A COMPREHENSIVE ANALYSIS OF NON-PLANAR TOOLPATH OPTIMIZATION IN MULTI-AXIS 3D PRINTING: EVALUATING THE EFFICIENCY OF CURVED LAYER SLICING STRATEGIES

Abstract

Non-planar toolpath optimization has emerged as a pivotal advancement in multi-axis additive manufacturing, offering transformative potential for overcoming the limitations of traditional layer-by-layer 3D printing. This comprehensive analysis investigates the current state-of-the-art in non-planar toolpath generation and curved layer slicing strategies, focusing on their efficacy in enhancing surface finish, structural integrity, and overall print efficiency. In Asia, Japanese and South Korean industries have rapidly adopted 5-axis AM systems for high-precision mold and die fabrication, leveraging robotic arms for deposition control. China has emerged as a global leader in large-scale multi-axis printing, exemplified by the use of robotic extruders in constructing concrete buildings with non-planar reinforcement layers. The United States, through institutions like MIT, Carnegie Mellon, and Oak Ridge National Laboratory, continues to push boundaries in toolpath generation algorithms, real-time sensor feedback, and hybrid subtractive-additive platforms By synthesizing recent developments in kinematic modeling, machine control, and slicing algorithms, this study critically examines the computational and mechanical complexities introduced by multi-axis motion. The evaluation includes a comparative assessment of adaptive slicing, curved layer deposition, and hybrid manufacturing approaches, considering both simulation-based and experimental findings. Furthermore, the analysis highlights the challenges associated with collision avoidance, motion planning, and printhead orientation, particularly in 5-axis and 6-axis systems. Emphasis is placed on the interplay between geometric complexity and slicing strategy, demonstrating how optimized curved layers can reduce support material usage, improve print continuity, and expand the design space for functional parts. The study concludes with a discussion on future research directions, including the integration of AI-based optimization techniques, real-time sensing, and feedback-driven path planning, aiming to foster more intelligent and autonomous multi-axis 3D printing systems. This work serves as a foundational reference for researchers and engineers seeking to improve the fidelity, speed, and versatility of advanced additive manufacturing processes.