Manufacturing Context
Connect inspection images with lot, material, process, equipment, and corrective-action history.
Semantic Graph connects industrial vision and sensing, Physics AI and digital twins, and Knowledge Graph/GraphRAG to support manufacturing decisions from physical evidence to prediction and explanation.
We distinguish technologies used in current development from technologies planned for evaluation and expansion.
AI vision, SWIR, thermal imaging, cameras, and process sensors capture physical conditions and inspection evidence.
Physics constraints, boundary conditions, CAE, and measured data support thermal/process prediction and surrogate models.
Lot, material, process, equipment, defect, and action knowledge supports evidence-grounded RCA and engineering decisions.
Thermal equations, material properties, geometry, and boundary conditions define physics constraints.
Scientific Machine Learning complements CAE and sensor data with GPU-accelerated surrogate models.
Used in current AI development workloads. PhysicsNeMo remains a planned evaluation area.
Targets temperature fields, hotspots, thermal resistance, reliability risk, and sensor-guided calibration.
Connect inspection images with lot, material, process, equipment, and corrective-action history.
Retrieve similar cases, SOPs, historical defects, and connected evidence paths for explainable RCA.
Use x86_64 GPU servers as the primary operating platform, adding Jetson Orin/TensorRT only for validated low-latency inference close to industrial cameras and sensors.
A preliminary prior-art review indicates that generic PINN thermal prediction, digital twins, knowledge-graph RCA, and closed-loop optimization already have related art. Our IP strategy therefore focuses on manufacturing-specific combinations and validation logic.
Packaging-specific multi-fidelity calibration across geometry, materials, power, boundary conditions, thermal sensors, CAE/PINN residuals, hotspot and thermal-resistance prediction, plus model validity checks.
Align AI Vision, SWIR, thermal sensors, lot/equipment/process data, and physics residuals in a causal/knowledge graph to generate evidence paths for root-cause candidates.
Generate process-adjustment candidates with GraphRAG/AI agents, validate safety and physics consistency in a digital twin, then route only validated actions for engineer approval.
We review HBM Thermal Digital Twin, SWIR inspection, GraphRAG, x86 Physical AI Server, and selective edge-deployment opportunities.