Polydimethylsiloxane (PDMS) is one of the most popular elastomers and has been used in different fields, especially in biomechanics research. Among the many interesting features of this material, its hyperelastic behavior stands out, which allows the use of PDMS in various applications, like the ones that mimic soft tissues. However, the hyperelastic behavior is not linear and needs detailed analysis, especially the characterization of shear strain. In this work, two approaches, numerical and experimental, were proposed to characterize the effect of shear strain on PDMS. The experimental method was implemented as a simple shear testing associated with 3D digital image correlation and was made using two specimens with two thicknesses of PDMS (2 and 4 mm). A finite element software was used to implement the numerical simulations, in which four different simulations using the Mooney–Rivlin, Yeoh, Gent, and polynomial hyperelastic constitutive models were performed. These approaches showed that the maximum value of shear strain occurred in the central region of the PDMS, and higher values emerged for the 2 mm PDMS thickness. Qualitatively, in the central area of the specimen, the numerical and experimental results have similar behaviors and the values of shear strain are close. For higher values of displacement and thicknesses, the numerical simulation results move further away from experimental values.

The manufacturing processes involving thermal transitions have been more used in industries nowadays, being the welding one of the most widely used. The requirement to design and predict adverse conditions are fundamental to the development of any mechanical project. As a result, the market needs have motivated the companies to find faster and more effective solutions, being one of a recent tools an ACT (Ansys Customization Toolkit) called "Moving Heat Source", in which is executed the Gaussian heat source to model welding and laser processes. Based on this, the present work proposes to evaluate the accuracy of that extension implementing a finite element model for the MAG/TIG welding processes in DINCK20 steel and Al6082-T6 aluminium alloy, comparing with one of the first mathematical model proposed by the literature (Rosenthal) and with a recent analytical method of high precision already validated experimentally. The results showed a smaller global error for MAG process (3~10%) when compared to TIG (15~18%) and, the temperatures measured on the surface of the plate presented errors lower than the bottom in both alloys.

In the search of sustainable process and products, ecofriendly policies have been developed over the years, aiming at reducing the environmental impacts as a step toward sustainability. Among the environmental impacts, alternatives to mitigate the greenhouse gas emissions - GHG stand out due to the concerns with climate change. Then, the development and use of renewable resources become relevant. Considering that supply chains are intense in energy consumption and GHG emissions (since involves processes related to supply, production, transport, consumption), it becomes relevant to investigate if the management of sustainable supply chain are considering the renewable energies in their processes. Therefore, this paper aims at mapping the role of renewable energies in the context of sustainable supply chain, analyzing the literature published at Web of Science database - WoS about the subject. The main researchers, organizations, collaboration networks were presented, and the 21 most cited studies were mapped in this paper. The research was carried out with the papers published at WoS until 2019, using VantagePoint software to handle information. The findings show that the research about renewable energy in the context of sustainable supply chain has been growing, especially since 2010. Moreover, biomass, biofuels and photovoltaic energy were the most recurrent sources of renewable energy studied by most cited papers. However, the theme presented itself as new and that there are still potential to be explored.