A technique for the out of machine calibration of analog probes for CMM is presented in this paper. The process and the results obtained in different tests carried out on two different probes are shown. This calibration procedure allows to find out the measuring errors of these systems in a quick and reliable way.

In practice, ninety percent of the engineering faults initiate from the surfaces by means of the mechanisms of fatigue crack wear behavior, corrosion and erosion. In this point of view, researchers are attracted by measuring surface and near-surface zone suitability of work pieces and evaluation studies. Surface roughness is a factor not only in composing the work piece aspect but also in defining the performance of the work pieces in coating processes. In this study, 3 different test specimens are subjected to molten glass that was in forming temperature and the changing surface roughness parameters have been monitored with precise measuring techniques. The obtained data is evaluated by SPSS 13.0 statistical package program. Results are informative about the product quality either.

In the paper analysis of surface topography influence on emissivity of metals was shown. This was performed for infrared band comprising wavelength region of 7.5 – 13 micrometers. Appropriate characterization and description of object emissivity has a crucial influence on accuracy of IR system for remote temperature measurement, e.g. IR thermography or pyrometry. These properties depend on many factors, including surface topography of material, where especially cavities play a very important role. In references so far emissivity are discussed mainly as influence of type of material (metal, dielectric), its temperature, wavelength or direction of emission. In the paper characterizing emissivity of construction materials in connection with 3D surface topography.

This paper describes a non-contact technique to assess the differences in surface characteristics of the ground components. The computer vision based system is used to analyze the pattern of scattered light from the surface to asses the surface roughness of the component. The ground specimens were manufactured using varying machining parameters. The images of the specimens are captured using a CCD camera. The image parameters based on the wavelet transform are evaluated. Then, the evaluated parameters along with the cutting parameters were used to train the artificial neural network to predict the surface roughness parameters R_a, which is measured using the stylus instrument. The comparison of stylus R_a and that predicted using ANN are presented and analyzed in this paper.

In this paper, a new methodology for the uncertainty analysis of coordinate measurements is presented. The methodology can be applied on any kind of measurements performed by a CMM, and it is intended to be especially useful when performing reference measurements of geometrical and dimensional characteristics of workpieces. Master parts calibrated using this approach can be used in connection with ISO/TS 15530-3 for the uncertainty assessment of other CMMs, when measuring similar parts. Consistent measurement strategies and the real measurand definition according to the technical drawing are the foundations of this methodology, generally based in high-density measurement points. Assessment of uncertainties is performed using models in complete agreement with the measurement procedure, considering all major uncertainty contributions. Resources from artificial intelligence were applied to accelerate the development of adequate measurement procedures, simplifying the work of the CMM operator. To validate the proposed methodology, a cast iron steering case was calibrated and compared to simulation results obtained with PUNDIT/CMM, a software solution for task-specific uncertainty evaluation of coordinate measurements.