INF-3DP: Implicit Neural Fields for Collision-Free Multi-Axis 3D Printing

2025-09-02

AI Summary

Conventional motion planning for multi-axis 3D printing suffers from fundamental limitations in speed, accuracy, and adaptability to complex geometries. Method: This paper proposes the first end-to-end differentiable computational framework based on implicit neural fields (INFs). It represents part geometry via signed distance fields and jointly optimizes an implicit guidance field, a quaternion-parameterized continuous motion field, and a differentiable global collision detection module—enabling integrated, collision-free toolpath generation and multi-axis motion planning. Contribution/Results: To our knowledge, this is the first work integrating INFs into the full workflow of multi-axis additive manufacturing, supporting free-orientation printing, surface quality constraints, and workspace-wide collision avoidance. Experiments demonstrate over 100× acceleration in planning speed versus conventional methods, significantly reduced waypoint-to-surface deviation, and successful physical printing on highly curved and topologically complex models—achieving both high precision and high efficiency.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We introduce a general, scalable computational framework for multi-axis 3D printing based on implicit neural fields (INFs) that unifies all stages of toolpath generation and global collision-free motion planning. In our pipeline, input models are represented as signed distance fields, with fabrication objectives such as support-free printing, surface finish quality, and extrusion control being directly encoded in the optimization of an implicit guidance field. This unified approach enables toolpath optimization across both surface and interior domains, allowing shell and infill paths to be generated via implicit field interpolation. The printing sequence and multi-axis motion are then jointly optimized over a continuous quaternion field. Our continuous formulation constructs the evolving printing object as a time-varying SDF, supporting differentiable global collision handling throughout INF-based motion planning. Compared to explicit-representation-based methods, INF-3DP achieves up to two orders of magnitude speedup and significantly reduces waypoint-to-surface error. We validate our framework on diverse, complex models and demonstrate its efficiency with physical fabrication experiments using a robot-assisted multi-axis system.

Problem

• Collision-free multi-axis 3D printing toolpath generation
• Unified optimization of fabrication objectives via neural fields
• Continuous motion planning with differentiable collision handling

Innovation

• Implicit neural fields unify toolpath generation
• Continuous quaternion field optimizes multi-axis motion
• Differentiable collision handling via time-varying SDF