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  • Additive manufacturing of stiff and strong structures by leveraging printing-induced strength anisotropy in topology optimization
Title: Additive manufacturing of stiff and strong structures by leveraging printing-induced strength anisotropy in topology optimization
Anisotropy in additive manufacturing (AM), particularly in the material extrusion process, plays a crucial role in determining the actual structural performance, including the stiffness and strength of the printed parts. Unless accounted for, anisotropy can compromise the objective performance of topology-optimized structures and allow premature failures for stress-sensitive design domains. This study harnesses process-induced anisotropy in material extrusion-based 3D printing to design and fabricate stiff, strong, and lightweight structures using a two-step framework. First, an AM-oriented anisotropic strength-based topology optimization formulation optimizes the structural geometry and infill orientations, while assuming both anisotropic (i.e., transversely isotropic) and isotropic infill types as candidate material phases. The dissimilar stiffness and strength interpolation schemes in the formulation allow for the optimized allocation of anisotropic and isotropic material phases in the design domain while satisfying their respective Tsai–Wu and von Mises stress constraints. Second, a suitable fabrication methodology realizes anisotropic and isotropic material phases with appropriate infill density, controlled print path (i.e., infill directions), and strong interfaces of dissimilar material phases. Experimental investigations show up to 37% improved stiffness and 100% improved strength per mass for the optimized and fabricated structures. The anisotropic strength-based optimization improves load-carrying capacity by simultaneous infill alignment along the stress paths and topological adaptation in response to high stress concentration. The adopted interface fabrication methodology strengthens comparatively weaker anisotropic joints with minimal additional material usage and multi-axial infill patterns. Furthermore, numerically predicted failure locations agree with experimental observations. The demonstrated framework is general and can potentially be adopted for other additive manufacturing processes that exhibit anisotropy, such as fiber composites.  more » « less
Award ID(s):
2127134 2047692
PAR ID:
10492196
Author(s) / Creator(s):
;
Publisher / Repository:
ScienceDirect
Date Published:
Journal Name:
Additive Manufacturing
Volume:
75
Issue:
C
ISSN:
2214-8604
Page Range / eLocation ID:
103730
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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