用神经场统一生成层与路径，直接控制避碰和轨迹形状。
Implicit Neural Field-Based Process Planning for Multi-Axis Manufacturing: Direct Control over Collision Avoidance and Toolpath Geometry
- 用正弦激活网络建模制造层和刀具路径为隐式场。
- 可任意点评估场值与导数，实现显式避碰与联合优化。
- 适用于增材与减材制造，适合需精准轨迹控制的场景。
现有基于弯曲层的多轴制造工艺规划方法仅间接处理碰撞问题，并在后处理阶段生成刀具路径，导致路径几何无法在优化中控制。本文提出一种基于隐式神经场的多轴制造工艺规划框架，将层生成与路径设计整合于单一可微流水线中。采用正弦激活神经网络将层与路径表示为隐式场，可在任意空间点直接计算场值及其梯度，从而实现显式碰撞规避，并对制造层与刀具路径进行联合优化。进一步研究了网络超参数与目标函数定义对奇点行为及拓扑变化的影响，提供内置正则化与稳定性控制机制。该方法在增材与减材制造实例中均得到验证，证明其通用性与有效性。
原文摘要 · Abstract (English)
Existing curved-layer-based process planning methods for multi-axis manufacturing address collisions only indirectly and generate toolpaths in a post-processing step, leaving toolpath geometry uncontrolled during optimization. We present an implicit neural field-based framework for multi-axis process planning that overcomes these limitations by embedding both layer generation and toolpath design within a single differentiable pipeline. Using sinusoidally activated neural networks to represent layers and toolpaths as implicit fields, our method enables direct evaluation of field values and derivatives at any spatial point, thereby allowing explicit collision avoidance and joint optimization of manufacturing layers and toolpaths. We further investigate how network hyperparameters and objective definitions influence singularity behavior and topology transitions, offering built-in mechanisms for regularization and stability control. The proposed approach is demonstrated on examples in both additive and subtractive manufacturing, validating its generality and effectiveness.
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