1,721,073 research outputs found
Improvement of thermal properties of micro head engine electroplated by graphene: experimental and thermal simulation
No full textThe 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
A thermographic technique for in-plane thermal diffusivity measurement of electroplated coatings
No full textIn the present work, a thermographic procedure is developed and tested with the aim to determine the in-plane
thermal diffusivities of electroplated coatings with different thickness on an aluminium alloy (AA6082). For the
tests, a single pulse of a diode laser source was adopted to generate a thermal contrast on the specimen’s surface.
An infrared camera was adopted to acquire the surface gradient temperature and a MATLAB algorithm was
developed to calculate the thermal diffusivity. In order to study the influence of the laser parameters set on the
technique, two different laser pulses were adopted to heat the specimens; moreover, the influence of the starting
acquiring time by IR camera was also evaluated. From the results, the technique is demonstrated to be effective
for the in-plane thermal diffusivity measurement of thin coatings and, within the investigated parameters
window, to be not affected by the checked parameters
Neural network implementation for the prediction of secondary phase precipitation and mechanical feature in a duplex stainless steel
No full textNeural network implementation for the prediction of secondary phase precipitation and mechanical feature in a duplex stainless steel
No full textDuplex stainless steels are extremely valuable materials in the manufacturing environment, featuring remarkable mechanical and physical characteristics. Anyway, the exploitation of this material often requires the creation of welded joints; this is a critical process for the duplex steel, entailing the precipitation of secondary phases. These precipitates undermine the peculiar features of the duplex steels and particularly toughness and corrosion resistance. For the design of welding processes or thermal cycles in general, literature presents several models aimed at the prediction of the sigma-phase precipitation furtherly to the precipitation diagram. In this paper, the presence of secondary phases within a duplex stainless steel 2205 microstructure thermally treated was evaluated with several techniques. At a later stage, an indentation test with a flat-ended cylinder was carried out, obtaining load-indentation depth curves that allow the evaluation of the yield stress. The data acquired during the experimental activities, which highlighted a correlation between secondary phases amount and yield stress, were used for the training of two artificial neural networks aimed at secondary phase amount and indentation curve prediction. The networks implemented are connected in series. The first network predicts the secondary phases’ amount with an error of the magnitude of 1% and can be used as starting point for the second network, while the accuracy in the indentation curve prediction allows a precise evaluation of the yield stress
Scratch resistance of ‘fast-cured’ metal flake powder coatings
No full textThe 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
Pulp and paper characterization by means of artificial neural networks for effluent solid waste minimization—A case study
No full textPaper 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
Analysis of void growth in superplastic materials
No full textA 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)
Design and thermal comparison of random structures realized by indirect additive manufacturing
No full textAdditive manufacturing (AM) processes are used to fabricate three-dimensional complex geometries. There are several technologies that use laser or electron beam over metal powder beds. However, the direct AM processes have inconveniences such as specific set of materials, high thermal stress traced, high local energy absorbed, poor surface finish, anisotropic properties, high cost of material powder, and manufacturing with high-power beams. In this paper, an alternative process was developed. An indirect additive manufacturing (I-AM) combining a 3D print of castable resin and metal casting in order to obtain a cellular structure similar in shape to commercial metal foams but completely definable as design features was developed. Design of the cellular structure was made by the graphical algorithm editor Grasshopper®. Designed structures were realized by a lost-wax casting process and compared with commercial foam specimens by a system designed for this work. The designed metal foams showed a performance superior to that of commercial metal foam; in particular, the heat thermal coefficient of designed metal foams in the better case was 870W/m2·K, almost doubled in comparison with the commercial foam tested in this work
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