Search

Primary aluminum recycling has been demonstrated to play a pivotal role in sustainable manufacturing. Nevertheless, among all the Life Cycle Assessment (LCA) studies existing in the literature on aluminum products, very few focus on the rolling stage. In this work, LCA of two semi-finished products, plates and coated strips, made of AA8011 aluminum alloy have been carried out. Inventory data were collected by the Laminazione Sottile plant, except for coil coating data for pharmaceutical caps, which were collected in the Italcoat plant. For both semi-finished products, hot rolling and fusion mostly affect the environmental impact of the manufacturing. Moreover, results show that the substitution of virgin primary aluminum with secondary aluminum results in a remarkable minimization of the environmental impact. Indeed, a 50% reduction of primary aluminum results in about a 70% reduction of GWP to produce AA 8011 coated strips. [ABSTRACT FROM AUTHOR]

Currently, sustainability plays a central role in the response to global challenges, strongly influencing decisions in various sectors. From this perspective, global efforts to explore inventive and eco-friendly solutions to address the demands of industrialization and large-scale production are being made. Bio-based composites needed for lightweight applications benefit from the integration of natural fibers, due to their lower specific weight compared to synthetic fibers, contributing to the overall reduction in the weight of such structures without compromising the mechanical performance. Nevertheless, challenges arise when using natural fibers in composite laminates and hybridization seems to be a solution. However, there is still a lack of knowledge in the literature regarding the strategies and possibilities for reducing laminate thickness, without sacrificing the mechanical performance. This work aims to fill this knowledge gap by investigating the possibility of reducing the laminate thickness in hybrid flax/basalt composites made of plies, organized in the same stacking sequence, through only varying their number. Tensile, Charpy, flexural, and drop-weight tests were carried out for the mechanical characterization of the composites. The results obtained confirm the feasibility of achieving thinner hybrid composites, thus contributing to sustainability, while still having acceptable mechanical properties for structural applications. [ABSTRACT FROM AUTHOR]

Electron Beam Melting (EBM) is typically used to produce high performance titanium parts, but it falls short in ensuring the high surface finishing always requested for aerospace applications, and this leads to the need of a post-building finishing resulting in additional resources and energy consumption. An optimization of the post-processing is necessary and the literature lacks guidelines in this regard. In this study, 18 cylindrical samples were produced by EBM and then postprocessed by wet and dry turning by using the same process parameters. Surface roughness, tool wear rate, specific cutting energy and energy consumption were investigated and compared to understand the best compromise to remove the same total depth of cut (2.4 mm). Results showed that dry turning can result in saving up 9% in total energy consumption. Also, feed rate and depth of cut can be varied by fixing the spindle speed without significantly affecting surface roughness by saving up to 73% in total energy consumption. [ABSTRACT FROM AUTHOR]

Selective laser melting (SLM) is a sustainable process that offers various environmental benefits. However, the parts produced from SLM process require post-processing treatments that increase the energy consumption. Therefore, there is a need for optimization of SLM input parameters to minimize the same. For this purpose, the data set on selectively laser-melted Inconel 718 parts was obtained from the reference. An evolutionary neural net has been employed to model the objective functions: specific energy consumption, relative density and surface roughness in the present study. The neural net strategy was successful in capturing the important trend of the three objectives by achieving a maximum correlation coefficient of 85% in each of them. Subsequently, the trained model is used in tri-objective optimization to yield the optimum input parameters. A close agreement is observed between the predicted optimum parameters and experimentally obtained parameters, proving the formulated strategy to be reliable and effective. [ABSTRACT FROM AUTHOR]