Orientador: Raquel Gonçalves / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-06T00:21:33Z (GMT). No. of bitstreams: 1 Silva_SergioAugustoMelloda_D.pdf: 3060293 bytes, checksum: 3514c54e017be522e62c07231642a540 (MD5) Previous issue date: 2003 / Resumo: As tecnologias para produção das chapas denominadas ¿Medium Density Fiberboard¿ (MDF), confeccionadas com fibras de madeiras de reflorestamento de baixo custo e resinas fenólicas provenientes de uréia, representam para vários setores industriais uma possibilidade de agregar valores às espécies com pouco interesse comercial, possibilitando para os seguimentos industriais investimento promissores. Um aspecto importante a ser considerado sobre a utilização de resinas fenólicas na confecção de chapas MDF é que produzem emissões tóxicas poluidoras da natureza e nocivas ao ser humano. Dentro deste contexto, de acordo com o ¿Protocolo de Quioto¿ de 1997, os países industrializados precisam diminuir suas emissões combinadas de gases de efeito estufa em pelo menos 5 % até 2012. Considerando-se as exigências do ¿Protocolo de Quioto¿, este trabalho objetivou avaliar o desempenho de chapas MDF confeccionadas com poliuretana (PU) derivada do óleo de mamona, pois este produto apresenta baixos teores de toxidez sendo classificado como não poluente e não tóxico ao ser humano. A caracterização das chapas foi realizada considerando-se a utilização do equipamento de ultra-som da Marca Steinkamp modelo BP7 com transdutoes de 45 kHz e a realização de ensaios físicos e mecânicos propostos pela EuroMDFBoard - EMB. A avaliação desenvolveu-se em três fases distintas: 1. Na fase I foram realizados estudos exploratórios com o objetivo de verificar as características do PU sendo utilizado como adesivo para confecção de chapas de Pinus caribaea. Nesta fase foram confeccionadas chapas com 5 e 10 % de PU3070, que de acordo com a caracterização física e mecânica observou-se que as chapas apresentaram resistências compatíveis com as exigências da EMB, entretanto, a forte exalação de solvente inviabilizou seu manuseio do PU3070. 2. Na fase II foram confeccionadas chapas com fibras de Pinus caribaea e Eucalyptus grandis com uma nova síntese de adesivo, denominada neste trabalho de PU7030. A caracterização física e mecânica determinou valores de resistências compatíveis com as exigências da EMB. Entretanto, verificou-se a necessidade de se ajustar o teor de umidade a temperatura e a pressão de prensagem. 3. Na fase III novas chapas foram confeccionadas com fibras de Pinus caribea e Eucalyptus grandis modificando-se o teor de umidade, a temperatura e a pressão de prensagem. Com o resultado da caracterização física e mecânica, observou-se que as alterações nas umidades, temperatura e pressão de prensagem propiciaram resistências compatíveis com a EMB, com vantagens de utilização de menores teores de PU7030 e diminuição na temperatura de prensagem, representando economia no consumo de energia. Outro aspecto muito importante estudado neste trabalho foi a caracterização das chapas utilizando ensaios não-destrutivos. Neste caso foram realizadas medições, por meio de equipamento de ultra-som, utilizando-se transdutores de faces exponenciais e planas aplicados nas direções x, y e z das chapas. A partir das medições dos tempos de propagação das ondas ultra-sônicas determinaram-se as velocidades das ondas e as constantes dinâmicas das chapas. Estas variáveis foram comparadas com as propriedades de resistência das chapas obtidas nos ensaios estáticos. Em seguida desenvolveu-se análise estatística buscando-se avaliar as correlações entre os resultados de ensaios não-destrutivos e destrutivos tendo sido possível concluir que a utilização dos métodos de ensaios não-destrutivos é viável para inferir sobre as propriedades físicas e mecânicas das chapas. Com relação à utilização da PU7030, concluiu-se que essa poliuretana possibilita a substituição da resina fenólica na confecção das chapas MDF / Abstract: While employing low cost reforestation lumber, the technologies of the fabrication of fiber plates denominated ¿Medium Density Fiberboard¿ (MDF) offer to several industrial sectors the opportunity to aggregate worth to species with little or no commercial interest, opening to them the possibility of promising profitable investments. However, the phenolic resins used in the production of MDF plates are well-known pollutants to the environment and therefore harmful to the human beings in such a way that environmentally friendly alternatives have been eagerly sought. According to that, the present work has been aimed to evaluate the physical, chemical and mechanical properties of MDF manufactured with Polyurethane Adhesive (PU) derived from Castor Oil, due to the low toxicity and to the non-polluting characteristics of the resulting adhesive. The characterization of the plates has been accomplished with the help of Steinkamp BP7 ultrasonic equipment using 45 kHz transducers and the physical and mechanical tests recommended by Euro MDF Board - EMB. The work has been developed along three distinct phases. Initially a better understanding of the characteristics of the polyurethane adhesive derived from castor oil has been acquired. It has been learnt that the adhesive exhibits the main characteristic of a monocomponent synthesis, stabilized by air humidity. During that phase, plates with 5% and 10% of polyurethane adhesive have been manufactured, called Adhesive PU3070 in this work, corresponding to 30% of solids and 70% of solvents. The properties of the plates manufactured with this synthesis have been determined and it has been found that although they have met EMB demands, the strong exhalation of solvent has ruled out its usage for MDF manufacture. As for the second phase, fiber plates have been produced with a new synthesis, called Adhesive PU7030, that is, 70% of solids and 30% of solvents. Again mechanical properties have satisfied EMB regulations. However, during the tests, it has been noticed that in order to control the quality of the final product, a fine adjusting to the values of some important Variables in the manufacture process, such as humidity level, temperature and pressing pressure, would be necessary. Accordingly, in the last phase, using Pinus caribea as fiber material, three experimental conditions have been elaborated, one for each controlled Variable, and repeated again for Eucalyptus gandis. Resulting from the adjusting of the variables, it has been observed that during the manufacture process, plates using much less adhesive than the former standards still have satisfied EMB demands fully, with advantages of a significant decrease in the pressing temperature and the corresponding energy savings. The characterization of the plates using non-destructive tests has been an important feature of the present work. Measurements have been performed in the plates by means of ultrasound equipment, using exponential as well as plane face transducers. From the readings, the propagation times of the ultrasonic waves have been evaluated, allowing to the determination of the propagation velocity of the ultrasonic waves through the material and consequently its dynamic parameters, which have been correlated to the mechanical properties of the plates, by means of a suitable statistic model. It has been concluded from the results that non-destructive test methods could as well be employed for the characterization of the physical and mechanical properties of the plates / Doutorado / Construções Rurais / Doutor em Engenharia Agrícola

Abstract Barkhausen noise measurement is an intriguing non-destructive testing method suitable for ferromagnetic materials. It is based on the stochastic movements of magnetic domain walls when the tested sample is placed in an external varying magnetic field. Barkhausen noise is typically utilised so that some features are calculated from the signal and then compared with the studied material properties. Typical features are, for example, the root-mean-square value (RMS), peak height, width and position. Better utilisation of the method, however, requires quantitative predictions of material properties. The aim of this thesis is to study and select a suitable methodology for the quantitative prediction of material properties based on Barkhausen noise measurement. The prediction considered is divided into four steps: feature generation, feature selection, model identification and model validation. In feature generation, a large set of features is calculated with different mathematical procedures. This feature set is explored in the feature selection step to find the most significant features in terms of predictions. A model with the selected features is identified and some independent data are usually used for model validation. This thesis presents the developed procedures required in feature generation and the results of the studies using different feature selection strategies and modelling techniques. The studied feature selection methods are forward selection, simulated annealing and genetic algorithms. In addition, two-step algorithms are investigated where a pre-selection step is used before the actual selection. The modelling techniques used are multivariable linear regression, partial least squares regression, principal component regression and artificial neural networks. The studies also consider the use and effect of different objective functions. The results of the studies show that the proposed modelling scheme can be used for the prediction task. The models identified mainly include reasonable terms and the prediction accuracy is fairly good considering the challenge. However, the application of Barkhausen noise measurement is very case-dependent and thus conflicts may occur. Furthermore, the changes in unmeasured material properties may lead to the unexpected behaviour of some features. The results show that linear models are adequate for capturing the major interactions between material properties and Barkhausen noise but indicate that the use of neural networks would lead to better model performance. The results also show that genetic algorithms give better selection results but at the expense of the computational cost. / Tiivistelmä Barkhausen-kohina-mittaus on ferromagneettisille materiaaleille soveltuva materiaalia rikkomaton testausmenetelmä. Mittaus perustuu magneettisten alueiden välisten rajapintojen stokastisiin liikkeisiin, kun testattava kappale asetetaan vaihtuvaan magneettikenttään. Tyypillisesti Barkhausen-kohina-mittaussignaalista lasketaan piirteitä, joita sitten verrataan tutkittaviin materiaaliominaisuuksiin. Usein käytettyjä piirteitä ovat signaalin keskineliön neliöjuuri (RMS-arvo) sekä piikin korkeus, leveys ja paikka. Menetelmää voidaan soveltaa paremmin, jos tutkittavia materiaaliominaisuuksia voidaan ennustaa kvantitatiivisesti. Tämän tutkimuksen tavoitteena on tutkia ja valita menetelmiä, jotka soveltuvat materiaaliominaisuuksien kvantitatiiviseen ennustamiseen Barkhausen-kohina-mittauksen perusteella. Ennustusmallit luodaan neljässä vaiheessa: piirteiden laskenta, piirteiden valinta, mallin identifiointi ja mallin validointi. Piirteiden laskennassa yhdistellään erilaisia matemaattisia laskutoimituksia, joista tuloksena saadaan suuri joukko erilaisia piirteitä. Tästä joukosta valitaan ennustukseen soveltuvimmat piirteiden valinta -vaiheessa. Tämän jälkeen ennustusmalli identifioidaan ja viimeisessä vaiheessa sen toimivuus todennetaan riippumattomalla testausaineistolla. Väitöskirjassa esitetään piirteiden laskentaan kehitettyjä algoritmeja sekä mallinnustuloksia käytettäessä erilaisia piirteiden valintamenetelmiä ja mallinnustekniikoita. Tutkitut valintamenetelmät ovat eteenpäin valinta, taaksepäin eliminointi, simuloitu jäähtyminen ja geneettiset algoritmit. Väitöskirjassa esitellään myös kaksivaiheisia valintamenettelyjä, joissa ennen varsinaista piirteiden valintaa suoritetaan esivalinta. Käytetyt mallinnustekniikat ovat monimuuttujaregressio, osittainen pienimmän neliösumman regressio, pääkomponenttiregressio ja neuroverkot. Tarkasteluissa huomioidaan myös erilaisten kustannusfunktioiden vaikutukset. Esitetyt tulokset osoittavat, että käytetyt menetelmät soveltuvat materiaaliominaisuuksien kvantitatiiviseen ennustamiseen. Identifioidut mallit sisältävät pääasiassa perusteltavia termejä ja mallinnustarkkuus on tyydyttävä. Barkhausen-kohina-mittaus on kuitenkin erittäin tapauskohtainen ja täten ristiriitoja kirjallisuuden kanssa voidaan joskus havaita. Näihin ristiriitoihin vaikuttavat myös ei-mitattavat muutokset materiaaliominaisuuksissa. Esitetyt tulokset osoittavat, että lineaariset mallit kykenevät ennustamaan suurimmat vuorovaikutukset materiaaliominaisuuksien ja Barkhausen-kohinan välillä. Tulokset kuitenkin viittaavat siihen, että neuroverkoilla päästäisiin vielä parempiin mallinnustuloksiin. Tulokset osoittavat myös, että geneettiset algoritmit toimivat piirteiden valinnassa paremmin kuin muut tutkitut menetelmät.

L’objectif de cette thèse était de concevoir, fabriquer, et tester des transducteurs ultrasonores multi – éléments pour des applications en environnements sévères. Dans ce contexte, des techniques de caractérisation des matériaux ont été développées afin d’adapter la conception des transducteurs en fonction des contraintes de l’environnement, et plus particulièrement la température. Deux types de conceptions ont été étudiées selon les applications visées : La première application consistait à développer un transducteur ultrasonore permettant de réaliser des contrôles non destructifs dans des réacteurs de génération IV (refroidi au sodium liquide) lors d’un arrêt de tranche. Dans ce cas, l’environnement sévère était caractérisé par une température de 200 °C, et la présence de sodium liquide. Des radiations pouvaient également être présentes. Pour cette application, un transducteur (nommé LiNa) sous la forme d’un projecteur ultrasonore linéaire de 20 éléments, fonctionnant à 2.5MHz a été fabriquée, puis testé en eau et en sodium liquide. La seconde application consistait à développer un transducteur ultrasonore permettant de réaliser des contrôles non destructifs en contact de pièces massives en cours de soudage. Une pièce massive est par exemple une cuve de réacteur nucléaire. Ce transducteur permet de détecter des défauts dans une soudure très rapidement après sa réalisation, et ainsi corriger directement cette dernière, sans attendre que les pièces soient refroidies. Pourcette application, un transducteur (nommé MaCo) sous forme de matrice 8 × 8 éléments, fonctionnant à 5MHz a été développé, et caractérisé de la température ambiante, jusqu’à 240 °C. Avec ce capteur, Avec ce capteur, des échos en ondes transversales ont été obtenus jusqu’à la température limite, sans refroidissement. / The goal of this thesis was to design, fabricate and test phased arrays ultrasonic transducers for harshenvironment applications. In this context, characterization methods were developed in order to adapt transducerdesign to the environment. We designed two types of ultrasonic phased array transducers according to theconsidered application:The first application aimed at developping an ultrasonic transducer for non destructive testing in IVth generationnuclear reactors (cooled with liquid sodium) during reactor outages. In this case, the harsh environment wascharacterized by a temperature of 200 °C, and the presence of liquid sodium. Radiations could also be present.For this application, a 20 elements 2.5MHz immersion linear projector (called LiNa) was fabricated then testedunder water and under liquid sodium.The second application aimed at developping an ultrasonic transducer in order to realize non destructive testingin contact with massive pieces during welding. A massive piece for example is a tank for nuclear plant reactor.The transducer will permit to quickly detect defects in a weld after its realization, and then correct it directlyif necessary, without waiting for the pieces to cool down at ambient temperatures. For this application,a 64elements 5MHz matrix transducer (named MaCo) were developed and tested from ambient to 240 °C. Thetransducer worked up to the limit temperature without any cooling system, and some typical defects weredetected.

Les matériaux composites sont largement utilisés dans l'industrie en raison de leur bonne tenue mécanique et de leur faible densité. La diversité des domaines d’application des matériaux composites donne lieu à une grande variété de modes de sollicitation et d’endommagement. De ce fait, l’évaluation de leurs propriétés et le contrôle de leur état présentent un grand intérêt industriel. Dans ce travail, une nouvelle méthode d’évaluation et de contrôle non destructif dite par sonothermographie est explorée. Cette méthode est basée sur l'analyse du champ thermique induit par des ondes ultrasonores de puissance dans les matériaux absorbants tels que les composites. Deux applications complémentaires sont étudiées, d’une part l’évaluation des propriétés thermiques du matériau et d’autre part le contrôle non destructif de structures par thermographie infrarouge. Dans ce cadre, le problème direct de la sonothermographie est résolu numériquement à partir d’un modèle par éléments finis. Ce modèle permet de simuler le champ thermique induit par la propagation d’ondes ultrasonores dans un matériau absorbant dont les propriétés sont connues. Les simulations réalisées permettent de montrer l’applicabilité de la sonothermographie à la détection de défauts. Une nouvelle approche de caractérisation thermique est également développée. Cette approche basée sur la formulation faible de l’équation de conduction de la chaleur permet une estimation robuste de la diffusivité thermique du matériau à partir du champ thermique induit par les ondes ultrasonores de puissance. Des résultats expérimentaux sont présentés pour le cas de plaques minces. / The composite materials are widely used in industry because of their high mechanical resistance and low density. The diversity of composite materials application fields gives rise to a large variety of solicitation and damage conditions. For this reason, the evaluation of their properties and their health monitoring are of great industrial interest. In this work, a new method of evaluation and non-destructive testing named sonothermography is explored. This method is based on the analysis of thermal fields induced by ultrasonic waves in absorbent materials such as composites. Two additional applications are studied: the evaluation of the thermal properties of the material and the non-destructive testing of structures by infrared thermography. In this framework, the direct problem of sonothermography is solved numerically using a model based on the finite element method. This model allows to simulate the thermal field induced by the propagation of ultrasonic waves in absorbent material whose properties are known. The simulations carried out show the applicability of the sonothermography for the detection of defects. An innovative approach for thermal characterization is also developed. This approach based on the weak formulation of the heat conduction equation allows a robust estimate of the thermal diffusivity of the material from the thermal field induced by ultrasonic waves. Experimental results are presented for thin plates.

TRAPIL est une société Française ayant à charge l'exploitation et l'entretien de pipelines d'hydrocarbures. L'entretien de pipelines enterrés nécessite le passage de racleurs équipés de sondes ultrasons réalisant une cartographie de la structure du pipeline, qui est ensuite analysée à la main afin de détecter et d'identifier les différents défauts pouvant apparaître ou évoluer.L'objectif de ce travail de thèse est d'apporter une solution algorithmique permettant d'accélérer et de compléter le travail des analystes à l'aide des méthodes modernes de traitement d'images et du signal.Notre approche suit le mode opératoire des experts et est découpée en trois partie.Tout d'abord nous réalisons une détection des soudures d'aboutage permettant de séparer le pipelines en les différents tubes qui le composent. Les signaux de sondes représentant la circonférence du tube sont regroupés et compressés dans une détection de rupture par comparaison de moyenne à court et long terme, puis les signaux résultants sont fusionnés à l'aide d'une pondération unique permettant une augmentation majeure du contraste entre bruit et soudure, offrant une détection et une localisation presque sans faille.Les tubes subissent ensuite une première segmentation visant à éliminer le plus grand nombre de pixels sains. Usant de modélisation d'histogramme des valeurs d'épaisseur par un algorithme EM initialisé pour notre problématique, l'algorithme suit un principe récursif comparable aux méthodes de type split and merge pour détecter et isoler les zones dangereuses.Enfin, Les zones dangereuses sont identifiées à l'aide d'une foret aléatoire, apprise sur un grand nombre d'exemples de défauts. Cette troisième partie est centrée sur l'étude de différentes méthodes de reconnaissance de forme appliquées à notre nouvelle problématique.Au travers de ces différentes étapes, les solutions que nous avons apportées permettent à TRAPIL un gain de temps significatif sur les tâches les plus fastidieuses du processus d'analyse (par exemple 30% sur la détection de soudures) et leur offre de nouvelles possibilités commerciales, par exemple la possibilité de fournir un pré-rapport à leur clientèle en quelques jours pendant que l'analyse manuelle est réalisée pouvant prendre plus d'un mois. / TRAPIL is a French society who is in charge of exploitation and maintenance of oil pipelines. Maintenance of buried pipeline implies the use of ultrasonic sensor-equipped devices, providing thickness and structural maps of the pipe, which are analysed by experts in order to detect and identify defects that may appear or evolve.The objective of this work is to provide an algoritmic solution allowing to accelerate and aid the experts's work with modern image and signal processing methods.Our approach follows the experts's operating mode and is divided in three sections.First, a weld detection is realized allowing to split the pipe in tubes. The signals of probes representing the circumference of the pipe are regrouped and compressed through an abrupt change detection, using short and long-term average comparison, then the resulting signals are merged using a unique weightening function allowing a massive increase of the contrast between welds and noise, offering near-perfect detection and localization.The tubes then undergoes a first segmentation aiming at eliminating a large amount of sane pixels. Using histogram modelization through an EM algorithm tuned specially for our purpose, the algorithm follows a recursive approach comparable to split and merge methods to detect and isolate dangerous areas.Finally, those dangerous areas are identified with a Random Forest, which has been learnt on a large amount of defect examples. This third part is greatly focused on the study of different pattern recognition methods applied on our new problematic.Through those different steps, the solution we brought allows TRAPIL to save a lot of time on the most tedious tasks of the analysis process (for example 30% of gain in processing time for the weld detection) and offers new commercial possibilities, like for example the possibility to provide their clients a first report in a matter of days, while the manual analysis is completed, which can take more than a month.

[EN] This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability densities, greatly simplifying the modeling of the data. ICA mixture models (ICAMM) provide further flexibility by alleviating the independency requirement of ICA, thus allowing the model to obtain local projections of the data without compromising its generalization capabilities. Here are explored new possibilities of ICAMM for the purposes of estimation and classification of signals. The thesis makes several contributions to the research in non-Gaussian mixtures: (i) a method for maximum-likelihood estimation of missing data, based on the maximization of the PDF of the data given the ICAMM; (ii) a method for Bayesian estimation of missing data that minimizes the mean squared error and can obtain the confidence interval of the prediction; (iii) a generalization of the sequential dependence model for ICAMM to semi-supervised or supervised learning and multiple chains of dependence, thus allowing the use of multimodal data; and (iv) introduction of ICAMM in diverse novel applications, both for estimation and for classification. The developed methods were validated via an extensive number of simulations that covered multiple scenarios. These tested the sensitivity of the proposed methods with respect to the following parameters: number of values to estimate; kinds of source distributions; correspondence of the data with respect to the assumptions of the model; number of classes in the mixture model; and unsupervised, semi-supervised, and supervised learning. The performance of the proposed methods was evaluated using several figures of merit, and compared with the performance of multiple classical and state-of-the-art techniques for estimation and classification. Aside from the simulations, the methods were also tested on several sets of real data from different types: data from seismic exploration studies; ground penetrating radar surveys; and biomedical data. These data correspond to the following applications: reconstruction of damaged or missing data from ground-penetrating radar surveys of historical walls; reconstruction of damaged or missing data from a seismic exploration survey; reconstruction of artifacted or missing electroencephalographic (EEG) data; diagnosis of sleep disorders; modeling of the brain response during memory tasks; and exploration of EEG data from subjects performing a battery of neuropsychological tests. The obtained results demonstrate the capability of the proposed methods to work on problems with real data. Furthermore, the proposed methods are general-purpose and can be used in many signal processing fields. / [ES] Esta tesis considera nuevas aplicaciones de las mezclas no Gaussianas dentro del marco de trabajo del procesado estadístico de señal y del reconocimiento de patrones. Las mezclas no Gaussianas fueron implementadas mediante mezclas de analizadores de componentes independientes (ICA). La hipótesis fundamental de ICA es que las señales observadas pueden expresarse como una transformación lineal de un grupo de variables ocultas, normalmente llamadas fuentes, que son estadísticamente independientes. Esta independencia permite factorizar la función de densidad de probabilidad (PDF) original M-dimensional de los datos como un producto de densidades unidimensionales, simplificando ampliamente el modelado de los datos. Los modelos de mezclas ICA (ICAMM) aportan una mayor flexibilidad al relajar el requisito de independencia de ICA, permitiendo que el modelo obtenga proyecciones locales de los datos sin comprometer su capacidad de generalización. Aquí se exploran nuevas posibilidades de ICAMM para los propósitos de estimación y clasificación de señales. La tesis realiza varias contribuciones a la investigación en mezclas no Gaussianas: (i) un método de estimación de datos faltantes por máxima verosimilitud, basado en la maximización de la PDF de los datos dado el ICAMM; (ii) un método de estimación Bayesiana de datos faltantes que minimiza el error cuadrático medio y puede obtener el intervalo de confianza de la predicción; (iii) una generalización del modelo de dependencia secuencial de ICAMM para aprendizaje supervisado o semi-supervisado y múltiples cadenas de dependencia, permitiendo así el uso de datos multimodales; y (iv) introducción de ICAMM en varias aplicaciones novedosas, tanto para estimación como para clasificación. Los métodos desarrollados fueron validados mediante un número extenso de simulaciones que cubrieron múltiples escenarios. Éstos comprobaron la sensibilidad de los métodos propuestos con respecto a los siguientes parámetros: número de valores a estimar; tipo de distribuciones de las fuentes; correspondencia de los datos con respecto a las suposiciones del modelo; número de clases en el modelo de mezclas; y aprendizaje supervisado, semi-supervisado y no supervisado. El rendimiento de los métodos propuestos fue evaluado usando varias figuras de mérito, y comparado con el rendimiento de múltiples técnicas clásicas y del estado del arte para estimación y clasificación. Además de las simulaciones, los métodos también fueron probados sobre varios grupos de datos de diferente tipo: datos de estudios de exploración sísmica; exploraciones por radar de penetración terrestre; y datos biomédicos. Estos datos corresponden a las siguientes aplicaciones: reconstrucción de datos dañados o faltantes de exploraciones de radar de penetración terrestre de muros históricos; reconstrucción de datos dañados o faltantes de un estudio de exploración sísmica; reconstrucción de datos electroencefalográficos (EEG) dañados o artefactados; diagnóstico de desórdenes del sueño; modelado de la respuesta del cerebro durante tareas de memoria; y exploración de datos EEG de sujetos durante la realización de una batería de pruebas neuropsicológicas. Los resultados obtenidos demuestran la capacidad de los métodos propuestos para trabajar en problemas con datos reales. Además, los métodos propuestos son de propósito general y pueden utilizarse en muchos campos del procesado de señal. / [CAT] Aquesta tesi considera noves aplicacions de barreges no Gaussianes dins del marc de treball del processament estadístic de senyal i del reconeixement de patrons. Les barreges no Gaussianes van ser implementades mitjançant barreges d'analitzadors de components independents (ICA). La hipòtesi fonamental d'ICA és que els senyals observats poden ser expressats com una transformació lineal d'un grup de variables ocultes, comunament anomenades fonts, que són estadísticament independents. Aquesta independència permet factoritzar la funció de densitat de probabilitat (PDF) original M-dimensional de les dades com un producte de densitats de probabilitat unidimensionals, simplificant àmpliament la modelització de les dades. Els models de barreges ICA (ICAMM) aporten una major flexibilitat en alleugerar el requeriment d'independència d'ICA, permetent així que el model obtinga projeccions locals de les dades sense comprometre la seva capacitat de generalització. Ací s'exploren noves possibilitats d'ICAMM pels propòsits d'estimació i classificació de senyals. Aquesta tesi aporta diverses contribucions a la recerca en barreges no Gaussianes: (i) un mètode d'estimació de dades faltants per màxima versemblança, basat en la maximització de la PDF de les dades donat l'ICAMM; (ii) un mètode d'estimació Bayesiana de dades faltants que minimitza l'error quadràtic mitjà i pot obtenir l'interval de confiança de la predicció; (iii) una generalització del model de dependència seqüencial d'ICAMM per entrenament supervisat o semi-supervisat i múltiples cadenes de dependència, permetent així l'ús de dades multimodals; i (iv) introducció d'ICAMM en diverses noves aplicacions, tant per a estimació com per a classificació. Els mètodes desenvolupats van ser validats mitjançant una extensa quantitat de simulacions que cobriren múltiples situacions. Aquestes van verificar la sensibilitat dels mètodes proposats amb respecte als següents paràmetres: nombre de valors per estimar; mena de distribucions de les fonts; correspondència de les dades amb respecte a les suposicions del model; nombre de classes del model de barreges; i aprenentatge supervisat, semi-supervisat i no-supervisat. El rendiment dels mètodes proposats va ser avaluat mitjançant diverses figures de mèrit, i comparat amb el rendiments de múltiples tècniques clàssiques i de l'estat de l'art per a estimació i classificació. A banda de les simulacions, els mètodes van ser verificats també sobre diversos grups de dades reals de diferents tipus: dades d'estudis d'exploració sísmica; exploracions de radars de penetració de terra; i dades biomèdiques. Aquestes dades corresponen a les següents aplicacions: reconstrucció de dades danyades o faltants d'estudis d'exploracions de radar de penetració de terra sobre murs històrics; reconstrucció de dades danyades o faltants en un estudi d'exploració sísmica; reconstrucció de dades electroencefalogràfiques (EEG) artefactuades o faltants; diagnosi de desordres de la son; modelització de la resposta del cervell durant tasques de memòria; i exploració de dades EEG de subjectes realitzant una bateria de tests neuropsicològics. Els resultats obtinguts han demostrat la capacitat dels mètodes proposats per treballar en problemes amb dades reals. A més, els mètodes proposats són de propòsit general i poden fer-se servir en molts camps del processament de senyal. / Safont Armero, G. (2015). New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/53913 / TESIS

Entre otras aplicaciones, el Grupo de Tratamiento de Señal (GTS) de la Universidad Politécnica de Valencia ha trabajado tradicionalmente en el campo de los ensayos no destructivos por ultrasonidos. el GTS ha desarrollado sofisticados algoritmos de procesado de señal para la detección y caracterización en materiales dispersivos. El procedimiento de inspección ultrasónica de materiales implica la obtención de un registro o señal mediante un equipo emisor-receptor de ultrasonidos. Este registro será posteriormente procesado para obterner información del material. La extracción de características puede realizarse calculando parámetros temporales y/o frecuenciales de los registros en cuestión. Estos parámetros contienen información que está relacionada con propiedades físicas de los materiales y que podrán ser empleados en definitiva para clasificar y caracterizar el material. El objetivo de la tesis es el de ampliar el conjunto de parémetros que se han venido usando de manera tradicional en el grupo. Los nuevos parámetros proponen un cambio de perspectiva mediante el uso de técnicas basadas en el tratamiento digital de imagen. Se presenta una colección de descriptores geométricos y de forma que serán aplicados a las imágenes obtenidas a partir de los diagramas tiempo-frecuencia. Los citados parámetros se aplicarán junto con los parámetros que componen el modelo tradicional, o versiones modificadas de éstos, a la caracterización ultrasónica de materiales dispersivos blandos. En la tesis se presenta un modelo matemático afín a los tipos de materiales que han sido evaluados en este trabajo. Se define así un esquema que modela en el dominio de la frecuencia el registro de señal procedente de la inspección ultrasónica del material. Se proponen y estudian además diferentes métodos con el objetivo de llevar a cabo el análisis espectral de la señal ultrasónica y se discuten diferentes alternativas para la obtención y mejora de los diagramas tiempo-frecuencia. / Gómez García, S. (2010). Descriptores Geométricos y de Forma: Aplicación a la caracterización ultrasónica de materiales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8722 / Palancia

<div> <div> <div> <p>Composite materials have become widely used in engineering applications, in order to reduce the overall weight of structures while retaining their required strength. Due to their light weight, relatively high stiffness properties, and formability into complex shapes, discontinuous fiber composites are advantageous for producing small and medium size components. However, qualifying their mechanical properties can be expensive, and therefore there is a need to improve predictive capabilities to help reduce the overall cost of large scale testing. To address this challenge, a composite material consisting of discontinuous glass fibers in a polypropylene matrix is studied at the microstructural level through coupled experiments and simulations, in order to uncover the mechanisms that cause microvoids to initiate and progress, as well as certain fiber breakage events to occur, during macroscopic tension. Specifically, this work coupled in-situ X-ray micro computed tomography (μ-CT) experiments with a finite element simulation of the exact microstructure to enable a 3D study that tracked damage initiation and propagation, and computed the local stresses and strains in the microstructure. In order to have a comprehensive 3D understanding of the evolution of the microstructure, high fidelity characterization procedures were developed and applied to the μ-CT images in order to understand the exact morphology of the microstructure. To aid in this process, ModLayer - an interactive image processing tool - was created as a MATLAB executable, and the 3D microstructural feature detection techniques were compared to traditional destructive optical microscopy techniques. For damage initiation, this work showed how high hydrostatic stresses in the matrix can be used as a metric to explain and predict the exact locations of microvoid nucleation within the composite’s microstructure. From a damage propagation standpoint, matrix cracking - a mechanism that has been notably difficult to predict because of its apparent stochastic nature - was studied during damage propagation. The analysis revealed the role of shear stress in fiber mediated flat matrix cracking, and the role of hydrostatic stress in fiber-avoidance conoidal matrix cracking. Overall, a sub-fiber simulation and an in-situ experimental analysis provided the microstructural physical phenomena that govern certain damage initiation and progression mechanisms, further enabling the strength and failure predictions of short fiber thermoplastic composites. </p></div></div></div>

The need for reliable and cost-effective testing procedures for Additive Manufacturing (AM) is growing. In this Dissertation, the development of a new computational-experimental method based on the realization of specific testing artifacts to address this need is presented. This research is focused on one of the widely utilized AM technologies, Fused Deposition Modeling (FDM), and can be extended to other AM technologies as well. In this method, testing artifacts are designed with simplified boundary conditions and computational domains that minimize uncertainties in the analyses. Testing artifacts are a combination of thin and thick cantilever structures, which allow measurement of natural frequencies, mode shapes, and dimensions as well as distortions and deformations. We apply Optical Non-Destructive Testing (ONDT) together with computational methods on the testing artifacts to predict their natural frequencies, thermal flow, mechanical properties, and distortions as a function of 3D printing parameters. The complementary application of experiments and simulations on 3D printed testing artifacts allows us to systematically investigate the density, porosity, moduli of elasticity, and Poisson’s ratios for both isotropic and orthotropic material properties to better understand relationships between these characteristics and the selected printing parameters. The method can also be adapted for distortions and residual stresses analyses. We optimally collect data using a design of experiments technique that is based on regression models, which yields statistically significant data with a reduced number of iterations. Analyses of variance of these data highlight the complexity and multifaceted effects of different process parameters and their influences on 3D printed part performance. We learned that the layer thickness is the most significant parameter that drives both density and elastic moduli. We also observed and defined the interactions among density, elastic moduli, and Poisson’s ratios with printing speed, extruder temperature, fan speed, bed temperature, and layer thickness quantitatively. This Dissertation also shows that by effectively combining ONDT and computational methods, it is possible to achieve greater understanding of the multiphysics that governs FDM. Such understanding can be used to estimate the physical and mechanical properties of 3D printed components, deliver part with improved quality, and minimize distortions and/or residual stresses to help realize functional components.

Description of Ultrasonic Cleaning technology. Description of Non-destructive testing technology process. Design of Ultrasonic Cleaning equipment. Design of equipment for the ultrasonic cleaning machine. Design of non-destructive testing equipment (Liquid Penetrant Inspection and Magnetic Testing). Feasibility study of designed technologies and supporting processes.