Cohesive law estimation of adhesive joints in mode II condition

The adhesive bonding technique enables both weight and complexity reduction in structures that require some joining technique to be used on account of fabrication/component shape issues. Because of this, adhesive bonding is also one of the main repair methods for metal and composite structures by the strap and scarf configurations. The availability of strength prediction techniques for adhesive joints is essential for their generalized application and it can rely on different approaches, such as mechanics of materials, conventional fracture mechanics or damage mechanics. These two last techniques depend on the measurement of the fracture toughness (GC) of materials. Within the framework of damage mechanics, a valid option is the use of Cohesive Zone Modelling (CZM) coupled with Finite Element (FE) analyses. In this work, CZM laws for adhesive joints considering three adhesives with varying ductility were estimated. The End-Notched Flexure (ENF) test geometry was selected based on overall test simplicity and results accuracy. The adhesives Araldite® AV138, Araldite® 2015 and Sikaforce® 7752 were studied between high-strength aluminium adherends. Estimation of the CZM laws was carried out by an inverse methodology based on a curve fitting procedure, which enabled a precise estimation of the adhesive joints’ behaviour. The work allowed to conclude that a unique set of shear fracture toughness (GIIC) and shear cohesive strength (ts0) exists for each specimen that accurately reproduces the adhesive layer’ behaviour. With this information, the accurate strength prediction of adhesive joints in shear is made possible by CZM.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Mechanical properties of chemically modified portuguese pinewood

To turn wood into a construction material with enhanced properties, many methods of chemical modification have been developed in the last few decades. In this work, mechanical properties of pine wood were chemically modified, compared and evaluated. Maritime pine wood (Pinus pinaster) was modified with four chemical processes: 1,3-dimethylol-4,5- dihydroxyethyleneurea, N-methylol melamine formaldehyde, tetra-alkoxysilane and wax. The following mechanical properties were assessed experimentally: Modulus of elasticity measured statically, stiffness stabilization efficiency in different climates (30 and 87% of relative humidity), modulus of rupture, work maximum load, impact bending strength, compression, tensile and shear strength at indoor conditions (65% of relative humidity). In both types of active principle of modification, cell wall or lumen fill, no significant changes on the bending stiffness (modulus of elasticity) were found. In the remaining properties analysed significant changes in the modified wood-material took place compared to unmodified wood control: - Cell wall modification was the most effective method to achieve high stiffness stabilization efficiency (up to 60%) and also increased compression strength (up to 230%). However, modulus of rupture, tensile, shear and the impact bending strength were reduced by both resins, but in a varying extent, where the N-methylol melamine formaldehyde endured less reduction than 1,3-dimethylol-4,5-dihydroxyethyleneurea resin. In the latter, reduction up to 60% can take place. - In the lumen fill modification: tetra-alkoxysilane has no effect in the mechanical properties. Although, a slight increase in shear strength parallel to the grain was found. Wax specimens have shown a slight increase in bending strength, compression, tensile and shear strength as well as in the absorption energy capacity.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

Caraterização das propriedades mecânicas de um adesivo estrutural de alta ductilidade

A tecnologia de ligação por adesivos estruturais tem vindo a ser utilizada ao longo de várias décadas, permitindo solucionar diversos problemas associados a técnicas chamadas "tradicionais" de ligação, como a soldadura, a rebitagem ou a ligação aparafusada. Esta é uma alternativa viável para substituir as ligações mecânicas, devido a diversos fatores como o menor peso estrutural, menor custo de fabricação e capacidade de união de diferentes materiais. O crescente recurso a materiais compósitos em diversas indústrias, nomeadamente a aeronáutica e naval, levaram ao consequente aumento da aplicação de ligações adesivas, por serem indicadas como forma de união destes materiais, onde é de enaltecer a sua elevada resistência à fadiga. Uma junta adesiva está maioritariamente sujeita a esforços de corte e arrancamento e portanto o conhecimento dos módulos de elasticidade à tração (E) ou corte (G) do adesivo, e ainda as resistências máximas à tração e ao corte, não é suficiente quando se pretende prever o comportamento da mesma. Na verdade, torna-se necessário abranger na análise a plastificação progressiva verificada nas juntas adesivas antes da rotura, sendo necessário o conhecimento de parâmetros tais como a taxa crítica de libertação de energia de deformação à tração (GIc) e corte (GIIc). Este trabalho pretende estudar um adesivo estrutural recentemente lançado no mercado, carecendo portanto da sua caracterização, para facilitar a previsão da resistência de estruturas adesivas ligadas com o mesmo. São 4 os ensaios a realizar: ensaios à tração de provetes em bruto, ensaios ao corte com a geometria Thick Adherend Shear Test (TAST), ensaios Double-Cantilever Beam (DCB) e ensaios End-Notched Flexure (ENF). Com a realização dos ensaios referidos, serão determinadas as propriedades mecânicas e de fratura à tração e ao corte, e serão fornecidos os parâmetros para a previsão da resistência de juntas adesivas com este adesivo por uma variedade de métodos, desde métodos analíticos mais expeditos até aos métodos numéricos mais avançados existentes atualmente. Os resultados foram de encontro aos disponibilizados pelo fabricante, sempre que estes se encontravam disponíveis, obtendo-se discrepâncias bastante reduzidas nos diversos parâmetros determinados.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Caraterização das propriedades macânicas e de fratura de um adesivo estrutural de alta ductilidade

As juntas adesivas são uma alternativa viável para substituir ligações comuns como as mecânicas ou soldadas, devido a diversas vantagens como a possibilidade de união de materiais de natureza diferente, maior leveza, menores custos inerentes ao fabrico e ainda prevenção da corrosão galvânica que pode ocorrer nas ligações entre dois materiais metálicos diferentes. A resistência de uma junta depende, para um determinado tipo de solicitação imposta, da distribuição de tensões no interior da junta. Por outro lado, a geometria das juntas, as propriedades mecânicas dos adesivos e os componentes a ligar vão influenciar a distribuição de tensões. O carregamento nas camadas adesivas de uma junta poderá induzir tensões de tração, compressão, corte, arrancamento ou clivagem, ou ainda uma combinação de duas ou mais destas componentes. O objetivo da presente dissertação é a caraterização completa de um adesivo estrutural de alta ductilidade recentemente lançado no mercado (SikaPower® -4720), para facilitar o projeto e otimização de juntas adesivas ligadas com o mesmo. São quatro os ensaios a realizar: ensaios à tração de provetes maciços (também denominados de bulk), ensaios ao corte com a geometria Thick Adherend Shear Test, ensaios Double-Cantilever Beam e ainda ensaios End-Notched Flexure. Com a realização dos ensaios referidos, são determinadas as propriedades essenciais à caraterização mecânica e de fratura do adesivo. Os resultados obtidos para cada ensaio resultaram em propriedades medidas com elevada repetibilidade, da mesma maneira que se revelaram de acordo com os dados disponibilizados pelo fabricante, sempre que estes estavam disponíveis.