Automatically Partitioning 3D Objects for Larger Printing Volumes

  • Marcelo Thielo Unipampa
  • Rafael Jarczewski Unipampa
  • Gustavo Tamiosso Unipampa
  • Douglas Dullius Unipampa
  • Eliezer Flores Unipampa


The additive manufacturing(AM) market, popularly known as 3D printing, has proven to be useful and revolutionary for various industry segments. However, 3D printing machines have a physical limitation linked to their printing volume, which leads to the partitioning of objects for larger printing. This work aims to present a 3D object partitioning approach taking into account the desired printing dimensions and the resulting parts' curvatures. We also developed a prototype that receives a 3D object as input and returns a set of cubic partitions, each one seeking maximum advantage of the print volume when possible, through minimization of a cost function. Our solution has an object reader, a visualization interface and the functionality to export the set of objects generated. We also provide a cost function to evaluate characteristics defining the quality of a partitioning, with tests performed manually and employing a genetic algorithm for optimization. As a future work, we intend to evaluate the proposed cost function with more complex objects as well as improving the optimization algorithm so to automatically obtain a set of parameters for partitioning 3D objects for high quality 3D printing.
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THIELO, Marcelo; JARCZEWSKI, Rafael; TAMIOSSO, Gustavo; DULLIUS, Douglas; FLORES, Eliezer. Automatically Partitioning 3D Objects for Larger Printing Volumes. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 36. , 2023, Rio Grande/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 43-48.