Accelerated computing
Physical AI, at the die train
Conductor moves at over a thousand metres a minute. Perception has to be local, deterministic and fast, so Draweon runs GPU inference at the plant edge and keeps training, simulation and optimisation in the cloud or on-prem.
Jetson Orin · DeepStream · Holoscan · TensorRT
Edge perception
4 to 12 cameras per line plus laser, eddy-current and spark streams fused at the line. Sub-50 ms alerting and sub-250 ms bounded control are the design targets. Jetson Thor is planned for higher sensor density.
Triton · NIM
Model serving
Vision, time-series, anomaly and quality models behind one orchestrator at the plant edge, with canary rollout by line and fallback to the previous validated version.
DGX or HGX · NeMo
Training
Defect classifiers, sensor-fusion models, time-series prognostics and cable-process reasoning models trained on reel telemetry and engineer corrections. Dedicated training capacity is planned.
Omniverse · OVX · Modulus
Line twin
As-run drawing, stranding, extrusion, thermal and tension simulation. Physics-informed surrogates are planned so recipes can be optimised at production cadence.
Cosmos · Omniverse Replicator
Synthetic data
Rare spark, pinhole, eccentricity, neckdown and break variants generated and validated against real gauge, spark and vision distributions before promotion. Planned.
cuOpt
Optimisation
Line sequencing, reel changeovers, energy allocation, takt and constrained setpoint proposals.
Isaac · Isaac Sim
Robot handling
Spool, reel and payoff and take-up motion planned and validated in simulation before it runs next to moving conductor.