This paper describes the theoretical background of electromagnetic induction from metal objects modelling. The response function of a specific case of object shape - a homogenous sphere from ferromagnetic and non-ferromagnetic material is introduced. Experimental data measured by a metal detector excited with a linearly frequency-swept signal are presented. As a testing target various spheres from different materials and sizes were used. These results should lead to better identification of the buried object.

Obtaining discrete data is inseparably connected with losing information on surface properties. In contact measurements, the ball tip functions as a mechanical-geometrical filter. In coordinate measurements the coordinates of the measurement points of a discrete distribution on the measured surface are obtained. Surface geometric deviations are represented by a set of local deviations, i.e. deviations of measurement points from the nominal surface (the CAD model), determined in a direction normal to this surface. The results of measurements depend both on the ball tip diameter and the grid size of measurement points. This article presents findings on the influence of the ball tip diameter and the grid size on coordinate measurement results along with the experimental results of measurement of a free-form milled surface, in order to determine its local geometric deviations. One section of the surface under research was measured using different measurement parameters. The whole surface was also scanned with different parameters, observing the rule of selecting the tip diameter d and the sampling interval T in the ratio of 2:1.

Surface roughness parameter prediction and evaluation are important factors in determining the satisfactory performance of machined surfaces in many fields. The recent trend towards the measurement and evaluation of surface roughness has led to renewed interest in the use of newly developed non-contact sensors. In the present work, an attempt has been made to measure the surface roughness parameter of different machined surfaces using a high sensitivity capacitive sensor. A capacitive response model is proposed to predict theoretical average capacitive surface roughness and compare it with the capacitive sensor measurement results. The measurements were carried out for 18 specimens using the proposed capacitive-sensor-based non-contact measurement setup. The results show that surface roughness values measured using a sensor well agree with the model output. For ground and milled surfaces, the correlation coefficients obtained are high, while for the surfaces generated by shaping, the correlation coefficient is low. It is observed that the sensor can effectively assess the fine and moderate rough-machined surfaces compared to rough surfaces generated by a shaping process. Furthermore, a linear regression model is proposed to predict the surface roughness from the measured average capacitive roughness. It can be further used in on-machine measurement, on-line monitoring and control of surface roughness in the machine tool environment.