## 1 Introduction  
There are many experiments and much research aimed at creating 3D models that serve to evaluate the quality of a cutting surface. These models are used to analyze and characterize various factors affecting the cut quality, such as tool geometry, the cutting process's parameters, the material, the cutting speed, and many others. Evaluation of cut quality is important because industrial processes often require precise and high-quality cut surfaces. The quality of the cutting process depends on many factors, such as the accuracy of the tool, the material, the cutting parameters, and the cutting conditions. The overall quality of the product is influenced by these factors and is conditioned by three types of accuracy, namely shape, size, and area measurement, which are characterized by the roughness parameters [1]. The spatial characteristics of the surface provide new possibilities for the expression and presentation of the structure of the surface. By including an additional dimension and a significantly larger amount of data, they enable a more objective assessment of the surface and potentially predict its functionality and evolution during operation. Spatial analyses of the structure of the measured surface provide us with a graphical representation of the profile from different points of view, such as a topographic map or a record of the coordinates' intensity. These measurement methods provide a large amount of information that can be used by an experienced observer [2]. Spatial methods are measurement techniques that serve to obtain information about topographical deviations of the surface of objects. These deviations are referred to as z (x,y) and represent the distance of the point (x,y) from the reference plane [3]. The reference plane can be defined either through physical calibration, where a known reference surface is used to adjust the measurement system, or by the principle of the method used, which can have its own internal reference plane [4]. The field of spatial methods has undergone significant development in recent decades. This progress was made possible mainly due to the development of computing technology, optical detectors, and light sources [5]. Enhancing the longevity and operational dependability of components hinges on the superior quality of their functional surfaces, encompassing dimensional precision, the shape's fidelity, and the surface texture. As progressive construction materials with constrained machinability become more prevalent, alongside advancements in cutting materials and machine tools, there is a growing imperative for the evolution of measurement techniques and assessment methods to ensure the quality of the components' surfaces [6]. An important factor in the evaluation of the quality of the surfaces of the components is the roughness of the surface, which is an important indicator of the technology and processes used to create it [7]. In contemporary machining technologies such as AWJ (abrasive water jet), laser, or additive methods, it is commonplace for the surface roughness to be different between the entry point into the material and the exit point [8]. Scoring itself harms the quality of machined surfaces, as it can cause unevenness, increased roughness, and undesirable shape deviations. Therefore, it is important to divide the machined surface into several zones to better characterize the quality of the surface and allow possible correction or optimization of the processes. Zones can be defined on the basis of various criteria, such as the depth of grooving, roughness, shape deviation, or other factors affecting the surface's quality. In this way, it is possible to identify areas with high roughness, undesirable shape deviations, or other problems and focus on their optimization and improvement [9; 10]. Surface roughness analysis is an important tool in the evaluation and control of machining technology and surface treatment processes.