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ARRTSM Digital Twin ​

ARRTSM Digital Twin with a look-ahead function and real-time simulation driven by AI combines several powerful technologies to create a highly advanced and responsive system. Including real-time process and system simulation and algorithms for collision prediction.

Our Digital Twin represents a virtual replica of the autonomous digital Factory, process representing the complete system. It incorporates real-time data from the physical counterpart, such as sensors, IoT devices, manufacturing data, to create a synchronized virtual model.

Real-Time Simulation: Our ‘Digital Twin’ is not a static representation but operates in real time, mirroring the behavior of the complete Factory and its process. We use Real-time simulation algorithms and techniques to process the manufacturing data from the physical counterpart and update the Digital Twin accordingly, ensuring that the virtual model reflects the current state of the physical system. To give our client the best possible view of the Factory from any part of the world. 
 ARRTSM look-ahead function utilizes AI techniques, such as predictive analytics with powerful machine learning, to forecast or anticipate future events or behaviors based on historical data, patterns, and the current state of the Digital Twin. By analyzing the available data and employing predictive models, our system can project possible future scenarios or outcomes, this leads to correction of the robotic path for the best possible action considering the cycle time. Our ARRTSM AI can also enable the digital twin to adapt and learn from real-time feedback, allowing it to improve its accuracy and performance over time.

We combined, these components to create a powerful system that enables proactive decision-making and optimization. Our real-time simulation keeps it up to date, and the look-ahead function powered by AI provides insights into possible future scenarios. This integrated approach allows for better monitoring, analysis, and decision support, leading to improved efficiency, predictive maintenance, and enhanced performance across various industries.