This paper focuses on evaluating the roughness of heterogeneous surfaces, aiming to interpret data effectively for thorough assessment. Previous research highlights the significant impact of surface roughness on final coatings. Beam-cutting machining generates surfaces with position-dependent roughness parameter changes. However, there is inconsistency in the methods for investigating roughness in such surfaces, leading to the loss of crucial information and potentially inaccurate results. This could result in flawed coating preparation and subsequent defects. This paper proposes a suitable evaluation method involving an optical 3D profilometer and a stabilizing support system for reliable measurements. It provides a detailed description of the materials and methods used. The objective is to establish a more consistent and accurate approach to assessing roughness for coating preparation. Technical applications demonstrate up-to-fivefold fluctuations in surface topography parameters, as illustrated in this manuscript. Overall, this paper seeks to address these challenges and provide a robust framework for evaluating roughness in heterogeneous surfaces, thereby enhancing surface coating preparation processes.

This paper presents the measurement and evaluation of the surfaces of molds produced using additive technologies. This is an emerging trend in mold production. The surfaces of such molds must be treated, usually using laser-based alternative machining methods. Regular evaluation is necessary because of the gradually deteriorating quality of the mold surface. However, owing to the difficulty in scanning the original surface of the injection mold, it is necessary to perform surface replication. Therefore, this study aims to describe the production of surface replicas for in-house developed polymer molds together with the determination of suitable descriptive parameters, the method of comparing variances, and the mean values for the surface evaluation. Overall, this study presents a new summary of the evaluation process of replicas of the surfaces of polymer molds. The nonlinear regression methodology provides the corresponding functional dependencies between the relevant parameters. The statistical significance of a neural network with two hidden layers based on the principle of Rosenblatt’s perceptron has been proposed and verified. Additionally, machine learning was utilized to better compare the original surface and its replica.