Accelerating 3D printing for surface wettability research

The wettability of a surface is affected by its physical and chemical properties, but it can be modulated by patterning it. Researchers use many different techniques for surface patterning, each one with different trade-offs in terms of cost, flexibility, convenience and realizable geometries. Very high-resolution 3D printing technologies (such as stereolithography by two-photon absorption) have the potential to greatly increase the range of realizable surface geometries, but they are currently not in wide use because they are too slow for printing the relative large surface areas required for wetting experiments. To enable the use of these 3D techniques, we are developing new slicing algorithms able to speed up 3D-printing technologies.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Multiresolution Layered Manufacturing

PURPOSE: Two-photon polymerization (TPP) has become one of the most popular techniques for stereolithography at very high resolutions. When printing relatively large structures at high resolutions, one of the main limiting factors is the printing time. The goal of this work is to present a new slicing algorithm to minimize printing times. DESIGN/METHODOLOGY/APPROACH: Typically, slicing algorithms used for TPP do not take into account the fact that TPP can print at a range of resolutions (i.e. with different heights and diameters) by varying parameters such as exposure time, laser power, photoresist properties, and optical arrangements. This work presents Multiresolution Layered Manufacturing (MLM), a novel slicing algorithm that processes 3D structures to separate parts manufacturable at low resolution from those that require a higher resolution. FINDINGS: MLM can significantly reduce the printing time of 3D structures at high resolutions. The maximum theoretical speed-up depends on the range of printing resolutions, but the effective speed-up also depends on the geometry of each 3D structure. RESEARCH LIMITATIONS/IMPLICATIONS: MLM opens the possibility to significantly decrease printing times, potentially opening the use of TPP to new applications in many disciplines such as microfluidics, metamaterial research or wettability. ORIGINALITY/VALUE: There are many instances of previous research on printing at several resolutions. However, in most cases, the toolpaths have to be manually arranged. In some cases, previous research also automates the generation of toolpaths, but they are limited in various ways. MLM is the first algorithm to comprehensively solve this problem for a wide range of true 3D structures.NANO3D (a BEWARE Fellowship from the Walloon Region, Belgium, part of the Marie Curie Programme of the ERC). IAP 7/38 MicroMAST (Interuniversity Attraction Poles Programme from the Belgian Science Policy Office, the Walloon Region and the FNRS)

Propuesta de un marco de trabajo para el diseño de procesos de desarrollo bioinspirados basados en estructuras tensegritales

Los algoritmos evolutivos se inspiran en la evolución biológica como metáfora de su modus operandi: se consideran poblaciones (conjuntos) de individuos (soluciones a problemas), donde cada individuo se caracteriza por su genotipo (conjunto de parámetros que componen la solución), y se le asigna un fitness que mide cuán adaptado es (cómo de buena es la solución). La cuestión es que las distintas clases de algoritmos evolutivos aparecieron en la segunda mitad del siglo XX, en una época en la que la complejidad de los seres vivos se interpretaba como la complejidad de sus correspondientes genotipos [35]. Así, en los algoritmos evolutivos se suele poner el acento sobre el diseño de un buen genotipo, y la transformación de genotipo en fenotipo suele ser trivial, siguiendo el paradigma de esta interpretación biológica. Actualmente, se está descifrando el enigma del desarrollo de los seres vivos poco a poco, y el genotipo va perdiendo paulatinamente su papel estelar. La complejidad de los seres vivos se asigna cada vez más a su proceso de desarrollo, que el genotipo modula y coordina antes que dirige [35]. Esto ha motivado la aplicación de este paradigma a diversas disciplinas, como redes neuronales [1, 17], agentes autónomos [16], o diseño de hardware [15]. En este trabajo, nos proponemos explorar este paradigma desde el punto de vista del diseño computacional de estructuras. Concretando, en este trabajo pretende esbozar un marco de trabajo con el que estudiar procesos de desarrollo bioinspirados de estructuras tensegritales. El carácter de este trabajo se puede entender como una exploración del espacio de posibilidades en los estadios iniciales de la tesis

Vida artificial: en la encrucijada

En las últimas décadas, la línea divisoria entre la Biología y las Ciencias de la Computación se ha ido difuminando paulatinamente. Por un lado, una gran cantidad de métodos computacionales (colectivamente conocidos como métodos bioinspirados) han surgido de una amplia variedad de sistemas o procesos biológicos, como los algoritmos evolutivos, las redes neuronales artificiales o la computación basada en membranas. Por otro lado, el avance científico en la inmensa mayoría de las disciplinas de la Biología ha abocado a los investigadores a depender completamente de sistemas informáticos y métodos computacionales para el procesado, análisis y síntesis de la información, como es el caso de la Genómica, la Proteómica y otras disciplinas biológicas auxiliadas por las herramientas de la Bioinformática. Mención aparte merece la Biología Computacional, que a grandes rasgos consiste en el desarrollo de técnicas computacionales para la simulación y el análisis de sistemas o procesos biológicos a múltiples niveles. A las conocidas técnicas experimentales comúnmente llamadas in vivo (con el organismo vivo) e in vitro (con células o tejidos cultivados en material de laboratorio), la Biología Computacional añade una tercera: in silico, o experimentación mediante simulación informática. Pertenecen a la Biología Computacional disciplinas tan dispares como la Neurociencia Computacional, la Biología de Sistemas o la Proteómica (en lo que se refiere a la predicción del plegado y la función de las proteínas). Es en este contexto en el que podemos introducir la Vida Artificial como una disciplina que intenta entender los fenómenos asociados a la vida a través de modelos computacionales

Experiencias en implementación de lenguajes funcionales: la máquina STG

Descripción de un back-end para compilación de lenguajes funcionales

AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía

On the atomic decomposition length of graphs and tensegrities

In this study, a complexity measure for graphs and tensegrities is proposed, based on the concept of atomic decomposition.We state several results on the relationship between atomic decompositions and spaces of self-stresses for generically rigid graphs, and study the computational complexity of finding atomic decompositions of minimal length.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. The present work was developed during a stay of Jose David Fernández Rodríguez at the Universidad de Alcalá, partially supported by the Departamento de Matemáticas de la Universidad de Alcalá. Jose David Fernández Rodríguez is partially supported by the Ministerio de Educación del Gobierno de España through a FPU grant (AP2007-03704). David Orden Martín is partially supported by grants MTM2008- 04699-C03-02/MTM and MTM2011-22792

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

MREM: Una red recurrente con estados neuronales generalizados

Presentación de una generalización de las redes de Hopfiel