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Title: Lightweight parametric design optimization for 4D printed parts
Authors: Paz, Rubén 
Pei, Eujin
Monzón Verona, Mario Domingo 
Ortega Andrade, Francisco 
Suárez-García, Luis A.
UNESCO Clasification: 331324 Maquinaria de impresión y reproducción
3313 Tecnología e ingeniería mecánicas
Keywords: Finite element analysis (FEA)
Genetic algorithms (GAs)
Lightweight design optimization
Shape memory polymers (SMPs), et al
Issue Date: 2017
Journal: Integrated Computer-Aided Engineering 
Abstract: 4D printing is a technology that combines the capabilities of 3D printing with materials that can transform its geometry after being produced (e.g. Shape Memory Polymers). These advanced materials allow shape change by applying different stimulus such as heating. A 4D printed part will usually have 2 different shapes: a programmed shape (before the stimulus is applied), and the original shape (which is recovered once the stimulus has been applied). Lightweight parametric optimization techniques are used to find the best combination of design variables to reduce weight and lower manufacturing costs. However, current optimization techniques available in commercial 3D CAD software are not prepared for optimization of multiple shapes. The fundamental research question is how to optimize a design that will have different shapes with different boundary conditions and requirements. This paper presents a new lightweight parametric optimization method to solve this limitation. The method combines the Latin Hypercube design of experiments, Kriging metamodel and specifically designed genetic algorithms. The optimization strategy was implemented and automated using a CAD software. This method recognizes both shapes of the part as a single design and allows the lightweight parametric optimization to retain the minimum mechanical properties for both shapes.
ISSN: 1069-2509
DOI: 10.3233/ICA-170543
Source: Integrated Computer-Aided Engineering[ISSN 1069-2509],v. 24, p. 225-240
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