What are the "Digital Twins"
Engineering has been using digital twin technology since the computer age, they have created models and model systems to be able to represent the real characteristics of a product or processes, with mathematical models, in this way a model is as good as that its real characteristics are represented in the digital.
With the new technologies, modeling or "Digital Twins" has jumped to another level, especially with super computers and processors; which normally requires the processing of huge data packets coming from your real model containing multiple sensors capable of collecting data from a system, for example, a manufacturing execution system, ERP software or specific data collection tool which is then transmitted if it requires it in real time and/or processed by artificial intelligence to increase its efficiency.
Currently it can be used in any area that is required, in the Aerospace Industry, in the Automotive Industry with virtual car testing, vehicle maintenance, development of autonomous vehicles, aerodynamic efficiency, consumption, resistance of materials, etc.
In Medicine and Health Care, in a clinical center evaluating operational strategies, unit capabilities, staffing, healthcare models; evaluation of the lifestyle of patients to provide personalized attention.
Creation of a logistics network of information on the traffic situation, the layout of the road to design distribution routes and inventory storage locations.
To understand how a team is performing in real time,
organize its use in industry, improving its design, efficiency and effectiveness.
Advantages and disadvantages in the use of this technology.
There are many advantages of this technology, I could mention the following:
- It allows the development of prototypes for the simulation of products or services, which, if made physically, would be very expensive and would take a long time. Example: a Digital Twin of industrial equipment that simulates its way of working and use in real time.
- Reduces maintenance costs thanks to predictive maintenance: For example, analysis of changes in parts of industrial equipment components can be carried out, and in this way evaluate the result of changes in conditions of temperature, pressure or other property of the process , this allows failures to be predicted and offers the possibility of focusing on maintenance tasks that are so necessary in industries.
- Improves Productivity, due to the Reduction of unplanned downtime due to predictive maintenance and the improvement of performance and efficiency with the application of AI.
- Test and Validate before Manufacturing.
I can't imagine the disadvantages with such a new tool.
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