An Evolutionary approach to microstructure optimisation of stereolithographic models.

Abstract- The aim of this work is to utilize an evolutationary algorithm to evolve the microstructure of an object created by a stereolithography machine. This should be optimised to be able to withstand loads applied to it while at the same time minimizing its overall weight. A two part algorithm is proposed which evolves the topology of the structure with a genetic algorithm, while calculating the details of the shape with a separate, deterministic, iterative process derived from standard principles of structural engineering. The division of the method into two separate processes allows both flexibility to changed design parameters without the need for re-evolution, and scalability of the microstructure to manufacture objects of increasing size. The results show that a structure was evolved that was both light and stable. The overall shape of the evolved lattice resembled a honeycomb structure that also satisfied the restrictions imposed by the stereolithography machine.

Optimising continuous microstructures: a comparison of gradient-based and stochastic methods

This work compares the use of a deterministic gradient based search with a stochastic genetic algorithm to optimise the geometry of a space frame structure. The goal is not necessarily to find a global optimum, but instead to derive a confident approximation of fitness to be used in a second optimisation of topology. The results show that although the genetic algorithm searches the space more broadly, and this space has several global optima, gradient descent achieves similar fitnesses with equal confidence. The gradient descent algorithm is advantageous however, as it is deterministic and results in a lower computational cost

Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines

Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods

Procedural function-based spatial microstructures

We propose a new approach to modelling heterogeneous objects containing internal spatial geometric structures with size of details orders of magnitude smaller than the overall size of the object. The proposed function-based procedural representation provides a compact, precise, and arbitrarily parameterized model allowing for modelling coherent microstructures, which can undergo blending, offsetting, deformations, and other geometric operations, and can be directly rendered and fabricated without generating any auxiliary representations. In particular, modelling of regular lattices and porous media is discussed and illustrated. Examples of microstructure models rendering and fabrication using a variety of digital fabrication machines and materials are presented

Curved photovoltaic surface optimization for BIPV: an evolutionary approach based on solar radiation simulation

The paper attempts to address the problem of the optimization of curved photovoltaic surfaces that may become the alternatives of the traditional flat PV surfaces in BIPV. The proposed method combines three parts: an evolutionary algorithm (Genetic Algorithm) for optimization, an adaptive simulation tool based on Hay’s anisotropic radiation model, and a comparison module for analysis. The cladding problem of curved PV modules is geometrically solved that may serve as the starting point for practical links with architectural and PV engineering considerations. A systematical approach is established for the comparisons between the curved and flat surfaces according to various surface angle-settings (tilt angle and azimuth angle) and solar condition setups (latitude and radiation mode), involving specific 3D and 2D radiation plots and related data recording system. Through a series of experiments, the paper presents the characteristics of curved surfaces in terms of the solar energy gain, such as the stabilization characteristic and the mean total annual solar radiation, etc. The capacities of the algorithm are confirmed and several findings are discussed and concluded so as to be used as references for BIPV projects and other practical photovoltaic appliances

Projecto inteligente de scaffolds obtidos por prototipagem rápida

Doutoramento em Engenharia MecânicaA engenharia de tecidos é um domínio tecnológico emergente em rápido desenvolvimento que se destina a produzir substitutos viáveis para a restauração, manutenção ou melhoria da função dos tecidos ou órgãos humanos. Uma das estratégias mais predominantes em engenharia de tecidos envolve crescimento celular sobre matrizes de suporte (scaffolds), biocompatíveis e biodegradáveis. Estas matrizes devem possuir não só elevadas propriedades mecânicas e vasculares, mas também uma elevada porosidade. Devido à incompatibilidade destes dois parâmetros, é necessário desenvolver estratégias de simulação de forma a obter estruturas optimizadas. A previsão real das propriedades mecânicas, vasculares e topológicas das matrizes de suporte, produzidas por técnicas de biofabricação, é muito importante para as diversas aplicações em engenharia de tecidos. A presente dissertação apresenta o estado da arte da engenharia de tecidos, bem como as técnicas de biofabricação envolvidas na produção de matrizes de suporte. Para o design optimizado de matrizes de suporte foi adoptada uma metodologia de design baseada tanto em métodos de elementos finitos para o cálculo do comportamento mecânico, vascular e as optimizações topológicas, como em métodos analíticos para a validação das simulações estruturais utilizando dados experimentais. Considerando que as matrizes de suporte são estruturas elementares do tipo LEGO, dois tipos de famílias foram consideradas, superfícies não periódicas e as superfícies triplas periódicas que descrevem superfícies naturais. Os objectivos principais desta dissertação são: i) avaliar as técnicas existentes de engenharia de tecidos; ii) avaliar as técnicas existentes de biofabricação para a produção de matrizes de suporte; iii) avaliar o desempenho e comportamento das matrizes de suporte; iv) implementar uma metodologia de design de matrizes de suporte em variáveis tais como a porosidade, geometria e comportamento mecânico e vascular por forma a auxiliar o processo de design; e por fim, v) validar experimentalmente a metodologia adoptada.The design of optimized scaffolds for tissue engineering is a key topic of research, as the complex macro- and micro- architectures required for a scaffold depends not only on the mechanical properties, but also on the physical and molecular queues of the surrounding tissue within the defect site. Thus, the prediction of optimal features for tissue engineering scaffolds is very important for its mechanical, vascular or topological properties. The relationship between high scaffold porosity and high mechanical properties is contradictory, as it becomes even more complex due to the scaffold degradation process. A scaffold design strategy was developed, based on the finite element method, to optimise the scaffold design regarding the mechanical and vascular properties as a function of porosity. Scaffolds can be considered as a LEGO structure formed by an association of small elementary units or blocks. In this research work, two types of family elementary scaffold units were considered: non-triple periodic minimal surfaces and triple periodic minimal surfaces that describe natural existing surfaces. The main objectives of this research work are: i) The evaluation of the Tissue Engineering methodology and its different strategies; ii) The evaluation of the existing biofabrication technologies used to produce tissue engineering scaffolds; iii) The evaluation of the scaffold’s requirements involved in the scaffold’s design; iv) The development of an integrated design strategy based on material, porosity, geometry, mechanical and vascular properties, in order to aid the scaffold design process; and v) The experimental validation of the adopted design strategy

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

Topological Self-Organisation: Using a particle-spring system simulation to generate structural space-filling lattices

The problem being addressed relates to the filling of a certain volume with a structural space frame network lattice consisting of a given number of nodes. A method is proposed that comprises a generative algorithm including a physical dynamic simulation of particle-spring system. The algorithm is able to arrange nodes in space and establish connections among them through local rules of self-organisation, thus producing space frame topologies. In order to determine the appropriateness of the method, an experiment is conducted that involves testing the algorithm in the case of filling the volume of a cube with multiple numbers of nodes. The geometrical, topological and structural aspects of the generated lattices are analysed and discussed. The results indicate that the method is capable of generating efficient space frame topologies that fill spatial envelopes