Optimizing PCBA e-waste management: Intelligent inspection sequencing and recovery strategies using graph neural networks and Reinforcement Learning

Electronic waste management faces critical challenges due to the complexity and variability of printed circuit board assemblies (PCBAs), which contain both high-value recoverable materials and hazardous components. Current inspection and recovery processes are predominantly manual and static, resulting in inefficiencies and limited scalability. This paper proposes a novel framework that integrates Graph Neural Networks (GNNs) with Reinforcement Learning (RL) to enable adaptive, real-time inspection sequencing and recovery decision-making for PCBAs. By modelling each board as a graph of interconnected components, the GNN encodes structural and defect-related information, providing a dynamic state representation for the RL agent. The agent then chooses a sequence of inspections or recovery strategies, such as reuse, repair or recycle, balancing the cost of diagnostics against the potential value of recovery. A case study on an industrial I/O device demonstrates the approach's effectiveness with simulations showing that the system learns profitable inspection and recovery policies under uncertainty while reducing unnecessary tests. A comparative analysis of state-of-the-art graph architectures reveals that Graph Attention Networks (GAT) outperform standard Graph Convolutional Networks (GCN). Results confirm the potential of GNN-RL integration to improve economic viability and sustainability in PCBA inspection for e-waste management.