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. 2020 Apr 10;6(2):260.
doi: 10.18063/ijb.v6i2.260.. eCollection 2020.

Effects of Topology Optimization in Multimaterial 3D Bioprinting of Soft Actuators

Ali Zolfagharian  1 , Martin Denk  2 , Abbas Z Kouzani  1 , Mahdi Bodaghi  3 , Saeid Nahavandi  4 , Akif Kaynak  4
Affiliations

Effects of Topology Optimization in Multimaterial 3D Bioprinting of Soft Actuators

Ali Zolfagharian et al. Int J Bioprint. .

Erratum in

  • ERRATUM.
    [No authors listed] [No authors listed] Int J Bioprint. 2020 Sep 17;6(4):309. doi: 10.18063/ijb.v6i4.309. eCollection 2020. Int J Bioprint. 2020. PMID: 33102924 Free PMC article.

Abstract

Recently, there has been a proliferation of soft robots and actuators that exhibit improved capabilities and adaptability through three-dimensional (3D) bioprinting. Flexibility and shape recovery attributes of stimuli-responsive polymers as the main components in the production of these dynamic structures enable soft manipulations in fragile environments, with potential applications in biomedical and food sectors. Topology optimization (TO), when used in conjunction with 3D bioprinting with optimal design features, offers new capabilities for efficient performance in compliant mechanisms. In this paper, multimaterial TO analysis is used to improve and control the bending performance of a bioprinted soft actuator with electrolytic stimulation. The multimaterial actuator performance is evaluated by the amplitude and rate of bending motion and compared with the single material printed actuator. The results demonstrated the efficacy of multimaterial 3D bioprinting optimization for the rate of actuation and bending.

Keywords: Multimaterial; Soft actuator; Soft robot; Three-dimensional bioprinting; Topology optimization.

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Figures

Figure 1
Figure 1
Computer-aided design models of the actuators (A) two-material three-dimensional printing, (B) mechanical forces and boundary constraints.
Figure 2
Figure 2
Schematics of two-material topology optimization filtering.
Figure 3
Figure 3
Two-material topology optimization algorithm utilized in this study.
Figure 4
Figure 4
Two-material topology optimization layers’ configuration of bioprinted actuator.
Figure 5
Figure 5
Objective and volume fraction results over iterations at different layers with the constant total volume fraction (A) layer 1; B) layer 2; and (C) layer 3.
Figure 6
Figure 6
(A) Three-dimensional bioprinting; (B) two-material topology optimized bioprinted actuators.
Figure 7
Figure 7
(A) Bending index of the actuator and (B) the measurement set up.
Figure 8
Figure 8
(A and B) Experimental results of three-dimensional-printed actuators entirely made of material 1 and material 2 under cyclic input signal of 8 V.
Figure 9
Figure 9
A schematic comparison of bioprinted actuators end point after 40 s; (A) material 2; (B) material 1; (C) two-material topology optimized.
Figure 10
Figure 10
(A and B) Deflection of bioprinted actuators under 8 V input signal. The standard deviations of the average triplet sample results were calculated as 3.21°, 2.88°, and 2.49° for material 1, two-material topology optimization, and material 2 actuators.
See this image and copyright information in PMC

References

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