TY - GEN
T1 - Path Optimization for Multi-material 3D Printing Using Self-organizing Maps
AU - Pinochet, Diego
AU - Tsamis, Alexandros
N1 - Funding Information:
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.
Publisher Copyright:
© 2022, Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - 3D printing
KW - Algorithms
KW - Functional gradient materials
KW - Machine learning
KW - Multi-material 3d printing
KW - Path optimization
UR - http://www.scopus.com/inward/record.url?scp=85127672890&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-1280-1_21
DO - 10.1007/978-981-19-1280-1_21
M3 - Conference contribution
AN - SCOPUS:85127672890
SN - 9789811912795
T3 - Communications in Computer and Information Science
SP - 329
EP - 343
BT - Computer-Aided Architectural Design. Design Imperatives
A2 - Gerber, David
A2 - Pantazis, Evangelos
A2 - Bogosian, Biayna
A2 - Nahmad, Alicia
A2 - Miltiadis, Constantinos
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Computer-Aided Architectural Design Futures, CAAD Futures 2021
Y2 - 16 July 2021 through 18 July 2021
ER -