Reinforcement Learning for Laser Additive Manufacturing Scan-Order Optimisation: A Bilevel Proxy--FEA Diagnostic Framework for Reward and World-Model Diagnosis

• URL: http://arxiv.org/abs/2605.25063v1
• Date: Sun, 24 May 2026 13:17:05 GMT
• Title: Reinforcement Learning for Laser Additive Manufacturing Scan-Order Optimisation: A Bilevel Proxy--FEA Diagnostic Framework for Reward and World-Model Diagnosis
• Abstract summary: Reinforcement learning offers a promising approach for scan-order optimisation in laser additive manufacturing.<n>Full finite-element analysis is computationally prohibitive for dense in-the-loop evaluation.<n>This paper investigates a bilevel Proxy--FEA diagnostic framework for reward and world-model diagnosis.
• Score: 21.97902184672397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning offers a promising approach for scan-order optimisation in laser additive manufacturing, where sequential scan decisions critically influence thermal accumulation, residual stress, distortion, and final part quality. A central challenge in applying RL to this domain lies in reward and world-model fidelity: full finite-element analysis is computationally prohibitive for dense in-the-loop evaluation, while cheap thermo-inspired proxy metrics, though efficient, may capture only partial aspects of the true thermo-mechanical objectives. This paper investigates a bilevel Proxy--FEA diagnostic framework for reward and world-model diagnosis in reinforcement-learning-guided scan-order optimisation. The lower level employs lightweight scan-path and thermo-inspired proxies for rapid candidate generation and preliminary policy-side screening, while the upper level utilises sparse Abaqus FEA simulations to provide simulation-based reference labels. The framework is examined on a simplified whole-track heating LDED32 stripe benchmark comprising ten representative scan strategies. Final-cooling residual Mises stress, U3 vertical distortion, and PEEQ plasticity metrics reveal an observed stress--distortion trade-off rather than a single monotonic quality objective. Within the evaluated set, the center_out strategy emerges as a robust compromise candidate, while raster_left_to_right and edge_in form opposing endpoints of the trade-off. Proxy--FEA alignment analysis shows that current cheap path-based metrics predominantly capture distortion-related (U3) behaviour and exhibit only weak correlation with the sparse FEA reference labels. These findings highlight that proxy-only reward designs risk misalignment in future RL training and underscore the value of sparse FEA reference signals for diagnostic-guided reward and world-model refinement prior to large-scale policy optimisation.

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