Common Practices and Methodologies in Scientific Functional Characterization of Surface Topography

Abstract

More and more surfaces are required to fulfill functional characteristics that are embodied by their surface topography. In the process of measuring and characterizing the corresponding surfaces, many research activities have been conducted, and a broad variety of measuring principles and evaluation strategies have been developed. However, in industrial practice, there is still a lack of experience and a significant unhinged potential in this field. To predict which techniques will most likely be transferred more commonly into industrial applications, a study to identify the most frequently used measurement principles, methods, and surface texture parameters for characterizing functional surfaces through a systematic literature review of scientific research studies is conducted here. It can be shown that optical measuring instruments have emerged significantly, whereas the analysis is mostly performed using traditional and simple amplitude-based surface texture parameters. Based on the results, untapped potential in functional analysis can be revealed and the use of, e.g., function-oriented parameters or a direct measurement of the angular distribution can be recommended for a wider range of applications.

1. Introduction

A component’s surface plays a crucial role in the interactions with its environment and other components. More precisely, the surface as a boundary dictates the functionality of the workpiece. Particularly, a surface’s wear, friction, lubrication, wettability, and other tribological properties are heavily dependent upon its microstructure [1]. The surface texture that contains these microstructures can be interpreted as the fingerprint of the manufacturing process [2]. In the early stages of surface metrology, mostly parameters that could be determined using analog circuits were assessed, but in many cases were not relevant to the functionality of the surface. Additionally, the regional preferences varied: the UK used the center line average, the USA focused on root mean square, and many applicants in continental Europe emphasized the peak-to-valley height, primarily for controlling the manufacturing process rather than assessing the functionality of the workpiece [2]. Through the decades, more and more surface texture parameters were developed, and today, >100 surface texture parameters exist which are partially redundant [3] and by the 1980s, it was already advised to consider their relevance for functionality in the standardization when Whitehouse criticized the “parameter rash” [2]. At the same time, two different approaches particular to functional characterization were developed: the parameters Rk, Rpk, and Rvk using a segmentation of the Abbott-curve were developed in Germany, leading to the standard ISO 13565-2 [4], and the parameters Rpq, Rvk, and Rmq using the material probability curve were developed in the USA, resulting in ISO 13565-3 [5,6]. These profile parameters have been transferred to ISO 21920 [7], and with the emergence of areal surface texture measurements starting in the late 1980s to 1990s, their areal pendants were also considered in the subsequent standardization and introduced as Sk, Spk, Svk, Spq, Svq, and Smq by ISO 25178-2 [8] in 2012. Amongst many others, Zeng et al. highlight that the underlying representation of the amplitude distribution function and Abbott–Firestone curve, with the latter being crucial for evaluating surface lubrication retention, wear, and load-bearing capacity, are useful approaches for functional characterization [9]. In addition, besides the use of standardized surface texture parameters, other mathematical methods to characterize the surface texture and functionality have emerged, e.g., characterization using the autocorrelation function or power spectral density function [10]. This broad variety of evaluation techniques illustrates that there is an extensive range of methods for assessing surface functional behavior that has been researched, suggested, and in many cases also standardized, whereas the frequency of their application often remains undocumented both with regard to scientific and industrial applications. The objective of the present work is to identify the most frequently used techniques, methods, parameters, and measurement principles in scientific research in recent years that are applied in the context of functional surface characterization. Based on the results, it is possible to predict which methods can be expected to be transferred into the industrial application and give recommendations about unhinged potential in this field.

2. State-of-the-Art

Before the functionality can be assessed, a measurement of the surface topography is required, which can be performed profile-based or areal-based. Traditionally, tactile measurement is by far the most common measurement principle, and for, e.g., additively manufactured metals, Townsend et al. concluded that the use of stylus profilometers (SP) was dominant in 40% of the explored literature [11]. Focus variation microscopy (FV) and confocal microscopy (CM) were both present in 11% of the literature examined by the authors, white light interferometry (WLI) in 7%, atomic force microscopy (AFM) was noted to be rarely used, and the use of a scanning electron microscope (SEM in secondary electron mode) was observed in 11% of the studies [11]. Areal surface texture parameters were used in 20% of the examined literature, while profile surface texture parameters accounted for 80% of the applications, with the arithmetic mean roughness Ra being the most frequently used parameter, followed by the root mean square roughness Rq and the total height of the roughness profile Rt [11]. Studies by other researchers confirmed that SP, WLI, and CM are the most commonly used measurement principles for surface characterization [12]. Even though, as described, profile surface texture measurement is still the most common approach, a paradigm shift from profile to areal surface characterization is generally observable [13]. SEM has not gained widespread use in all areas [14]. Another paradigm shift is that defined microstructures are being used to embody a function of the workpiece [13]. Figure 1 gives a few examples of functional characteristics that are mapped by the microstructure of the surface in both nature and technology: the lotus effect is used in “self-cleaning” surfaces, leading to desired adhesion characteristics, and the flow friction of golf balls or airplanes is optimized using defined structures on the surface. The same effect can be observed on shark skin. And in many engineering components like turbines, engines, or artificial hip joints, desired tribological properties with regard to friction, wear, and lubrication need to be fulfilled and are included as a requirement in the technical drawing.

These examples illustrate the functional significance of surface topography. To assess the associated characteristics, not only various measuring principles, but also many profile and areal surface texture parameters are available. However, previous research suggests that generally, not all surface texture parameters are utilized equally: in their survey about surface texture parameters, Todhunter et al. highlight issues like limited understanding of parameters’ impact on functionality, their descriptions, appropriate usage, and overuse [15]. The results illustrate that amplitude-based surface texture parameters are by far the most applied. However, functional surface texture parameters, such as those in ISO 13565 [4,6], are also becoming increasingly popular, especially for research institutions and the metrology sector [15]. Optical measurement principles are gaining traction in industries, with 66% of respondents using them alone or with other methods. The arithmetic mean height remains the most popular parameter, while total height, skewness, and kurtosis are also gaining acceptance, indicating a better understanding of surface texture parameters [15]. Research institutions have significantly adopted areal surface texture parameters, with Sa, Sz, Sq, Sku, Ssk, Sp, Sv, Std, Sk, and Spk being the most used [15]. However, these prior studies do not specifically address functional characterization, which is the focus of the subsequently introduced study to explore which methods can be commonly found in research.

Since there is a growing complexity and diversity of surface characterization techniques and parameters, the question of which of them are actually applied in research arises, particularly with regard to a function-oriented characterization of surface topography, and thus may be transferred more commonly to industrial applications in the future. By systematically identifying the most commonly used approaches, the objective of the study is to provide a clear overview of current research trends, highlight gaps between academic research and industrial practice, and identify underutilized methods with potential for functional surface analysis. This mapping is essential for guiding future research, standardization efforts, and technology transfer to industry.

3. Methodology

To gain insights into functional characterization, this study employs a meta-analysis of the current literature by a comprehensive literature review of all accessible works related to functional surface characterization. Representatives were chosen based on specific databases such as Google Scholar, ResearchGate, ScienceDirect, the National Institute of Standards, Springer, the American National Standards Institute, university publications, and similar databases. This research focuses on evaluating common techniques used in functional analysis and related scientific studies. The literature was selected using a combination of broad, thematic, and snowball search strategies across multiple databases, with strict inclusion criteria focusing on studies from 2012 onward to ensure relevance to areal surface parameters. Quantitative analysis using frequency and percentage calculations to rank the usage of different techniques and parameters across 42 selected studies was performed. The determined studies provide a reliable foundation and sample size for identifying trends in surface metrology and support future research and industrial applications, with the limitation of possible terminology inconsistencies between the different references. Since most references follow the terminology given in international standardization, the impact of this limitation can be considered and limited by a thorough review of all mentioned references.

4. Results

All results of the references [9,14,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55] are summarized in

Table A1. Results of the literature review.

Study	Applied Measurement Principles	Parameters for Functional Surface Characterization	Additional Methods for Surface Characterization
(Abadías et al., 2015) [16]	AFM	Ra, Rq, Rsk, Rku	-
(Abbasian et al., 2017) [17]	DHM	Sa, Sq, Ssk, Sku, Sp, Sv, Sdq, Sdr, Sλq, Sλa, Sda, Ssc, St	-
(Alnoush et al., 2021) [18]	AFM, WLI	Rq	-
(Bellantone et al., 2022) [19]	CM	Sa, Sq, Sz, Ssk, Sku, Sp, Sv	-
(Berkmans et al., 2024) [20]	WLI	Sdr	Fractal analysis
(Gao et al., 2020) [21]	SP	Ra, Rz, Rt, RSm	-
(Gockel et al., 2019) [22]	CT, SLS	Sa, Sv	-
(Grimm et al., 2015) [23]	CM	Sal, Str, Sdq, Sdr	Fractal analysis
(Grover & Singh 2018) [24]	SP	Ra	-
(Grundke et al., 2014) [25]	SFM	Sa	-
(Grzesik & Żak 2016) [26]	SP	Sa, Sq, Sz/Rz, Ssk, Sku, Smr, Sxp, Sal, Str, Std, Rdq, Vmp, Vmc, Vvc, RSm, Rx, Spk, Sdc, Sk, Svk, Sds, Ssc	Fractal analysis, PSD, ACF, BAC, ADF, Motif analysis
(Guo et al., 2022) [27]	WLI	Sa, Sq, Sz, Ssk, Sku	Discrete wavelet transform
(Jiang et al., 2022) [28]	CM	Sa, Sq, Sz, Ssk, Sku, Sdq, Sdr	-
(Klink et al., 2017) [29]	SEM	Ra, Rz, Rdq, RSm	-
(Krishna et al., 2020) [30]	SP	Rp	PSD
(Krolczyk et al., 2018) [31]	FV	Sa, Sq, Sz, Ssk, Sku, Sp, Sv, Sal, Vmp, Vmc, Vvc, Vvv, Sa1, Sa2, Spk, Sk, Svk, Smr1, Smr2	ACF, FFT
(Leksycki & Królczyk, 2020) [32]	CM, FV	Ra/Sa, Rz	-
(Leksycki et al., 2020) [33]	CM	Sa, Sq, Sz	-
(Li et al., 2023) [34]	WLI	Sa, Sq, Sz, Ssk, Sku, Sp, Sv, Sdq, Sdr	FEA
(Liu et al., 2021) [35]	WLI	Rq	Wavelet transform
(Merson et al., 2017) [14]	CM	Sa, Sq, Rs	-
(Moreau et al., 2024) [36]	CM, FV, CSI	Sa	-
(Newton et al., 2023) [37]	CM	Sa	-
(Niemczewska-Wójcik, 2017) [38]	WLI	Sq, Sz, Ssk, Sku, Spk, Sk, Svk, Smr1, Smr2	-
(Niemczewska-Wójcik et al., 2022) [39]	WLI	Ra, Sq, Ssk, Sku, Sp, Sv, Spk, Sk, Svk, Smr1, Smr2	-
(Niemczewska-Wójcik & Wójcik, 2020) [40]	WLI	Ra, Sq, Sz, Ssk, Sku, Spk, Sk, Svk, Smr1, Smr2	-
(Pakuła et al., 2019) [41]	AFM, CM	Ra, Rq, Rz	-
(Park et al., 2015) [42]	CM, WLI	Ra/Sa, Rq/Sq, Rz/Sz, Sdr, Rt	-
(Reddy et al., 2018) [43]	SP	Ra, Rz, Rp, Rv, Rpc, Rdc, Rsm	-
(Romoli et al., 2013) [44]	SHFM	Rq, Rz	-
(Sedlaček et al., 2016) [45]	SP	Sa, Sq, Ssk, Sku	-
(Sedlaček et al., 2020) [46]	FV	Ra	-
(Stach et al., 2019) [47]	AFM	Sa, Sq, Vmp, Vmc, Vvc, Vvv, Spk, Sk, Svk, Smr1, Smr2	Fractal analysis, BAC, ADF, Multi fractal analysis
(Tălu et al., 2013) [48]	AFM	Sa, Sq, Sz, Ssk, Sku, Sp, Sv, Smr, Smc, Sxp, Sal, Str, Std, Sdq, Sdr, Vm, Vv, Vmp, Vmc, Vvc, Vvv, Spd, Spc, S5v, Sha, Shv	Fractal analysis
(Tălu, 2021) [49]	AFM	Sa, Sq, Ssk, Sku	Motif analysis
(Tian et al., 2012) [50]	CM	Sq, Sv, Sdr, Vvc, S5p, S10z, Sda	-
(Torrent-Burgués & Sanz, 2014) [51]	AFM	Ra, Rq, Rz, Rsk, Rku	-
(Walczak et al., 2023) [52]	CSI, CF	Sa, Sq, Sz, Ssk, Sku, Sp, Sv	-
(Wang et al., 2017) [53]	CM	Sa/Ra, Sq/Rq, Rc, Rsk/Ssk, Rku/Sku, Sp, Sv	-
(Webb et al., 2012) [54]	AFM	Ra, Rq, Rz, Rsk, Rku, Rpc, Rsa	-
(Zhu et al., 2020) [55]	FV	Sa, Sq, Ssk, Sku, Sv	-
(Zeng et al., 2018) [9]	SEM, WLI	Sa/Ra, Sq/Rq, Rt, Ssk/Rsk, Sku/Rku, Rpk, Rvk, Sal, Sdq, Sdr, Rk, Mr1, Mr2, Svi, Sci	PSD, ACF, BAC, ADF

in the Appendix A.1 and visualized in Figure 2, Figure 3 and Figure 4.

Table A2. List of Abbreviations.

Abbreviation	Full Form	Abbreviation	Full Form
ACF	Autocorrelation Function	Svi	Valley fluid retention index
ADF	Amplitude Density Function	Sda	Mean Pit Area
AFM	Atomic Force Microscopy	Sdc	Height difference of inverse Material Ratio
BAC	Bearing Area Curve	Sdq	Root Mean Square Gradient
CF	Confocal Fusion	Sdr	Developed Interfacial Area Ratio
CM	Confocal Microscopy	Sds	Peak/Summit Density
CSI	Coherence Scanning Interferometry	Sha	Mean Hill Area
CT	Computed Tomography	Shv	Mean Hill Volume
DHM	Digital Holographic Microscopy	Sk	Core Roughness Depth
FEA	Finite Element Analysis	Sku	Kurtosis
FFT	Fast Fourier Transform	Smc	Inverse Material Ratio
FV	Focus Variation	Smr	Material Ratio
PSD	Power Spectral Density	Smr1, Smr2	Upper and Lower Material Ratio
Ra	Arithmetic Mean Roughness	Sp	Maximum Peak Height
Rdc	Material Ratio Height Difference	Spc	Mean Peak Curvature
Rdq	Root Mean Square Slope (profile)	Spd	Density of Peaks
Rk	Core Roughness depth (profile)	Spk	Reduced Peak Height
Rpc	Peak Count (profile)	Sq	Root Mean Square Height
Rpk	Reduced Peak Height (profile)	Ssc	Mean Summit Curvature
Rq	Root Mean Square Roughness	Ssk	Skewness
Rsk	Skewness (profile)	St	Total Height of the Areal Roughness
RSm	Mean Profile Element Spacing	Std	Texture Direction
Rt	Total Height of the Roughness Profile	Str	Texture Aspect Ratio
Rv	Mean Pit Depth (profile)	Sv	Maximum Pit Depth
Rvk	Reduced Pit Depth (profile)	Svk	Reduced Pit Depth
Rx	Largest Motif Height	Sxp	Extreme Peak Height
Rz	Maximum Height of the Profile	Sz	Maximum Height of the Surface
S10z	Ten-Point Height	Sλq	Root Mean Square of Spatial Wavelength
S5p	Five-Point Pit Height	Sλa	Spatial Average Wavelength
S5v	Five-Point Valley Depth	Vmc	Material Volume Core
Sa	Arithmetic Mean Height (areal)	Vmp	Material Volume Hill
Sa1, Sa2	Hill / Dale areas of the Material Ratio Curve	Vvc	Void Volume Core
Sal	Autocorrelation Length	Vvv	Valley Void Volume
Sci	Core Fluid Retention Index	WLI	White Light Interferometry

in Appendix A.2 provides an overview of all used abbreviations.

Figure 2 summarizes the applied measurement principles that were observed in the examined literature and shows that in research, the optical, areal measurement principles CM and WLI have become the most common. In combination, the interferometric principles CM, CSI, and DHM represent the most popular methods. Stylus profilometry, which is by far still the most common principle in industrial applications, ranks 4th. This proves that optical metrology is emerging for functional characterization, and the common principles CM, WLI, FV, CSI, and DHM account for about 70% of all applications.

The results clearly prove the increasing application of optical measuring principles in the research context. However, whereas the analysis of scientific studies indicates the increasing adoption of optical metrology, in the industrial environment, within versatile key sectors, including automotive, aerospace, biomedical, and precision manufacturing, it can still be observed that traditionally, profile measurement is the most common approach [56]. Throughout the different industrial sectors, however, the adoption differs: in some industries, the transition from traditional tactile profilometry to non-contact optical methods has been driven by the need for faster, more flexible, and non-destructive measurements, although challenges in standardization and parameter interpretation persist [57,58]. In this area, however, progress has been made in recent years so that it can be expected that these barriers are slowly disappearing. This statement is also supported by the observations of Figure 2, which clearly show that optical metrology is already frequently applied in the scientific context.

In contrast, when the surface texture parameters are considered as shown in Figure 3, a very traditional result can be observed: the amplitude-based surface texture parameters Ra/Sa, Rq/Sq, Rz/Sz, and others are still applied for many applications, even though for decades their limited information content with regard to the functional behavior of surfaces has been proven [59]. In the plot in Figure 3, these parameters are highlighted in red. For areal measurement, Sv, Sp, Ssk, and Sku are also commonly applied, just as well as Sdr and Sdq, the hybrid parameters that, due to the required numerical derivatives, can feature large uncertainties [60]. The latter category of parameters is just as common as the parameters Sk, Spk, and Svk, which are intended for functional characterization, and their profile equivalents were only used in one reference. The group of function-oriented parameters is highlighted in green. This shows that the variety of possibilities for functional characterization provided by the ISO standardization is not commonly known to many applicants who still focus on amplitude-based parameters. These parameters, in contrast, are well-known but very limited in their information content. Surprisingly, the functional surface texture parameters like Rk, Sk, and more are by far not as commonly used as the amplitude-based parameters. This shows that further training is necessary to increase awareness and understanding of these parameters, given their suitability for specialized applications that has been proven by various scientific studies. For example, the Rk-parameters have been commonly applied for the functional assessment of cylinder running surfaces and cutting edges of artificial hip joints [61,62,63,64,65,66].

The angular parameters depending on the surface’s slope generally provide important information, e.g., with regard to the tribological behavior of the surface, however when evaluating them using surface texture measuring instruments, they feature a larger measurement uncertainty. Thus, a direct angular measurement, e.g., using angular scattering light measurements is more meaningful in this context. Since it has been demonstrated that the distribution of height and angular information provides complementary information [67], in the future, an angular-based assessment can also be expected more frequently, however most likely by using angular measuring principles like scattering light measurement.

Overall, it can be observed that even though they do not feature high information content in many applications, the amplitude-based parameters are by far the most commonly used ones. Angular parameters are used to some extent, whereas the function-oriented parameters of, e.g., the Sk and Rk family, which are intended for a function-oriented assessment of surfaces, are not as widely applied.

The additional evaluation methods besides surface texture parameters are summarized in Figure 4: fractal analysis, the auto-correlation function and its pendant in the frequency domain, the power spectral density, the amplitude density function, the bearing area curve, and motif analysis can be found in multiple studies. This illustrates that in the scientific context, it is common to apply other methods in addition to surface texture parameters that can provide a deeper insight into the characteristics of a surface. Since the evaluation and interpretation of the associated methods is more time consuming than a process monitoring by standardized parameters, it is questionable to what extent these approaches will be transferred to the industrial application of functional characterization.

5. Correlation Analysis

After the general distribution of the measuring principles, surface texture parameters, and additional evaluation methods has been illustrated, heatmaps were generated to visualize the co-occurrence frequency between the different sets of variables. Figure 5 shows the applied measurement principles and surface texture parameters across the reviewed studies. The x-axis represents individual surface parameters, while the y-axis lists the various measurement principles employed. Each cell indicates the number of studies in which a specific measurement principle was used in combination with a given parameter. The color intensity reflects the relative frequency of these combinations, with darker shades denoting higher usage. The analysis reveals that certain parameters, such as Sa, Sq, Ssk, and Sdr, are frequently assessed across multiple measurement techniques, particularly with AFM, CM, and WLI. Conversely, some measurement principles, such as SHFM and CF, are applied more selectively. This distribution highlights that the most common practices are the profile-based and areal evaluation of amplitude-based parameters using AFM, WLI, and CM. The distribution again illustrates the strong focus on amplitude-based parameters and shows that even advanced measuring principles like AFM measurements are mostly evaluated using simple parameters that describe the height distribution, even though in the case of the AFM measurements, individual application of other parameters can also be observed. For FV, mostly amplitude-based parameters are applied for surface texture assessment. For the WLI measurements, in some studies an evaluation of the function-oriented parameters of the Sk family can also be observed.

Figure 6 illustrates the frequency of application of various advanced evaluation techniques in relation to specific surface texture parameters. The y-axis lists the range of observed evaluation methods. The analysis reveals that certain methods, such as fractal analysis and ACF, are commonly combined with multiple parameters, while others, like FEA and discrete wavelet transform, are used more selectively. Additionally, it shows that both groups of parameters-the amplitude-based and functional surface texture parameters-are often combined with other evaluation techniques, most commonly fractal analysis, followed by evaluations of the ACF, PSD, ADF, BAC, and motif analysis. This proves that many applicants are aware of the limited information content that parameters like Sa/Ra provide, so in some cases more advanced evaluation strategies are used as an addition to surface texture parameters. However, also the Sk-parameters are sometimes combined with other approaches, even though their nature is directly function-oriented. Many other parameters are rarely or never combined with other evaluation strategies.

In the last heatmap shown in Figure 7, the frequency of co-occurrence between advanced surface evaluation techniques and measurement principles is shown. The analysis reveals that most commonly, the measured data of AFM, WLI, and SP are combined with advanced evaluation methods, whereas, e.g., CM, although it is the most applied measuring principle, is not commonly used together with advanced evaluation routines. One reason could be that in some cases, short-wavelength structures that matter significantly for many advanced evaluation techniques can be acquired more reliably with AFM and WLI measurements. For some measuring principles like DHM, CT, SLS, SFM, SHFM, CSI, and CF in the examined studies, no combination at all with advanced evaluation methods could be observed.

It was generally demonstrated that both the measurement technique and the subsequent data analysis vary significantly depending on the specific application, and that there is no universally applicable approach for functional characterization. It can be recommended to use the function-oriented parameters more frequently since they contain more information than amplitude-based surface texture parameters for many applications. In addition to the fundamental methods of topographic metrology, alternative approaches must also be considered. For example, Popov has shown that surface inclination angles play a critical role in determining the tribological behavior of surfaces [68]. Consequently, angle-resolved scattered light measurement techniques offer promising opportunities for functional surface characterization. It has already been demonstrated that the information content derived from height distributions and angular distributions is complementary, highlighting the potential of combining these modalities for a more comprehensive understanding of surface functionality [67].

Building on these examples, we propose facilitating the transition from theoretical development to industrial practice. The recommendations include a comprehensive evaluation of the surface, including height and angular information, and the wider application of functional surface texture parameters on the one hand and the formulation of simplified, application-specific parameter sets on the other.

6. Conclusions

Even though in the industrial context tactile measurement using stylus instruments is still by far the most common measurement principle, in scientific applications of functional characterization, the presented study illustrated that optical surface characterization methods have significantly emerged, accounting for approximately 70% of all measurements. Correspondingly, areal surface characterization parameters have gained a significance dominance, which has emerged in the past decade and is in contrast to the previous studies. Amplitude-based parameters are still most common, while in accordance with the literature, the skewness and kurtosis Ssk and Sku have achieved significant usage, as well as the hybrid parameters that are determined based on angular information. In contrast to earlier predictions, however, many parameters that are intended for functional characterization have not gained a common application.

Even though the surface characterization process is case specific, it can be recommended that the variety of standardized parameters, especially function-oriented parameters, should be considered to achieve a higher information content than with, e.g., Ra, Rq, and Rz. The general potential of functional surface texture parameters, hybrid parameters, and feature-based evaluation is not yet fully exploited. Also, an angular assessment using scattering light measurement can unleash potential for direct functional monitoring and assessment of surface topography. It can, however, be observed that additional evaluation methods like fractal analysis are more commonly being applied, leading to improved possibilities for functional characterization. In the correlation analysis, it was shown that mostly AFM, WLI, and SP were combined with advanced evaluation techniques.

Earlier studies have investigated this topic for different user groups. The given results indicate that research changes have occurred, and optical metrology has become the most common method for the functional assessment of surface topography. By choosing alternative parameters to the amplitude-based parameters, more information content with regard to the functional behavior can be extracted from the measured surface topography, and it is recommended to provide further training about the evaluation possibilities that the latest ISO standardization provides. Otherwise, it can be expected that the observed trends will further spread into industrial application.