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Currently, vegetable oils have been studied as biolubricants in order to reach new environmental standards. Besides being non-renewable, mineral oils from petroleum bring consequences to the environment due to its low biodegradability. Thus, the aim of this work is to develop a biolubricant and to add oxide nanoparticles (ZnO and CuO) in order to improve abrasion resistance and friction. This product must be biodegradable and has better performance under boundary lubrication. The methodology consisted of the synthesis of biolubricants using vegetable oils (soybean and sunflower) by epoxidation reaction. The tribological performance was evaluated by HFRR (High Frequency Reciprocating Rig). The developed biolubricants showed good tribological properties besides being more adapted to the environment. Also, it was possible to verify that biolubricants without additives are slightly more tribologically effective than lubricants with additives.

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There are several parameters that highly influence material quality and printed shape in laser Directed Energy Deposition (L-DED) operations. These parameters are usually defined for an optimal combination of energy input (laser power, scanning speed) and material feed rate, providing ideal bead geometry and layer height to the printing setup. However, during printing, layer height can vary. Such variation affects the upcoming layers by changing the printing distance, inducing printing to occur in a defocus zone then cumulatively increasing shape deviation. In order to address such issue, this paper proposes a novel intelligent hybrid method for in-process estimating the printing distance ($$Z_s$$ Z s ) from melt pool images acquired during L-DED. The proposed hybrid method uses transfer learning to combine pre-trained Convolutional Neural Network (CNN) and Support Vector Regression (SVR) for an accurate yet computationally fast methodology. A dataset with 2,700 melt pool images was generated from the deposition of lines, at 60 different values of $$Z_s$$ Z s , and used for training. The best hybrid algorithm trained performed with a Mean Average Error (MAE) of 0.266 and a Mean Absolute Percentage Error (MAPE) of $$6.7\%$$ 6.7 % . The deployment of this algorithm in an application dataset allowed the printing distance to be estimated and the final part geometry to be inferred from the data.

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