Topology optimization of 3D self-supporting structures for additive manufacturing

By Matthijs Langelaar. 2016

450 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/849da2d967b485b010c9043af8dfc2931837215c

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In Civil and Structural Engineering

In Computational Theory and Mathematics

In Industrial and Manufacturing Engineering

Citations in the corpus

3D printing of Aluminium alloys: Additive Manufacturing of Aluminium alloys using selective laser melting (2019, background): “However, optimising solely towards reducing the use of support structures can impose undesirable penalties on the part’s functional performance [86].”
Self-supporting structure design in additive manufacturing through explicit topology optimization (2017, background): “The problem associated with the filter approach invented in [21] and [22] is that the filter functions introduced are highly nonlinear and dependent on the regular mesh used for finite element analysis.”
Deep Generative Models in Engineering Design: A Review (2021, background): “Common areas of application include solid mechanics [114, 115], fluid dynamics [116, 117], additive manufacturing [118, 119], and heat transfer [120, 121].”
Undercut and overhang angle control in topology optimization: A density gradient based integral approach (2017, methodology): “A layerwise filtering scheme was developed in [30] for designing self-supporting structures.”
An additive manufacturing filter for topology optimization of print-ready designs (2016, background): “The extension to 3D is straightforward but will be discussed elsewhere (Langelaar 2016).”
Fatigue of additively manufactured 316L stainless steel: The influence of porosity and surface roughness (2019, unknown): “4,5 In recent years, AM is becoming accepted as a manufacturing method for industries like aerospace, medical, energy, and auto-motive.”
Shape optimization of a layer by layer mechanical constraint for additive manufacturing (2017, background): “For instance, if f is smooth, it is well known (see [3]) that the objective (2) is shape differentiable when deformations are restrained to (cid:7) k , k ≥ 1, and that its shape derivative reads:”
Current and future trends in topology optimization for additive manufacturing (2018, methodology): “Langelaar (2017, 2016) proposed another density filter which achieves a similar overhang-free effect compared to (Gaynor and Guest 2016).”
Support Structures for Additive Manufacturing: A Review (2018, background): “Langelaar [98] proposed a topology optimization formulation that can exclude unprintable geometries from the design space, resulting in fully self-supporting optimized designs.”