Optimal 3D printing of complex objects in a 5–axis printer uri icon

abstract

  • This work was developed under the FIBR3D project titled Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI–01–0145–FEDER–016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
  • Three-dimensional (3D) printing, also known as additive manufacturing (AM), has emerged in the last decades as an innovative technology to build complex structures. It enables increasing design complexity and low-cost customization with a vast range of materials. AM capabilities contributed to a widespread acceptance of 3D printing in different industries such as the aerospace and the automotive. However, important issues and limitations still need to be addressed, namely in printing complex objects where supports and material roughness surface are to be minimized. In this work we consider a 5–axis printer with the three traditional xyz movements and two additional degrees of freedom on the printer table bed. These extra degrees of freedom (table bed rotation and tilt) allow printing of more complex objects, and we propose an approach which consists on the decomposition of complex objects into simpler parts, allowing each part to be printed in an optimal way. We aim to reduce the number of supports needed and attain high final object quality due to lower material surface roughness. The optimal printing direction (or, equivalently, rotation) and sequencing of the object parts is determined by solving a combinatorial sequencing optimization problem. All the local or global optimal parts rotations are obtained by solving a global optimization sub-problem for each part, and are taken as input parameters for the sequencing optimization problem. We provide a heuristic approach for the combinatorial sequencing optimization problem, and a multistart multisplit search methodology for computing all the local or global optimal parts rotations for the sub-problems.

publication date

  • April 2021