The use of material extrusion (MEX) has increased rapidly due to the affordability of 3D printers. This has led to a growing demand for improved print quality, high fidelity, strength, or fast print times. In this study, a non-planar approach for better surface quality is investigated. The hardware of a 3-axis MEX printer was developed together with testing new software for non-planar slicing. The aim was to identify the most influential parameter combinations using design of experiments. A novel method for measuring surface quality was presented together with future research work.

Offshore wind turbines intend to take a rapidly growing share in the electric mix. The design, installation, and exploitation of these industrial assets are regulated by international standards, providing generic guidelines. Constantly, new projects reach unexploited wind resources, pushing back installation limits. Therefore, turbines are increasingly subject to uncertain environmental conditions, making long-term investment decisions riskier (at the design or end-of-life stage). Fortunately, numerical models of wind turbines enable to perform accurate multi-physics simulations of such systems when interacting with their environment. The challenge is then to propagate the input environmental uncertainties through these models and to analyze the distribution of output variables of interest. Since each call of such a numerical model can be costly, the estimation of statistical output quantities of interest (e.g., the mean value, the variance) has to be done with a restricted number of simulations. To do so, the present paper uses the kernel herding method as a sampling technique to perform Bayesian quadrature and estimate the fatigue damage. It is known from the literature that this method guarantees fast and accurate convergence together with providing relevant properties regarding subsampling and parallelization. Here, one numerically strengthens this fact by applying it to a real use case of an offshore wind turbine operating in Teesside, UK. Numerical comparison with crude and quasi-Monte Carlo sampling demonstrates the benefits one can expect from such a method. Finally, a new Python package has been developed and documented to provide quick open access to this uncertainty propagation method.

Research on additive manufacturing has highlighted methods and guidelines to optimise the design process and improving finished product quality. There is still room for improvement in making AM as reliable as more traditional processes when considering industrial use. In terms of manufacturing, managing print parameters properly can improve reproducibility and repeatability of a part, in addition to its fidelity to the basic geometric model. However, a topological optimised geometry requires more than good parameterisation. Efforts are therefore being made to formalise knowledge so that it is explicit and accessible to designers. This paper proposes an approach based on the spatio-temporal evolution of a geometry during printing to quantify data at the meso scale. Previous studies have been conducted on the description of features in time, space and space-time, and on the influence of their arrangement within a part. Building on this work, a parameterised test specimen was designed to measure the quantitative impact of these arrangements on the final product. The method is then presented and illustrated through a case study to help the designer with quantitative predictive values of geometric parameters.

The interaction between chalcopyrite and illite particles was analysed using ζ-potential measurements and flotation tests. Statistically designed tests were used to examine the factors controlling flotation (frother concentration, dispersant concentration, froth height, airflow rate and amount of clay). Furthermore, the significance levels of the impacts of these factors on responses (chalcopyrite grade/recovery, pyrite grade/recovery, dynamic froth stability and mean bubble diameter) were determined. Chalcopyrite and pyrite ζ-potentials were measured in the presence of illite. The addition of 15% illite to the chalcopyrite, especially between pH 11 and 12, shifted the ζ-potential values closer to that of pure illite, indicating complete surface coating of chalcopyrite with illite. In the flotation experiments, better results were obtained in terms of chalcopyrite grade at a low airflow rate and a high froth height. With increasing froth height there was a decline in the gangue mineral recovery as the residence time of the froth increased. The most significant factor increasing pyrite recovery was the amount of clay. Although illite is considered to be the least problematic clay mineral for flotation, as reported in the literature, an illite content of as low as 5% in the ore decreased chalcopyrite grade by 3.83%. While K and Na contents of 4% were detected after flotation without the addition of illite, their abundance increased to 5.7% after the addition of illite.

Eco-tools and techniques often lack guidance and a robust methodology for improving the environmental performance of products with an active use phase. A case study was undertaken to investigate the role of Design of Experiments (DoE) in providing insights to improve the environmental performance of two product categories with active use phases: showers and tapware. The results show how varying the components can reduce energy use and demonstrated how DoE can be used as an objective method for optimising a products environmental performance when user behaviour can influence the results.

This paper describes the design optimisation study used to aerodynamically optimise the fairings that cover the rear wheels of the Land Speed Record vehicle, BLOODHOUND SuperSonic Car (SSC). Initially, using a Design of Experiments approach, a series of Computational Fluid Dynamics simulations were performed on a set of parametric geometries, with the goal of identifying a fairing geometry that was aerodynamically optimised for the target speed of 1,000 mph. Several aerodynamic properties were considered when deciding what design objectives the fairings would be optimised to achieve; chief amongst these was the minimisation of aerodynamic drag. A parallel, finite-volume Navier–Stokes solver was used on unstructured meshes in order to simulate the complex aerodynamic behaviour of the flow around the vehicle’s rear wheel structure, which involved a rotating wheel, and shockwaves generated close to a supersonic rolling ground plane. It was found that the simple response surface fitting approach did not sufficiently capture the complexities of the optimisation objective function across the high-dimensional design space. As a result, a Nelder–Mead optimisation approach was implemented, coupled with Radial Basis Function design space interpolation to find the final optimised fairing design. This paper presents the results of the optimisation study as well as indicating the likely impact this optimisation will have on the ultimate top speed of this unique vehicle.

The machining of shape memory alloys (SMAs) is fairly essential and integral part in the manufacture of components for utilizing in engineering applications. An effort has been made in the present work to study the effect of wire electro discharge machining process parameters such as pulse on time (Ton), pulse off time (Toff) and servo voltage (SV) have been analyzed on material removal rate and surface roughness. The investigation clearly reveals that an increased pulse on time with decrease in pulse off time as well as SV increases the amount of material removed in machining of SMAs. On the other hand, the surface roughness increases with increased pulse on time and decreases with increased pulse off time as well as SV. The surface topography of the machined surface was analyzed using scanning electron microscope (SEM) and confocal micrographs. Phase changes on the machined surface with respect to pulse on time and SV were evaluated from X-ray diffractometer (XRD) analysis.

The purpose of this paper is to provide a tutorial of data analysis methods for answering questions that arise in analyzing data from wine-tasting events: (i) measuring agreement of two judges and its extension to m judges; (ii) making comparisons of judges across years; (iii) comparing two wines; (iv) designing tasting procedures to reduce burden of multiple tastings; (v) ranking of judges; and (vi) assessing causes of disagreement. In each case we describe one or more analyses and make recommendations on the conditions of use for each. (JEL Classifications: C10, C12, C13, C59, C90)

On-line quality assessment has become one of the most critical requirements for improvingthe efficiency and the autonomy of automatic resistance spot welding (RSW) processes. Anaccurate and efficient model to perform non-destructive quality estimation is an essentialpart of the assessment process. This paper presents a structured and systematic approachdeveloped to design an effective ANN-based model for on-line quality assessment in RSW.The proposed approach examines welding parameters and conditions known to have aninfluence on weld quality, and builds a quality estimation model step by step. Themodeling procedure begins by examining, through a structured experimental design, theeffect of welding parameters (welding time, welding current, electrode force and sheetmetal thickness) and welding conditions represented by typical characteristics of thedynamic resistance curves on multiple welding quality indicators (indentation depth,nugget diameter and nugget penetration) and by analyzing their interactions and theirsensitivity to the variation of the dynamic process conditions. Using these results and bycombining an efficient modeling planning method, neural network paradigm, multi-criteriaoptimization and various statistical tools, the identification of the model form and thevariables to be included in the model is achieved by executing a systematic modeloptimization procedure. The results demonstrate that the proposed approach can lead to ageneral ANN-based model able to accurately and reliably provide an appropriate assessmentof the weld quality under diverse and variable welding conditions.

La formation des cellules (FC) de production se traduit par le groupement des produits en familles et le partage des machines en sous-groupes. Le traitement de ce problème devient plus complexe en présence de gammes alternatives. Dans ce contexte, nous proposons une nouvelle approche qui traite ce problème en deux niveaux. Le premier niveau porte sur l'application de la méthode des plans d'expériences, l'objectif étant de choisir, pour chaque produit, la gamme préférentielle permettant l'optimisation des indicateurs de performance de FC. En plus, ce niveau de traitement permet une analyse de la sensibilité de la solution finale ignorée par les techniques antérieures de la FC. Nous avons proposé et justifié un nouvel indice de la FC défini par le pourcentage de conservation des données de la matrice d'incidence initiale. Le second niveau de traitement consiste à appliquer la méthode de la FC basée sur l'analyse des corrélations statistiques entre les différents produits et machines. Pour expliquer l'approche proposée, nous avons détaillé deux exemples numériques. Les résultats d'une analyse comparative appliquée à des problèmes reconnus en littérature, ont permis de valider l'approche proposée.

Nous proposons dans cet article une méthode d'essai accéléré applicable pour des systèmes mécaniques complexes dont la durée de vie ne peut être exprimée par une relation analytique du niveau de sollicitation. Dans de tels cas, une approche numérique peut rester envisageable. Il s'agit alors d'ajuster le modèle numérique à partir de tests sévérisés au cours desquels les défaillances apparaissent plus rapidement qu'en conditions nominales. Il est ainsi possible de prévoir à partir d'essais très courts des défaillances susceptibles d'apparaître après une longue période d'utilisation. Cette méthode est mise en œuvre dans le cas de sollicitations en fatigue, l'endommagement cumulé conduisant à la rupture ne pouvant être modélisé que par un code numérique en éléments-finis.

Randomized controlled trials of veterinary vaccines are essential if we are to have a reasonable understanding of how those vaccines can be expected to perform when used in the field. This manuscript reviews a few (but certainly not all) of the key elements that need to be considered in the design of veterinary vaccine trials. The first step in the design of such a trial is to have a clear statement of the objective of the trial that reflects what is expected of the vaccine (e.g. should it minimize clinical disease or does it need to prevent infection?). Because domestic animals are often managed in groups, the ‘unit of concern’ used in a vaccine trial becomes of great importance. Whether the trial should be carried out at the individual or group level will depend on the objectives of the trial and the extent of concern about ‘group effects’ affecting the trial. Sample sizes will also be influenced heavily by the choice of unit of concern and the nature of the primary outcome being assessed. Finally, while there is no easy solution (except to conduct group-level trials, which may be logistically impossible), the potential for group effects to influence the trial outcome must be considered.