Digital Twins
Digital twins are virtual representations of physical entities or processes that come with digital connectivity. Unlike typical digital representations of physical entities, digital twins are always an accurate reflection of the original physical object or process, as sensors continuously collect relevant data and transmit it to the digital twin.
Moreover, digital twins can send information back to the physical entity, making it possible to effect changes during the process. Digital twins are often compared with Hardware-in-the-Loop (HIL) systems, which differ from digital twins in that they use a software model to control the system, and input and output from the physical object under test are provided to observe how the object performs.
In contrast, HIL models use the core of the physical object to construct a software model, with the hardware circuit connected directly to evaluate the performance of the object being tested.
Digital twins make it possible to model various scenarios and obtain timely answers to questions about the optimal approach. As the Industrial IoT continues to advance and the cost of edge computing decreases, it is becoming increasingly feasible and cost-effective to create a complete digital twin of a factory, encompassing all its assets and processes.
In manufacturing plants, equipment runs continuously, and any downtime can lead to significant production losses and strain on moving parts. Digital twins provide remote visibility into equipment and processes, eliminating the need for human intervention in dangerous situations. By offering insights, information, and precise data, digital twins enable factories to make better decisions, enhancing safety, productivity, optimization, and profitability.
Digital twins offer a range of applications, including:
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Increasing productivity by remote monitoring, predictive maintenance, and effective asset management.
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Enhancing safety within the factory by detecting anomalies and malfunctions before they become major issues.
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Optimizing asset performance and utilization through diagnostics and root cause analysis.
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Assessing the impact of process control and workflow changes or new tools on the factory's performance, safety, efficiency, and competitiveness.
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Fostering innovation by enabling quick and risk-free testing of new methods and scenarios through a "fail fast" approach.
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Supporting collaboration between different teams and departments, whether on-site or remote.
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Developing training simulations that shorten on-the-job training from years to just a few months, ensuring employees are prepared for rare, abnormal, and dangerous operations.
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Achieving Lights Out Manufacturing through Robotic Process Automation (RPA), enabling factories to operate with little or no human intervention.
