BRAINBOX DIGITAL TWIN
DIGITAL MERE SELF OF INDUSTRIAL ASSETS
Connecting the best of two worlds of Physics and Machine Learning
solves the critical issue of operating, maintaining and repairing, even when you aren't near them physically, thus improving productivity and reducing downtime
THE TWIN CHARACTERISTICS
The Brainbox Digital Twin creates a comprehensive physical and functional structure of any industrial component, product or system.
The Twin is a smart geometric tool which links digital artefacts including engineering data, operational data and behavioural models via numerically and empirically simulated models.
The Twin design evolves along with the real system throughout the lifecycle and generates a seed which is shared between knowledge and historical system
The Twin is used to describe behaviour, trend, patterns, changes due to applied physics phenomena and also to derive solutions relevant for the real system
Its a next generation Digital Twin which can bring an impeccable change in future designs of these systems
BENEFITS OF BRAINBOX TWIN
Predicting what is happening on your production line, and predicting what will happen in future, is essential for your overall manufacturing productivity, and business profitability.
Achieve increased reliability of equipment's and production lines
Improved OEE through reduced downtime
Improved productivity and performance
Reduced risk in product availability, marketplace reputations
Lower maintenance cost by predicting maintenance issues
Increases new business opportunities such as mass customization, operational optimizations and more
Improved product quality, and deep insight into the performance of your products
All the above combined Brainbox Twin brings in result in the ultimate benefit of improved profits
BRAINBOX TWIN FOR INDUSTRY
ELEMENTS OF A BRAINBOX DIGITAL TWIN SHIP
whole structure, member elements, stiffeners and hydrodynamics
Information Models Model specific systems & components, including vibrations, stress, thermal, noise signatures
Numerically simulated Governing physics models and empirical models for influencing parameters
Time domain models of components and systems. Geometric model for stress and loads
Real-Time Sensors, probes, process and environmental loading data from the real vessel.
Software driven control algorithms, knowledge system and historian models