Encircled energy measurement for focusing lens based on scanning Hartmann test

The encircled energy of a focusing lens is one of the parameters directly affecting the target efficiency in high-power laser facilities. The direct measurement method of the encircled energy for the focusing lens based on the scanning Hartmann test is proposed in this paper. With the scanning Hartmann test setup, the information in the whole aperture of the focusing lens can be achieved. The encircled energy can be obtained by analyzing the spot diagram on the focal plane of the focusing lens. In experiments, the encircled energy of an aspheric focusing lens is measured using this method. The measurement result is in good agreement with that derived from measurement data by an interferometer and the difference is 7.7%. (C) 2006 Elsevier GmbH. All rights reserved.

Group delay dispersion measurement of Yb : Gd2SiO5, Yb : GdYSiO5 and Yb : LuYSiO5 crystal with white-light interferometry

We report the measured group delay dispersion (GDD) of new crystals Yb:Gd2SiO5 (Yb:GSO), Yb:GdYSiO5 (Yb:GYSO) and Yb:LuYSiO5 (Yb:LYSO) over wavelengths from 1000nm to 1200nm, with a white-light interferometer. Those GDD data should be useful for the dispersion compensation for femtosecond pulse generation in the lasers with these new crystals as the gain media. (C) 2007 Optical Society of America

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.