Use Cases Digital Twin

Digital Twin

The digital twin is a digital replica of a living or non-living physical entity. Digital twin refers to a digital replica of potential and actual physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an object operates throughout its lifecycle. Definitions of digital twin technology emphasize two important characteristics. Firstly, each definition emphasizes the connection between the physical model and the corresponding virtual model. Secondly, this connection is established by generating real-time data using sensors to provide an evolving perspective into the current status of the physical object. The concept of the digital twin can be compared to other concepts such as cross-reality environments or co-spaces and mirror models, which aim to, by and large, synchronise part of the physical world with its cyber representation.

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Leveraging the IoT to Gain a Competitive Edge in International Competition
Leveraging the IoT to Gain a Competitive Edge in International Competition
Many large manufacturers in and outside Japan are competing for larger market share in the same space, expecting a growing demand for projectors in the areas of entertainment, which requires glamor and strong visual performance as well as digital signage that can attract people’s attention. “It is becoming more and more difficult to differentiate ourselves with stand-alone hardware products,” says Kazuyuki Kitagawa, Director of Service & Support at Panasonic AVC Networks. “In order for Panasonic to grow market share and overall business, it is essential for us to develop solutions that deliver significant added value.” Panasonic believes projection failure and quality deterioration should never happen. This is what and has driven them to make their projectors IoT-enabled. More specifically, Panasonic has developed a system that collects data from projectors, visualizes detailed operational statuses, and predicts issues and address them before failure occurs. Their projectors are embedded with a variety of sensors that measure power supply, voltage, video input/ output signals, intake/exhaust air temperatures, cooling fan operations, and light bulb operating time. These sensors have been used to make the projector more intelligent, automatically suspending operation when the temperature rises excessively, and automatically switching light bulbs. Although this was a great first step, Panasonic projectors were still not equipped with any capability to send the data over a network.
Intralox Using Demo3D Case Study
Intralox Using Demo3D Case Study
Intralox strives to create significant economic value for our customers by optimizing their conveyance systems. Intralox wants to emulate real-world production environments for end users and OEMs working in multiple industries, including packaging, warehousing, parcel, beverage, brewery, food, tire, and consumer goods. They frequently had to perform physical tests to demonstrate to customers how ARB equipment would handle their products.
ThyssenKrupp employs Visual Rules BRM
ThyssenKrupp employs Visual Rules BRM
ThyssenKrupp Steel Europe found itself in the position of needing to replace, step by step, the models it had programmed itself to manage the production of various grades of steel with a solution equipped to face the demands of the future. The solution had to be easy to integrate and designed to be rolled out step by step. ThyssenKrupp Steel Europe uses its automation systems to manage the whole production process at its company steel plants. This is done using clever algorithms that map the functional relationships across all stages of the steel production process. However, there are still a host of other changeable factors that influence the quality of the product obtained, and these also need to be factored in in the form of rules or formulae. What ThyssenKrupp Steel Europe was looking for was a suitable piece of software to fulfill this rule-based management function.
What is the business value of this IoT use case and how is it measured?
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Improved Asset Performance and Reliability: Businesses leverage digital twins to monitor, analyze, and optimize the performance of assets, such as machinery, equipment, buildings, and infrastructure. By creating digital twins of assets, businesses can gain insights into asset behavior, identify potential issues, and proactively address maintenance and performance optimization opportunities to improve reliability and uptime.

Enhanced Product Development and Innovation: Digital twins enable businesses to simulate and validate product designs, test different scenarios, and optimize product performance before physical prototypes are built. By leveraging digital twins in product development processes, businesses can accelerate time-to-market, reduce development costs, and deliver innovative products that meet customer requirements and market demands.

Who is involved in purchasing decisions, and who are the primary system users?
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Executives and Decision-makers: Executives view digital twins as a strategic investment that enables data-driven decision-making, improves operational efficiency, and drives business growth. They prioritize digital twin initiatives to gain insights into asset performance, optimize resource utilization, and enhance customer satisfaction by delivering high-quality products and services.

Product Managers and Engineers: Product managers and engineers leverage digital twins to streamline product development processes, validate design concepts, and optimize product performance. They view digital twins as valuable tools for accelerating innovation, reducing development costs, and delivering products that meet customer expectations for quality, reliability, and functionality.

 

Which technologies are used in a system and what are the critical technology?
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3D Modeling and Simulation Tools: 3D modeling and simulation tools enable businesses to create realistic virtual representations of physical assets, processes, or systems. These tools support the creation of detailed digital twins that accurately reflect the geometry, topology, and behavior of assets, enabling stakeholders to visualize and interact with digital twins for analysis and decision-making.

IoT and Sensor Technologies: IoT devices and sensor technologies provide real-time data about asset performance, condition, and environmental factors. By integrating IoT sensors into digital twin platforms, businesses can collect and analyze sensor data to create dynamic digital twins that reflect the current state of physical assets, enabling predictive maintenance, performance optimization, and operational efficiency.

Cloud Computing and Edge Computing: Cloud computing and edge computing platforms provide scalable, cost-effective infrastructure for storing, processing, and analyzing large volumes of data generated by digital twins. These platforms support real-time data processing, analytics, and visualization, enabling stakeholders to access and interact with digital twins from anywhere, at any time, using any device.

What data is obtained by the system and what are the critical data management decision points?
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Sensor Data and IoT Connectivity: Sensor data from connected devices and IoT sensors provide real-time information about asset behavior, performance, and environmental conditions. By collecting and analyzing sensor data, businesses can create accurate digital twins that reflect the current state of physical assets, enabling predictive maintenance, performance optimization, and operational efficiency.

Historical Data and Simulation Models: Historical data from past operations, maintenance records, and performance metrics are used to calibrate simulation models and validate digital twin predictions. By integrating historical data into digital twin simulations, businesses can analyze past performance, forecast future behavior, and optimize asset management strategies to improve reliability and performance.

What business, integration, or regulatory challenges could impact deployment?
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Software Platforms and Tools: Deployment includes the selection, configuration, and integration of digital twin software platforms and tools that support asset modeling, simulation, visualization, and analytics. These platforms provide a user-friendly interface for creating and managing digital twins, enabling stakeholders to access and interact with virtual representations of assets for analysis and decision-making.

Data Integration and Connectivity: Deployment involves integrating data from various sources, such as IoT devices, enterprise systems, and external databases, into digital twin platforms. Data integration ensures that digital twins are populated with accurate, up-to-date information about asset behavior, performance, and condition, enabling stakeholders to make informed decisions based on real-time insights.

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