Geometric Computing Based Enabler for Multi-axis Additive Manufacturing

  • Tianyu Zhang

Student thesis: Phd

Abstract

This doctoral thesis focuses on multi-axis additive manufacturing, where extrusion-based printing technology is the main research object. The system comprehensively covers the entire process of curved surface additive manufacturing, decomposing the three-dimensional model into two-dimensional slices, and further decomposing it into one-dimensional curves. The waypoint information in the workpiece coordinates system is transferred to the machine coordinate system through kinematics to complete the final implementation. Specifically, this project presents a comprehensive exploration of multi-axis additive manufacturing, highlighting the innovative concept of curved-layer printing within a multi-axis framework. To address the limitations of traditional additive manufacturing methods and take full advantage of multi-axis additive manufacturing technology, this research is structured into three interconnected components. The first component introduces the S3 slicing method. Complemented by a rotation-driven deformation framework and a developed robotic setup, this work significantly advances additive manufacturing technology by improving print quality, reinforcing model strength, and reducing support requirements. The second component delves into support generation for curved layer printing, proposing a skeleton-based algorithm that enhances efficiency and compactness while ensuring reliability through implicit presentation. This work contributes to the robustness and comprehensive application of multi-axis additive manufacturing techniques. The third component focuses on motion planning in multi-axis additive manufacturing (MAAM), addressing challenges related to singularity in rotational axes. The proposed singularity-aware motion planning pipeline can bring collision-free motion and better fabricational quality. These results are disseminated through published articles and integrated into collaborative projects with companies, demonstrating value in real manufacturing processes.
Date of Award1 Aug 2024
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorCharlie C.L. Wang (Supervisor)

Keywords

  • Computational Geometry
  • Robotics
  • Multi-Axis 3D printing

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