Simulate, optimize, visualize

 | 
19 Maio 2022
 | 
Tempo de leitura
2 minutos

ROBOTIC SIMULATION AND VIRTUAL REALITY TO PREVIEW THE POTENTIAL OF LEAS SOLUTIONS.

Previewing a project, before it takes shape, has enabled our customers to fully appreciate the quality and top technology of LEAS systems, even before they have them installed in their factories. It also instills peace of mind in their investment choice.

Virtual reality systems as well as robotic simulation systems, which are used by our teams throughout the design phase, perfectly control the automations and movements in all aspects and thus also preview how the robots will interact with the production line.

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This system supports LEAS design and engineering teams as they define the automation concept in order to make it completely linear, efficient, and safe from the outset, avoiding costly errors and wasted time and resources in the design and construction phase of solutions. In the definition of the Gartner Digital Twin, this is a digital representation of a real-world entity or system. A digital twin implementation is a captured object or software model that mirrors a physical object, process, organization, person, or other unique object. Data from multiple digital twins can be aggregated for a composite view of a set of real-world entities, such as a power plant or a city, and the related processes.

This hot-button topic harkens back to the Digital Twin concept that originated in the work of Michael Grieves and John Vickers at NASA in 2003. As they write in the book "Transdisciplinary Perspectives on Complex Systems:

"Systems do not simply pop into existence. They progress through lifecycle phases of creation, production, operation, 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 therefore understand in these few lines the relevance of these digital representations, which, by modeling and predicting the system's lifecycle, not only make it possible to avoid costly errors at the system's inception, but also support its development and ensure safe "human interactions."

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