Data Collection and Analysis

The process begins with an audit of existing processes, machinery, and robotic systems. Key performance metrics are recorded: operation speed and accuracy, energy consumption, mechanical load, and production cycles. A map of system interactions and potential failure points is created.

Digital Twin Modeling

Based on collected data, precise digital twins of machinery, production lines, and robotic systems are created. Simulations account for motion dynamics, physical constraints, control algorithms, sensor parameters, and automation systems.

Simulation and Optimization

Digital twins are used to:

• test new control algorithms and schedules,
• optimize machine utilization and process flows,
• predict wear and maintenance needs,
• model failure and emergency scenarios without risk to real equipment.

Simulations run on Nerve Engine with high performance, realistic physics, and integration support for industrial systems (PLC, SCADA, ROS).

Validation and Integration

Simulation results are validated against historical data and pilot scenarios. Models are integrated into existing monitoring and management systems, enabling real-time decision support using digital twins.

Measurable Impact

• Reduces testing and deployment time for new processes by 30–50%.
• Decreases downtime and failure events by up to 25%.
• Optimizes resources and energy consumption by 15–30%.
• Enables prediction and prevention of critical failures without affecting actual production.
• Creates a scalable platform for automation and Industry 4.0 adoption.