Digital Twin
Assembly Line
Delta and Subsidiary Universal Instruments Unveil AI-based Automation and Digital Twins for Semiconductor Manufacturing
Delta, a global leader in power management and a world-class provider of industrial automation solutions, along with its U.S.-based subsidiary Universal Instruments, unveiled at SEMICON Taiwan 2024, a set of next-generation solutions designed to enable digital twins and AI-based automation in semiconductor manufacturing applications. These include the DIATwin Virtual Machine Development Platform, which accelerates new product development by up to 20%. Leveraging its extensive experience in industrial automation, Delta develops the Wafer Edge Profile Measurement Solution for semiconductor front-end processing, which features highly customizable features for added equipment value.
At SEMICON Taiwan 2024, Delta is highlighting the DIATwin Virtual Machine Development Platform. In addition, Delta is demonstrating its innovative digital twin platform developed on the NVIDIA OmniverseTM platform. Delta’s digital twin platform can virtually link specific production lines and aggregate data from a diverse range of equipment and systems to generate synthetic data to train its computer models to achieve 90% accuracy.
Upgrading Safety Asset Planning and Incident Investigations with Digital Twins
Using the Optelos Safety Asset Planning capabilities with digital twins, facilities can more effectively manage the placement and oversight of safety equipment, create turnaround safety plans, investigate incidents, and more easily and effectively present safety data to leadership and employees.
Recent technology developments put significant safety improvements and risk reduction within reach. The powerful combination of drone data collection, digital twin reality models, and visual data management can enhance site safety and reduce risks to personnel through geolocation of safety-related assets in a digital twin model, enabling visualization of safety and incident response data.
Meet a Factory Digital Twin From Wistron
Streamlining Cell Tower Inspections and Site Assessments with Visual Data Management
Tower owners, operators, and contractors are turning to new visual data management solutions to provide the accurate and reliable data needed to support efficient cell tower maintenance and technology rollout programs. The availability of accurate and up-to-date site assessment data streamlines operations, enhances decision-making, and significantly reduces operational costs. By leveraging new visual data management systems such as Optelos in combination with drone data collection, 3D digital twin point cloud models and AI technologies, companies in the telecom industry are revolutionizing the process for how to inspect, manage and maintain their tower assets, yielding significant cost savings and operational efficiencies.
RF engineering can use the digital twin models to verify the RF design was properly implemented and built to specification (antenna placement & orientation of the RAD center). The RAN group can evaluate the impact of the overall network and performance for each individual cell tower. The ability to house all CAD drawings, 3D models, shelter photos, inventory, inspections and other required documents in one location can speed up collaborative work dramatically. One major telecom provider reported that site assessment team meetings were now 60% shorter, and decisions were made in the meetings, versus leaving the meeting with a series of “go gets” for required information.
Improve tire manufacturing effectiveness with a process digital twin
In the rubber-mixing stage, the recipe of various raw material constituents like rubber, chemicals, carbon, oil, and other additives plays a vital role in the control of process standards and final product quality. In the current schema of things, parameters like Mooney viscosity, specific gravity, and Rheo (the level of curing that can be achieved over the compound) are fairly manual and offline. In addition, the correlation of these parameters is conducted either on a standard spreadsheet solver or statistical package. Because of the delay in such correlation and interdependency, the extent of control a process engineer has on the deviation (such as drop temperature, mixing time, ram pressure, injection time, and so on) are limited.
There are four steps to operationalize, the first being data acquisition and noise removal—a process of 3–6 weeks with the built-in and external connectors. Next is model tuning and ascertaining what is fit for our purpose. Since we are considering a list of defect types, we are talking about another four weeks for training, validating, creating test sets, and delivering a simulation environment with minimum error. The third step is delivering the set points and boundary conditions for each grade of compound.
For example, the process digital twin cockpit has three desirable sub-environments:
- Carcass level—machine ID, drum width, drum diameter, module number, average weight, actual weight, and deviation results
- Tread roll level—machine number, average weight, actual weight, deviation, and SKU number
- Curing level—curing ID, handling time, estimated curing time, curing schedule, and associated deviations in curing time
The final step is ascertaining the model outcome and computing the simulation result (bias, Sum of Squares Error (SSE), deviation, and so on) with respect to the business outcome like defect percentage, speed of work, overall accuracy, and so on.
Yokogawa and TIM Solution Ink Global Partnership to Offer 8D Digital Twin to Customers
Yokogawa Electric Corporation (TOKYO: 6841) announces that it has entered into a partnership with TIM Solution and will act as a global value-added reseller for this company’s 8D* digital twin platform. The addition of TIM Solution’s digital twin platform will complement Yokogawa’s existing digital twin portfolio, covering a broader spectrum of asset lifecycle solutions from design and engineering to operation and maintenance, thereby improving the intelligence of industrial sites. Through this agreement, Yokogawa will be able to manage implementation, integration, and support for TIM Solution’s digital twin platform. The partnership will help strengthen Yokogawa’s capabilities as a global digital transformation (DX) integrator while expanding the provision of TIM Solution’s digital twin platform in the global market.
TIM Solution’s digital twin platform is offered as a PaaS. Functioning as a universal digital twin platform, it can be applied in various fields to enhance manufacturing processes by integrating and simulating data from oil & gas, shipbuilding, aerospace, automotive, and other industries with respect to IT/OT and engineering technology. It is a specialized digital platform that, when coupled with Yokogawa asset management offerings such as OpreX Asset Health Insights, can provide customers with a single platform for the access and utilization of company-wide data. TIM Solution’s digital twin platform can also be placed on top of Yokogawa’s OT process digital twin offerings, providing an even more holistic view of asset data.
8D digital twin: A digital twin platform that adopts an eight-dimensional approach (document, drawing, spatial, real time, cost, maintenance, sustainability, safety) to the integration of data.
Intel® AFS and Digital Twins: Optimizing Factory Performance
Today, the ever-increasing technological capabilities of computers equipped with high performance processors mean we can digitize the concept of modeling, raising it to new, dynamic levels. Now, instead of simple physical replicas, we can build Digital Twins (DTs)—data sets that simulate not only the physical attributes of entities (such as shape, color, and size) but also more abstract characteristics (such as strength, elasticity, conductivity, and many more). Plus, once created, Digital Twins of different objectives can be combined into Digital Twin systems, their behavior mimicking that of their real-world counterparts. That behavior can be recorded, analyzed, tested, and revised cyclically. Intel® Automated Factory Solutions is a suite of products that embody the experience Intel has gained implementing Digital Twins. Designed specifically for high-end technical manufacturers, these products are custom-fit to meet each customer’s unique operational challenges.
Unlocking the Full Potential of Manufacturing Capabilities Through Digital Twins on AWS
In this post, we will explore the collaboration between Amazon Web Services (AWS) and Matterport to create a digital twin proof of concept (POC) for Belden Inc. at one of its major manufacturing facilities in Richmond, Indiana. The purpose of this digital twin POC was to gain insights and optimize operations in employee training, asset performance monitoring, and remote asset inspection at one of its assembly lines.
The onsite capture process required no more than an hour to capture a significant portion of the plant operation. Using the industry-leading Matterport 3D Pro3 capture camera system, we captured high-resolution imagery with high-fidelity measurement information to digitally recreate the entire plant environment.
The use of MQTT protocol to natively connect and send equipment data to AWS IoT Core further streamlined the process. MQTT, an efficient and lightweight messaging protocol designed for Internet of Things (IoT) applications, ensured seamless communication with minimal latency. This integration allowed for quick access to critical equipment data, facilitating informed decision making and enabling proactive maintenance measures.
Throughout the plant, sensors were strategically deployed to collect essential operational data that was previously missing. These sensors were responsible for monitoring various aspects of machine performance, availability, and health status, including indicators such as vibration, temperature, current, and power. Subsequently, the gathered operational data was transmitted through Belden’s zero-trust operational technology network to Belden Horizon Data Operations (BHDO).
A Unified Industrial Large Knowledge Model Framework in Smart Manufacturing
The recent emergence of large language models (LLMs) shows the potential for artificial general intelligence, revealing new opportunities in industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes an Industrial Large Knowledge Model (ILKM) framework emphasizing their potential to revolutionize the industry in smart manufacturing. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, “6S Principle” is proposed as the guideline for the development of ILKMs in smart manufacturing.
Monitor factory operations using digital twins from AWS and Matterport
AWS IoT TwinMaker is a solution from Amazon Web Services (AWS) that makes it easy for industrial companies to create digital twins of real-world systems, such as buildings, factories, industrial equipment, and production lines. Matterport is the leading spatial data company focused on digitizing and indexing the built world. The Matterport 3D data platform enables anyone to turn a space into an accurate and immersive digital twin, which is used to design, build, operate, promote, and understand any space.
With AWS IoT TwinMaker and Matterport integration, developers leverage this technology to combine the data from the manufacturing floor with the 3D models of the factory. This helps to create a fully integrated digital twin of the factory or remote facility. All of this is done in a short period of time and at a low cost, giving the customers the spatial data insights they need to monitor and manage their operations more efficiently than ever before.
Predictive Maintenance 3D Simulation Use Case
Introducing 'Factory View' a virtual look inside Aisin factories
Factory View is a tool that uses images and point cloud data to recreate a factory in virtual space, allowing users to virtually see the inside of the factory, very similar to the benefit Google Street View provides. By accessing the portal site from a computer, users can freely move around factories in Japan and overseas, and view desired locations and equipment in nearly all directions from anywhere at any time.
The measurement device used is manufactured by NavVis, a German company, and enables quick and efficient data acquisition. A worker wearing the device can acquire images and point cloud data simultaneously by walking around in a factory.
The use of Factory View may be expanded and used in overseas plants and group companies, where significant cost and time saving benefits can be expected. The system can also be used as a Business Continuity Plan measure in instances such as damage caused by large-scale earthquakes and other natural disasters.
🇺🇸 Why Build a New Factory in the US? Logistics, Not Politics
Siemens is almost as excited about the guts of the Fort Worth facility as it is about the demand that supports the additional capacity. The company has digitally simulated the entire process of setting up a new plant, including the construction design, the layout of the factory floor and the product development but also the day-to-day manufacturing workflows. “We optimize it, we shift it around and when we like it — not before that — we start bringing in excavating machines on the site or putting machines into it,” Busch said. This lets Siemens get the construction right the first time — which is important at a time of high inflation — but it also sets up a virtuous cycle of productivity improvements whereby plant managers can test out tweaks digitally and carry them out with much less equipment downtime, and sensor-packed equipment can yield insights from the field that spark yet more tweaks.
Digital simulation can be game changer — for Siemens itself and for its customers. For example, when a beverage manufacturer rolls out a new product, the viscosity of the liquid will affect the speed at which it can run its filling machines. Traditionally, this was just a trial and error process that resulted in a lot of spilled beverages. “What we can do is we can simulate it — the viscosity and whatnot, the whole plant. And then you just have a new mixture and you run it seamlessly without fooling around,” Busch said. It’s almost like a video game but for a factory — and much more sophisticated.
Debug, Checkout and Startup
After months of developing processes, creating drawings and integrating components, your automated assembly machine is finally complete. All that needs to be done now is hit the start button, right? Not so fast. Debug, checkout and startup come first. “Debug” means to search for and eliminate malfunctioning elements. “Checkout” refers to the test of a machine or system for proper functioning. “Startup” is the act of initiating a production process on the plant floor.
“Debug, checkout and startup are the most important parts of any automation project,” claims Sean Dotson, PE, former president and chief technology officer of systems integrator RND Automation. “I’ve seen some companies that cut corners, thinking everything is good enough just so they can get a machine out the door on time. Saying ‘we’ll fix it at the customer’s site’ is a recipe for disaster. A machine like that will never be 100 percent correct. “Debug is what gets you to the factory acceptance test,” explains Dotson. “If a machine works at that stage, then it should work when it arrives on the floor of the customer’s facility.
Digital Robotics Twin in Omniverse
Prevu3D: The next generation of digital twin
It Takes Two: Why Digital Twins Need Both Humans and Machines
When Western Digital expanded its hard drive manufacturing site in Thailand, the first time the assembly lines were turned on wasn’t on the factory floor; it was on a laptop 8,000 miles away. Before any physical machinery found its place within the newly constructed walls, teams of engineers meticulously crafted its virtual counterpart. This digital twin could mimic the operations down to every tool, robot arm, and even the pace of human operators, flawlessly simulating the assembly of the company’s most advanced enterprise hard drives. Engineers could quickly test different layouts and operation scenarios without touching the production line.
For most projects, Sanguanpong could go into the factory and measure parameters like cycle times, yield, output, or level of automation. Here, she needed to extrapolate data from experts about machines and processes that had yet to materialize. “Because there is no physical operation in the building, we the advanced analytics team needed to validate our findings with the subject matter experts, making sure our simulation model fit the expected action,” she said. Data needed to constantly flow in and out of the model, relying not only on algorithms but on the capacities of human communication and imagination.
Why will intelligent digital twins become an industrial manufacturing must-have?
For an illustrative example, look at a large thermal processing equipment manufacturer. With a legacy of serving clients across 40 countries for the past 60 years, the manufacturer faced critical challenges, including expensive and time-consuming diagnostics of on-field equipment and complexity in managing equipment monitoring software for multiple customers.
To address these problems, the company partnered with Saviant Consulting to build a platform to create digital twins on Microsoft Azure for its customers’ melt shops. Saviant designed a multitenant, loosely coupled architecture to create this scalable platform, which helped reduce overheads while also managing multiple equipment for its customers.
The Race to Build a $6.3BN Railway for the Olympics
Meetkai creates a digital twin of sprawling $2B Silicon Box chip packaging factory
Meetkai — which is pioneering tech in the metaverse and conversational AI — created a digital twin of the planned $2 billion Silicon Box chip packaging factory coming soon to Singapore.
Silicon Box recently celebrated the private grand opening of its physical 800,000 square-feet facility in Tampines, Singapore, while simultaneously introducing MeetKai’s digital replica in the metaverse. By leveraging the power of advanced AI and metaverse technologies, MeetKai has created a virtual replica of Silicon Box’s new facility, offering opportunities for business growth and talent development.
The factory will be home to thousands of jobs in the future, and the Meetkai digital twin will survey as a recruiting tool to help bring those employees on board, making it easier to visualize the kind of jobs that those employees will be doing. Meetkai used generative AI to try to improve the tech.
Data-Driven Design: Leveraging Synthetic Data for Engineering Simulations
A key feature in this recent chapter of the digitization of design is that synthetic data and digital twins have dramatically improved collaboration and communication among stakeholders involved in the product design process. Virtual replicas are far easier to share and visualize than their physical counterparts, and the results of these twins being used alongside synthetic data are far-reaching.
By harnessing the power of synthetic data and digital twins, developers gain deeper insights into product performance. The aviation industry demonstrates a perfect example of this. As a result of using digital twin technologies, Boeing recently saw a 40% improvement in first-time quality of its systems and parts.
Creating comprehensive digital twins that capture the complexity of physical systems may require significant computational resources and integration with IoT devices. At Treble Technologies, acoustic engineers achieve this through benchmark testing. Having successfully simulated a device’s performance in one complex real-life room, the same benchmarks such as geometry detail or boundary conditions can then be used to simulate other hypothetical rooms of similar complexity. To evaluate the authenticity of synthetic data, benchmark datasets comprising real-world data can be created.
Digital twins for the rapid startup of manufacturing processes: a case study in PVC tube extrusion
In this work, a soft sensor–based digital twin (DT) was developed to reduce the startup time in manufacturing plastic tubes and enable real-time product quality monitoring, i.e., the weight per unit length and the inner and outer diameters of the tube. An experimental campaign was conducted on a real tube extrusion line using three polyvinyl chloride (PVC) compounds and different process conditions, and machine learning regression algorithms were trained and tested to create the models of the extruder and the extrusion die the DT is based on. The characterization of the considered material, whose properties were given as input to the digital models, was carried out according to a procedure based only on the data collected by the production line. The DT was tested for the startup of the production of a single-layer tube and allowed to achieve the specified customer requirements (thickness and weight) in a few minutes. The proposed solution thus proved to be a valuable tool for reducing the setup time, thus increasing the efficiency of the process.
A Comparative Analysis of Data Modelling Standards for Smart Manufacturing
In essence, adopting data modeling standards can facilitate seamless data exchange across the entire value chain, enhancing overall efficiency and cooperation among various applications and machines. Crucial to this evolution is semantic modeling, allowing machines to deduce meaning without human intervention. Thus, the concept of information modeling, encapsulating not only data but its meaning, is paramount to facilitating intelligent, autonomous decisions.
The Digital Twin Definition Language (DTDL) language follows JSON syntax but is based on JSON-LD. JSON-LD, or JSON for Linked Data, is a method of encoding Linked Data using JSON. It is a World Wide Web Consortium (W3C) standard that provides a way to enrich your data by contextualizing it with schemas (vocabularies) that you choose. This makes it easy to define complex models and relationships between different parts of a system.
Sparkplug and OPC UA, on the other hand, provide a way to structure data and ensure interoperability. Sparkplug uses MQTT and Protocol Buffers, focusing on SCADA/IIoT solutions and efficient data encoding, while OPC UA provides a more generalized approach, offering industry-specific guidelines through companion specifications.
🤝 Hexagon collaborates with NVIDIA to transform industrial digital twin solution
Hexagon AB, the global leader in digital reality solutions combining sensor, software and autonomous solutions technologies, announced a collaboration with NVIDIA to enable industrial digital twin solutions that unite reality capture, manufacturing twins, AI, simulation and visualisation to deliver real-time comparison to real-world models.
The collaboration will connect industry-leading technologies from Hexagon and NVIDIA to enable seamless, multi-user workflows through a unified view for factory planning and design, as well as process quality optimisation and operations. As part of the collaboration, Hexagon’s HxDR reality capture platform and Nexus manufacturing platform will be connected to NVIDIA Omniverse, a platform for developing and operating industrial metaverse applications, based on the Universal Scene Description (USD) framework. The connected platforms provide complementary technologies that enable customers to advance manufacturing for digital factories and accelerate the power of digital twins for intelligent cities, construction and infrastructure.
Assystem Creates a Digital Twin for Nuclear Plants with Altair
🔏🚗 In-Depth Analysis of Cyber Threats to Automotive Factories
We found that Ransomware-as-a-Service (RaaS) operations, such as Conti and LockBit, are active in the automotive industry. These are characterized by stealing confidential data from within the target organization before encrypting their systems, forcing automakers to face threats of halted factory operations and public exposure of intellectual property (IP). For example, Continental (a major automotive parts manufacturer) was attacked in August, with some IT systems accessed. They immediately took response measures, restoring normal operations and cooperating with external cybersecurity experts to investigate the incident. However, in November, LockBit took to its data leak website and claimed to have 40TB of Continental’s data, offering to return the data for a ransom of $40 million.
Previous studies on automotive factories mainly focus on the general issues in the OT/ICS environment, such as difficulty in executing security updates, knowledge gaps among OT personnel regarding security, and weak vulnerability management. In light of this, TXOne Networks has conducted a detailed analysis of common automotive factory digital transformation applications to explain how attackers can gain initial access and link different threats together into a multi-pronged attack to cause significant damage to automotive factories.
In the study of industrial robots, controllers sometimes enable universal remote connection services (such as FTP or Web) or APIs defined by the manufacturer to provide operators with convenient robot operation through the Control Station. However, we found that most robot controllers do not enable any authentication mechanism by default and cannot even use it. This allows attackers lurking in the factory to directly execute any operation on robots through tools released by robot manufacturers. In the case of Digital Twin applications, attackers lurking in the factory can also use vulnerabilities in simulation devices to execute malicious code attacks on their models. When a Digital Twin’s model is attacked, it means that the generated simulation environment cannot maintain congruency with the physical environment. This entails that, after the model is tampered with, there may not necessarily be obvious malicious behavior which is a serious problem because of how long this can go unchecked and unfixed. This makes it easy for engineers to continue using the damaged Digital Twin in unknown circumstances, leading to inaccurate research and development or incorrect decisions made by the factory based on false information, which can result in greater financial losses than ransomware attacks.
Deloitte and Siemens Model-Based Enterprise: Now, Near, Far
Using Carbon Capture and Storage Digital Twins for Net Zero Strategies
One of the key challenges for keeping CCS solutions economical is the cost of proving duration and reliability of storage using numerical modeling. Traditional simulators for carbon sequestration are time-consuming and computationally expensive. Machine learning models provide similar accuracy levels while dramatically shrinking the time and costs required.
This post presents a new approach to carbon capture and storage that is substantially close to what is needed in industrial settings. It is readily available for real-world applications using NVIDIA Modulus and NVIDIA Omniverse. This CCS approach works on high-resolution, two-meter digital twin simulations over large spatial domains, handles a varying number of injection wells, and considers dipping and heterogeneous reservoirs. Most importantly, this new CCS method handles multiple wells and their interactions.
How Digital Twins are Shaping the Future of Defense System Design
The longer that military aircraft, combat tanks, and warships continue in service, the more difficult it becomes to manufacture or source their required parts. Maintaining a healthy supply chain and ensuring optimal performance can, therefore, become increasingly difficult. Since manufacturers can produce exact parts based on a digital model, the DoD is turning to digital twin technology to reduce lead time on part acquisition.
Another challenge is working with more sensitive IP, for example, in military system design and development as mentioned above. Having a fully functioning digital twin creates a serious cybersecurity threat since anyone gaining access to the digital twin could arguably recreate the product in the physical world. This potential threat requires putting limits in place regarding access to various aspects of the digital model, further complicating the idea of data creation, ownership, and access.
3D Printing A Bridge With A Twin
The world’s first 3D-printed steel bridge showcases technology that could reduce the amount of material used in structures. It has a network of sensors that continuously feed data into a ‘digital twin’; that will monitor how the bridge behaves over time and help refine the design of similar structures in future. Hugh Ferguson reports and looks at how a similar approach to monitoring is being adopted across civil engineering projects.
The origins of this bridge lie within a small creative design studio in Amsterdam, Joris Laarman Lab, headed by designer and artist Joris Laarman. In about 2014, excited by opportunities presented by emerging technologies, the team decided to develop designs in 3D-printed stainless steel. This presented an immediate challenge: no-one had before produced large steel objects using 3D printing or additive manufacturing. The process requires molten metal to be deposited in multiple layers. At the time, there were already tools for metal inert gas (MIG) welding. In this arc welding process, a continuous solid wire – usually 1.2 millimetre in diameter – is electrically heated and fed from a welding gun. There were also robots on which the tools could be mounted. However, no-one had used robots with MIG welding. Robots were generally used for repetitive ‘pick and place’ tasks, rather than complex welding control.
🚙 Digital Twins: The Benefits and Challenges of Revolutionary Technology in Automotive Industries
With the advent of Industry 4.0, an increasing number of organizations have implemented digital twin technology to optimize their performance, enhance their educational initiatives, or facilitate advanced maintenance. Even the automotive industry has readily embraced this transformational technology. However, organizations must acknowledge that the adoption of digital twin technology may simultaneously expose them to potential cyber threats. Thus, securing digital twins within an organization should be viewed as an essential priority, on par with their implementation.
One of the challenges of implementing digital twin technology is maintaining consistency between the physical and virtual twins. In the case of a model corruption attack, it can be difficult to detect the issue, as developers may not notice the problem until they inspect the repository or run jobs on an infected digital twin. Running an infected digital twin not only leads to inconsistencies, but it can also compromise the CPS, as the malicious code sent by the infected twin may cause additional harm.
Using Data Models to Manage Your Digital Twins
A continuously evolving industrial knowledge graph is the foundation of creating industrial digital twins that solve real-world problems. Industrial digital twins are powerful representations of the physical world that can help you better understand how your assets are impacting your operations. A digital twin is only as useful as what you can do with it, and there is never only one all-encompassing digital twin. Your maintenance view of a physical installation will need to be different from the operational view, which is different from the engineering view for planning and construction.
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
Cosmo Tech collaborates with Microsoft to drive strategic sustainability outcomes with Simulation Digital Twins
Cosmo Tech is collaborating with Microsoft to integrate Microsoft Azure Digital Twins capabilities with the addition of its strategic 360° Simulation Digital Twin technology. The combined technologies enable Microsoft’s enterprise customers to monitor systems in near real-time and to simulate the evolution of complex organization in uncertain environments over time. This will allow strategic optimizations at all levels of enterprise planning, decision making and financial functions; enabling outcomes that are robust, resilient, and sustainable.
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.
The Smartest Website You Haven't Heard of
Finally, one of the most brilliant parts of McMaster’s product is that for nearly every part, they have a CAD file that you can instantly download into your 3D models. Mechanical engineers mock up designs in CAD programs before actually building them, and having access to pre-modeled parts saves time. (Imagine having to manually model all your nuts and bolts.) McMaster even has extensions for popular CAD programs which allow you to import part files directly, instead of using their website. This makes engineer’s lives 10x easier (not to mention making them more likely to purchase from McMaster-Carr). The closest analogy to this is AR try-on, but that’s not even very accurate. The point of AR try-on is to determine whether you like the item you’re about to buy, whereas the point of McMaster’s CAD downloads is to speed up an engineer’s workflow. In most cases, they already know which part they need, it’s just a matter of completing the CAD model before they can start building the real thing.
The Digital Factory framework: An International Standard for Semantic Interoperability
“Smart Manufacturing” is an internationally agreed concept of an ideal state of the manufacturing industry. To achieve this, systems with different architectures must exchange information without compromising its meaning. In other words, systems must not only connect to, but also understand, each other. This crucial requirement is called semantic interoperability. The Digital Factory framework is an international standard that Yokogawa has contributed to its development. Its purpose is to achieve semantic interoperability and thus establish a foundation for Smart Manufacturing. This standard defines the structure of common model elements and their usage rules based on common concept dictionaries and integrates various information of a “system of systems” related to production. When related implementation technologies worldwide comply with this standard, digital information representing production systems (Digital Factories) will be available to all parties throughout the lifecycle of production systems while keeping up-to-date. This paper outlines the Digital Factory framework, the significance of international standardization for Smart Manufacturing, and Yokogawa’s commitment to this effort.
The IEC 62832 Digital Factory framework was developed by IEC TC 65/WG 16 and published in October 2020. It provides the basic structures of model elements needed to digitally represent an entire production system and their usage rules. It consists of the following three parts. ● IEC 62832-1 General principles (Part 1)(9) ● IEC 62832-2 Model elements (Part 2)(10) ● IEC 62832-3 Application of Digital Factory for lifecycle management of production systems (Part 3)(11)
To establish Semantic Interoperability and allow different systems to understand each other, dictionaries that define concepts in an identifiable and understandable way are needed (e.g., IEC 61360-4 - Common Data Dictionary(8)). A method that shares structures for combining the shared concepts and using them as complex information is also needed.
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.
Digital twins improve real-life manufacturing
Real-world data paired with digital simulations of products—digital twins—are providing valuable insights that are helping companies identify and resolve problems before prototypes go into production and manage products in the field, says Alberto Ferrari, senior director of the Model-Based Digital Thread Process Capability Center at Raytheon.
The concept has started to take off, with the market for digital-twin technology and tools growing by 58% annually to reach $48 billion by 2026, up from $3.1 billion in 2020. Using the technology to create digital prototypes saves resources, money, and time. Yet the technology is also being used to simulate far more, from urban populations to energy systems to the deployment of new services.
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.
Real working Squidgame robot
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.
A Digital Factory Approach to Data-driven Management in Factories
Yokogawa’s solutions and know-how play an important role in accelerating digital transformation (DX) of operational technology (OT) in the manufacturing industry. When proposing these solutions and know-how to customers, it is persuasive to be able to show that Yokogawa has actually improved productivity in its own factories using its OT operations data. This specific example will help customers to understand the effectiveness of the proposal. To achieve data-driven management with OT operation data, three requirements must be satisfied: (1) OT Data Lake, which is a framework for gathering operational data from Yokogawa’s factories worldwide into a single database and improving productivity on a global scale, (2) AI optimization and automation that use operational data and images, and (3) remote operation that ensures the continuity of business even when people’s access is restricted, for example, due to the COVID-19 pandemic. Yokogawa defines a factory that satisfies these three items as a Digital Factory and is working hard to make its own factories as such. Although this approach is one of Yokogawa’s Internal DX measures, the results can be used to develop know-how for External DX, which will increase value for customers, expedite DX in existing businesses, create new DX businesses, and strengthen Yokogawa’s presence in DX. This paper introduces Yokogawa’s approach to Internal DX, its roadmap, and progress toward external DX.
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 twin for load monitoring of wind turbine blade
Recently, the lifetime extension of wind turbines has increasingly attracted attention as one way to reduce levelized cost of energy. To explain, generally, wind turbines are designed under the wind condition defined by design standards such as the International Electrotechnical Commission (IEC), however, real wind conditions do not always correspond to the design condition. Therefore, the actual lifetime of wind turbines can be extended when the real wind condition is less severe than the design condition. For the lifetime extension, however, it is important to have an accurate evaluation of remaining useful lifetime (RUL). To accurately evaluate RUL, we should know historical data of loads applied to a structure of wind turbine but unfortunately, often there are not enough sensors to provide a full set of data to evaluate the loads. Thus, while the simple solution would be to add more sensors for the load evaluation, this would defeat the purpose as it would entail additional costs, and thus reduce the goal of trying to reduce the levelized cost of energy through lifetime extension. So, the challenge is to accurately estimate the load from the sensor data available.
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.
BMW Group and NVIDIA take virtual factory planning to the next level
The BMW Group and NVIDIA are generating a completely new approach to planning highly complex manufacturing systems – with the Omniverse platform. The virtual factory planning tool integrates a range of planning data and applications and allows real-time collaboration with unrestricted compatibility. As industry leaders, the BMW Group and NVIDIA are setting new standards in virtual factory planning.
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.
Westinghouse ML, AI, and Digital Twin Developments for Nuclear Power Applications
A Platform Based on the Semantic Data Model That Makes Full Use of Design Data throughout the Plant Lifecycle
Design data are created in multiple systems because their purpose and specialty are different. Yokogawa has been developing a plant data transformation platform that checks the consistency among data distributed across various systems and enables the interoperability of the data by applying ontology technology to database operation and management. This platform will make it possible to quickly and reliably resolve data gaps and inconsistencies between the plant design and instrumentation systems, ensure their reliability, and provide high-quality engineering services. This paper describes through the value architecture analysis how this platform technology will also help solve social issues related to the SDGs and explains its core technologies and application examples.
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, as shown in Figure 3 and 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.
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.