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Reinforcement Learning Tames DLP Peel Forces For Fragile Prints

A new research paper introduces geometry‑adaptive reinforcement learning (RL) to control the peel step in Digital Light Processing (DLP) resin printing. By extracting slice‑level features such as cured area and perimeter, the RL agent dynamically adjusts lift speed, dwell time, tilt angle and Z‑hop to minimize peak separation forces. Early results suggest the method can protect thin walls, lattices and cantilevers while reducing cycle time on easy layers. The study highlights sensor requirements and questions about cross‑printer generalization before commercial rollout.