PhysixAI offers a hybrid digital twin platform that links real physical PLCs of any brand to virtual 3D models of custom automation equipment, allowing equipment builders to commission PLCs virtually. The system has been proven in pilot projects across steel processing, lithium‑battery manufacturing, and ultrasonic welding, cutting commissioning time, cost, and risk. Future upgrades will add AI‑driven predictive maintenance, anomaly detection, and operational optimization using live runtime data.
Funding
Funding not disclosed
Founders
Product
Problem
Commissioning custom automation lines requires manual integration of physical PLCs with equipment, which is time‑consuming, costly, and prone to errors, especially for non‑standard or one‑off machinery. Delays and risks during commissioning can hinder manufacturers’ ability to bring advanced production capabilities to market quickly.
Solution
PhysixAI offers a hybrid digital‑twin platform that links any brand of physical PLC to a virtual 3D model of custom automation equipment. By mirroring the real PLC’s logic in a simulated environment, equipment builders can perform full commissioning virtually, identifying and resolving issues before hardware is installed. The platform reduces commissioning time, cost, and risk, and is being extended with AI that ingests runtime data from the twin to provide predictive maintenance, anomaly detection, and operational optimization. Validated in steel processing, lithium‑battery, and ultrasonic‑welding lines, the solution helps U.S. manufacturers accelerate deployment of complex production lines and remain competitive globally.
Target Audience
Primary customers are equipment manufacturers and system integrators that design and build custom automation lines for industries such as steel processing, battery production, and ultrasonic welding.
Features
- Real‑time bidirectional connection between physical PLCs (any brand) and a 3D digital twin of custom equipment
- Virtual commissioning environment that replicates PLC logic and equipment behavior for offline testing
- AI layer that combines live runtime data with the twin to predict failures, detect anomalies, and suggest performance improvements
- Support for non‑standard, one‑off automation designs without requiring pre‑built templates
- Cloud‑based platform enabling remote collaboration among engineering teams and rapid iteration