The article studies the friction coefficient in elastohydrodynamic lubrication (EHL) by means of analytically obtained equations for different contact geometries. The introduction of some simplifications allows for the simultaneous consideration of piezoviscous, pseudoplastic and thermal phenomena, resulting in complete and realistic models, which provide results in a quick and easy manner. The predictive potential of this analytical approach is analyzed by comparing the estimates of friction with full-EHL simulations and experimental data under different operating conditions. The results obtained allow us to discuss the influence of some assumptions taken into account and the scope of applicability of the models, in order to determine their usefulness and limitations.

Robot-based Non-Planar Additive Manufacturing, as a manufacturing technique, may be a candidate for the fabrication of soft robotics compliant mechanisms, owing to the increased design freedom and final part mechanical properties of these methods compared to conventional Additive Manufacturing. The non-planar slicing method applied in this work involves the bijective homomorphism between the Cartesian map and a slicing map, used for planar part design. This paper explores this novel Design for Non-Planar Additive Manufacturing technique and applies it on a case study of a Yoshimura-style origami compliant mechanism-based grasping end-effector. Three different build platforms are proposed for different to-be-grasped applications. For all three cases, the complete methodology is applied, starting from CAD model preparation, model isomorphism onto the Cartesian map, planar additive manufacturing trajectory generation and toolpath isomorphism for manufacturing. This case study showcases the possibilities of Robot-based Non-Planar Additive Manufacturing for the manufacture of improved mechanical properties soft robotics compliant mechanisms, and suggests the transversality of the methodology for other areas of knowledge, including aerospace, embedded electronics, and biomedical industries.

Desde que se acuñó el término tribología en la década de los sesenta se han logrado numerosos avances que reducen la fricción en máquinas. Aun con estos avances el estudio de la fricción en máquinas es un área activa de investigación, ya que todavía se emplea el 20% de la energía mundial en vencer esta resistencia pasiva. El microtexturizado de superficies ha resultado ser una técnica efectiva para reducir la fricción en contactos mecánicos con condiciones de trabajo que pueden dificultar la formación de una lubricación en película gruesa. En la literatura hay escasos estudios que modelen los fenómenos físicos de este tipo de contactos lubricados con superficies microtexturizadas debido a la dificultad que conlleva la influencia de numerosas variables, como las condiciones de trabajo, la geometría que define el patrón de texturas y su interrelación. Estudios recientes muestran la aplicabilidad de los algoritmos de Inteligencia Artificial (IA) para la predicción de la fricción. Por ello, en este estudio se emplean redes neuronales para determinar el coeficiente de fricción en contactos lubricados microtexturizados. A diferencia de estudios previos en los que se utilizan algoritmos de IA entrenados con datos que no tienen en consideración la incertidumbre de la microfabricación, el algoritmo propuesto se desarrolla empleando datos que se han obtenido midiendo los patrones de texturizado fabricados tanto por fotolitografía y ataque químico como mediante ablación láser. De esta forma, se comprueba que la influencia de la precisión de la fabricación es relevante en la correcta predicción del coeficiente de fricción.

Background/Objectives: Screw loosening and vertebral fractures remain common after vertebral body tethering (VBT). Because tightening torque sets screw preload, its biomechanical effect warrants explicit modeling. In this paper, a Finite Element (FE) model, supported by ex vivo porcine vertebral tests, was developed and validated that incorporates torque-induced pre-tension to quantify vertebral stress, aiming toward customizable VBT planning. Methods: An FE model with pre-tension and axial extraction failure was parameterized using ex vivo tests on five porcine vertebrae. A laterally inserted surgical screw in each specimen was tightened to $5.9\pm 0.80$ Nm. Axial extraction produced failure loads of $2.1\pm 0.31$ kN. This is also considered in the FE model to validate the failure scenario. Results: Torque alone generated peak von Mises stresses of $16.1\pm 0.86$ MPa (cortical bone 1) and $2.1\pm 0.13$ MPa (trabecular), lower than prior reports. With added axial load, peaks rose to $141.1\pm 0.70$ MPa and $19.7\pm 0.23$ MPa, exceeding typical ranges. However, predicted failure agreed with experiments, showing $0.58$ mm displacement and a conical displacement distribution around the washer. Conclusions: Modeling torque-induced pre-tension is essential to reproduce realistic stress states and anchor failure in VBT. The framework enables patient-specific assessment (bone geometry/density) to recommend safe tightening torques, potentially reducing screw loosening and early fractures.

Robotic manipulators play a crucial role in precision machining, and understanding their dynamic behavior is essential for optimizing performance. This study presents the dynamic characterization of a six-degree-of-freedom serial robotic manipulator for precision machining operations. A simplified dynamic model is proposed using Frequency Response Functions (FRFs), pole-zero estimation, and regression analysis. The robot's dynamic behavior is represented through the mechanical admittance matrix, linearized by transfer functions dependent on joint states. Experimental data were collected through automated torque impacts within a defined Region of Interest and analyzed using Fast Fourier Transforms. The resulting FRFs guided the estimation of transfer functions, optimizing model complexity to balance accuracy and computational efficiency. Regression analysis indicated that cubic models with interaction terms best capture the system's dynamics. The identified models closely approximate experimental data, though minor discrepancies suggest potential areas for refinement. This methodology has may enable improvements in the precision and dynamic accuracy of robotic manipulators in machining tasks, as well as broader applications in automated manufacturing.