Evolutionary Decomposition for 3D Printing (2017)
Eric Yu, Jin Yeom, Cem C. Tutum, Etienne Vouga, Risto Miikkulainen
Capabilities of extrusion-based 3D-printers have progressed significantly, but complex forms are still challenging to print. One major problem is overhanging surfaces. These surfaces require extra support structure to be printed, wasting material and time. Furthermore, delicate parts of the object can be damaged when these structures are removed. One potential solution is to print the object in parts, but decomposition is difficult. This paper proposes an evolutionary approach for determining optimal object decompositions for 3D printing. Two alternative methods, with different complementary strengths, are tested: Multi-objective Genetic Algorithm (MOGA) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). MOGA is able to evolve a set of decompositions at variable complexity, i.e. number of pieces, whereas CMA-ES is able to find a limited number of comparable decompositions with significantly less computational time.
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To Appear In Proceedings of The Genetic and Evolutionary Computation Conference (GECCO 2017) (nominated for Best Paper Award), 8 pages, Berlin, Germany, July 2017.
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Risto Miikkulainen Faculty risto [at] cs utexas edu
Cem C Tutum Research Scientist tutum [at] cs utexas edu