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Creation and Benefits of Digital Twins

The University of Tokyo_031422A
[The University of Tokyo]

- Overview

A digital twin is a digital representation of a physical object, person, or process, contextualized in a digital version of its environment. Digital twins can help an organization simulate real situations and their outcomes, ultimately allowing it to make better decisions.

Digital twins are used across the manufacturing lifecycle, from planning and designing to maintaining facilities. They can help companies in many ways, including:

  • Research and design: Digital twins can help companies research and design products more effectively. They can create a lot of data about likely performance outcomes, which can help companies refine products before production.
  • Testing and validation: Digital twins can help companies test and validate products before they exist in the real world. They can help engineers identify process failures before a product goes into production.
  • Decision-making: Digital twins can help companies improve decision-making. They can provide insights into all aspects of a production line and manufacturing process. They can also automate the decision-making process by recalibrating equipment, production lines, processes, and systems.
  • Predictive capabilities: Digital twins can help companies make accurate forecasts and predict future outcomes. This can help companies produce more cost-efficient and effective products and services.
  • Remote monitoring: Digital twins can connect real-time data with virtual representations for remote monitoring. They can also help companies monitor equipment at all times and analyze performance data.
  • Collaboration: Digital twins can help stakeholders collaborate. For example, digital twins can help companies share data with engineering, production, sales, and marketing.

- Creation of Digital Twins

A digital twin (DT) is a detailed and dynamically updated virtual copy of a physical object or process, used to monitor performance, test different scenarios, predict problems and identify opportunities for optimization. Unlike traditional computer-aided design and engineering (CAD/CAE) models, DTs always have a unique real-world counterpart from which they receive real-time data and change accordingly to mimic the original data throughout its lifecycle. However, twinning doesn't just happen in a vacuum. This process involves many parts working as a unified system.

Digital twins can be created during the design phase of an object's lifecycle, enhancing the creative phase of inventing new products and refining them into detailed product models. At this stage, the digital twin can effectively assess the impact of design decisions on product quality and functionality early on, reducing the need to develop expensive physical prototypes. After the design phase, there is a physical phase, where the digital twin begins to exist. 

The digital twin of the design generates physical objects and updates when there are any deviations. Use sensors and AutoID devices to monitor the current and historical state and condition of physical products during operational use. 

Additionally, digital twins can be used to remotely control objects via actuators. Finally, the processing phase occurs, where physical objects are processed, but conceptual objects may be retained for a period of time, such as for traceability, compliance, and learning.


- Benefits of Digital Twins

Digital twins will transform many processes. One of the main benefits is that they enable companies to detect problems earlier and fix them faster. They can alert and interact with any future failures, events or operational anomalies. They can even work autonomously by analyzing the situation, proposing optimized solutions and implementing them. 

When a company builds digital copies of its products, the environments in which they operate, and production systems, it can predict almost everything that will happen in the physical world. 

Digital twins are extremely useful for engineers because, with a detailed history of previous models, they can correct mistakes and create new, more reliable versions. The data bears this out.

By helping teams visualize how data-rich systems perform and serving as shared sources of truth, digital twins have been shown to cut costs; lower energy consumption and carbon emissions; and improve product quality, delivery speed, and operational efficiency.


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