Passive harmonic mode-locking fiber laser is experimentally demonstrated with high pulse energy and excellent signal-to-noise-ratio by employing monolayer graphene and multi-mode fiber. A repetition rate of 20.26 MHz corresponding to the 3rd harmonic mode-locking has been achieved, with a pulse duration of ~ 603 fs, and a high single-pulse energy of ~1.04 nJ. The spectral width of the pulses is found to be decreased with the increase of the harmonic order. Such a fiber laser is suitable for optical access network or material processing applications.
The polishing convolution theory is widely used in CCOS optical manufacture. In the paper, it is found that the practical amount of material removal is largely different to the theoretical results when the polishing pad does an accelerated motion. The change of the feed rate will cause a huge deviation while the change of the direction will not cause the deviation. Several experiments have finished by using ABB robot polisher and laser interferometer. The cause of the deviation primarily lies in the accumulation of the abrasive grains. To ensure the stability of the amount of material removal in the sub-aperture polishing process, the large change of feed rate should be avoided and the effect on the change of direction can be neglected.
Freeform surfaces are widely used in precision components to realize novel functionalities. In order to evaluate the form qualities of the manufactured freeform parts, surface matching/fitting is required. The uncertainty of the obtained form deviations needs to be estimated to assess the reliability of form error evaluation. The GUM approach is extensively adopted for uncertainty assessment in precision metrology, but it is not suited for assessing the nonlinear matching/fitting problems of freeform models. In this paper a Monte-Carlo method is developed to estimate the uncertainty of the fitted position, shape and form error metrics. Based on the correlation analysis, the effects of objective functions in numerical optimization, noise amplitudes in measurement, shapes of freeform surfaces and so on are determined. Then the significant factors dominating the reliability of the fitted results can be identified. Henceforth the matching/fitting procedures can be arranged appropriately to reduce the uncertainty of the evaluation results and improve the reliability of freeform surface characterization.
In this paper, robotic reposition error measurement method based on laser interference remote positioning is presented, the geometric error is analyzed in the polishing system based on robot and the mathematical model of the tilt error is presented. Studies show that less than 1 mm error is mainly caused by the tilt error with small incident angle. Marking spot position with interference fringe enhances greatly the error measurement precision, the measurement precision of tilt error can reach 5 um. Measurement results show that reposition error of the polishing system is mainly from the tilt error caused by the motor A, repositioning precision is greatly increased after polishing system improvement. The measurement method has important applications in the actual error measurement with low cost, simple operation.
Uncertainty evaluation, which is an effort to set reasonable bounds for the measurement results, is important for assessing the performances of precision measuring systems. The three dimensional measurement is affected by a large number of error sources. The distributions of the primary error sources are analyzed in this paper. The multiple-try Metropolis (MTM) algorithm is applied for sampling and propagation of uncertainty for these error sources due to its advantage in dealing with large dimensional problems. The uncertainties of the three coordinates of a measured point on the workpiece r, z, and θ are evaluated before and after error separation, respectively. The differences between the two types of uncertainties are compared to find out the influence of the error separation to the uncertainty. Finally, numerical experiments are implemented to demonstrate the uncertainty assessment process.
Micro optical components are more and more widely used in precision engineering due to their small sizes and novel functionalities. Characterization of the surface topography of these components is very difficult due to the existence of sharp edges and complex features. Conventional filtering algorithms cannot be used directly for non-smooth structured surfaces. In this paper we present a filtering algorithm using the non-local means method. Instead of assigning weights according to the closeness or similarity between individual data points, this method are based on the similarity of the patches surrounding data points. This method can effectively separate the detailed textures of non-smooth surfaces while preserving primary features. Proper adaptation and improvement are made for the applications in precision engineering. The k-means clustering method is used to reduce the computational cost. Numerical experiments prove that the non-local means method is able to separate small-scaled textures from the primary surface shapes without ruining the sharp features.
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