This review paper provides a comprehensive overview of selected 3D surface texture parameters defined by ISO 25178-2, with a focus on their metrological aspects in high-resolution measurements using atomic force microscopy (AFM). The parameters Sa, Sz, Sq, Sdq, and Sdr are analyzed in terms of their practical application, sensitivity to measurement conditions, and role in assessing surface functionality. Through a review of the literature and simulations of surface profiles with controlled geometric variations, the study demonstrates how the selected parameters respond to changes in step pitch, step width, slope, and lateral calibration errors. Experimental AFM measurements performed on a certified step height standard further illustrate the impact of calibration on the quality of measurement results. Special emphasis is placed on the importance of evaluating measurement uncertainty. The results confirm the need for rigorous instrument calibration and uncertainty assessment to ensure reliable and comparable surface characterization across different instruments and laboratories.

The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers’ calculations. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer’s risk. If a product does not meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with upwards openings. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties were determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model’s performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.

This paper provides an overview of key methods for assessing the quality of measurement systems and measurement results. Fundamental concepts are analyzed and their definitions compared across various metrological standards. Particular emphasis is placed on the issue of terminological inconsistency, as the same terms are interpreted differently in different documents, which may lead to misinterpretation or incorrect application of methods. This paper also presents practical examples of how these methods are applied to real data. Clear indications are provided regarding the conditions under which each method is recommended, depending on factors such as model complexity, data availability, and intended application. In addition, it offers a discussion and recommendations for future directions, highlighting the need for harmonized terminology, standardization of evaluation procedures, and the adoption of advanced technologies such as artificial intelligence and machine learning in the assessment of measurement quality.

The atomic force microscope (AFM) enables the measurement of sample surfaces at the nanoscale. Reference standards with calibration gratings are used for the adjustment and verification of AFM measurement devices. Thus far, there are no guidelines or guides available in the field of atomic force microscopy that analyze the influence of input parameters on the quality of measurement results, nor has the measurement uncertainty of the results been estimated. Given the complex functional relationship between input and output variables, which cannot always be explicitly expressed, one of the primary challenges is how to evaluate the measurement uncertainty of the results. The measurement uncertainty of the calibration grating step height on the AFM reference standard was evaluated using the Monte Carlo simulation method. The measurements within this study were conducted using a commercial, industrial atomic force microscope.

The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers calculation. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer's risk. If a product doesn't meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended, and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with an opening upwards. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties are determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model's performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.

The concept of Industry 4.0 envisions the flexibilization of production and high levels of automation of existing manufacturing processes. As an extension of this concept, Industry 5.0 assumes the adaptation of products to the individual needs of users, with a particular emphasis on creativity, personalization, sustainability, and an ethical approach to business. Within these concepts, the development of metal forming technology is also recognized. In addition to the usual mass production, the development of various roboforming ideas is steering this branch of industry towards greater flexibilization, as well as personalization of production. This supports the idea of sustainability, enables more precise determination of the quantity of required material and energy resources, and emphasizes quality over quantity. This paper provides an overview of metal forming technologies that belong to the domain of roboforming of sheet metals, from the basic idea in 1960s to the present day. These technologies have seen a marked tendency to accelerate in development over the past few years. The technologies are classified, and an overview of scientific papers proposing their development and application is provided, with a discussion of the fundamental production parameters that influence product quality as well as the production trend towards Industry 5.0.