1,720,977 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
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
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
Pulp and paper characterization by means of artificial neural networks for effluent solid waste minimization—A case study
No full textDesign and analysis of compound structures integrated with bio-based phase change materials and lattices obtained through additive manufacturing
No full textPhase 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
Artificial neural network in fibres length prediction for high precision control of cellulose refining
No full textPaper, 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
Evaluation of the effects of the metal foams geometrical features on thermal and fluid-dynamical behavior in forced convection
Full text linkMetal 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
FEM Simulations for the Optimization of the Inlet Gate System in Rapid Investment Casting Process for the Realization of Heat Exchangers
No full textOver 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 processe
Design and mechanical characterization of voronoi structures manufactured by indirect additive manufacturing
Full text linkAdditive manufacturing (AM) is a production process for the fabrication of threedimensional 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
Improvement of thermal, electrical, and tribological performances of GnPs composites produced by selective laser sintering
Full text linkIn the present work, Graphite nanoPlatelets/Polyamide-12 (GnPs/PA-12) particle composites were produced through additive manufacturing (AM). The selective laser sintering (SLS) technology was used to manufacture 3D-printed composite components by means of a powder mix of GnPs and PA-12. The analyzed combination of technology and materials allows to obtain parts with improved thermal, electrical, and tribological properties, while maintaining a low production cost. In total were realized 5 different scenarios, each one with a different wt% of the GnPs reinforcement (2-4-6-8-10 wt%), and compared the results to the PA-12 matrix. Experimental tests were performed to study the morphology (profilometry, SEM, wettability), the electrical conductivity under different normal loads (0.1–1 kN), the thermal performance, and the tribological properties of each sample. The results show that the increase of GnPs particles dispersed in the matrix leads to a hydrophobic behavior of the surface. An improvement in electrical conductivity (from 10−11 S/cm of the pure PA-12 matrix to 10−4 S/cm of the 10 wt% GnPs) and thermal performance (33,6% improvement for the best-case scenario compared to the bare matrix) was observed. Tribological tests underlined a reduction of 25% in friction coefficient and an improvement of 80% in wear resistance compared to the PA-12 matrix. Highlights: 3D printed GnPs/PA-12 composites does not exhibit any significant geometrical alteration. GnPs enable hydrophobic surfaces with increased contact angles. Electrical conductivity improved from 10−11 S/cm of the unfilled PA-12 matrix up to 10−4 S/cm, for the 10 wt% GnPs sample. Thermal performance improves up to 33.6% with GnPs reinforcement. 10 wt% GnPs reduces friction by 25% and wear by 81%
- …
