Search

This paper describes the machining and forming process of a Pitman arm, proposed to be used in the automotive industry, from austempered ductile cast iron (ADI) as an alternative to the commonly used material, AISI 1045 steel. Samples were austenitized at 900 °C for 180 min and subsequently austempered in a salt bath over a range of temperatures, from 340 to 360 °C, for 60 min, to obtain favorable mechanical properties. The results of metallographic testing show that the pieces formed with ADI have an average nodularity of 92.5%, and the average nodule count is 159 nodules/mm2. Mechanical tests results of strength, elongation, hardness, and impact energy are comparable to that of ASTM standard A897-15 grade 1. A finite element simulation and an experimental analysis were performed to determine the forming force and the material strength. The deformation limits for ADI were identified. These limits and the simulation results show that ADI is an attractive alternative high-strength material for the Pitman arm application.

En este artículo se aborda el problema de la distribución de un conjunto de cámaras en un espacio de trabajo cerrado, con el objetivo de visualizar un área de interés en su totalidad en cada instante de tiempo. La metodología a seguir en este proyecto se basa en modelos de geometría computacional y proyectiva, así como características de detección y algoritmos de búsqueda. La experimentación se realiza empleando la misma metodología, variando únicamente los algoritmos de búsqueda que se implementarán, siendo estos algoritmos genéticos y búsqueda codiciosa (greedy search en inglés), con la finalidad de realizar una comparación de los resultados obtenidos con cada uno de los algoritmos mencionados, evaluando la cobertura del área de interés obtenida, así como el tiempo de computo involucrado en el análisis.

The main goals of a fault diagnosis system in a vehicle are to prevent dangerous situations for occupants. This domain is a complex system that turns the monitoring task a very challenging one. On one hand, there is an inherent uncertainty caused by noisy sensor measurements and unmodeled dynamics and in the other hand the existence of false alarms that appears in a natural way due to the high correlation between several variables. The present work is a variant of a proposal made by the author. This paper presents a new approach based on history data process that can manage the variable correlation and can carry out a complete fault diagnosis system. In the first phase, the approach learns behavior from normal operation of the system using an autoassociative neural network. On a second phase a fuzzy system is carried out in order to diminish the presence of false alarms that could be originated by the noise presence and then a competitive neural network is used to give the final diagnosis. Results are shown for a ten variables vehicle monitoring.

The reasons because power systems monitoring is a challenging task are the complexity and high degree of interconnection present in electrical power networks, the presence of dynamic load changes in normal operation mode, the presence of both continuous and discrete variables, as well as noisy information and lack or excess of data. Therefore, in order to increase the efficiency of diagnosis, the need to develop more powerful approaches has been recognized, and hybrid techniques that combine several reasoning methods start to be used. This paper proposes a methodology based on the system’s history data. It combines two techniques in order to give a complete diagnosis. The proposal is composed by two phases. The first phase is in charge of the fault detection by using Multidimensional Scaling (MDS). MDS acts like a first filter that gives the most probably state of each system’s node. The second phase gives the final diagnosis using an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) over the node(s) given by the first phase in order to look for the faulty line(s) and the time when the fault starts. This proposal can detect the presence of either symmetrical or asymmetrical faults. A set of simulations are carried out over an electrical power system proposed by the IEEE. To show the performance of the approach, a comparison is made against similar diagnostic systems.