Benito, J.; Cobo, R.; Calvo, J.; Cabrera, J. Materials science and engineering A. Structural materials properties microstructure and processing Vol. 655, p. 310-320 DOI: 10.1016/j.msea.2016.01.004 Data de publicació: 2016-02-08 Article en revista
Con el objetivo de que el funcionamiento de una máquina sea el correcto es imperativo
asegurar que existe un buen mantenimiento predictivo. Es conveniente tener un sistema
inteligente y dispositivos capaces de detectar fallos en fases tempranas. Los fallos más
comunes en máquinas industriales son todos aquellos relacionados con los sistemas de
transmisión de potencia. La técnica de emisión acústica (EA) es el último enfoque para
detectar e identificar defectos en rodamientos, cajas de cambio y uniones mecánicas.
La emisión acústica (EA) es el fenómeno de generación de ondas elásticas transitorias
en materiales bajo tensión. Cuando el material está sometido a un cierto nivel de
tensión, una liberación rápida de energía de deformación tiene lugar en forma de
ondas elásticas, las cuales pueden ser detectadas mediante transductores colocados en
la pieza en cuestión.
El objetivo de este trabajo es proporcionar una caracterización de las ondas elásticas
que emanan de grietas localizadas entre el flanco y el valle de un engranaje. Las
señales han sido registradas usando transductores colocados en la superficie del
engranaje (a media distancia entre el eje y los dientes).
El método de los elementos finitos ha sido utilizado para simular las ondas elásticas
emitidas durante el crecimiento de las grietas. El modelo de simulación está basado en
suposiciones elásticas y se ha llevado a cabo mediante Abaqus. Estos resultados han
sido comparados con los resultados experimentales.
In order to have a machine that functions well it is imperative to ensure that there is a
good predictive maintenance. An intelligent system and devices able to detect the fault
in its early stage is then convenient. The most common failures in industrial machines
are those related to the power transmission systems. Acoustic Emission (AE) is the
latest approach in detecting and identifying faults in bearings, gearboxes and
mechanical couplings. Acoustic Emission (AE) is the phenomenon of transient elastic
wave generation in materials under stress. When the material is subjected to stress at a
certain level, a rapid release of strain energy takes place in the form of an elastic wave
which can be detected by transducers placed on it. The objective of this work is to provide a characterization of elastic waves emanating
from cracks located between the flank and the valley of a gear. The signals have been
recorded using transducers attached to the surface of the gear (midway between the
shaft and the teeth).
FE modeling has been used to simulate the elastic waves emitted from fatigue crack
growth. The model is based on linear elasticity assumptions and undertaken using
Abaqus. These results have been compared with the experimental ones.
'Unexpected failure in an industrial production chain does not only involve the costs of failed parts replacement and the associated man-hour labour, but downtime costs have also have to be considered. To keep a machine functioning well it is a must to have good predictive maintenance, as it helps to reduce operating risk, avoids plant failures, provides reliable equipment, reduces operating costs, eliminates defects in operating plant and maximises production. Acoustic Emission (AE) is a phenomenon of transient elastic wave generation in materials under stress. When the material is subjected to stress at a certain level, a rapid release of strain energy takes place in the form of elastic wave which can be detected by transducers placed on it. Plastic deformation and growth of cracks are among the main sources of AE in metals. Though AE can came form any system under movement, the main source is doubtlessly from rotating machinery. Sources of AE in rotating machinery include impacting, cyclic fatigue cracks, friction, turbulence, material loss, cavitation, leakage, etc. In most cases the SMEs machine owner would be satisfied with a simple affordable device that is able to warn them from critical equipment failure.
Recent developments in sensing technology, microprocessors, and miniaturised radio transceivers has enabled a new generation of Wireless Sensors Networks. The future of these sensors is to have an ubiquitous sensing nodes that will autonomously report on operating conditions, and that this data will be used to facilitate structural health monitoring, embedded test & evaluation, and condition based maintenance of critical industrial rotating machinery without the use of expensive cabling. In addition, in order to provide sensing networks which are truly autonomous, chemical batteries must be eliminated from the sensor and some kind of energy harvesting has to be foreseen. Piezoelectric materials have demonstrated their ability to convert vibration energy from vibrating machinery and rotating structures into electrical energy for powering a wireless sensing node. Hence, an acoustic emission self-powered wireless sensor is one of the main objectives to be achieved in this project. The sensor will measure using frequency as opposed to time which is an advancement from the state of the art.'
In this paper, the variation of the Young’s modulus with plastic deformation during unloading in a tensile test with has been determined for a wide range of Dual-phase steels, covering from DP 1400 to DP 780. As a general rule, the higher strength steels have a lower variation (8%) and as the strength decreases, this diminution increases, arriving to 20%. However, in the softer steels, there are appreciable differences in young’s modulus variation although their mechanical properties are very similar. In order to explain this, the role of ferrite content in the DP steels and the presence of bainite have been analyzed as well as the initial dislocation density and its evolution with plastic deformation.
In this paper the evolution of the Young’s Modulus (E) during unloading with plastic deformation has been studied for different Dual-Phase AHSS from DP780 to DP1400. During unloading, all the DP steels studied showed the presence of microplasticity so an Apparent Young’s Modulus (EA) has been defined. Although that in all cases EA decreased with a non-linear behavior as the plastic strain was increased, it has been observed that the final percentage of decrease seems to be related to the microstructure of DP steels. As the ferrite content increased as in the lower strength DP steels, the reduction of EA is larger, reaching a 21%.
The introduction of the variation of the elastic response during unloading in the simulation of a bending operation has allowed obtaining an improvement of the accuracy in springback prediction in all the DP steels studied. For the low strength DP steels the final shape obtained by simulation is in fact the same than the real one. As the strength of steel is increased, the accuracy is less, especially in the DP 1400 steel, in which differences in bending angle higher than a 15% are still found.