Journal ArticleDOI
Optimal design of functionally graded materials using a procedural model and particle swarm optimization
TLDR
It is demonstrated, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials.Abstract:
A new method for the optimal design of Functionally Graded Materials (FGM) is proposed in this paper. Instead of using the widely used explicit functional models, a feature tree based procedural model is proposed to represent generic material heterogeneities. A procedural model of this sort allows more than one explicit function to be incorporated to describe versatile material gradations and the material composition at a given location is no longer computed by simple evaluation of an analytic function, but obtained by execution of customizable procedures. This enables generic and diverse types of material variations to be represented, and most importantly, by a reasonably small number of design variables. The descriptive flexibility in the material heterogeneity formulation as well as the low dimensionality of the design vectors help facilitate the optimal design of functionally graded materials. Using the nature-inspired Particle Swarm Optimization (PSO) method, functionally graded materials with generic distributions can be efficiently optimized. We demonstrate, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials. The underlying reason for this performance boost is also elucidated with the help of benchmarked examples.read more
Citations
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Journal ArticleDOI
A survey of the design methods for additive manufacturing to improve functional performance
Yunlong Tang,Yaoyao Fiona Zhao +1 more
TL;DR: A comprehensive review of the state-of-the-art design methods for additive manufacturing technologies to improve functional performance is provided in this article, where a novel classification method is used to categorize existing design methods.
Journal ArticleDOI
A review on optimization of composite structures Part II: Functionally graded materials
TL;DR: In this paper, a review of the literature on FG structures is presented and the key outputs of each publication are represented to make this article an asset source for mechanical engineers since there has not been any comprehensive review article on optimal designs of FG structures in the literature.
Journal ArticleDOI
Spec2Fab: a reducer-tuner model for translating specifications to 3D prints
TL;DR: This paper proposes an abstraction mechanism that simplifies the design, development, implementation, and reuse of algorithms for multi-material 3D printing by providing an application programming interface for specifying the desired object and for defining parameters for the reducer tree and tuner network.
Journal ArticleDOI
Data-driven finite elements for geometry and material design
TL;DR: Data-Driven Finite Elements is introduced, which constructs a metamaterial library which is reusable for subsequent simulations, regardless of object geometry and/or material composition, and is applicable to non-linear material models.
Journal ArticleDOI
Material optimization of functionally graded plates using deep neural network and modified symbiotic organisms search for eigenvalue problems
TL;DR: This paper aims at improving computational cost enhanced by a new combination of deep neural network (DNN) and modified symbiotic organisms search (mSOS) algorithm for optimal material distribution of functionally graded (FG) plates.
References
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