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

TL;DR

This paper introduces an implicit neural field framework for multi-axis manufacturing process planning, enabling direct control over collision avoidance and toolpath geometry within a unified differentiable system.

Contribution

It presents a novel neural network-based method that integrates layer generation and toolpath design, improving control and optimization in multi-axis manufacturing.

Findings

Enables explicit collision avoidance during process planning

Demonstrates effectiveness in both additive and subtractive manufacturing

Provides insights into network hyperparameters affecting topology transitions

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.