Lucas F.M. da Silva

Mode II Fracture Toughness of a Brittle and a Ductile Adhesive as a Function of the Adhesive Thickness

The main goal of this study was to evaluate the effect of the thickness and type of adhesive on the Mode II toughness of an adhesive joint. Two different adhesives were used, Araldite ® AV138/HV998 which is brittle and Araldite 2015 which is ductile. The end notched flexure (ENF) test was used to determine the Mode II fracture toughness because it is commonly known to be the easiest and widely used to characterize Mode II fracture. The ENF test consists of a three-point bending test on a notched specimen which induces a shear crack propagation through the bondline. The main conclusion is that the energy release rate for AV138 does not vary with the adhesive thickness whereas for Araldite 2015, the fracture toughness in Mode II increases with the adhesive thickness. This can be explained by the adhesive plasticity at the end of the crack tip.

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.

Moulds design for adhesive bulk and joint specimens manufacturing

Purpose – The purpose of this paper is to provide an insight into the techniques which are used and developed for adhesive bulk and joint specimens manufacturing.Design/methodology/approach – After a short introduction, the paper discusses various techniques for adhesive bulk and joint specimens manufacturing and highlights their advantages and limitations. A number of examples in the form of different bulk and joint specimens of different types of adhesives are used to show the methods for determining the adhesives mechanical properties needed for design in adhesive technology. In order to predict the adhesive joint strength, the stress distribution and a suitable failure criterion are essential. If a continuum mechanics approach is used, the availability of the stress‐strain curve of the adhesive is sufficient (the bulk tensile test or the TAST test is used). For fracture mechanics‐based design, mode I and mode II toughness is needed (DCB and ENF tests are used). Finally, single lap joints (SLJs) are u...

Aluminium Friction-stir Weld-bonded Joints

A push towards more energy-efficient transport solutions has led to an increasing lightweight trend in structural design, requiring new materials, manufacturing, and assembly processes. The development of solid-state welding techniques, such as friction-stir welding (FSW), and the continuous improvement of adhesive technology, has created opportunities for new structural design concepts. Although FSW is capable of producing sound defect-free welds with high tensile strength efficiency in butt joint configuration, in the case of lap joints, the formation of a “hook”-like defect results in worse properties than base material. The combination of adhesive bonding (AB) with FSW aims to overcome this issue and create a hybrid joining technique. This work aims to develop a hybrid technique combining FSW and AB aggregating static strength testing and numerical modelling efforts. AB joints showed a 60% higher strength than FSW lap joints, but when combining FSW with adhesive, the hybrid joint managed to match the adhesive joints strength. Finite elements method (FEM) models developed for both AB and FSW lap joint showed some level of agreement, but when attempting to combine both models to discretize the hybrid joints the developed model failed to mimic the more complex failure mode.

Exploring the use of deep neural networks for sales forecasting in fashion retail

Abstract In the increasingly competitive fashion retail industry, companies are constantly adopting strategies focused on adjusting the products characteristics to closely satisfy customers requirements and preferences. Although the lifecycles of fashion products are very short, the definition of inventory and purchasing strategies can be supported by the large amounts of historical data which are collected and stored in companies databases. This study explores the use of a deep learning approach to forecast sales in fashion industry, predicting the sales of new individual products in future seasons. This study aims to support a fashion retail company in its purchasing operations and consequently the dataset under analysis is a real dataset provided by this company. The models were developed considering a wide and diverse set of variables, namely products physical characteristics and the opinion of domain experts. Furthermore, this study compares the sales predictions obtained with the deep learning approach with those obtained with a set of shallow techniques, i.e. Decision Trees, Random Forest, Support Vector Regression, Artificial Neural Networks and Linear Regression. The model employing deep learning was found to have good performance to predict sales in fashion retail market, however for part of the evaluation metrics considered, it does not perform significantly better than some of the shallow techniques, namely Random Forest.