Reinforcement Learning for Laser Additive Manufacturing Scan-Order Optimisation: A Bilevel Proxy--FEA Diagnostic Framework for Reward and World-Model Diagnosis
arXiv:2605.25063v1 Announce Type: new 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 obj
The paper leverages recent advancements in reinforcement learning to address a perennial challenge in advanced manufacturing, where traditional simulation methods are computationally prohibitive.
Optimizing additive manufacturing processes through AI can significantly enhance material quality, reduce waste, and accelerate production cycles for critical components, impacting multiple industrial sectors.
The proposed framework allows for more efficient and accurate AI-driven optimization of complex manufacturing techniques like laser additive manufacturing, potentially lowering costs and improving product integrity.
- · Advanced manufacturing sector
- · AI/ML research and development
- · Aerospace and defense industries
- · Material science engineers
- · Traditional manufacturing processes
- · Companies reliant on less efficient simulation tools
Improved quality and throughput of 3D printed metal components become achievable through enhanced process control.
Broader adoption of AI-driven optimization in other complex material science and manufacturing applications could follow.
This could lead to a decentralization of high-precision manufacturing, enabling more agile and resilient supply chains.
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Read at arXiv cs.LG