# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 2 Materials and Methods
### Algorithms for Orchestration and Analytics  
The orchestration engine of the DTT ecosystem is supported by an integrated ensemble of algorithmic modules that enable adaptive scenario assignment, skill analytics, privacy-preserving data management, and knowledge-graph-based learning path optimization. These modules operate in coordination to form the system's adaptive intelligence core, ensuring that training pathways dynamically adjust to individual learner profiles while maintaining regulatory compliance and operational efficiency. Figure 3: Cloud–edge deployment architecture for digital training twin delivery. At the foundation of the orchestration process is a combination of rule-based agents and reinforcement learning (RL) strategies. In the initial deployment stage, rule-based policies govern scenario assignment by applying deterministic logic based on competence gap severity and predefined fidelity allocation thresholds. These rules ensure reliable system performance under regulatory constraints. However, the architecture is designed to support progressive evolution toward reinforcement learning agents that optimize training pathways over time through reward-based learning cycles. In this configuration, the RL agent observes learner state transitions, selects optimal training scenarios, and adjusts its policy through trial-and-reward updates where reward functions are based on competence gap reduction and time-to-convergence metrics. To extend personalization while preserving learner privacy, the system incorporates federated learning (FL) protocols. FL enables distributed model training across multiple edge nodes without exposing individual learner data to centralized servers. Each node trains its local model on learner-specific performance data, transmitting only encrypted model updates to the cloud-based aggregator. These aggregated models enable system-wide skill analytics and predictive performance modeling while ensuring compliance with data protection regulations such as GDPR and institutional data governance policies. In parallel, the system uses a knowledge graph curriculum model to represent the hierarchical structure of aviation maintenance competencies. Each skill domain is modeled as a node, while prerequisite relationships between skills are encoded as directed edges. This graph-based representation enables the orchestration engine to enforce dependency constraints during content sequencing, ensuring that learners are only exposed to advanced scenarios after mastering prerequisite competencies. The knowledge graph also enables gap clustering, early bottleneck detection, and real-time path re-optimization based on dynamic learner performance. As an example, the knowledge graph of the ATA 36 Pneumatic (PN) Systems curriculum with six modules (PN-1..., PN-6) is shown in Figure 4.  
Each node represents a skill domain, and directed edges indicate prerequisite relationships. This structure allows the orchestration engine to ensure learners acquire foundational knowledge before advancing to dependent concepts like Leak Detection Procedures or Safety Relief Systems. This visual emphasizes the sequential and conditional structure of the learning path, making it suitable for integration into orchestration algorithms and learner progression planning. The integration of these algorithmic subsystems is illustrated in Figure 5, which summarizes their functional roles and interactions within the orchestration process. In this architecture, the orchestration engine operates as the central hub, receiving inputs from both reinforcement learning agents and Rule-Based Scenario Selection Modules for adaptive decision-making. Simultaneously, federated skill analytics modules aggregate encrypted local learning models into institution-wide analytics without violating data privacy, while the knowledge graph curriculum model informs structural learning dependencies to ensure valid training progression. This multi-layered architecture enables  
Figure 4: Knowledge graph representation of Pneumatic Systems curriculum.