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Tires play a critical role in vehicle safety. Proper modeling of tire–road interaction is essential for optimal performance of active safety systems. This work studies the influence of temperature, longitudinal vehicle speed, steering frequency, vertical load, and inflation pressure on lateral tire dynamics. To this end, a tire test bench that allows the accurate control of these parameters and the measurement of the variables of interest was used. The obtained results made it possible to propose a simple model that allowed the determination of relaxation length as a function of tire vertical load and vehicle linear speed, and the determination of a representative tread temperature. Additionally, a model has been proposed to determine the lateral friction coefficient from the aforementioned temperature. Finally, results also showed that some variables had little influence on the parameters that characterize lateral dynamics. [ABSTRACT FROM AUTHOR]

In the last years, a large number of new biocompatible materials for 3D printers have emerged. Due to their recent appearance and rapid growth, there is little information about their mechanical properties. The design and manufacturing of oral appliances made with 3D printing technologies require knowledge of the mechanical properties of the biocompatible material used to achieve optimal performance for each application. This paper focuses on analysing the mechanical behaviour of a wide range of biocompatible materials using different additive manufacturing technologies. To this end, tensile and bending tests on different types of recent biocompatible materials used with 3D printers were conducted to evaluate the influence of the material, 3D printing technology, and printing orientation on the fragile/ductile behaviour of the manufactured devices. A test bench was used to perform tensile tests according to ASTM D638 and bending tests according to ISO 178. The specimens were manufactured with nine different materials and five manufacturing technologies. Furthermore, specimens were created with different printing technologies, biocompatible materials, and printing orientations. The maximum allowable stress, rupture stress, flexural modulus, and deformation in each of the tested specimens were recorded. Results suggest that specimens manufactured with Stereolithography (SLA) and milling (polymethyl methacrylate PMMA) achieved high maximum allowable and rupture stress values. It was also observed that Polyjet printing and Selective Laser Sintering technologies led to load–displacement curves with low maximum stress and high deformation values. Specimens manufactured with Digital Light Processing technology showed intermediate and homogeneous performance. Finally, it was observed that the printing direction significantly influences the mechanical properties of the manufactured specimens in some cases. [ABSTRACT FROM AUTHOR]

This paper is devoted to developing a biological-based algorithm to simulate the control of a human arm by means of a Spiking Neural Network (SNN) with a pre-set structure similar to those found in reflex arcs. The replication of the behavior of biological systems is a primary step to understand how the brain and the neural networks that comprise it work. The study of these systems began with the analysis of animals with low neural complexity as well as with small control neural networks such as those present in the reflex acts of the human body. However, the study of the brain, due to its intricate structure, still presents many unknowns and challenges to be solved. Meanwhile, one way to understand how biological systems work is to emulate their behavior through computer simulations. Artificial neural networks (ANNs) offered the opportunity to replicate neural structures to understand and reproduce their behavior and performance. Many types of ANNs are based on the use of activation functions. However, these ANNs are simplified models that do not replicate the behavior of complex biological neural systems accurately. For this reason, spike-based models have been developed to reproduce real biological systems more faithfully. This work proposes simulating the motor behavior of the central nervous system to control the position of an arm. To this end, a spiking neural network has been developed to emulate motion control using a fixed structure that reproduces reflex arcs. A channel-based synapse model and a control scheme based on the equilibrium point hypothesis have been proposed to improve biological similarity of the controller. Furthermore, the developed controller has learning capabilities thanks to the reproduction of the synapse plasticity process that takes place in real biological systems through a supervised Spike-Timing Dependent Plasticity (STDP) learning strategy. The performance of the proposed approach has been demonstrated by simulating the control of the movement of an arm using Hill's Muscle Model. Results suggest that the proposed control algorithm resembles the response of biological systems adequately in terms of speed and temporal response. [ABSTRACT FROM AUTHOR]