This work deals with investigative methods used for evaluation of the surface quality of selected metallic materials’ cutting plane that was created by CO₂ and fiber laser machining. The surface quality expressed by Rz and Ra roughness parameters is examined depending on the sample material and the machining technology. The next part deals with the use of neural networks in the evaluation of measured data. In the last part, the measured data were statistically evaluated. Based on the conclusions of this analysis, the possibilities of using neural networks to determine the material of a given sample while knowing the roughness parameters were evaluated. The main goal of the presented paper is to demonstrate a solution capable of finding characteristic roughness values for heterogeneous surfaces. These surfaces are common in scientific as well as technical practice, and measuring their quality is challenging. This difficulty lies mainly in the fact that it is not possible to express their quality by a single statistical parameter. Thus, this paper’s main aim is to demonstrate solutions using the cluster analysis methods and the hidden layer, solving the problem of discriminant and dividing the heterogeneous surface into individual zones that have characteristic parameters.

The danger of terrorism is a result of the increased risk of critical infrastructure. We will focus on enumerating each area then listing of possible real uses in security and protection and finally we will focus in the list of used 3D sensors (in practice focused mainly on the use of laser scanning of space, camera sensing and subsequent transfer to cyberspace or scanning with infrared sensors).