Implicit Neural Field-Based Process Planning for Multi-Axis Manufacturing: Direct Control over Collision Avoidance and Toolpath Geometry

arXiv cs.RO / 4/22/2026

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Key Points

Existing curved-layer methods for multi-axis manufacturing handle collision avoidance only indirectly and optimize toolpaths without direct control of their geometry during the optimization process.
The paper proposes an implicit neural field-based framework that combines layer generation and toolpath design into a single differentiable pipeline for joint optimization.
Sinusoidally activated neural networks represent layers and toolpaths as implicit fields, enabling explicit collision avoidance and evaluation of field values and derivatives at arbitrary spatial points.
The authors analyze how neural network hyperparameters and objective definitions affect singularity behavior and topology transitions, and they include mechanisms for regularization and stability control.
Experiments in both additive and subtractive manufacturing demonstrate the method’s generality and effectiveness for multi-axis process planning.

Abstract

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.