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Additive manufacturing (AM) has been one of the most highlighted processes of the last few years. AM prints complex parts and items from 3D files regarding different materials, such as polymers. Moreover, there are different AM techniques available for polymers, such as selective laser sintering. In the SLS technology, polyamides 11 and 12 lead 88% of the market. These materials are high-cost and use an average of 50% of virgin material at each printing. It is possible to use lower rates of virgin material, but at least 30% is recommended. Low rates of virgin material decrease mechanical properties.

This study aims to evaluate the influence on the mechanical properties of the percentage of reused PA12 in parts manufactured by the SLS process. The specimens of PA12 were manufactured with a percentage of virgin/reused polymer of 50/50, 40/60, 30/70, 20/80 and 10/90. We considered three distinct printing directions to compare the mechanical properties of the specimens: horizontal, perpendicular and vertical.

The results showed that when the percentage of reused material increases, the tensile strength limit (TSL), flexural strength limit and Shore D hardness decrease. Another aspect visualized was the fragile behavior presented in the vertical specimens. In addition, DSC analysis indicated a 2% reduction of crystallinity. Scanning electron microscopy images revealed spherical voids and unfused particles of PA12 at the fracture of tensile test specimens. The material thermal history and unfused particles could decrease the material properties.

We observed that the mechanical properties, such as the TSL, flexural strength limit and hardness, decrease as the percentage of reused material increases. In addition, the process presented a printing-direction dependence, where the vertical direction presented as the more brittle between the ones used.

This paper aims to develop a simple model for estimating sustainable development goals index using the capabilities of artificial neural networks.

Sustainable development has three pillars, including social, economic and environmental pillars. Three clusters corresponding to the three pillars were created by extracting sub-indices of three 2018 global reports and performing cluster analysis on the correlation matrix of sub-indices. By setting the sustainable development goals index as the target variable and selecting one indicator from each cluster as input variables, 20 artificial neural networks were run 30 times.

Artificial neural networks with seven nodes in one hidden layer can estimate sustainable development goals index by using just three inputs, including ecosystem vitality, human capital and gross national income per capita. There is an excellent similarity (>95%) between the results of the artificial neural network and the sustainable development goals index.

Instead of calculating 232 indicators for determining the value of sustainable development goals index, it is possible to use only three sub-indices, but missing 5% of precision, by using the proposed artificial neural network model.

The study provides additional information on the estimating of sustainable development and proposes a new simple method for estimating the sustainable development goals index. It just uses three sub-indices, which can be retrieved from three global reports.