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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.

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.

Wine's high economic value requires the development of methods to assess its quality. Anyway, winemaking is often managed by operators that decode the physicochemical phenomena occurring during manufacture. Integrating their skills in a control framework would represent an enhancement of the quality control procedure. The anthocyanin amount in wine is influenced by the fermentation process parameters, is responsible for red wine colour and suitable for intensity and tonality classification. In this paper, an artificial neural network was implemented to predict the intensity and tonality of wine's colour as a function of different parameters, which occur during the fermentation process.

Over the last decades, additive manufacturing (AM) has become the principal production technology for prototypes and components with high added value. In the production of metallic parts, AM allows producing complex geometry with a single process. Also, AM admits a joining of elements that could not be realized with traditional methods. In addition, AM allows the manufacturing of components that could not be realized using other types of processes like reticular structures in heat exchangers. A solid mold investment casting that uses printed patterns overcomes typical limitations of additive processes such as expensive machinery and challenging process parameter settings. Indeed, rapid investment casting provides for a foundry epoxy pattern reproducing the component to exploit in the lost wax casting process. In this paper, aluminium radiators with flat heat pipes seamlessly connected with a cellular structure were conceived and produced. This paper aims at defining and investigating the principal foundry parameters to achieve a defect-free heat exchanger. For this purpose, different device CAD models were designed, considering four pipes’ thickness and length. Finite element method numerical simulations were performed to optimize the design of the casting process. Three different gate configurations were investigated for each length. The numerical investigations led to the definition of a castability range depending on flat heat pipes geometry and casting parameters. The optimal gate configuration was applied in the realization of AM patterns and casting processes

Paper mills are among the most polluting industries, responsible for many organic and inorganic compounds emissions. The fibres electro-kinetic features strongly affect the ability to retain fillers since the fillers–fibres interactions are charge induced. The control and the prediction of these parameters would represent a precious aid for process management, allowing the fillers retention enhancement, a lower environmental impact and the paper sheet properties streamlining. The work presented deals with the implementation and training of four artificial neural networks (ANNs) for the prediction of the main electrochemical and physical features of cellulose pulp and paper. First, two ANNs predict the electrochemical parameters. Following, they were applied to predict the paper sheet properties and fillers retention. The neural models implemented showed outstanding prediction performance, with R2 in the order of 0.999 and a low mean error. The results demonstrate how Artificial Neural Networks may be a valuable instrument for paper mill pollutant reduction. However, they suggest a more inclusive investigation for a better fibres behaviour representation.

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&reg, The metal foams produced by AM were cast with a lost wax process and compared with commercial metal foams by means of compression tests.

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.

Metal foams are a material, featuring interesting characteristics for the aeronautical and automotive fields because of their low specific weight, high thermal properties, and mechanical performances. In particular, this paper deals with thermal and fluid dynamic study of 24 open-cell aluminum EN43500 (AlSi10MnMg) metal foams produced by indirect additive manufacturing (I-AM), combining 3D printing and metal casting to obtain a controllable morphology. A study of foam behavior function of the morphological features (pores per inch (PPI), branch thickness (r), and edges morphology (smooth-regular)) was performed. The samples produced were heated by radiation and tested in an open wind circuit gallery to measure the fluid dynamic properties such as pressure drop (Δp), inertial coefficient (f), and permeability (k), in an air forced convection flow. The thermal characterization was performed evaluating both the theoretical (kth) and effective (keff) thermal conductivity of the foams. Also, the global heat transfer coefficient (HTCglobal) was evaluated with different airflow rates. Analysis of variance (ANoVA) was performed to figure out which geometrical parameters are significant during both thermal and fluid dynamic processes. The results obtained show how the controllable foam morphology can affect the involved parameters, leading to an ad hoc design for industrial applications that require high thermo-fluid-dynamical performances.

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.

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.

The present work deals with to improve knowledge of the mechanism of deposition of graphene on a complex geometry. The component of this study is an aluminum micro head engine that represents an interesting study case for its application in the field of heat dissipation. It has been coated with copper and graphene nanoplatelets by an electrodeposition process. The tests are conducted by realizing a system heat source similar to engine thermal behavior. The analysis has been developed on a micro head engine with a comparison between thermography results and finite element method (FEM) thermal analysis by commercial software Ansys. A three-dimensional heat conduction model in the coating structure was built, based on which FEM simulation was done. The influence of convection conditions has been evaluated by a comparison with FEM analysis without computational fluid dynamics simulations. The increase of thermal conductivity of coated specimen has been evaluated with the original one. Data analysis was performed by a comparison with 2-norm of fitting curves between the laboratory tests and simulations.

In the present study, laser surface treatment of CFRP made of PPS thermoplastic matrix by means of a 30 W Q-Switched Yb:YAG fiber laser, is investigated with the aim to improve adhesive bonding. The process parameters pulse power, scanning speed, hatch distance and scanning strategy, were varied to the aim to study the influence of the process condition on the first top resin layer removal and fibre damage. The operating window was experimentally determined. The effectiveness of laser treatment was verified by single lap shear test.

The present investigation deals with the application of low-curable powder paints on epoxy-carbon laminates. Carbon laminates were first peened to corrugate their surface, hence increasing the wettability and allowing a better adhesion of the overlying coatings. Powder coatings were then electrostatically sprayed onto peened and unpeened substrates and baked into a convection oven. Their aesthetic and tribological performance were comparatively evaluated. Powder coated peened carbon laminates exhibited good adhesion and visual appearance as well as noteworthy scratch resistance and tribological performance. © 2009 Elsevier B.V. All rights reserved.

The economy of high quality metal flakes powder coatings process is remarkably influenced by the curing procedure involving two cost and time consuming steps in convective oven. Significant savings can be achieved accelerating the baking process by IR pre-curing the outermost layers of the basecoat and following this with a conventional oven-baking of the whole coating system. In the present investigation, the IR pre-curing process of the basecoat was analyzed by studying the influence of the IR radiation intensity and exposure time on the adhesion strength and scratch resistance of the coating varying the contact load of the scratching indenter. © 2009 Elsevier B.V. All rights reserved.

A number of materials is subject to the cavitation during superplastic deformation. The cavities nucleate at sites such as grain boundaries, second phase particles, and triple points; subsequently, they grow and interlink with the neighbouring cavities. Cavitation usually leads either to the undesirable post-forming characteristics or to the premature tensile failure. It is also apparent that the cavities can pre-exist in the form of cracks and decohered interfaces, which develop during thermomechanical processing necessary to produce the superplastic microstructures. Evidently, extensive cavitation imposes significant limitations on their commercial application. The effect of material properties such as the cavity growth rate of intentionally pre-machined voids on specimens subject to the tensile deformation and to the biaxial deformation has been determined. The tensile tests have been conducted at constant crosshead velocity, using a fine-grained Pb–Sn alloy that presents superplastic properties at room temperature. The results of the experiments are in agreement with the numerical predictions obtained using a code based on the finite element method (FEM).