Determinação da tenacidade à fratura de adesivos estruturais em modo misto pelo ensaio Single-Leg Bending

A utilização de juntas adesivas em aplicações industriais tem vindo a aumentar, em detrimento dos métodos tradicionais tais como a soldadura, brasagem e ligações aparafusadas e rebitadas. Este facto deve-se às vantagens que estas oferecem, como o facto de serem mais leves, comportarem-se bem sob cargas cíclicas ou de fadiga, a ligação de materiais diferentes e menores concentrações de tensões. Para aumentar a confiança no projeto de estruturas adesivas, é importante conseguir prever com precisão a sua resistência mecânica e respetivas propriedades de fratura (taxa crítica de libertação de energia de deformação à tração, GIC, e corte, GIIC). Estas propriedades estão diretamente relacionadas com a Mecânica da Fratura e são estimadas através de uma análise energética. Para este efeito, distinguem-se três tipos de modelos: modelos que necessitam da medição do comprimento de fenda durante a propagação do dano, modelos que utilizam um comprimento de fenda equivalente e métodos baseados no integral J. Como na maioria dos casos as solicitações ocorrem em modo misto (combinação de tração com corte), é de grande importância a perceção da fratura nesta condições, nomeadamente das taxas de libertação de energia relativamente a diferentes critérios ou envelopes de fratura. Esta comparação permite, por exemplo, averiguar qual o melhor critério energético de rotura a utilizar em modelos numéricos baseados em Modelos de Dano Coesivo. Neste trabalho é realizado um estudo experimental utilizando o ensaio Single-Leg Bending (SLB) em provetes colados com três tipos de adesivos, de forma a estudar e comparar as suas propriedades de fratura. Para tal, são aplicados alguns modelos de redução da taxa de libertação de energia de deformação à tração, GI, e corte, GII, enquadrados nos modelos que necessitam da medição do comprimento de fenda e nos modelos que utilizam um comprimento de fenda equivalente. Numa fase posterior, procedeu-se à análise e comparação dos resultados adquiridos durante a fase experimental de GI e GII de cada adesivo. A discussão de resultados foi também feita através da análise dos valores obtidos em diversos envelopes de fratura, no sentido de averiguar qual o critério de rotura mais adequado a considerar para cada adesivo. Foi obtida uma concordância bastante boa entre métodos de determinação de GI e GII, com exceção do adesivo mais dúctil, para o qual o método baseado no comprimento de fenda equivalente apresentou resultados ligeiramente superiores.

Adhesive selection for single lap bonded joints: Experimentation and advanced techniques for strength prediction

The integrity of multi-component structures is usually determined by their unions. Adhesive-bonding is often used over traditional methods because of the reduction of stress concentrations, reduced weight penalty, and easy manufacturing. Commercial adhesives range from strong and brittle (e.g., Araldite® AV138) to less strong and ductile (e.g., Araldite® 2015). A new family of polyurethane adhesives combines high strength and ductility (e.g., Sikaforce® 7888). In this work, the performance of the three above-mentioned adhesives was tested in single lap joints with varying values of overlap length (LO). The experimental work carried out is accompanied by a detailed numerical analysis by finite elements, either based on cohesive zone models (CZM) or the extended finite element method (XFEM). This procedure enabled detailing the performance of these predictive techniques applied to bonded joints. Moreover, it was possible to evaluate which family of adhesives is more suited for each joint geometry. CZM revealed to be highly accurate, except for largely ductile adhesives, although this could be circumvented with a different cohesive law. XFEM is not the most suited technique for mixed-mode damage growth, but a rough prediction was achieved.

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.

Strength and damage analysis of composite-aluminium adhesively-bonded single-lap joints

With the need to find an alternative way to mechanical and welding joints, and at the same time to overcome some limitations linked to these traditional techniques, adhesive bonds can be used. Adhesive bonding is a permanent joining process that uses an adhesive to bond the components of a structure. Composite materials reinforced with fibres are becoming increasingly popular in many applications as a result of a number of competitive advantages. In the manufacture of composite structures, although the fabrication techniques reduce to the minimum by means of advanced manufacturing techniques, the use of connections is still required due to the typical size limitations and design, technological and logistical aspects. Moreover, it is known that in many high performance structures, unions between composite materials with other light metals such as aluminium are required, for purposes of structural optimization. This work deals with the experimental and numerical study of single lap joints (SLJ), bonded with a brittle (Nagase Chemtex Denatite XNRH6823) and a ductile adhesive (Nagase Chemtex Denatite XNR6852). These are applied to hybrid joints between aluminium (AL6082-T651) and carbon fibre reinforced plastic (CFRP; Texipreg HS 160 RM) adherends in joints with different overlap lengths (LO) under a tensile loading. The Finite Element (FE) Method is used to perform detailed stress and damage analyses allowing to explain the joints’ behaviour and the use of cohesive zone models (CZM) enables predicting the joint strength and creating a simple and rapid design methodology. The use of numerical methods to simulate the behaviour of the joints can lead to savings of time and resources by optimizing the geometry and material parameters of the joints. The joints’ strength and failure modes were highly dependent on the adhesive, and this behaviour was successfully modelled numerically. Using a brittle adhesive resulted in a negligible maximum load (Pm) improvement with LO. The joints bonded with the ductile adhesive showed a nearly linear improvement of Pm with LO.