A comprehensive method for the elastic calculation of the two-point wheel-rail contact

This work shows the method developed to solve the wheel-rail contact problem via a look-up table with a three-dimensional elastic model. This method enables introduction of the two contact point effect on vehicle movement using three-dimensional analysis of surfaces including the influence of the angle of attack. This work presents several dynamic simulations and studies the impact that the introduction of the two contact points on three dimensions has on wear indexes and derailment risk against traditional bidimensional analysis. Furthermore, it studies advantages and disadvantages of using a look-up table against an on-line resolution of the problem.

Amara Berri Sistemaren Baliagarritasuna Aztertzen

XX. mendean eman zen hezkuntza sistemaren aldaketa dela eta agertutako “Eskola Berria”ren mugimenduaren barnean dagoen Amara Berri Sistema mugimendu garrantzitsua eta oso berritzailea da. Hezkuntza kalitatea hobetzeko nahiarekin, hogei hezkuntza zentro baino gehiagok lan-sare bat osatzen dute, non sistema honen finkatzeari eta garapenari erreferentzia egiten dioten helburuak ezartzen diren. Ikerketa lan honetan ikasleen kalifikazio akademikoak eta irakasleek Amara Berri Sistemaren gainean duten pertzepzioa ageri dira. Horrela, Amara Berri Sistemak eskolari eta bertako kideei dakarkien onurak anitzak direla eta egiaztaturik daudela baieztatzen dugu.

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.