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International Conference on Computer-Aided Architectural Design Futures

CAAD Futures 2021: Computer-Aided Architectural Design. Design Imperatives: The Future is Now pp 329–343Cite as

Path Optimization for Multi-material 3D Printing Using Self-organizing Maps

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1465)

Abstract

Shape generation based on scalar fields opened up the space for new fabrication techniques bridging the digital and the physical through material computation. As an example, the development of voxelized methods for shape generation broadened the exploration of multi-material 3d printing and the use of Functionally Gradient Materials (FGM) through the creation of shapes based on their material properties known as Property representations (P-reps) as opposed to Boundary representations (B-reps) [1]. This paper proposes a novel approach for the fabrication of P-reps by generating optimized 3d printing paths by mapping shape internal stress into material distribution through a single optimized curve oriented to the fabrication of procedural shapes. By the use of a modified version of the traveling salesman problem (TSP), an optimized Spline is generated to map trajectories and material distribution into voxelized shape’s slices. As a result, we can obtain an optimized P-Rep G-code generation for multi-material 3d printing and explore the fabrication of P-Rep as FGMs based on material behavior.

Keywords

  • 3D printing
  • Path optimization
  • Multi-material 3d printing
  • Algorithms
  • Machine learning
  • Functional gradient materials

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Notes

  1. 1.

    Fusion deposition modeling.

  2. 2.

    https://github.com/tsamis/VSpace.

  3. 3.

    Michalatos, Panagiotis- Autodesk https://www.food4rhino.com/app/monolith.

  4. 4.

    In the experiments, we see that with the exception of the random distribution, the simple TSP performs poorly in length and self intersections number.

  5. 5.

    Through Radical component from Digital Space Exploration plugin for Grasshopper http://digitalstructures.mit.edu/page/tools#design-space-exploration-tool-suite-for-grasshopper.

  6. 6.

    Through Silvereye optimization plugin for Grasshopper. https://www.food4rhino.com/en/app/silvereye-pso-based-solver.

  7. 7.

    Through the use of Octopus for Grasshopper. https://www.food4rhino.com/en/app/octopus.

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Acknowledgements

I would like to thank Dr. Caitlin Mueller and Yijiang Huang for all the support and great lectures at 4.450 Computational Structural design and Optimization at MIT. Their knowledge and dedication motivated me to propose and develop an atypical project that helped me move forward with important areas of my Ph.D. research. Also, I would like to thank the Design and Computation group at MIT and the school of design of Adolfo Ibañez University for their valuable support.

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Authors and Affiliations

  1. School of Design, Adolfo Ibañez University, Santiago, Chile

    Diego Pinochet

  2. Massachusetts Institute of Technology, Cambridge, USA

    Diego Pinochet

  3. Rensselaer Polytechnic Institute, Troy, USA

    Alexandros Tsamis

Authors
  1. Diego Pinochet
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  2. Alexandros Tsamis
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Corresponding authors

Correspondence to Diego Pinochet or Alexandros Tsamis .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    David Gerber

  2. University of Southern California, Los Angeles, CA, USA

    Evangelos Pantazis

  3. Florida International University, Miami, FL, USA

    Biayna Bogosian

  4. University of Calgary, Calgary, Canada

    Alicia Nahmad

  5. Aalto University, Espoo, Finland

    Constantinos Miltiadis

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Pinochet, D., Tsamis, A. (2022). Path Optimization for Multi-material 3D Printing Using Self-organizing Maps. In: Gerber, D., Pantazis, E., Bogosian, B., Nahmad, A., Miltiadis, C. (eds) Computer-Aided Architectural Design. Design Imperatives: The Future is Now. CAAD Futures 2021. Communications in Computer and Information Science, vol 1465. Springer, Singapore. https://doi.org/10.1007/978-981-19-1280-1_21

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  • DOI: https://doi.org/10.1007/978-981-19-1280-1_21

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