Global behaviour of a composite stiffened panel in buckling. Part 2: Experimental investigation

The present study analyses an aircraft composite fuselage structure manufactured by the Liquid Resin Infusion (LRI) process and subjected to a compressive load. LRI is based on the moulding of high performance composite parts by infusing liquid resin on dry fibres instead of prepreg fabrics or Resin Transfer Moulding (RTM). Actual industrial projects face composite integrated structure issues as a number of structures (stiffeners, …) are more and more integrated onto the skins of aircraft fuselage. A post-buckling test of a composite fuselage representative panel is set up, from numerical results available in previous works. Two stereo Digital Image Correlation (DIC) systems are positioned on each side of the panel, that are aimed at correlating numerical and experimental out-of-plane displacements (corresponding to the skin local buckling displacements of the panel). First, the experimental approach and the test facility are presented. A post-mortem failure analysis is then performed with the help of Non-Destructive Techniques (NDT). X-ray Computed Tomography (CT) measurements and ultrasonic testing (US) techniques are able to explain the failure mechanisms that occured during this post-buckling test. Numerical results are validated by the experimental results

Machine Learning for Microcontroller Performance Screening

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of Fmax (the maximum operating frequency). Traditional speed-binning techniques are not feasible to be applied to mass production, due to the high cost of the needed test equipment. Literature has proven that data extracted from on-chip ring oscillators (ROs) can model the Fmax of integrated circuits by means of machine learning models able to predict the actual operating frequency of the devices. Those models, once trained, can be easily applied to the ROs data coming from every produced device with low effort and no need for high-cost equipment. This research aims to develop machine learning methodologies to be deployed in the MCU screening process, allowing for a more efficient and accurate Fmax estimation, as well as improved speed binning. The effectiveness of this approach has been demonstrated on a real world dataset of microcontroller data

Monitoring UF membrane performance treating surface-groundwater blends: limitations of FEEM-PARAFAC on the assessment of the organic matter role

The decrease of water quantity and quality in water scarcity areas is palliated by improving water treatments with membrane technologies. System performance and efficiency, and then cost, is mainly affected by membrane fouling, which is still not well understood and controlled appropriately. In this study, the influence of content and composition of dissolved organic matter (DOM) on a membrane ultrafiltration (UF) stage from a full-scale UF stage in a drinking water treatment plant (DWTP) fed with surface water, groundwater (or blends of them) was investigated. Excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) was used to characterize and assess DOM changes in water samples Water streams feeding the UF stage showed high variability in DOM content and composition. FEEM-PARAFAC analysis allowed the differentiation of seven different organic components. Additionally to the characterization and monitoring of DOM in the full-scale UF stage, a bench scale UF pilot was run to experimentally correlate the impact of water quality with membrane performance. The experiments included testing synthetic solutions of model foulants (synthetic humic acid and bovine serum albumin) and blends of complex waters. To quantify fouling, the total fouling index (TFI) and the hydraulically irreversible fouling index (HIFI) were calculated for each filtration run. According to the results obtained, the correlation plots between the PARAFAC components and the fouling indices pointed at microbial byproducts (C1) and humic-like components (C2, C4, C5) as the ones showing higher correlations

New non-destructive testing techniques based on mechanical waves: Development and application to damage characterization on non-homogeneus materials

Tesis por compendioEsta tesis doctoral se encuentra desarrollada en los ámbitos de materiales, ensayos no destructivos y procesado de señal. Su propósito es aportar nuevas técnicas no destructivas centrándose tanto en su desarrollo como en su aplicación a sistemas reales. La evaluación de durabilidad y daño en materiales no homogéneos es el eje central de este documento, articulándolo en dos capítulos fundamentales. En primera instancia se evalúa el comportamiento del cemento reforzado con fibra de vidrio bajo esfuerzo de flexión. Múltiples señales ultrasónicas fueron adquiridas en todo el proceso de deformación de los elementos implementando una nueva técnica de adquisición multifrecuencia. En la fase posterior a la adquisición se determinaron parámetros lineales y no lineales de las señales ultrasónicas pulsadas correlacionando dichos parámetros con las curvas tensión - deformación descritas por los elementos ensayados. En el último bloque se expone el análisis de espectroscopía por impacto sobre probetas de mortero dañadas química y térmicamente. Se profundizó sobre el comportamiento dinámico no-lineal e histéretico de los elementos de mortero. El análisis del ablandamiento del módulo dinámico con el incremento de la amplitud de excitación es un tema de gran interés para la evaluación de daño, siendo la técnica actual dependiente de la adquisición de múltiples señales. En este contexto, se propone nueva técnica de procesado de señal capaz de extraer parámetros de esta naturaleza a partir de una única señal.Aquesta tesi doctoral està desenvolupada als àmbits de materials, assajos no destructius i processament de senyals. El seu propòsit és aportar noves tècniques no destructives, centrades tant en el seu desenvolupament com en la seua aplicació a sistemes reals. L'avaluació de durabilitat i dany en materials no homogenis és l'eix central d'aquest document, format per dos capítols fonamentals. En primer lloc s'avalua el comportament del ciment reforçat amb fibra de vidre sotmès a esforç de flexió. Diverses senyals ultrasòniques van ser adquirides durant tot el procés de deformació dels elements que implementa una nova tècnica d'adquisició multifreqüència. En la fase posterior a l'adquisició es van determinar paràmetres lineals i no lineals de les senyals ultrasòniques polsades que correlacionen aquests paràmetres amb les corbes de tensió-deformació descrites pels elements assajats. Al tercer bloc s'exposa l'anàlisi d'espectroscòpia per impacte en provetes de morter malmeses químicament i tèrmicament. Es va aprofundir en el comportament dinàmic no lineal i histerètic dels elements de morter. L'anàlisi de l'estovament del mòdul dinàmic amb l'increment de l'amplitud de l'excitació és un aspecte molt interessant per a l'avaluació de dany, tècnica que actualment depèn de l'adquisició de diverses senyals. En aquest context, es proposa una nova tècnica de processament de senyal capaç d'extraure paràmetres d'aquesta natura a partir d'una única senyal.This doctoral thesis is developed in materials, Non-Destructive Testing (NDT) and signal processing fields. Its purpose is to make a contribution in new non destructive techniques focusing in its development and application to real systems. Durability and damage evaluation in non-homogeneous materials is the keystone of this document, assembling the structure in two main chapters. In the first instance the behaviour of Glass-fiber Reinforced Cement under bending test is evaluated. Multiple ultrasonic signals during the deformation process of the specimens were acquired, implementing a new multi-frequency acquisition technique. After the acquisition stage, linear and non-linear parameters were calculated from the pulsed ultrasonic signals correlating those parameters with the stress strain curve described during the test. In the third chapter, the impact spectroscopy analysis applied to chemical and thermal damaged mortar samples is exposed. The non-linear histeretic behaviour of the mortar specimens was studied more in depth. The analysis of the dynamic modulus softening with increment of the excitation amplitude is a hot spot at this moment for damage evaluation, being the current technique depending on the multiple signals acquisition. In this context, a new technique is proposed which allows a non-linear parameters extraction from a single reverberation signal.Genovés Gómez, V. (2018). New non-destructive testing techniques based on mechanical waves: Development and application to damage characterization on non-homogeneus materials [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/114799TESISCompendi

Semi-Supervised Deep Learning for Microcontroller Performance Screening

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of F_max (the maximum operating frequency). Data extracted from on-chip ring oscillators (ROs) can model the F_max of integrated circuits using machine learning models. Those models are suitable for the performance screening process. Acquiring data from the ROs is a fast process that leads to many unlabeled data. Contrarily, the labeling phase (i.e., acquiring F_max) is a time-consuming and costly task, that leads to a small set of labeled data. This paper presents deep-learning-based methodologies to cope with the low number of labeled data in microcontroller performance screening. We propose a method that takes advantage of the high number of unlabeled samples in a semi-supervised learning fashion. We derive deep feature extractor models that project data into higher dimensional spaces and use the data feature embedding to face the performance prediction problem with simple linear regression. Experiments showed that the proposed models outperformed state-of-the-art methodologies in terms of prediction error and permitted us to use a significantly smaller number of devices to be characterized, thus reducing the time needed to build ML models by a factor of six with respect to baseline approaches

Corrosion in Concrete: Inhibitors and Coatings

Improving the durability of reinforced concrete structures is a mandatory strategy to reduce the environmental impact of construction materials together with aiming to limit carbon dioxide emissions, energy consumption and natural raw materials depletion. Hence, for both new and existing concrete structures, proper protective techniques are required to prevent premature failure induced by environmental aggressive agents. The book presents the most innovative findings both in designing durable new constructions and toward solving durability deficiencies in existing concrete structures, such as innovative special coatings, hydrophobic impregnations and pore blocking treatments that are very efficient in severe aggressive environments (seawater, biocorrosion, etc.). Moreover, migrant corrosion inhibitors applied on concrete surfaces are able to greatly improve resistance against both chloride and CO2. Finally, special reinforcements and fibers, together with a proper designed cathodic protection, can greatly contribute to obtaining long-life reinforced concrete structures, significantly increasing the sustainability of concrete construction

A Multi-Label Active Learning Framework for Microcontroller Performance Screening

In safety-critical applications, microcontrollers have to be tested to satisfy strict quality and performances constraints. It has been demonstrated that on-chip ring oscillators can be be used as speed monitors to reliably predict the performances. However, any machine-learning model is likely to be inaccurate if trained on an inadequate dataset, and labeling data for training is quite a costly process. In this paper, we present a methodology based on active learning to select the best samples to be included in the training set, significantly reducing the time and cost required. Moreover, since different speed measurements are available, we designed a multi-label technique to take advantage of their correlations. Experimental results demonstrate that the approach halves the training-set size, with respect to a random labelling, while it increases the predictive accuracy, with respect to standard single-label machine-learning models

A Novel Differential Time-of-Arrival Estimation Technique for Impact Localization on Carbon Fiber Laminate Sheets

Composite material structures are commonly used in many industrial sectors (aerospace, automotive, transportation), and can operate in harsh environments where impacts with other parts or debris may cause critical safety and functionality issues. This work presents a method for improving the accuracy of impact position determination using acoustic source triangulation schemes based on the data collected by piezoelectric sensors attached to the structure. A novel approach is used to estimate the Differential Time-of-Arrival (DToA) between the impact response signals collected by a triplet of sensors, overcoming the limitations of classical methods that rely on amplitude thresholds calibrated for a specific sensor type. An experimental evaluation of the proposed technique was performed with specially made circular piezopolymer (PVDF) sensors designed for Structural Health Monitoring (SHM) applications, and compared with commercial piezoelectric SHM sensors of similar dimensions. Test impacts at low energies from 35 mJ to 600 mJ were generated in a laboratory by free-falling metal spheres on a 500 mm × 500 mm × 1.25 mm quasi-isotropic Carbon Fiber Reinforced Polymer (CFRP) laminate plate. From the analysis of many impact signals, the resulting localization error was improved for all types of sensors and, in particular, for the circular PVDF sensor an average error of 20.3 mm and a standard deviation of 8.9 mm was obtained

Development of a Novel Methodology to Study Fatigue Properties using the Small Punch Test

Small scale mechanical test methods are now widely recognised as an established and quantifiable means of obtaining useful mechanical property information from limited material quantities. Much research has been gathered employing such approaches, but to date these methods have largely been restricted to characterising the creep, tensile and fracture characteristics of numerous materials and alloys through the small punch (SP) test. Clearly, a key element that is missing from this list of fundamental mechanical properties is understanding the cyclic response of the material, a significant form of damage that accounts for a large proportion of in-service failures in critical structural components. Therefore, in order to profit from the numerous benefits that SP testing has to offer, including a small sample size and hence reduced cost, a small scale fatigue testing methodology is now required to provide a holistic mechanical property evaluation. Such an innovative approach would provide real potential benefit to the engineering mechanical characterisation community. This paper will discuss the development and implementation of this highly bespoke SP fatigue testing methodology that can accommodate alternative loading ratios and frequencies to mimic conventional fatigue data. A number of novel experiments have been performed on the titanium alloy Ti-6Al-4V with accompanying analysis and fractography detailed. Numerical correlations to uniaxial fatigue data is also presented through the use of Finite Element Analysis

Impact-Acoustic Evaluation Method of Internal Crack in Rubber Composite Structure

The main objective of this work is to investigate the impact acoustic method as a means of nondestructive testing (NDT) for internal cracks in a rubber composite structure, such as that found in a tire. As demonstrated in this dissertation, this approach is an effective and economical alternative to the current NDT methods for tires casing integrity inspection. There are two separate aspects of the impact acoustic signals considered in this work: the impact force signal and the resultant acoustic signal. First, a contact dynamics model is developed based on the Hertz’s impact theory and modified for rubber composite materials. The model generates prediction of major impact dynamics quantities, which are theoretically proven to be sensitive to the existence of internal structural cracks. For the purpose of applying the impact acoustic method for tire casing integrity inspection, models are developed for simplified tire structures, which are a cubic shape comprised of rubber compound material without reinforcements. The prepared cubic rubber samples are designed to roughly approximate the profile of the sectional tire casing and the cracks embedded at the belt edge in the shoulder area. The rationality of the simplification is explained both theoretically and experimentally. Based on comprehensive theoretical analysis of the impact acoustic signals, several direct and indirect experimental features are identified that are equivalent to the theoretical dynamic quantities, thus correlated to the presence of internal crack. The experimental discriminators can be extracted from either impact force signal or acoustic time- and frequency-domain signal. They are verified as promising indicators of internal crack in both simplified cubic rubber structures and complicated tire casings. Integration of the experimentally extracted discriminators helps to mitigate the deficiencies and noise caused by relying heavily on a single discriminator, while providing an integrated index identifying the damage conditions with good accuracy and robustness