
We propose a reference architecture centred on Jetson edge-AI modules deployed directly within production cells or lines. The solution comprises:
Place a Jetson-based unit next to the machine or inspection station, secure power, network and I/O interfaces.
Link cameras, depth sensors or IoT sensors to collect raw data (e.g., image streams, vibration, temperature).
Containerise AI models (e.g., defect-detection, anomaly detection) and deploy on Jetson using JetPack + NVIDIA TensorRT for optimised performance.
Edge node processes streams in real-time, detects anomalies or quality faults, and triggers local responses (e.g., stop line, alert operator).
Send metadata/results upstream to MES/SCADA or cloud for audit, analytics, model retraining. Collect new data for continuous improvement.
Monitor edge performance (latency, throughput, reliability), update models remotely, push firmware/SDK updates.
Modern manufacturing lines are under immense pressure to deliver higher throughput, tighter quality tolerances, and near-zero downtime all while reducing operational costs. Traditional approaches often rely on centralised systems, batch analytics, and human inspection, which lead to:
Delays in detecting defects or anomalies, resulting in scrap or rework.
High latency and bandwidth costs when sending data off-site or to the cloud for inference.
Difficulty scaling computer vision or AI-driven inspection across many machines and environments (especially harsh factory conditions).
Integration headache with legacy PLCs, sensors, and control systems.
Lack of real-time decision making at the edge: many systems still operate on “see-analyse-react” with delays.
In short: manufacturing needs intelligent edge computing that can process vision, sensor and operational data in real time, on-site, under factory conditions and that’s where Jetson enters.