Design and mechanical characterization of voronoi structures manufactured by indirect additive manufacturing

Additive manufacturing (AM) is a production process for the fabrication of three-dimensional items characterized by complex geometries. Several technologies employ a localized melting of metal dust through the application of focused energy sources, such as lasers or electron beams, on a powder bed. Despite the high potential of AM, numerous burdens afflict this production technology; for example, the few materials available, thermal stress due to the focused thermal source, low surface finishing, anisotropic properties, and the high cost of raw materials and the manufacturing process. In this paper, the combination by AM of meltable resins with metal casting for an indirect additive manufacturing (I-AM) is proposed. The process is applied to the production of open cells metal foams, similar in shape to the products available in commerce. However, their cellular structure features were designed and optimized by graphical editor Grasshopper®. The metal foams produced by AM were cast with a lost wax process and compared with commercial metal foams by means of compression tests

Investigating curcumin/intestinal epithelium interaction in a millifluidic bioreactor

Multidrug resistance is still an obstacle for chemotherapeutic treatments. One of the proteins involved in this phenomenon is the P-glycoprotein, P-gp, which is known to be responsible for the efflux of therapeutic substances from the cell cytoplasm. To date, the identification of a drug that can efficiently inhibit P-gp activity remains a challenge, nevertheless some studies have identified natural compounds suitable for that purpose. Amongst them, curcumin has shown an inhibitory effect on the protein in in vitro studies using Caco-2 cells. To understand if flow can modulate the influence of curcumin on the protein’s activity, we studied the uptake of a P-gp substrate under static and dynamic conditions. Caco-2 cells were cultured in bioreactors and in Transwells and the basolateral transport of rhodamine-123 was assessed in the two systems as a function of the P-gp activity. Experiments were performed with and without pre-treatment of the cells with an extract of curcumin or an arylmethyloxy-phenyl derivative to evaluate the inhibitory effect of the natural substance with respect to a synthetic compound. The results indicated that the P-gp activity of the cells cultured in the bioreactors was intrinsically lower, and that the effect of both natural and synthetic inhibitors was up modulated by the presence of flow. Our study underlies the fact that the use of more sophisticated and physiologically relevant in vitro models can bring new insights on the therapeutic effects of natural substances such as curcumin

Neural network implementation for the prediction of load curves of a flat head indenter on hot aluminum alloy

The indentation test performed by means of a flat-ended indenter is a valuable non-destructive method for assessment of metals at a local scale. Particularly, from the indentation curves it is possible to achieve several mechanical properties. The aim of this paper is the implementation of an artificial neural network for the prediction of the indentation load as a function of the penetration depth for an aluminium substrate. In particular, the neural network is addressed to the mechanical characterization of the bulk in function of temperature and indentation rate. The results obtained showed a high accuracy in curves prediction

Artificial neural network in fibres length prediction for high precision control of cellulose refining

Paper, a web of interconnected cellulose fibres, is widely used as a base substrate. It has been applied in several applications since it features interesting properties, such as renewability, biodegradability, recyclability, affordability and mechanical flexibility. Furthermore, it offers a broad possibility to modify its surface properties toward specifics additives. The fillers retention and the fibres bonding ability are heavily affected by the cellulose refining process that influences chemical and morphological features of the fibres. Several refining theories were developed in order to determine the best refining conditions. However, it is not trivial to control the cellulose refining as different phenomena occur simultaneously. Therefore, it is intuitively managed by experienced papermakers to improve paper structures and properties. An approach based on the machine learning aimed at estimating the effects of refining on the fibres morphology is proposed in this study. In particular, an artificial neural network (ANN) was implemented and trained with experimental data to predict the fibres length as a function of refining process variables. The prediction of this parameter is crucial to obtain a high-performance process in terms of effectiveness and the optimisation of the final product performance as a function of the process parameter. To achieve these results, data mining of the experimental patterns collected was exploited. It led to the achievement of excellent performance and high accuracy in fibres length prediction

Electro-deposition of graphene nanoplatelets on CPU cooler—experimental and numerical investigation

This manuscript deals with to improve knowledge of the mechanisms of deposition of graphene on a flat surface. The analysis has been developed on a CPU cooler with experimental results and FEM analysis. The experiments are conducted by mounting the system over a heat source, while the force convection is facilitated by means of a blower. The transient temperature distribution in the CPU cooler is also observed. A model has been performed for modeling the thermal behavior of assembly structures with thin layers. The experimental observations are verified by simulation using a commercial FEM software. It was used a model of orthotropic thermal conductivity with variable values. The goal of the simulation has been to individuate the value of coefficient of thermal conductivity of the layer of graphene. The FEM results show how the graphene was deposited on the surface of CPU cooler and consequently, how this determine the coefficient of thermal conductivity

Design and analysis of compound structures integrated with bio-based phase change materials and lattices obtained through additive manufacturing

Phase change materials (PCMs) are an interesting category of materials employed in latent heat thermal energy storage, such as ad hoc designed heat exchangers. Nowadays, there are several typologies of PCMs, which derive from the wastes of the agricultural industry, which could be used for this kind of design. Each material made of biological waste has a different melting/solidification point and latent heat of fusion/solidification, which means flexibility of design on the heat exchangers by considering the different thermal proprieties of the chosen material. Also, using recycled material from wastes can lead to an overall improvement of the resources and goes hand in hand with the need of today's society to aim more and more at a Circular Economy. The industrial development of this kind of material is limited by its thermal properties, such as poor thermal conductivity both in liquid and solid phases, leading to low heat transfer effectiveness. To overcome these limitations, in this paper, the bio-based PCMs were integrated into a metallic reticular structure made of copper and aluminium and realised through Indirect-Additive Manufacturing, to improve the overall thermal conductivity of the system and increase the efficiency of the heat transfer. Four compound structures filled each time with four different PCMs were realised and tested, in order to thermally characterise each combination of materials used and choose which one has an overall better thermal behaviour. The results showed how the thermal storage/release was improved by 10% for the copper reticular structure, even if must be considered the tradeoff between better thermal management and the increase of the costs and the weight of the designed heat exchanger

Image-based system and artificial neural network to automate a quality control system for cherries pitting process

This work proposes a non-destructive quality control for a pitting process of cherries. A system composed of a video camera and a light source records pictures of backlit cherries. The images processing in MATLAB environment provides the dynamic histograms of the pictures, which are analysed to state the presence of the pit. A feedforward artificial neural network was implemented and trained with the histograms obtained. The network developed allows a fast detection of stone fractions not visible by human inspection and the reduction of the accidental reject of properly manufactured products