The Digital Twin of Wire Harness Manufacturing
Meet the organization helping aviation companies harness digital twins
NIAR works with government agencies, eVTOL manufacturers, and commercial aircraft OEMs like Boeing to test parts for compliance with FAA regulations, and with the FAA itself on certification by analysis methodologies for airframe crashworthiness and ditching, according to Gerardo Olivares, senior research scientist and director at NIAR. The industry has outsourced parts of these processes to organizations like NIAR in an effort to lower costs.
Olivares told Emerging Tech Brew that NIAR uses digital twins for flight testing, design, and test safety in devices like pilot seats, and to assist in FAA certification. He said its digital twin tech is developed with the help of Altair, a tech company that specializes in simulation software, among other things.
Reality Show: X-ray Vision Can See Through Metal
A typical aircraft maintenance inspection involves maintenance technicians and engineers walking around an aircraft recording new defects and damage with a pencil in a notebook. Locations are often described in language like ‘3 inches from the left side of the window.’ The inspection can often take hours or days. But what if you could hold a digital device and see locations of all previous damage and repairs highlighted in 3D?
What Is the Link between Digital Twin and Configuration Lifecycle Management (CLM)?
Because Configuration Lifecycle Management provides a single source of truth on all valid, potential and available combinations of product components and options, it plays a key role in the design, manufacturing, sales and service of the product. When this information is shared with existing systems, including Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM), the entire organization operates from the same data, thus eliminating errors due to manual entries, data handover, multiple configuration data sources, and overlapping versions.
Manufacturers wanting to build a Digital Twin representation of each product delivered need access to the same, real-time configuration information. Since Configuration Lifecycle Management solutions are designed with open interfaces allowing integration with any platform, the Digital Twin can be hosted using any application, including a PLM system, a dedicated application, or a distributed model. The product configuration data remains maintained by the Configuration Lifecycle Management (CLM) platform, easily accessed by the Digital Twin.
Overview of the Digital Twin Lifecycle
Productionizing digital twins in an industrial, regulated environment is challenging. From connecting to a variety of data lakes and cleaning data to make it human or machine useable, all the way to visualization, modeling, and exporting of key model outputs to various stakeholders, there are a dozen different steps organizations need to get right to effectively benefit from digital twin technologies. In today’s age of aspirational Industry 4.0, many organizations are at various stages of their digitalization journeys. On one end, some may be working at sorting and centralizing their data onto cloud-based data lakes, while others may be further along and already have numerous sophisticated models built to represent their assets and related processes.
The core of productionizing digital twins is subject matter expertise across multiple teams to work synchronously to meet stringent engineering, regulatory, and cybersecurity requirements. From an engineering perspective, digital twins need to be explainable and grounded in the physical system’s physics, biology, and/or chemistry. From a regulatory perspective, diligent record-keeping is required for auditability (i.e., tracing when models were built, what data was used for training, how model outputs were consumed, etc). Lastly, from a cybersecurity perspective, IT departments often require significant controls on how digital twins may interface directly or indirectly with control systems and/or other mission-critical databases.
This article provides an overview of the digital twin lifecycle through a TwinOps workflow shown in the figure below. TwinOps is focused on the lifecycle of taking digital twins from design to production, and then providing the infrastructure to maintain and monitor them once operationalized.
Industrial DataOps: The data backbone of digital twins
What is needed is not a single digital twin that perfectly encapsulates all aspects of the physical reality it mirrors, but rather an evolving set of “digital siblings.” Each sibling shares a lot of the same DNA (data, tools, and practices) but is built for a specific purpose, can evolve on its own, and provides value in isolation.
The data backbone to power digital twins needs to be governed in efficient ways to avoid the master data management challenges of the past—including tracking data lineage, managing access rights, and monitoring data quality, to mention a few examples. The governance structure has to focus on creating data products that may be used, reused, and collaborated on in efficient and cross-disciplinary ways. The data products have to be easily composable and be constructed like humans think about data ; As a graph where physical equipment are interconnected both physically and logically. And through this representation select parts of the graph may be used to populate the different digital twins in a consistent and coherent way.
Introduction to Hybrid Modelling for Digital Twins
Physics-informed Machine Learning (PIML) involves embedding established domain knowledge (i.e. physics, chemistry, biology) with machine learning (ML) to effectively model dynamic industrial systems. While these dynamic systems face challenges such as high sensor noise and sparse measurements, they often are characterized by some fundamental scientific/engineering knowledge. There are 3 general ways to embed domain knowledge with ML, including:
- Introducing observational bias to the data
- Introducing inductive bias into the model structure
- Introducing learning bias to how models are trained
Physics-informed neural networks (PINNs) are a novel approach that integrate the information from both process data and engineering knowledge by embedding the ODEs into the loss function of a neural network. PIML integrates data and mathematical models seamlessly even in noisy and high- dimensional contexts.Thanks to its natural capability of blending physical models and data as well as the use of automatic differentiation, PIML is well placed to become an enabling catalyst in the emerging era of digital twins.
Ford's Vijayakumar Kempuraj on Digital Twin Adoption | Future Says
Building Autonomous Rail Networks in NVIDIA Omniverse with Digitale Schiene Deutschland
NVIDIA launches Omniverse Cloud to support industrial metaverse ‘digital twins’
During the company’s virtual GTC 2022 conference for developers, Nvidia announced the launch of Omniverse Cloud, a comprehensive cloud-based software-as-a-service solution for artists, developers and enterprise teams to use Omniverse to design, publish and operate metaverse applications anywhere in the world.
Omniverse Cloud runs on specially designed cloud-computing architecture within Nvidia’s data centers and hardware running Nvidia OVX architecture for graphics and simulation and Nvidia HGX servers for advanced artificial intelligence workloads. It uses the Nvidia Graphics Delivery Network, a global-scale distributed data center network for delivering low-latency metaverse content that the company learned from its experience with GeForce Now, its low-latency cloud-based video game streaming service.
Using a digital twin of the entire network built into Omniverse that runs alongside the actual railway network at the same time, being fed the same data in real time, it will be able to use AI to monitor sensors and other data and simulation to predict and prevent incidents. “With Nvidia technologies, we’re able to begin realizing the vision of a fully automated train network,” said Ruben Schilling of the Lead Perception Group at DB Netz, part of Deutsche Bahn.
The Digital Twin takes the first steps with the development of AAS
An Asset Administration Shell or AAS is a virtual representation of such an asset consisting of a series of sub-models, made up of various properties, in which all the information and functionalities of the asset are described. The following figure exemplifies the concept starting from an electrical axis as an asset, in which two examples of sub-models for specific functionalities can be seen with their associated properties: energy efficiency and positioning mode.
In addition to this virtual representation and modelling, the AAS also allows communication through standard interfaces and models, using technologies such as OPC-UA, AutomationML or REST APIs for interaction with each other or with external entities not modelled with AAS. This facilitates the interoperability of Digital Twins by means of open languages, understandable by all interested parties.
Digital Twins and AI Reshape Biopharmaceutical Manufacturing
The foundation of any control strategy is process understanding. And, according to the ICH’s Q8 guidance,1 modeling is the best way to generate process understanding and meet regulators’ quality-by-design expectations. The models should describe the relationship between process parameters and drug quality and performance attributes.
Statistical models—predictions based on available data—have proven to be the most popular approach so far. Many manufacturers have used data-based models to guide development, scale-up, and process control. But their predictive power is limited to the range of data available, and they require significant experimental effort.
For this reason, mechanistic models—assumptions based on known principles rather than just data—are gaining in popularity. Mechanistic models “can provide a full description of the system, higher prediction power, as well as the potential to extrapolate well outside of calibration space,” Li explains. “They are valuable tools for predicting scale-up process performance, thereby de-risking large-scale manufacturing runs.”
Building Industrial Digital Twins on AWS Using MQTT Sparkplug
Even better, a Sparkplug solution is built around an event-based and publish-subscribe architectural model that uses Report-By-Exception for communication. Meaning that your Digital Twin instances get updated with information only when a change in the dynamic properties is detected. Firstly, this saves computational and network resources such as CPU, memory, power and bandwidth. Secondly, this results in a highly responsive system whereby anomalies picked up by the analytics system can be adjusted in real-time.
Further, due to the underlying MQTT infrastructure, a Sparkplug based Digital Twin solution can scale to support millions of physical assets, which means that you can keep adding more assets with no disruptions. What’s more, MQTT Sparkplug’s definition of an MQTT Session State Management ensures that your Digital twin Solution is always aware of the status of all your physical assets at any given time.
Process Modeling Flow Editor
AVEVA E3D Design Overview
The Metaverse Goes Industrial: Siemens, NVIDIA Extend Partnership to Bring Digital Twins Within Easy Reach
Silicon Valley magic met Wednesday with 175 years of industrial technology leadership as Siemens CEO Roland Busch and NVIDIA Founder and CEO Jensen Huang shared their vision for an “industrial metaverse” at the launch of the Siemens Xcelerator business platform in Munich. Pairing physics-based digital models from Siemens with real-time AI from NVIDIA, the companies announced they will connect the Siemens Xcelerator and NVIDIA Omniverse platforms.
The partnership also promises to make factories more efficient and sustainable. Users will more easily be able to turn data streaming from the factory floor PLCs and sensors into AI models. These models can be used to continuously optimize performance, predict problems, reduce energy consumption, and streamline the flow of parts and materials across the factory floor.
Industry 4.0 at Škoda
Over the past few years, Škoda has invested millions of dollars in state-of-the-art assembly technologies to increase productivity, improve worker safety, and decrease the company’s environmental footprint. As part of an overall Industry 4.0 strategy, the company has implemented additive manufacturing, artificial intelligence, augmented reality, autonomous mobile robots and other technology.
Adding a new workstation to an assembly line requires careful planning—especially if regular operations are expected to continue at the same time. When engineers at Škoda’s assembly plant in Vrchlabí, Czech Republic, wanted to integrate a new robot into a gearbox production line, the project was fully operational in just three weeks—thanks to digital twin technology. Within a cycle time of less than 30 seconds, the new workstation installs bearings into each gearbox. Robots install the bearings to meet the precision requirements of the application.
Optikon uses mathematical combinatorial analysis methods to find various solutions to what is known as the “knapsack problem.” It addresses the question of how certain objects can be optimally fitted into a limited space. While the classic knapsack problem only takes into account the weight and value of the items to be packed, Optikon also considers floor space, the volume of the item, and when the goods have to be shipped.
Digital twin: Empowering power systems with real-time training and predictive simulation
Uncontrolled operation and neglected maintenance of electrical systems increase safety and financial risks in such facilities, often resulting in unplanned outages that can cause equipment damage and injuries to on-site personnel.
Consider the average cost of power outages in the following critical industries:
- Oil and Gas- $800K to $3M per outage event (per Schneider Electric’s internal Voice of Customer study).
- Semiconductor- $3.8M for a single electrical event
- Data Center -30% of all reported outages cost more than $250,000, with many exceeding $1M
Leveraging digital twin technology, fully digitized electrical single-line diagrams can help address these concerns by boosting operational efficiency and reducing safety exposures. This is an example of the same digital twin technology used during the design phase of an electrical system being applied in the operation and maintenance phases of the lifecycle.
Industrial dataOps capabilities to truly scale Simulation Digital Twins
For some time, the notion of digital twins has been ubiquitous in exemplifying the potential of digital technology for heavy-asset industries. With a digital representation of a real-world system of assets or processes, we can apply simulation and optimization techniques to deliver prescriptive decision support to end-users.
Simulation Digital Twins help industries to make decisions in an increasingly complex & uncertain environment, to balance competing constraints (revenue, cost, efficiency, resiliency, carbon footprint, ++), and to react quickly and adapt with agility to real-world changes.
In this article we are describing solutions that combine the capabilities of Microsoft Azure Digital Twins, Cognite Data Fusion and Cosmotech Simulation Digital Twins. In an integrated solution, Azure Digital Twins provides a digital twin model that reflects real time state from sensors and other real time source and orchestrates event processing. Cognite Data Fusion (CDF) delivers integration of schemas and metadata from IT, OT and ET data sources, including the generation of models and twin graphs for Azure Digital Twins. The Cosmotech Simulation Digital Twin platform adds deep simulation capabilities in a scalable, open framework.
NVIDIA Omniverse Ecosystem Expands 10x, Amid New Features and Services for Developers, Enterprises and Creators
There are also new connections to industrial automation and digital twin software developers. Bentley Systems, the infrastructure engineering software company, announced the availability of LumenRT for NVIDIA Omniverse, powered by Bentley iTwin. It brings engineering-grade, industrial-scale real-time physically accurate visualization to nearly 39,000 Bentley System customers worldwide. Ipolog, a developer of factory, logistics and planning software, released three new connections to the platform. This, coupled with the growing Isaac Sim robotics ecosystem, allows customers such as BMW Group to better develop holistic digital twins.
At GTC, NVIDIA announced NVIDIA OVX, a computing system architecture designed to power large-scale digital twins. NVIDIA OVX is built to operate complex simulations that will run within Omniverse, enabling designers, engineers and planners to create physically accurate digital twins and massive, true-to-reality simulation environments.
Make Digital Twins an Integral Part of Your Sustainability Program
Digital solutions provide the visibility, analysis and insight needed to address the challenges inherent in sustainability goals. A digital twin strategy as part of an overall digitalization plan can be a crucial capability for asset intensive industries such as refining and chemicals. A digital twin needs to encompass the entire asset lifecycle and value chain from design and operations through maintenance and strategic business planning.
Comprehensive sustainability solutions are stretching the capabilities of thermodynamic first principle-based digital twins and driving the need for the next generation of solutions. Reduced order hybrid models offer a critical capability to achieve digitalization, sustainability and business goals faster. Reduced-order models can abstract models to enterprise views which inform executive awareness and strategic decision-making. Site-wide models can run faster and more intuitively to drive agile decision-making and optimize assets to achieve safety, sustainability and profit.
Digital Twins Improve Plant Design and Operational Performance
Commissioning and start-up are two of the most crucial use cases for digital twins, as people become less dependent on physical devices. The value of the digital twin is in quicker configuration and modernization of lifecycle processes in a simulated environment.
Imagine operating with all the accuracy but without the boundaries of a physical device. The simulated device can understand the environment and sends values back to the user. The information model is coming directly from the device.
The Rapid Rise and Evolution of the Digital Twin
Digital twins have a well-established track record in the realm of high-end engineering, but the new technologies and trends will drive wider adoption and higher return on investment for digital twins. Jet-engine makers are veteran users of the technique to monitor performance and predict maintenance needs. For such complex and costly pieces of machinery, digital twins more than pay for themselves. Two new trends are underway that can make digital twins high-value propositions for more industries and applications: Sensor fusion and Access to data and compute.
Hyundai Motor to set up metaverse factory with Unity
Hyundai Motor Co., South Korea’s top automaker, is set to establish a digital virtual factory in a metaverse space with Unity, a US-based real-time 3D content platform, in order to become a smart mobility solutions provider through upgrades of plant operations and production innovations. The partnership is expected to realize Hyundai’s vision of becoming the first mobility innovator to build a Meta-Factory concept, a digital twin of an actual plant, supported by a metaverse platform.
The automaker plans to first apply the concept to Hyundai Mobility Global Innovation Center in Singapore (HMGICS), supporting Hyundai Motor Group’s initiative to create an open innovation hub for research and development. The group earlier planned to adopt digital twin technology to HMGICS’ design sector.
Boeing wants to build its next airplane in the metaverse
In Boeing Co’s factory of the future, immersive 3-D engineering designs will be twinned with robots that speak to each other, while mechanics around the world will be linked by $3,500 HoloLens headsets made by Microsoft.
Boeing’s holy grail for its next new aircraft is to build and link virtual three-dimensional “digital twin” replicas of the jet and the production system able to run simulations. The digital mockups are backed by a “digital thread” that stitches together every piece of information about the aircraft from its infancy - from airline requirements, to millions of parts, to thousands of pages of certification documents - extending deep into the supply chain. Overhauling antiquated paper-based practices could bring powerful change. More than 70% of quality issues at Boeing trace back to some kind of design issue, Hyslop said. Boeing believes such tools will be central to bringing a new aircraft from inception to market in as little as four or five years.
AWS Announces AWS IoT TwinMaker
Industrial companies collect and process vast troves of data about their equipment and facilities from sources like equipment sensors, video cameras, and business applications (e.g. enterprise resource planning systems or project management systems). Many customers want to combine these data sources to create a virtual representation of their physical systems (called a digital twin) to help them simulate and optimize operational performance. But building and managing digital twins is hard even for the most technically advanced organizations. To build digital twins, customers must manually connect different types of data from diverse sources (e.g. time-series sensor data from equipment, video feeds from cameras, maintenance records from business applications, etc.). Then customers have to create a knowledge graph that provides common access to all the connected data and maps the relationships between the data sources to the physical environment. To complete the digital twin, customers have to build a 3D virtual representation of their physical systems (e.g. buildings, factories, equipment, production lines, etc.) and overlay the real-world data on to the 3D visualization. Once they have a virtual representation of their real-world systems with real-time data, customers can build applications for plant operators and maintenance engineers that can leverage machine learning and analytics to extract business insights about the real-time operational performance of their physical systems. Because of the work required, the vast majority of organizations are unable to use digital twins to improve their operations.
Building digital twins, mixed reality and metaverse apps for businesses
Using digital twin for cost-efficient wind turbines
CBM of the wind turbine is usually conducted by monitoring vibration at many points on each component with dedicated sensors. Simply increasing the number of monitored points and components leads to an increase in monitoring cost. In our approach, the digital twin acts as virtual sensors for monitoring any component whose behavior can be simulated from a smaller number of sensors as input to the digital twin. Thus, CBM with the digital twin contributes to identifying critical turbines, components, and positions that need maintenance.
BMW uses Nvidia’s Omniverse to build state-of-the-art factories
BMW has standardized on a new technology unveiled by Nvidia, the Omniverse, to simulate every aspect of its manufacturing operations, in an effort to push the envelope on smart manufacturing. BMW has done this down to work order instructions for factory workers from 31 factories in its production network, reducing production planning time by 30%, the company said.
Product customizations dominate BMW’s product sales and production. They’re currently producing 2.5 million vehicles per year, and 99% of them are custom. BMW says that each production line can be quickly configured to produce any one of ten different cars, each with up to 100 options or more across ten models, giving customers up to 2,100 ways to configure a BMW. In addition, Nvidia Omniverse gives BMW the flexibility to reconfigure its factories quickly to accommodate new big model launches.
BMW succeeds with its product customization strategy because each system essential to production is synchronized on the Nvidia Omniverse platform. As a result, every step in customizing a given model reflects customer requirements and also be shared in real-time with each production team. In addition, BMW says real-time production monitoring data is used for benchmarking digital twin performance. With the digital twins of an entire factory, BMW engineers can quickly identify where and how each specific models’ production sequence can be improved. An example is how BMW uses digital humans and simulation to test new workflows for worker ergonomics and efficiency, training digital humans with data from real associates. They’re also doing the same with the robotics they have in place across plant floors today. Combining real-time production and process monitoring data with simulated results helps BMW’s engineers quickly identify areas for improvement, so quality, cost, and production efficiency goals keep getting achieved.
Unity moves robotics design and training to the metaverse
“The Unity Simulation Pro is the only product built from the ground up to deliver distributed rendering, enabling multiple graphics processing units (GPUs) to render the same Unity project or simulation environment simultaneously, either locally or in the private cloud,” the company said. This means multiple robots with tens, hundreds, or even thousands of sensors can be simulated faster than real time on Unity today.
According to Lange, users in markets like robotics, autonomous driving, drones, agriculture technology, and more are building simulations containing environments, sensors, and models with million-square-foot warehouses, dozens of robots, and hundreds of sensors. With these simulations, they can test software against realistic virtual worlds, teach and train robot operators, or try physical integrations before real-world implementation. This is all faster, more cost-effective, and safer, taking place in the metaverse.
“A more specific use case would be using Unity Simulation Pro to investigate collaborative mapping and mission planning for robotic systems in indoor and outdoor environments,” Lange said. He added that some users have built a simulated 4,000 square-foot building sitting within a larger forested area and are attempting to identify ways to map the environment using a combination of drones, off-road mobile robots, and walking robots. The company reports it has been working to enable creators to build and model the sensors and systems of mechatronic systems to run in simulations.
Expanding Omniverse: BMW Group Builds their Factory of the Future 2.0
Siemens Energy HRSG Digital Twin Simulation Using NVIDIA Modulus and Omniverse
12 factors heating up the popularity of digital twins and simulations
Observers see significant demand for multi-physics simulations that present a holistic view across different physical domains like electronics, structures, and heat. This is critical for areas like noise and vibration. Top simulation techniques include computational fluid dynamics (CFD), multi-body systems (MBS), or finite element analysis (FEA) technologies.
Others expect to see simulation advances used to improve various aspects of operations, particularly with the rise of the so-called “omniverse” for rendering models — referring to the use of things like VR and AR, automated data labeling, AI-powered physics, and improved supply chains.
A conversation with Dr. Michael Grieves, inventor of the digital twin concept.
Manufacturing Manakins for Medical Simulation and Training
Human patient simulators may mimic the human body with varying degrees of realism—or fidelity—and can be used in almost every aspect of healthcare education. The most effective medical training devices are those that have the ability to create accurate modeling of the underlying structures of the human body and replicating them digitally and physically, noted Alban. It is why Simetri’s anatomical models and medical training aides integrate electronic, mechanical and computational components and turns to materials science for innovations in soft and skeletal tissue.
The roadmap to digitization for Simetri, said Alban, started first on the mechanical side, when mechanical models started to go from sketches to using SolidWorks and 3D models, and then embedding sensors to capture data before writing the related software and then advancing the software development capability.
In another development, software can monitor when skin has been cut, and when and if the correct fascia (connective tissue encasing the muscle) has been cut. That data is transmitted digitally to the manakin, and the physiology model of that manakin is updated as a result of that new data and, therefore, displays new vital signs. “If you will have done it the right way, you will lose pulse at the foot, but if you do this procedure correctly, you will gain back pulse at the foot because you’re allowing circulation to flow through,” explained Alban.
Optimizing manufacturing processing and quality management with digital twins, IIoT
The application of IIoT and digital twin technologies in production process and quality management in steel production processes with the following characteristics:
- Integrate process design data, quality specification data, equipment operational real time data, quality measurement data into a holistic end-to-end closed-loop system, enabling comprehensive online monitoring and analytics of production process and supporting product quality traceability.
- Combine digital twin and Industrial Internet technology seamlessly into a holistic platform to support such an application.
- Enable digital twin for both equipment and product alike, dynamically bind product digital twins with equipment digital twins to enabling product process and quality online tracking, monitoring and traceability.
- Combine online data and analytic technologies with Lean management and Six Sigma concepts and best practice for production process and quality management, creating a digital Lean capability.
The Autonomous Factory: Innovation through Personalized Production at Scale
Personalized products are in high demand these days. Meeting this demand is leading companies to increasingly automate their production processes and even make parts of it autonomous. However, this approach presents a trade-off: with increasing personalization comes increasing complexity. Therefore, companies need to decide on the expedient extents and levels of automation to be implemented in their factories. Two strategies that may help along the way: 1. Limited implementation in selected areas. 2. Co-creation with trusted partners.
Mapped raises $6.5M to build API for the ‘digital twin of data infrastructure’
Mapped simplifies access to physical building assets through a standard vocabulary, while supporting a secured API perimeter. The company already provides access to 30,000 different types of equipment. This investment will help it expand to support more equipment types and integrations and grow its go-to-market efforts.
Digital Twins at Olympic Scale
Not unlike its steel competitors, the Xuanhua facility, a subsidiary of China’s second-biggest steelmaker, HBIS Group Co., is gunning to reorganize on the basis of new demands for competition and efficiency. Relocating the 89-year-old factory to the Leting Economic Development Zone in Tangshan City in China’s Hebei province includes plans to develop a digital model for the factory.
From Logs to Logging On: Paper Machines Built With Digital Manufacturing
ANDRITZ, an Austrian company that manufactures machinery for pulp and paper mills, is using digital manufacturing and artificial-intelligence (AI) processes to save millions of dollars. Skilled workers and engineers on ANDRITZ production lines are now able to take advantage of data-driven support as standard. 3D modeling and digital twins also give ANDRITZ a competitive advantage by guiding operators safely through maintenance and repairs and ensuring transparent access to data.
Complex machine validations performed with multiphysics simulation
When new materials and methods are applied to manufacturing, it increases product complexity. But the benefits can be significant: Products are now lighter, smaller and more easily customizable to meet consumer demands. Multiphysics simulations enable machine builders to explore the physical interactions complex products encounter, virtually. It tracks interactive data of product performance, safety and longevity.
A digital twin solved the risks associated with the 50m smart patching line made by Raute
The project consists of a digital twin and virtual commissioning of the production line to secure the project delivery for the new designed machine sections (material infeed and baseplate removal) of a patching line. Different scenarios could be created with the digital twin to optimize the design (i.e. avoidance of mechanical collisions etc.) and validate the concept before manufacturing the real machine sections.
Creating a Factory of the Future in Aerospace
One of the unique anomalies of aerospace manufacturing is how it transitions from automated to manual production. Many initial components are fabricated in highly automated machining or manufacturing systems. These systems are already Industry 4.0-enabled with integrated sensors and PLCs that capture and package production data for analysis and quality control.
As subassemblies are created and installed, final assembly and integration is much more manual. For example, the final tightening of thousands of fasteners on aircraft is often done with pneumatic and manual wrenches that are purely mechanical, with manual inspections and written verification on paper documents. However, aerospace manufacturers can improve this process by integrating smart, programmable tightening tools that document the amount of torque applied for each fastener and that can automatically reconfigure torque and rotation settings based on the assigned task.
Introducing Microsoft Cloud for Manufacturing
What makes the Microsoft Cloud for Manufacturing unique is our commitment to industry-specific standards and communities, such as the Open Manufacturing Platform, the OPC Foundation, and the Digital Twins Consortium, as well as the co-innovation with our rich ecosystem of partners.
Evolving control systems are key to improved performance
For decades, the control system was constrained by physical hardware: hardwired input/output (I/O) layouts, connected controllers and structured architectures including dedicated networks and server configurations. Now, the lower cost of processing power and sensing, the evolution of network and wireless infrastructure, and distributed architectures (including the cloud) are unlocking new opportunities in control systems. Additionally, emerging standards for plug-and-produce, such as advanced physical layer (APL) and modular type package (MTP) interfaces, will drive significant changes in the way plants design and use control systems over the next decade.
Precision of Digital Twin Data Models Hold Key to Success
As the industrial sector turns to digital twin technology for operational efficiency, digital twin data model accuracy is key to success of digital replicas.
Master the digital transformation with the Digital Twin
Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems
Systems do not simply pop into existence. They progress through lifecycle phases of creation, production, operations, and disposal. The issues leading to undesirable and unpredicted emergent behavior are set in place during the phases of creation and production and realized during the operational phase, with many of those problematic issues due to human interaction. We propose that the idea of the Digital Twin, which links the physical system with its virtual equivalent can mitigate these problematic issues. We describe the Digital Twin concept and its development, show how it applies across the product lifecycle in defining and understanding system behavior, and define tests to evaluate how we are progressing. We discuss how the Digital Twin relates to Systems Engineering and how it can address the human interactions that lead to “normal accidents.” We address both Digital Twin obstacles and opportunities, such as system replication and front running. We finish with NASA’s current work with the Digital Twin.
Origins of the Digital Twin Concept
While the terminology has changed over time, the basic concept of the Digital Twin model has remained fairly stable from its inception in 2002. It is based on the idea that a digital informational construct about a physical system could be created as an entity on its own. This digital information would be a “twin” of the information that was embedded within the physical system itself and be linked with that physical system through the entire lifecycle of the system. The concept of the Digital Twin dates back to a University of Michigan presentation to industry in 2002 for the formation of a Product Lifecycle Management (PLM) center. The presentation slide, originated by Dr. Grieves, was simply called “Conceptual Ideal for PLM.” However, it did have all the elements of the Digital Twin: real space, virtual space, the link for data flow from real space to virtual space, the link for information flow from virtual space to real space and virtual sub-spaces.