Multi-Agent System for Aerospace Composite Layup and Drill Inspection

Manual inspection of composite parts for wrinkles, gaps, and drill tolerance is slow and subjective, leading to escaped defects requiring costly rework or part scrapping. An automated vision and ultrasonic workflow catches deviations in real-time, preventing defective parts from advancing. The business case is direct: reducing scrap material (often aerospace-grade carbon fiber) and eliminating labor-intensive rework operations directly improves unit margin.

Traditional inspection of large composite structures involves manual staging, point-by-point ultrasonic checks, and visual review, taking hours or days. A coordinated multi-agent system using drones or robotic crawlers with on-board edge AI performs full-part scans autonomously. This parallelized inspection cuts throughput time dramatically, accelerating the release of work-in-progress and increasing overall production capacity without adding labor.

Aerospace mandates rigorous traceability. Manual records are brittle. This workflow automatically logs every inspection event—images, sensor data, pass/fail decisions—and updates the digital twin and MES (e.g., SAP, Oracle). Each part has a complete, immutable quality record, streamlining FAA/EASA audits and providing defensible evidence of compliance. This reduces administrative overhead and de-risks certification delays.

Detection is reactive; prevention is proactive. When the system identifies a trend (e.g., consistent gap in a specific layup zone), it doesn't just flag the part. An analysis agent correlates the flaw with layup robot parameters or drill press settings from the MES and can automatically generate a work order to calibrate the equipment or alert process engineers. This shifts quality from inspection to in-process control, preventing defect batches.

Skilled Non-Destructive Testing (NDT) technicians are a scarce, expensive resource. Automating the initial screening and anomaly detection with AI allows these experts to focus on reviewing only the flagged exceptions and conducting final certification, rather than routine scanning. This labor leverage improves technician productivity by 3-4x and mitigates operational risk from workforce shortages and turnover.

Implementation follows a phased pilot on a high-value, high-rework part line. The architecture—integrating drone/robot control (ROS), edge vision models (PyTorch/TensorRT), ultrasonic data fusion, and MES APIs—is designed for measurable outcomes from day one. Key performance indicators (KPIs) like First-Pass Yield (FPY), Cost of Quality (CoQ), and inspection hours per part are tracked to validate the business case for full-scale rollout.

An autonomous agent orchestrates drones equipped with ultrasonic or high-resolution cameras to scan large composite parts (e.g., wing skins, fuselage sections). It analyzes imagery for wrinkles, gaps, and fiber misalignment in real-time, comparing results against CAD specifications. This eliminates manual tap-testing, reduces inspection time by 70-80%, and creates a spatially mapped defect log for the digital twin.

A robotic agent, often integrated with a CNC or automated drill, uses 3D vision to inspect hole diameter, roundness, countersink depth, and burr presence post-drilling. It validates against tight aerospace tolerances (often within ±0.001"). Non-conforming holes trigger an immediate alert and can pause the drilling cell, preventing the assembly of defective parts and saving thousands in downstream re-drilling and part replacement costs.

A central orchestrator agent ingests inspection data from all agents and updates a federated digital twin (e.g., in Siemens Teamcenter or a custom platform). This creates a living as-built record for each part serial number, linking defects to specific manufacturing stages. The system automatically generates Non-Conformance Reports (NCRs) in the QMS and routes them for engineering review, ensuring full traceability for FAA/EASA compliance.

When defects are confirmed, a diagnostic agent correlates the flaw type, location, and timestamp with process data (e.g., autoclave cycle, material batch, drill bit ID). It suggests the most probable root cause and, for reparable defects, generates a tailored rework instruction packet—including approved repair procedures and tool paths—for a technician or automated repair cell. This slashes mean-time-to-repair (MTTR) and standardizes corrective actions.

A LangGraph or custom orchestration layer manages the handoffs between inspection, analysis, and reporting agents. It enforces business rules—for example, any critical defect (e.g., a wrinkle in a primary load path) is automatically flagged for mandatory engineer approval before the part can proceed. The gateway manages exception queues, provides a unified dashboard for quality engineers, and ensures human oversight is applied only where risk demands it.

Deployment involves containerized vision models (PyTorch, TensorRT) on ruggedized edge GPUs at the drill station and on the drone's companion computer. Agents communicate via a secure, low-latency factory network (5G or Wi-Fi 6) to a central orchestrator. Integration points include the MES (SAP, Oracle) for work orders, the QMS (ETQ, SAP QM) for NCRs, and robotic PLCs for cell control. A 6-8 week pilot focuses on a single part type to validate accuracy and ROI before line-wide rollout.

The primary operational owner. They are measured on First-Time Yield (FTY), scrap/rework costs, and audit readiness. Their buy-in is secured by demonstrating how the system automates manual ultrasonic scan review (saving 15-20 hours/week per inspector) and provides a digital, searchable record of every layup and drill for traceability. They define the critical-to-quality (CTQ) characteristics (e.g., wrinkle size thresholds, hole diameter tolerance) and approve the validation protocol.

Responsible for the data pipeline and system interoperability. They evaluate how the multi-agent orchestration layer (e.g., LangGraph) will interface with the factory network, PLCs controlling inspection drones/robots, the MES (e.g., SAP ME), and the digital twin platform (e.g., Siemens Teamcenter). Their key concern is security, latency (<100ms for drill feedback loops), and maintaining a clean audit trail for data lineage from sensor to record.

Approves the capital expenditure based on a clear ROI model. The business case hinges on hard cost avoidance: reducing scrap of high-cost carbon fiber pre-preg, eliminating rework labor on complex assemblies, and avoiding downstream warranty claims from latent defects. The workflow must instrument itself to report real-time yield metrics and attribute savings directly to cost centers in the ERP.

The delivery team architecting and deploying the custom multi-agent workflow. They translate CTQ specs into agent roles (e.g., Layup Inspector Agent, Drill Analysis Agent, Digital Twin Sync Agent), design the exception-handling logic for ambiguous cases, and implement the human-in-the-loop review queues in the operator dashboard. Their success is tied to system uptime, model accuracy in production, and meeting the defined KPIs for throughput and quality.

The end-users who interact with the system daily. Their adoption is critical. The workflow must provide a clear dashboard showing pass/fail status, highlight defect imagery, and route only true exceptions for their review. Training focuses on interpreting AI confidence scores and handling the override process. The system must reduce, not increase, their cognitive load during high-tempo production.

The governance stakeholder in highly regulated aerospace (AS9100, NADCAP). They mandate that the workflow's decision logic is documented, model performance is validated on held-out datasets, and all inspection records are immutable and traceable. The architecture must include an explainability layer that can retrieve the specific image evidence and analysis steps for any part's inspection history during an audit.