Neural field planning for hybrid manufacturing cuts estimated build time 68.3%
jiqizhixin · x · 2026-10-15
University of Manchester researchers present a neural field optimization method for hybrid additive-subtractive manufacturing (HAM), accepted to SIGGRAPH Asia 2026. It replaces discrete voxel-sequence search with continuous field optimization, cutting estimated fabrication time by 68.3% on the GE-Bracket model while preserving fine features, controlling distortion per FEA, and extending naturally to 3-axis additive plus 5-axis subtractive platforms.
More from Embodied
- 1X, DeepMind, Physical Intelligence Gather for Humanoid Robot Demos — lukas_m_ziegler · 2026-10-15
- NVIDIA recaps GTC Berlin: open models to physical AI — NVIDIA Developer · 2026-10-15
- AlphaNIST: one RL policy solves all 20 NIST assembly tasks with 98.2% success in simulation — hbouammar · 2026-10-15
- Leash Bio: DNA-encoded libraries and an RL sandbox for 10,000 drug-discovery campaigns a weekend — Latent Space · 2026-10-15
- Work photos are AI's first mile: if capture is wrong, no model can recover — arnabdotorg · 2026-10-15
- LEGO builds an omnidirectional vehicle for automated warehouses — glenbeer · 2026-10-15