Automation Workflow for Simulating Fatigue and Crack Propagation in Aircraft Components

Manual setup for FAA/EASA damage tolerance analysis (DTA) can take weeks per component, requiring engineers to define load spectra, mesh critical regions, and configure solvers. This workflow automates the ingestion of CAD/PLM data, applies regulatory load cases from a managed library, and auto-generates compliant simulation input decks. By eliminating repetitive setup, engineering teams can complete the critical path for certification submissions in days, not months, directly accelerating time-to-market for new aircraft and modifications.

Physical fatigue testing of full-scale airframes and components is extraordinarily expensive and time-consuming, often requiring dedicated test rigs and months of cyclic loading. This automated simulation workflow enables virtual testing of thousands of design and material permutations, identifying high-risk crack paths and failure modes before committing metal to a test article. The result is a dramatic reduction in the number of physical test iterations needed, saving millions in direct testing costs and freeing up scarce lab capacity for final validation.

Conservative, fixed-interval maintenance drives high operational costs for airlines. This workflow automates the generation of component-specific fatigue life curves and crack growth models under realistic operational spectra. By feeding these high-fidelity predictions into maintenance planning systems (e.g., integrating with airline MRO software), operators can shift from calendar-based to condition-based maintenance. This extends safe service intervals for robust components while focusing inspections on predicted critical zones, reducing aircraft downtime and lowering overall maintenance burden.

The traditional fatigue analysis process is fragmented across CAD designers, stress engineers, and specialists, relying on manual file transfers, email, and spreadsheet tracking. This workflow creates an integrated digital thread, using orchestration agents to automatically pull geometry from Siemens Teamcenter or Dassault ENOVIA, trigger ANSYS or Abaqus jobs on an HPC cluster, and post-process results into standardized reports. By automating these handoffs, engineering teams can reallocate 20-30 hours per analysis from administrative coordination to higher-value design interpretation and optimization.

Modifying in-service aircraft (e.g., adding new antennas, reinforcing structures) requires re-qualification of affected zones. Manually assessing the fatigue impact of these changes is slow and risks oversight. This workflow can be triggered automatically by a PLM change order, running a comparative battery of simulations on the new vs. baseline design. It flags any components where the damage accumulation rate increases beyond a safety threshold, providing engineers with quantified risk assessments early in the change process. This prevents costly late-stage discoveries and ensures retrofit programs stay on schedule and budget.

Scaling fatigue analysis across an entire aircraft or fleet is prohibitive with manual methods. This workflow institutionalizes simulation as a repeatable production service. It embeds governance through automated checkpoints—validating input quality, enforcing solver settings per company standards, and routing anomalous results for human review. By treating simulation as an automated factory line, aerospace OEMs can perform fleet-wide durability studies, sensitivity analyses, and what-if scenarios at a fraction of the cost, turning structural analysis from a bottleneck into a strategic, scalable capability for lifecycle engineering.

An autonomous agent extracts component geometry, material assignments, and boundary conditions from CAD (e.g., CATIA, NX) and PLM systems (e.g., Teamcenter, Windchill). It validates data completeness, repairs minor mesh issues, and prepares a normalized input package for the simulation engine, eliminating days of manual file preparation and data hunting per assembly.

This core orchestrator (built on frameworks like LangGraph or Prefect) manages the end-to-end simulation pipeline. It sequences jobs: first running a static stress analysis to identify critical regions, then deploying a fatigue life prediction agent to calculate SN curves or crack initiation points, and finally executing a fracture mechanics agent to model crack growth (e.g., using NASGRO or Abaqus). It handles HPC job submission (Slurm, Kubernetes) and monitors for convergence.

A rules-based and ML scoring agent analyzes simulation outputs (stress intensity factors, crack growth rates). It cross-references results against regulatory thresholds (e.g., FAA AC 25.571) and historical fleet data. Components are automatically ranked by failure criticality, and potential inspection intervals are calculated. This transforms raw simulation data into actionable maintenance intelligence, compressing a weeks-long engineering review into hours.

This agent synthesizes all findings into structured outputs for operations. It generates preliminary maintenance task cards, populates fields in Enterprise Asset Management (EAM) systems like IBM Maximo or SAP PM, and creates visualization packages (highlighting crack paths) for engineering review. It enforces approval gates, routing packages to designated sign-offs before release, ensuring governance is baked into the automated workflow.

A monitoring agent tracks pipeline health, simulation accuracy, and runtime costs. More critically, it manages a closed-loop calibration system. When new in-service inspection data or physical test results become available, this agent triggers a discrepancy analysis and can recommend updates to simulation parameters or material models, ensuring the digital twin fidelity improves over time and reducing predictive error.

This component manages the practical deployment of the workflow. It handles phased rollout—starting with a pilot on a single component family—and includes exception routing for edge cases (e.g., novel materials, complex joints) to a human-in-the-loop review queue. It integrates with enterprise SSO and logging systems to ensure access control and create a full audit trail for regulatory scrutiny, making the automation production-ready and governable.