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The materials selection can affect the design component radically, with effect on the manufacturing systems efficiency, environmental impact issues, and customer satisfaction. There are different methods employed for materials selection; however, two steps are usual for most of these methods: screening and ranking. The ranking step identifies among materials candidates those that can perform the function the best as possible. Multi-criteria methods have been widely employed to materials selection, especially in the ranking step. Most of these methods take advantage of fuzzy numbers and linguistic variables to process qualitative information and information with uncertainties. One of the approaches that have been developed to solve issues related to make decisions in multi-criteria methods using linguistic information is the 2-tuple linguistic computational model. The main advantage of this approach is taking the "loss of information" away, which provides a higher precision on results. This paper aims to present a multi-criteria method for materials selection ranking step based on 2-tuple linguistic variables. The steps and several equations needed to apply the proposed method are described. Two case studies are presented and compare results with other methods to demonstrate the proposed method potential.

This study compares two processing routes, selective laser melting (SLM) and flame spray (FS) to fabricate an Al/MWCNT composite layer over an aluminum alloy 6013 (AA6013) substrate. The final surface and cross section morphologies were evaluated by scanning electron microscopy (SEM) and optical microscopy (OM). The effect of these processing routes on the multiwall carbon nanotubes (MWCNT) was evaluated by X-ray diffraction (XRD) and Raman spectroscopy (RS). Finally, the mechanical properties were evaluated by Vickers microhardness. The Raman bands corresponding to carbon were identified in the spectrum of both samples processed by SLM and FS. However, the Al4C3 formation was also identified in the latter. The Vickers microhardness results show an increase in the hardness values of the FS and SLM processed coatings of 44% and 9%, respectively, when compared with the AA6013 substrate.

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