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.Materials 2020, 13, 1085 2 of 11 AM processes for metallic materials represent an interesting technology in manufacturing applications. The most applied AM processes for metals include laser beam melting (LBM), electron beam melting (EBM), and laser metal deposition (LMD). Metallic parts produced by AM are more suitable for industrial applications compared to polymeric parts. However, expensive machineries, as well as low surface finishing and demanding process settings, limit the application of these methodologies in industrial environments [7]. Metal foams are composed of biphasic and cellular materials, which combine good mechanical resistance with excellent thermal and acoustic properties [8].In particular, high specific strength [9][10][11][12][13] and strain [14][15][16], excellent energy absorption [17,18], acoustic insulation [19], and heat dissipation media [20][21][22][23][24] make this class of materials increasingly useful for several multifunctional applications. The main problem afflicting metal foams regards the manufacturing process, and specifically the porosity distribution [25,26], as well as the connection with other components. The latter is a critical factor in structural and heat-exchange devices, because welding and brazing processes are time-consuming, costly, and not suitable for the most common materials exploited in metal foams production. This burdens their feasibility in industrial applications [27,28]. The cellular configuration design is fundamental, as its purpose is the definition of a commercial foam-like structure that matches specific application requirements. As a consequence of their random structure, the foams offer very valuable materials for filters [29][30][31] or heat exchange processes [32][33][34][35].
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.
Magnesium alloys are widely employed in several industrial domains for their outstanding properties. They have a high strength-weight ratio, with a density that is lower than aluminum (33% less), and feature good thermal properties, dimensional stability, and damping characteristics. However, they are vulnerable to oxidation and erosion-corrosion phenomena when applied in harsh service conditions. To avoid the degradation of magnesium, several coating methods have been presented in the literature; however, all of them deal with drawbacks that limit their application in an industrial environment, such as environmental pollution, toxicity of the coating materials, and high cost of the necessary machinery. In this work, a plating of Al2O3 film on a magnesium alloy realized by the fluidized bed (FB) technique and using alumina powder is proposed. The film growth obtained through this cold deposition process is analyzed, investigating the morphology as well as tribological and mechanical features and corrosion behavior of the plated samples. The resulting Al2O3 coatings show consistent improvement of the tribological and anti-corrosive performance of the magnesium alloy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.