between an existing physical object and a digital object, and they are fully integrated in both directions, this constituted the reference “Digital Twin”. A change made to the physical object automatically leads to a change in the digital object and vice versa.

𝐒𝐨𝐮𝐫𝐜𝐞𝐬:
IEEE: https://lnkd.in/eXsk9HDj
IoT Analytics: https://lnkd.in/eEAARsYV

Digital twins started as an industrial idea. Technological breakthroughs, like IoT, helped in gathering finer aspects of physical assets. New data management capabilities, like Big Data and Cloud Computing, enabled processing and storing massive volumes of data. Stronger analytics and visualization techniques, like AI and 3D simulation, furthered our understanding of the asset’s life-cycle. The underlying diagnostics and prognostics capabilities, by combining existing and emerging technologies, ignited the minds to develop innovative digital twin use cases for the industries.

Gartner (2019) defines a Digital Twin as “a software design pattern that represents a physical object with the objective of understanding the asset’s state, responding to changes, improving business operations and adding value.” For those operating in the manufacturing and the process industries, quick access to the most up-to-date information in a Digital Twin from the plant floor to the boardroom enables better decisions, improved business processes, enhanced productivity, and the reduction of operational risk.

Here are some of the definitions that we have encountered based on our interactions with our partner companies:

– At Ansys – An analytics-driven, simulation-based digital twin is a connected, virtual replica of an in-service physical asset — in the form of an integrated multidomain system simulation —that mirrors the life and experience of the asset. Hybrid digital twins enable system design and optimization and predictive maintenance, and they optimize industrial asset management.

Ansys Twin Builder allows you to implement complete virtual prototypes of real-world systems. These can be deployed to manage the entire lifecycle of products and assets. This digital twin simulation paradigm allows you to exponentially increase efficiencies over time, scheduling maintenance around predictive methodologies that become more accurate with real-world testing and response. Access to this information allows engineers to unlock additional value from existing assets, which prevents unscheduled downtime and lowers operating costs, while working at optimal efficiency, and all with agnostic Internet of Things (IoT) platforms.

– At GE – Digital twins are a key piece of the digital transformation puzzle. They create an accurate virtual replica of physical objects, assets, and systems to boost productivity, streamline operations and increase profits.

– At IBM – A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

– At Hexagon Manufacturing Intelligence – Operating without a digital twin is like navigating a cross-country car journey with only a paper map. To help companies achieve these Digital Twin benefits, Hexagon provides the integrated Project Twin, Operational Twin and Situational Awareness solutions. The overall Digital Twin is the glue that connects these enterprise solutions and their data on one platform, enabling the “single version of the truth” concept. To prevent barriers to adoption, a Digital Twin can be built gradually using Hexagon solutions in different stages. The extent of Digital Twin maturity required is based on the level of digitalization already in place at an organization

A Digital Twin is a dynamic digital depiction comprised of physical entity information. It is the single version of truth unique to a user’s perspective for a point in the lifecycle with many digital levels.

Data is diffused seamlessly between the digital depiction and physical entity to enable co-existence. Then advanced technologies bring data, algorithms and context together. With this comes understanding, prediction and optimization for the physical entity to drive improved business outcome. Companies in the manufacturing and process industries are at various stages of digital maturity. Hexagon understands this and is providing Digital Twin solutions for all digital maturity stages across the asset lifecycle.

1. The first stage of a Digital Twin starts with a basic set of structured data and documents defining the facility configuration, designed by engineering teams in the Project Twin. For companies near the beginning of their digital transformation roadmap, this is an excellent start, empowering better decision making from more intelligent data and improving engineering-to-operations handover processes.
2. The second stage of connecting this intelligent data to 2D schematics, 3D models or laser scans allows for more intuitive viewing and navigation and begins to unlock the benefits of weaving engineering, operations and maintenance information in an Operational Twin.
3. The third stage further enhances the Operational Twin with increased interoperability by exchanging information and providing links to other information sources in the operations landscape, such as asset performance, data historian, maintenance management and real-time data solutions.
4. The fourth stage is where the major digital transformation business benefits will be realized, as the asset owners and operators can leverage a Digital Twin to manage value added work processes, such as human procedures, inspections, integrated safe systems of work and management of change. This ongoing stage of value addition can also include advanced analytics, artificial intelligence, machine learning and predictive and prescriptive analytics to reduce downtime.

When a comprehensive Digital Twin is deployed, the associated data needs to be efficiently dissected to understand it and to also transform this into actionable information. To help achieve this, the Hexagon Situational Awareness solution allows personnel to clearly see what’s happened, what’s happening, what could happen, what should happen and what’s scheduled to happen in a high-level operational dashboard that includes all the visual elements of a Digital Twin. Overall, the goal of any Digital Twin is to increase asset efficiency and offer a digital representation of current and historic plant configurations, along with related performance information. Enlightened, data-driven decision making becomes the norm, and the easy sharing of Digital Twin data with multiple departments increases collaboration and reduces operational risk. Hexagon solutions help people design, engineer, construct, operate and maintain industrial assets, and the Project Twin, Operational Twin and Situational Awareness solutions allow asset owners and operators to build and maintain a Digital Twin ecosystem throughout the asset lifecycle, allowing for a continuous journey of operational excellence.

– At Mathworks – A digital twin is an up-to-date representation, a model, of an actual physical asset in operation. It reflects the current asset condition and includes relevant historical data about the asset. Digital twins can be used to evaluate the current condition of the asset, and more importantly, predict future behavior, refine the control, or optimize operation. A digital twin can be a model of a component, a system of components, or a system of systems—such as pumps, engines, power plants, manufacturing lines, or a fleet of vehicles.  Digital twin models can include physics-based approaches or statistical approaches.  The models reflect the operating asset’s current environment, age, and configuration, which typically involves direct streaming of asset data into tuning algorithms.

– At Microsoft Azure – Azure Digital Twins is an Internet of Things (IoT) platform that enables you to create a digital representation of real-world things, places, business processes, and people. Gain insights that help you drive better products, optimize operations and costs, and create breakthrough customer experiences.

– At NVIDIA Omniverse – Utilize single source of truth to render virtual environments that replicate the physical world conditions utilizing physics, AI and GPU based raytracing to simulate systems real time.

– At PTC – A digital twin is a virtual representation of a physical product, process, person, or place that can understand and predict its physical counterparts. A digital twin has three components:

• a digital definition of its counterpart (generated from CAD, PLM, ),
• operational/experiential data of its counterpart (gathered from Internet of Things data, real-world telemetry, and beyond), and
• an information model (dashboards, HMIs, and more) that correlates and presents the data to drive decision making.

– At Siemens –  A digital twin is a virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics.

• Digital twins are used throughout the product lifecycle to simulate, predict, and optimize the product and production system before investing in physical prototypes and assets.
• By incorporating multi-physics simulation, data analytics, and machine learning capabilities, digital twins are able to demonstrate the impact of design changes, usage scenarios, environmental conditions, and other endless variables – eliminating the need for physical prototypes, reducing development time, and improving quality of the finalized product or process.
• To ensure accurate modelling over the entire lifetime of a product or its production, digital twins use data from sensors installed on physical objects to determine the objects’ real-time performance, operating conditions, and changes over time. Using this data, the digital twin evolves and continuously updates to reflect any change to the physical counterpart throughout the product lifecycle, creating a closed-loop of feedback in a virtual environment that enables companies to continuously optimize their products, production, and performance at minimal cost.
• The potential applications for a digital twin depend on what stage of the product lifecycle it models.

Generally speaking, there are three types of digital twin – Product, Production, and Performance. The combination and integration of the three digital twins as they evolve together is known as the digital thread. The term “thread” is used because it is woven into, and brings together data from, all stages of the product and production lifecycles.

To ensure accurate modelling over the entire lifetime of a product or its production, digital twins use data from sensors installed on physical objects to determine the objects’ real-time performance, operating conditions, and changes over time. Using this data, the digital twin evolves and continuously updates to reflect any change to the physical counterpart throughout the product lifecycle, creating a closed-loop of feedback in a virtual environment that enables companies to continuously optimize their products, production, and performance at minimal cost.

The potential applications for a digital twin depend on what stage of the product lifecycle it models. Generally speaking, there are three types of digital twin – Product, Production, and Performance, which are explained below. The combination and integration of the three digital twins as they evolve together is known as the digital thread. The term “thread” is used because it is woven into, and brings together data from, all stages of the product and production lifecycles.

– At Tesla the premise is to enable autonomous driving. The digital twin dashboard represents a real time view of the surrounding environment that is coordinated by the data gathered by 1 radar, 8 vision cameras and 16 ultra-sonic sensors that cocoon the car. The goal is to enable auto-driving and also inform the driver of inclement conditions. Tesla has sequentially progressed from AP0 (no auto-driving), AP1 (MoblEye vision based), AP2 (Nvidia GPU), and AP3 (Tesla TSD chip) and is now removing radar and ultra-sonic input so that processing speed can be increased without sacrificing safety.

– At TIBCO – A digital twin is when every process, service, or physical product gets a dynamic digital form or representation. The physical product can then be evaluated and manipulated based on analysis of the digital twin in a range of working environments. There are several kinds of simulators for digital twins: Product Twins, Process Twins and System Twins.

– At Unreal Engine – A digital twin is a 3D model of a physical entity like a building or city, but with live, continuous data updating its functions and processes in real time, providing a means for analyzing and optimizing a structure. When live data from the physical system is fed to the digital replica, it moves and functions just like the real thing, giving you instant visual feedback on your processes. The collected data can be used to calculate metrics like speed, trajectory, and energy usage, and to analyze and predict efficiencies.

– At Unity – A digital twin is a dynamic virtual copy of a physical asset, process, system, or environment that looks like and behaves identically to its real-world counterpart. A digital twin ingests data and replicates processes so you can predict possible performance outcomes and issues that the real-world product might undergo.

– At MxD, the institute of smart manufacturing, cybersecurity and supply chain formulations, the digital twin is the blurring of the lines between the physical and cyber spaces by making processes more efficient, more efficiently. It:

• Provides an up-to-date representation of the physical asset and process in operation
• Reflects and evaluates the condition of the physical asset and process
• Runs in parallel to the real assets and process, and immediately flags operational behavior that deviates from expected behavior
• Lowers maintenance costs by predicting maintenance issues before breakdowns occur
• Provides enhanced insight into the performance of the process

– At Digital Twin Consortium’s (DTC) – A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity.

• Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action.
• Digital twins use real-time and historical data to represent the past and present and simulate predicted futures.
• Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems.

The groundwork for digital twin has been laid through digital thread and Industrial Internet of Things (IIoT) technology. Coupled with increasingly powerful analytics and simulation capabilities common in industrial enterprises, digital twin use cases are being adopted across the value chain. From engineering to operations and service, these real-world examples of digital twin deliver significant business value to industrial leaders today.

OUR TAKE

Digital twins are built on the core concept of a digital equivalent for a physical entity. From automotive to agriculture, every enterprise interaction with their customers involves physical entities. Digital twins are paving the path for enterprises to bring the benefits of software world onto the physical assets – providing an opportunity to better serve the needs of the digital customers.

At Numorpho Cybernetic Systems (NUMO), our basis is to understand cause and effect by assimilating digital twins and their associated digital threads to automate, harmonize, and optimize operations to enable robust digital strategies and appropriate actionable outcomes.

Digital Twins in our case will manifest as product, process, and production entities. These will be driven by Nvidia’s Omniverse platform utilizing its physics and data driven basis, and our own physmatics based simulation environment that would utilize tools from our partner companies – Hexagon Nexus, Ansys, PTC Thingworx and NTopology to cater to innovation, additive and smart manufacturing, and logistics-based scenarios. The rendering will also utilize Unity and the Unreal Engine for depiction of virtual and augmented reality world scenes to depict engineering simulations, shop floor locations, geographical movements, and HMI interfaces. Built up spaces will be pre-generated using the Matterport engine and be superimposed with virtual data.

Utilizing our Digital Twine reference architecture, we have embarked on creating several digital twins that encompass end-to-end process management to enable connecting the dots for automation in Industry 4.0 initiatives:

• The Operational Digital Twin enables localized production of parts in an operational bases that is devoid of supply chain logistics and in an austere environment. Utilizing 3D printing Additivie Manufacturing techniques frontline operations will be able to create spare parts on demand and in an expeditious manner.
• The Interoperable Digital Twin Framework (IDTF) will enable the coupling of engineering and manufacturing to create spare parts based on new material composites to replace old worn out parts by including optimization, simulation and generative design techniques.
• The Chicago Digital Twin is a project that will create a cyber-physical visual rendering of the City of Chicago using Virtual and Augmented reality to superpose information onto a physical 3D architectural rendering.
• The Armadillo Helmet Digital Twin will encompass our philosophy of Adaptive Engineering to showcase form, functionality and engineering basis of our folding helmet in its different phases of innovation, design for manufacturability, custom manufacturing and marketing testament enablement.

As we progress, we will be building other digital twins to not only enable to robust our processes within Numorpho but also to help our clients and partners to utilize data engineering to optimize and harmonize activities.

SUMMARY

Digital twins are digital equivalents of physical entities that allow enterprises to bring the benefits of the software world onto physical assets, providing an opportunity to better serve the needs of digital customers.

At Numorpho Cybernetic Systems (NUMO), we utilize digital twins to automate, harmonize, and optimize operations to enable robust digital strategies and actionable outcomes. Digital twins in our case manifest as product, process, and production entities and are driven by Nvidia’s Omniverse platform, analysis and simulation environments provided by our partners and our own process management reference architecture called the Digital Twine, part of our Mantra M5 ecosystem.

We utilize tools from partner companies to cater to innovation, additive and smart manufacturing, and logistics-based scenarios, and AR/VR tools for rendering virtual and augmented reality world scenes.

Utilizing our Digital Twine reference architecture, we have embarked on creating several digital twins to encompass end-to-end process management for automation in Industry 4.0 initiatives. Some of these include:

1. Operational Digital Twin – To enable remote manufacturing of parts in a forward operating base for the military so that issues related to supply chain and logistics can be mitigated.
2. Interoperable Digital Twin Framework (IDTF) – To enable the Additive Manufacturing using composite materials to replace old parts in equipment where OEM and 3rd party products are not available.

We plan to continue building digital twins to optimize and harmonize activities within Numorpho, as well as help our clients and partners.

NI+IN UCHIL Founder, CEO & Technical Evangelist
nitin.uchil@numorpho.com