The increase in accessibility of fused filament fabrication (FFF) machines has inspired the scientific community to work towards the understanding of the structural performance of components fabricated with this technology. Numerous attempts to characterize and to estimate the mechanical properties of structures fabricated with FFF have been reported in the literature. Experimental characterization of printed components has been reported extensively. However, few attempts have been made to predict properties of printed structures with computational models, and a lot less work with analytical approximations. As a result, a thorough review of reported experimental characterization and predictive models is presented with the aim of summarizing applicability and limitations of those approaches. Finally, recommendations on practices for characterizing printed materials are given and areas that deserve further research are proposed.

Improving the efficiency of the agricultural irrigation systems substantially contributes to sustainable water management. This improvement can be achieved through an automated irrigation system that includes a real-time control strategy based on the water, soil, and crop relationship. This paper presents a model driven control strategy applied to an irrigation system, in order to make an efficient use of water for large crop fields, that is, applying the correct amount of water in the correct place at the right moment. The proposed model uses a predictive algorithm that senses soil moisture and weather variables, to determine optimal amount of water required by the crop. This proposed approach is evaluated against a traditional irrigation system based on the empirical definition of time periods and against a basic soil moisture control system. Results indicate that the use of a model predictive control in an irrigation system achieves a higher efficiency and significantly reduce the water consumption.

Non-pathological mental fatigue is a recurring, but undesirable condition among people in the fields of office work, industry, and education. This type of mental fatigue can often lead to negative outcomes, such as performance reduction and cognitive impairment in education; loss of focus and burnout syndrome in office work; and accidents leading to injuries or death in the transportation and manufacturing industries. Reliable mental fatigue assessment tools are promising in the improvement of performance, mental health and safety of students and workers, and at the same time, in the reduction of risks, accidents and the associated economic loss (e.g., medical fees and equipment reparations). The analysis of biometric (brain, cardiac, skin conductance) signals has proven to be effective in discerning different stages of mental fatigue; however, many of the reported studies in the literature involve the use of long fatigue-inducing tests and subject-specific models in their methodologies. Recent trends in the modeling of mental fatigue suggest the usage of non subject-specific (general) classifiers and a time reduction of calibration procedures and experimental setups. In this study, the evaluation of a fast and short-calibration mental fatigue assessment tool based on biometric signals and inter-subject modeling, using multiple linear regression, is presented. The proposed tool does not require fatigue-inducing tests, which allows fast setup and implementation. Electroencephalography, photopletismography, electrodermal activity, and skin temperature from 17 subjects were recorded, using an OpenBCI helmet and an Empatica E4 wristband. Correlations to self-reported mental fatigue levels (using the fatigue assessment scale) were calculated to find the best mental fatigue predictors. Three-class mental fatigue models were evaluated, and the best model obtained an accuracy of 88% using three features, $\beta /\theta$ (C3), and the $\alpha /\theta$ (O2 and C3) ratios, from one minute of electroencephalography measurements. The results from this pilot study show the feasibility and potential of short-calibration procedures and inter-subject classifiers in mental fatigue modeling, and will contribute to the use of wearable devices for the development of tools oriented to the well-being of workers and students, and also in daily living activities.

In contrast to the traditional 3D printing process, where material is deposited layer-by-layer on horizontal flat surfaces, conformal 3D printing enables users to create structures on non-planar surfaces for different and innovative applications. Translating a 2D pattern to any arbitrary non-planar surface, such as a tessellated one, is challenging because the available software for printing is limited to planar slicing. The present research outlines an easy-to-use mathematical algorithm to project a printing trajectory as a sequence of points through a vector-defined direction on any triangle-tessellated non-planar surface. The algorithm processes the ordered points of the 2D version of the printing trajectory, the tessellated STL files of the target surface, and the projection direction. It then generates the new trajectory lying on the target surface with the G-code instructions for the printer. As a proof of concept, several examples are presented, including a Hilbert curve and lattices printed on curved surfaces, using a conventional fused filament fabrication machine. The algorithm’s effectiveness is further demonstrated by translating a printing trajectory to an analytical surface. The surface is tessellated and fed to the algorithm as an input to compare the results, demonstrating that the error depends on the resolution of the tessellated surface rather than on the algorithm itself.