1,721,102 research outputs found
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
Emissioni di VOC nel mechanical pulping
No full textPrimi risultati del «Control of VOC emission from
mechanical pulping beyond BAT», progetto volto
a valutare la fattibilità tecnico-economica di un sistema
di abbattimento ibrido dei composti organici volatili nella
cartiera di Portonogaro, che produce pasta legno meccanic
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
Pretreatments effects on mechanical and morphological features of copper coatings
No full textIn order to exploit the aluminum potential in industrial environments, the application of alloying is fundamental. Anyway, this entails drawbacks such as decrement of mechanical bulk properties and corrosion phenomena. The surface engineering, and particularly the surface coatings, allows the local modification of the samples properties to overtake these criticisms. Copper it is often applied as a viable alternative to aluminum in several applications. Indeed, combinations of both have already broadly exploited for functional components. However, surface pretreatments strictly affect the production of coatings. This article proposes a study on the effects of some of the pretreatments of major industrial interest on the deposits created through the electrodeposition process. The results achieved demonstrate how the samples preparation effectively affects the growth of the copper grain as well as the coatings performance. In fact, different grains morphologies were observed as well as different performance of the coatings in terms of wear and scratch resistance. In particular, smooth and extremely corrugated surfaces did not lead to the formation of a continuous copper coatings when low thickness is considered. To bridge the gap among several grains, the copper core deposited has a planar growth in opposition to the other pretreatment, which allowed a compact coating and a columnar growth. Furthermore, the larger are the grain the less adherent is the coating. Also, different breakage mechanisms were identified as a function of the coating thickness
Pulp and paper characterization by means of artificial neural networks for effluent solid waste minimization—A case study
No full textEvaluation 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
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
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
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