Surface Topography: Metrology and Properties

Surface textures have been of great interest within the tribology community with nearly 1500 papers published on this topic in the past two decades. With the pursuit of low emissions and environmental sustainability, the application of surface texturing to mechanical systems to lower friction and control wear is attracting increasing attention. There is no doubt that certain textured surfaces can have a beneficial effect on tribological performance but it is widely agreed that the optimization of textures should be carried out based on specific requirements of applications. The purpose of this review article is to summarize the current state of the art in surface texturing applied to mechanical applications (cutting tools, piston-ring & cylinder liners, sealing and journal bearings) from the following aspects: application requirements, numerical/experimental testing and validation, and tribological performance of textured surfaces (wear and friction), as well as the limitations in texture designs when applied to certain applications. Patterns/grooves in the micron-scale are the most typical shapes been studied, and benefits of partial texturing are applicable for most of these mechanical applications. Friction reduction of up to 34.5% in cutting tools, 82% in piston-ring & cylinder-liners, 65% in seals and 18% in journal bearings have been observed by experimental tests. Based on primary evidence from the literature, the last section provides general suggestions on current gaps in understanding and modelling and suggestions for future research directions.

The optimization of surface finish to improve performance, such as adhesion, friction, wear, fatigue life, or interfacial transport, occurs largely through trial and error, despite significant advancements in the relevant science. There are three central challenges that account for this disconnect: (1) the challenge of integration of many different types of measurement for the same surface to capture the multi-scale nature of roughness; (2) the technical complexity of implementing spectral analysis methods, and of applying mechanical or numerical models to describe surface performance; (3) a lack of consistency between researchers and industries in how surfaces are measured, quantified, and communicated. Here we present a freely-available internet-based application (available at https://contact.engineering) which attempts to overcome all three challenges. First, the application enables the user to upload many different topography measurements taken from a single surface, including using different techniques, and then integrates all of them together to create a digital surface twin. Second, the application calculates many of the commonly used topography metrics, such as root-mean-square parameters, power spectral density (PSD), and autocorrelation function (ACF), as well as implementing analytical and numerical calculations, such as boundary element modeling (BEM) for elastic and plastic deformation. Third, the application serves as a repository for users to securely store surfaces, and if they choose, to share these with collaborators or even publish them (with a digital object identifier) for all to access. The primary goal of this application is to enable researchers and manufacturers to quickly and easily apply cutting-edge tools for the characterization and properties-modeling of real-world surfaces. An additional goal is to advance the use of open-science principles in surface engineering by providing a FAIR database where researchers can choose to publish surface measurements for all to use.

This paper investigates the performance of bio-inspired Herringbone Grooved-Gas Foil Journal Bearings (HG-GFJBs) using numerical simulations based on the Reynolds equation. Drawing inspiration from the natural arrangement of feather riblets of birds, the study explores how biologically inspired groove geometries can improve the efficiency and functionality of Gas Foil Journal Bearings (GFJBs). These natural patterns, which have evolved to enhance pressure distribution, are applied to the surface topographies of gas foil bearings to examine their impact on static performance. The results indicate that the air-lubricated HG-GFJBs are capable of operating at high speeds and sustaining significant radial loads. Furthermore, an Artificial Neural Network (ANN) was trained using numerical data in order to assess performance using the regression coefficient. Decision surface plots of the Adaptive Neuro-Fuzzy Inference System (ANFIS) were then produced in order to determine the optimal bearing parameters. By leveraging principles derived from nature, the study opens up new possibilities for enhancing the performance and longevity of GFBs, making them more efficient and reliable for a variety of engineering applications.

Conventional field parameters for surface measurement use all data points, while feature characterization focuses on subsets extracted by watershed segmentation. This approach enables the extraction of specific features that are potentially responsible for the function of the surface or are a direct reflection of the manufacturing process, allowing for a more accurate assessment of both aspects. Feature characterization with the underlying watershed segmentation for areal surface topographies has been standardized for over a decade and is well established in industry and research. In contrast, feature characterization for surface profiles has been standardized recently, and the corresponding standard for watershed segmentation is planned to be published in the near future. Since the standards do not provide guidelines for implementation, this paper presents an unambiguous algorithm of the watershed segmentation and the feature characterization for surface profiles. This framework provides the basis for future work, mainly investigating the relationship between feature parameters based on feature characterization and the function of the surface or manufacturing process. For this purpose, recommendations for the configuration and extensions of the toolbox can also be developed, which could find their way into the ISO standards.

In turbomachinery, their blade leading edges are critical to performance and therefore fuel efficiency, emission, noise, running and maintenance costs. Leading edge damage and therefore roughness is either caused by subtractive processes such as foreign object damage (bird strikes and debris ingestion) and erosion (hail, rain droplets, sand particles, dust, volcanic ash and cavitation) and additive processes such as filming (from dirt, icing, fouling, insect build-up). Therefore, this review focuses on the changes in topography induced by during service to blade leading edges and the effect of roughness and form on performance and efforts to predict and model these changes. The applications considered are focused on wind, gas and tidal turbines and turbofan engines. Repair and protection strategies for leading edges of blades are also reviewed. The review shows additive processes are typically worse than subtractive processes, as the roughness or even form change is significant with icing and biofouling. Antagonism is reported between additive and subtractive roughness processes. There are gaps in the current understanding of the additive and subtractive processes that influence roughness and their interaction. Recent work paves the way forward where modelling and machine learning is used to predict coated wind turbine blade leading edge delamination and the effects this has on aerodynamic performance and what changes in blade angle would best capture the available wind energy with such damaged blades. To do this generically there is a need for better understanding of the environment that the blades see and the variation along their length, the material or coated material response to additive and/or subtractive mechanisms and thus the roughness/form evolution over time. This is turn would allow better understanding of the effects these changes have on aerodynamic/ hydrodynamic efficiency and the population of stress raisers and distribution of residual stresses that result. These in turn influence fatigue strength and remaining useful life of the blade leading edge as well as inform maintenance/repair needs.

The analysis of surface effects in powder bed fusion additive manufacturing is the subject of intensive research activities. The aim of this paper is to provide an overview of the current state of knowledge and to gain a comprehensive understanding of this subject area. The paper is intended to enable researchers to select specific articles for their own further research context. In addition, a bibliometric analysis validates the data base. A discussion of the findings suggests that the criticality of the surface should be considered as a quality factor in the field of additive manufacturing by powder bed fusion processes. An accurate and reliable measurement is crucial for predicting component quality. There is a clear trend from two-dimensional measurements to three-dimensional measurements. Conducting comprehensive research is essential to improve the reliability and comparability of measurement results and to promote broad acceptance and application of this technology in the industry.

Surface degradation of materials due to solid particles (Erosion) in high-temperature environments is a major contributor to wear in applications such as power plants, petrochemical plants, and aeroplane engines. This degradation can result in decreased efficiency, higher maintenance costs, and potential equipment failure. The erosion rate is influenced by a number of variables, including the characteristics of the target material and particle shape, velocity, and angle of impingement. Surface coating, especially HVOF spraying, improves erosion resistance leading to increase the material durability, reducing wear and extending equipment life. The high-temperature erosion behaviour of microstructured and nanostructured HVOF-sprayed Inconel 718 superalloy materials is investigated in an air jet erosion tester. Following the tests, the eroded samples are examined using scanning electron microscopy (SEM), Energy dispersive x-ray spectroscopy (EDX) and x-ray diffraction (XRD) to analyze the effects of erosion. The results indicate that uncoated samples experience higher erosion rates at all impact angles compared to both coated samples. Erosion rates for both microstructured and nanostructured coated samples are higher at a 30° impact angle compared to a 90° angle, indicating a ductile behaviour in response to impact. The lower porosity of the nanostructured coating is believed to enhance its ability to protect the substrate from erodent particles. The Cr₃C₂NiCrBSi nanostructured coating provides better erosion resistance and effectively protects the surface of the Inconel 718 superalloy.

We discuss the height and lateral resolution that can be achieved in vertical scanning white-light interferometry (SWLI). With respect to interferometric height resolution, phase-shifting interferometry (PSI) is assumed to provide the highest accuracy. However, if the noise dependence of SWLI phase evaluation and PSI algorithms is considered, SWLI measurements can be shown to be more precise. With respect to lateral resolution, the determination of the coherence peak position of SWLI signals seems to lead to better results compared to phase based-interferometric measurements. This can be attributed to the well-known batwing effect. Since batwing is a nonlinear effect applying nonlinear filters, e.g. a median filter, it reduces them significantly. If filtering is applied prior to the fringe order determination and phase evaluation, the number of artefacts known as ghost steps can be eliminated without changing the modulus of the phase. Finally, we discuss the dependence of measured height values on surface slope. We show that in interference microscopy there are additional limitations which are more rigid compared to the maximum surface slope angle resulting from the numerical aperture of the objective lens. As a consequence, the measurement precision breaks down at slope changes of steeper flanks even if the modulation depth of the interference signals is still good enough for signal analysis.

Roughness determines many functional properties of surfaces, such as adhesion, friction, and (thermal and electrical) contact conductance. Recent analytical models and simulations enable quantitative prediction of these properties from knowledge of the power spectral density (PSD) of the surface topography. The utility of the PSD is that it contains statistical information that is unbiased by the particular scan size and pixel resolution chosen by the researcher. In this article, we first review the mathematical definition of the PSD, including the one- and two-dimensional cases, and common variations of each. We then discuss strategies for reconstructing an accurate PSD of a surface using topography measurements at different size scales. Finally, we discuss detecting and mitigating artifacts at the smallest scales, and computing upper/lower bounds on functional properties obtained from models. We accompany our discussion with virtual measurements on computer-generated surfaces. This discussion summarizes how to analyze topography measurements to reconstruct a reliable PSD. Analytical models demonstrate the potential for tuning functional properties by rationally tailoring surface topography—however, this potential can only be achieved through the accurate, quantitative reconstruction of the PSDs of real-world surfaces.

Dietary information of fossil mammals can be revealed via the analysis of tooth morphology, tooth wear, tooth geochemistry, and the microscopic wear patterns on tooth surfaces resulting from food processing. Although dental microwear has long been used by anthropologists and paleontologists to clarify diets in a diversity of mammals, until recently these methods focused on the counting of wear features (e.g., pits and scratches) from two-dimensional surfaces (typically via scanning electron microscopes or low-magnification light microscopes). The analysis of dental microwear textures can instead reveal dietary information in a broad range of herbivorous, omnivorous, and carnivorous mammals by characterizing microscopic tooth surfaces in three-dimensions, without the counting of individual surface features. To date, dental microwear textures in ungulates, xenarthrans, marsupials, carnivorans, and primates (including humans and their ancestors) are correlated with known dietary behavior in extant taxa and reconstruct ancient diets in a diversity of prehistoric mammals. For example, tough versus hard object feeding can be characterized across disparate phylogenetic groups and can distinguish grazers, folivorous, and flesh consumers (tougher food consumers) from woody browsers, frugivores, and bone consumers (harder object feeders). This paper reviews how dental microwear textures can be useful to reconstructing diets in a broad array of living and extinct mammals, with commentary on areas of future research.

This study investigated the interfacial lubrication and friction behavior of piezoelectric materials. Two types of water-based lubrication solutions containing anionic and cationic surfactants were used in friction experiments with varying rotational speeds and loads. This study revealed that lubricant molecules adsorb onto the electrode solid–liquid interface, resulting in distinct friction behaviors. The piezoelectric positive electrode adsorbs anions, while the negative electrode surface adsorbs cations, forming a thicker adsorption film that reduces friction compared to a non-piezoelectric interface. Furthermore, the study reveals that the coupling of mechanical force, electrical effects, and surface adsorption influences lubrication performance. This study provides a method for enhancing lubrication performance in smart materials, with significant engineering applications.

In digital holographic microscopy, the application of a polarization camera with a micro-polarizer array allows simultaneous phase shifting, thus the complex amplitude associated with the surface under test can be obtained by one-shot image acquisition. However, since the four phase-shifted holograms extracted from the polarization camera are sub-sampled, there exist lateral shift between different holograms, which in turn inevitably introduces error in the reconstructed results. To address this problem, a super-resolution reconstruction method is proposed for digital holographic microscopy. A diffraction physics model is combined with a deep learning framework, and the outputs are updated in a self-supervised manner. The proposed algorithm is verified with both simulated and experimental data, and the reconstruction accuracy can be improved by over an order of magnitude.

Volcanic edge textures form when processing concave textures on the surfaces of ultra-high molecular weight polyethylene (UHMWPE) flat specimens using a CO₂ laser due to thermal effect. This paper investigates the influence of the arrangement and density of volcanic edge textures on the tribological performance of UHMWPE water-lubricated thrust bearings, along with the morphological evolution of these textures under wear. Results indicate that under heavy load conditions, volcanic edge textures reduce the contact area between the friction pairs, enhance the hydrodynamic effect, and contribute to a reduction in friction torque compared to non-textured specimens. For a constant texture area, as the number of textures increases, the water-lubricated friction coefficient initially increases and then decreases. At low speeds or under heavy loads, specimens with a single radial rectangular texture connected to the internal water tank exhibit the lowest and most stable friction coefficients. For the same texture configuration, specimens with 15 rectangular textures distributed circumferentially demonstrate the lowest friction coefficient and minimal wear height.

The aim of this work is to further enhance the tribological lubrication performance of journal bearings on the premise of a slight improvement in the bearing's load carrying capacity. To this end, the effect of such parameters as the circumferential distribution position, axial proportion, depth, inclined angle, and area rate of the texture on the lubrication and tribological characteristics of misaligned radial journal bearings is examined by machining the cylindrical micro-pit texture onto the bearing shell. The findings show that the bearing's load carrying capacity increases with the presence of cylindrical micro-pit texture in the boost area, particularly when the axial ratio is close to 55%. Furthermore, the journal bearing that incorporates this micro-pit texture within the boost area exhibits a lower friction coefficient relative to those within fully texturing, texturing within the bucking zone, or an entirely smooth surface. The micro-pit texture can both enhance or diminish the load capacity of the bearing, when the texture axial proportion is 55%, the depth is 2.5 μm and the texture inclined angle is 90°, the tribological and lubrication performance of the bearing with texture in the boost area is the best and the load carrying capacity is slightly improved. Additionally, there is a direct linear relationship between the area ratio of the cylindrical micro-pit texture and both the load carrying capacity and the friction force. Machining the texture of suitable parameters onto the bearing shell's smooth surface is conducive to improving the bearing's properties, with the effect becoming more pronounced as the journal misalignment angle was reduced. The analysis indicated that machining cylindrical micro-pit texture with the appropriate parameters onto the bearing shell's smooth surface can effectively lower the friction coefficient, reduce mechanical wear and improve bearing performance while ensuring a slight increase in the bearing load carrying capacity. In addition, this research can provide a beneficial parameter reference for texture design.

Conventional field parameters for surface measurement use all data points, while feature characterization focuses on subsets extracted by watershed segmentation. This approach enables the extraction of specific features that are potentially responsible for the function of the surface or are a direct reflection of the manufacturing process, allowing for a more accurate assessment of both aspects. Feature characterization with the underlying watershed segmentation for areal surface topographies has been standardized for over a decade and is well established in industry and research. In contrast, feature characterization for surface profiles has been standardized recently, and the corresponding standard for watershed segmentation is planned to be published in the near future. Since the standards do not provide guidelines for implementation, this paper presents an unambiguous algorithm of the watershed segmentation and the feature characterization for surface profiles. This framework provides the basis for future work, mainly investigating the relationship between feature parameters based on feature characterization and the function of the surface or manufacturing process. For this purpose, recommendations for the configuration and extensions of the toolbox can also be developed, which could find their way into the ISO standards.

The concept of enhancing surface friction through the implementation of surface texturing has garnered significant attention. In nature, numerous animal species are renowned for their reliable attachment pads, characterized by textured epidermal surfaces featuring a dense array of distinct geometries. These intriguing surface textures enable them to achieve robust friction necessary for locomotion or attachment on various surfaces. In the realm of technology, such concise yet efficient frictional designs have also been noteworthy due to their wide-ranging applications in areas such as biomimetic robots, tires, and wearable devices. However, despite the extensive development of artificial mimicked textures, their frictional performance still falls short compared to natural systems. This paper presents an overview of recent advancements in bio- and bioinspired textures aimed at augmenting friction, encompassing their architectural designs, inherent properties, underlying physical principles, and future research directions.

The analysis of surface effects in powder bed fusion additive manufacturing is the subject of intensive research activities. The aim of this paper is to provide an overview of the current state of knowledge and to gain a comprehensive understanding of this subject area. The paper is intended to enable researchers to select specific articles for their own further research context. In addition, a bibliometric analysis validates the data base. A discussion of the findings suggests that the criticality of the surface should be considered as a quality factor in the field of additive manufacturing by powder bed fusion processes. An accurate and reliable measurement is crucial for predicting component quality. There is a clear trend from two-dimensional measurements to three-dimensional measurements. Conducting comprehensive research is essential to improve the reliability and comparability of measurement results and to promote broad acceptance and application of this technology in the industry.

Surfaces are the privileged places of interaction between physical phenomena and objects. Roughness studies, especially when performing multiscale analysis, are tools of choice to understand physical phenomena and their scales of application. However, profilometers, especially optical systems, must compromise between field of measurement and resolutions. Stitching is an assembling technique aiming to solve this compromise by combining elementary maps, such as images or topographies. Stitching generates high resolution over a large field of measurement maps, which increases the measurable scale range and facilitates the correct identification of physical phenomena at their scales of application. This article proposes a review of 3D topography stitching algorithms. After explanations on the use cases of 3D topography stitching, the stitching procedure from elementary maps acquisition to the obtention of the stitched map is described step-by-step. Secondly, errors in measurement and stitching are presented with the sources of errors and the error evaluation methods. Lastly, the mathematical modelling of 3D topography is detailed to better understand the optimization process used in the in-plane and out-of-plane registration steps of the stitching algorithms. Comparison of algorithms involved in stitching are proposed so that researchers might find the most suitable algorithm to their needs. Overall, this work aims at introducing researchers and metrologists to important multidisciplinary notions for the use and design of 3D topography stitching algorithms and offers a tutorial-based approach.

Different components used in industries like power plant, petrochemical, automobile are subjected to severe wear and corrosion due to high temperature and pressure environments. Therefore, it is necessary to improve those components' wear and corrosion resistance properties. Different processes like laser cladding, CVD, PVD, and thermal spraying are widely used for upgrading surface properties of material. In recent days, it has been found that many researchers investigated the performance of tungsten inert gas (TIG) welding for cladding of superior material like ceramics, metal etc on different substrate materials. TIG cladding can fulfil the requirements of industries by developing a quality cladded layer with low cost and high productivity. This research paper has made an effort to compile the literature related to TIG cladding process for improving substrate properties. It has been observed that the superior materials like titanium carbide(TiC), silicon carbide(SiC), tungsten carbide(WC), cobalt-based alloys, and nickel-based alloys have been successfully cladded using TIG welding process. Researchers have also observed adequate improvement in properties like microhardness and wear resistance of different grades of steel substrate material, like 304, 316 stainless steel, 1010, and 1020 low-carbon steel. The process is also successfully utilized for cladding of superior material on nonferrous metals like Al, Ti alloy. The TIG clad quality and performance rely on different process parameters like current, scan speed, and shielding gas flow rate and also the properties of coating and substrate material.

Demand for higher wear and corrosion resistance components has attracted increasing interest in surface engineering. This line of research develops alternative processes for improving the surface properties of engineering materials. The traditional route seeks the development of new alloys. However, the cost and time associated with these developments become prohibitive in many cases. Currently, the application of plasma-assisted thermochemical treatments has been a technically and economically viable alternative to extend the lifespan of components exposed to severe environments. In this sense, the tooling industry is one of the oldest and most traditional users of plasma-assisted processes, since forming, injection and/or cutting tools are usually subjected to wear and corrosion degradation. Among the various materials used to make tools, we highlight the martensitic stainless steels, which are used in the manufacture of molds and inserts for injection of chlorinated and fluorinated thermoplastic and thermoset polymers. In these applications, martensitic stainless steels are exposed to severe deterioration conditions due to abrasive wear and corrosion by chloride and fluoride ions. Considering the variety of available plasma-assisted thermochemical treatments whose application allows improving metallic materials corrosion and wear resistance, it is a complex task to select the better process and its execution parameters to ensure the maximum performance in operation. In this work, it is proposed a systematic method to aid the process selection task, focused on thermochemical treatments of martensitic stainless steels, which integrates the processing conditions and the resulting microstructure, properties and performance. For this purpose, working envelop for selecting processes and processing parameters were elaborated, that allow qualify and quantify the correlations among each specific plasma-assisted thermochemical treatment (like nitriding, carburizing, nitrocarburizing, etc.) execution conditions, with the resulting properties and performance for treated martensitic stainless steels. In parallel, the genesis of plasma-assisted thermochemical treatments is also described, a bibliometric analysis is carried out on the publications on the subject, and also, a summary description of the surface characteristics of the treated materials is realized.

Product surfaces, when touched by consumers, play an important role in enhancing the Kansei (affective) value of products. Though the Kansei value of products made of metal alloys would be enhanced by studies concerning machined surfaces, there are few studies. In this study, participants were asked to rub machined surfaces to investigate the relationships between surface roughness, finger vibrations, roughness perception, and perceived comfort—and thereby reveal how roughness and comfort of the surfaces were perceived and evaluated. Experiments revealed that with increased surface roughness the perception of roughness increased and that of comfort (of the machined surfaces) decreased. Thus, the perceived comfort and roughness perception were negatively correlated. The area under the fast Fourier transform (FFT) and center of frequency–calculated as features of finger vibration—varied slightly between the tested surfaces; however, there were no statistically significant differences between the surfaces; the features of finger vibration were correlated with roughness perception and perceived comfort. On the other hand, the correlation coefficients varied with participants and the features of finger vibration (FFT area or center of frequency). These findings contribute to a deeper understanding of the relationships between mechanical stimulation and psychological feelings when humans rub machined surfaces.

In this work, color coatings were prepared based on hot-dip galvanization employing a Zn-0.4Mn zinc alloy bath, and the surface color exhibited a three-order periodic cycle of colors (yellow, purple, blue, and green). The element Mn has a greater thermodynamic tendency towards oxidation than Zn in the air. As a result, the Mn atoms will be oxidized preferentially to form an oxide film of diverse colors on the surface. The morphology and composition of the oxide film were investigated by scanning electron microscope (SEM), X-ray diffractometry (XRD), and X-ray photoelectron spectroscopy (XPS). It has been shown that the diverse colors of the oxide film result from the different thicknesses of the oxide film caused by light destructive interference. The main components of the oxide film are MnO and MnO2. As the oxidation time increases, the oxide film grows and thickens. The thicker film showing the third-order color will crack, which may be caused by the internal stress of the film layer. The results of electrochemical impedance spectroscopy (EIS) showed that the impedance increased sequentially with the color progression, but decreased with the third-order color coating, and the charge transfer resistance reaches a maximum at the second-order in purple and blue, with both values exceeding 4000 Ω·cm2. Furthermore, neutral salt spray tests (NSS) demonstrated that the thickening of the oxide film enhances the corrosion resistance of the coatings.

When coupling with high tensile steel in an electrolyte, aluminum alloys show elevated corrosion rates due to galvanic corrosion, resulting in severe damage to the aluminum alloy. Herein, a series of pure aluminum (Al) coatings were prepared using Direct current (DC) magnetron sputtering to investigate the influence of bias voltages on the corrosion resistance and enhancement of galvanic corrosion protection provided by these coatings. The deposited Al coatings significantly reduced the galvanic corrosion sensitivity of both steel and aluminum alloy from class E to class B levels, thereby effectively mitigating the galvanic corrosion rate. Furthermore, tafel tests, neutral salt spray tests, and immersion experiments were conducted to examine the effect of bias voltages on the anti-corrosion properties of the Al coatings. The results demonstrated that optimal anti-corrosion performance was achieved at a bias voltage of -75 V due to its dense structure and fine grains. At this bias voltage, the Al coating exhibited a corrosion current density of 9.12×10⁻⁸ A/cm², an average galvanic current density of 0.41 μA/cm², and a hardness value of 0.662 GPa. This study further reveals the mechanism of improved galvanic corrosion protection offered by Al coatings under different bias voltages.

Copper thin film is commonly applied in the microelectronics and battery industries, and environmentally friendly magnetron sputtering process is one of the main coating processes. The effects of grain size, dislocation density, and texture type of Cu target on sputtering efficiency were researched in this work. High-purity copper cast ingots were rolled into plates. Pieces were then cut from different surfaces of the plates and assembled into the targets named as Target 1, Target 2, and Target 3 for magnetron sputtering. To investigate the etching behavior of the three targets, EBSD technology was utilized for analysis, and the micro-morphologies of the etching grooves were observed using SEM. After the sputtering process, the weight loss rate and etching depth of the various targets were measured to compare the respective sputtering efficiency. Target 1 exhibited the highest sputtering weight loss rate at any time period compared to Target 2 and Target 3, and reached its maximum depth after every 15 minutes of sputtering. In the first 45 minutes, the etching depth of Target 2 was slightly greater than that of Target 3. While the etching depth of Target 3 increased significantly in the last 15 minutes compared to that of Target 2. The most significant factor influencing the sputtering efficiency of the target material was grain size, with finer grains leading to higher sputtering efficiency. The dislocation density had a greater impact in the first 30 minutes, but the texture type became more important as the sputtering process continued.

Conventional field parameters for surface measurement use all data points, while feature characterization focuses on subsets extracted by watershed segmentation. This approach enables the extraction of specific features that are potentially responsible for the function of the surface or are a direct reflection of the manufacturing process, allowing for a more accurate assessment of both aspects. Feature characterization with the underlying watershed segmentation for areal surface topographies has been standardized for over a decade and is well established in industry and research. In contrast, feature characterization for surface profiles has been standardized recently, and the corresponding standard for watershed segmentation is planned to be published in the near future. Since the standards do not provide guidelines for implementation, this paper presents an unambiguous algorithm of the watershed segmentation and the feature characterization for surface profiles. This framework provides the basis for future work, mainly investigating the relationship between feature parameters based on feature characterization and the function of the surface or manufacturing process. For this purpose, recommendations for the configuration and extensions of the toolbox can also be developed, which could find their way into the ISO standards.

This paper investigates the performance of bio-inspired Herringbone Grooved-Gas Foil Journal Bearings (HG-GFJBs) using numerical simulations based on the Reynolds equation. Drawing inspiration from the natural arrangement of feather riblets of birds, the study explores how biologically inspired groove geometries can improve the efficiency and functionality of Gas Foil Journal Bearings (GFJBs). These natural patterns, which have evolved to enhance pressure distribution, are applied to the surface topographies of gas foil bearings to examine their impact on static performance. The results indicate that the air-lubricated HG-GFJBs are capable of operating at high speeds and sustaining significant radial loads. Furthermore, an Artificial Neural Network (ANN) was trained using numerical data in order to assess performance using the regression coefficient. Decision surface plots of the Adaptive Neuro-Fuzzy Inference System (ANFIS) were then produced in order to determine the optimal bearing parameters. By leveraging principles derived from nature, the study opens up new possibilities for enhancing the performance and longevity of GFBs, making them more efficient and reliable for a variety of engineering applications.

Surface degradation of materials due to solid particles (Erosion) in high-temperature environments is a major contributor to wear in applications such as power plants, petrochemical plants, and aeroplane engines. This degradation can result in decreased efficiency, higher maintenance costs, and potential equipment failure. The erosion rate is influenced by a number of variables, including the characteristics of the target material and particle shape, velocity, and angle of impingement. Surface coating, especially HVOF spraying, improves erosion resistance leading to increase the material durability, reducing wear and extending equipment life. The high-temperature erosion behaviour of microstructured and nanostructured HVOF-sprayed Inconel 718 superalloy materials is investigated in an air jet erosion tester. Following the tests, the eroded samples are examined using scanning electron microscopy (SEM), Energy dispersive x-ray spectroscopy (EDX) and x-ray diffraction (XRD) to analyze the effects of erosion. The results indicate that uncoated samples experience higher erosion rates at all impact angles compared to both coated samples. Erosion rates for both microstructured and nanostructured coated samples are higher at a 30° impact angle compared to a 90° angle, indicating a ductile behaviour in response to impact. The lower porosity of the nanostructured coating is believed to enhance its ability to protect the substrate from erodent particles. The Cr₃C₂NiCrBSi nanostructured coating provides better erosion resistance and effectively protects the surface of the Inconel 718 superalloy.

Optical topography measurements are of high interest in a lot of industrial and academic fields. One of the most common associated measurement methods is coherence scanning interferometry, but even though it provides sub-nanometer axial resolution, its lateral resolution is diffraction limited. Not only the feature size is a limiting factor for optical measurements, but also steep surface slopes may lead to problems, since the acceptance angle of the objective lens limits the maximum surface slope angles that can be measured. Here we use a Linnik-type interferometer with objective lenses of numerical apertures of 0.95 in order to maximize the measurable surface slope angle. We demonstrate that silicon V-groove structures with a slope angle of 54.74° can be measured. We compare the directly measured surface slope angle with an angle calculated from light that is reflected two times by the V-grooves. To verify our measurement we compare the measurement results to rigorous FEM simulations.

The analysis of surface effects in powder bed fusion additive manufacturing is the subject of intensive research activities. The aim of this paper is to provide an overview of the current state of knowledge and to gain a comprehensive understanding of this subject area. The paper is intended to enable researchers to select specific articles for their own further research context. In addition, a bibliometric analysis validates the data base. A discussion of the findings suggests that the criticality of the surface should be considered as a quality factor in the field of additive manufacturing by powder bed fusion processes. An accurate and reliable measurement is crucial for predicting component quality. There is a clear trend from two-dimensional measurements to three-dimensional measurements. Conducting comprehensive research is essential to improve the reliability and comparability of measurement results and to promote broad acceptance and application of this technology in the industry.

Metal additively manufactured (AM) surfaces do not exhibit the same surface features as machined surfaces. Rather than cutting marks, the additive surface may display surface features such as spatter particles, weld tracks, cracks, and surface breaking pores. These features are not well described by surface height parameters that were developed for machined surfaces. Therefore, an AM specific surface characterisation approach is required; feature based surface characterisation is a promising approach, but it requires surface features to be manually segmented which is a subjective process. In this work, a U-Net spatter particle segmentation algorithm is developed that removes the subjectivity of manual surface feature segmentation. A U-Net model is trained to segment spatter particles from optical measurements of 20 different metal AM samples. The performance of the U-Net segmentation algorithm is compared to segmenting the spatter particles using manual thresholding. The results show that the U-Net segmentation approach outperforms manual segmentations for 2 of 3 test samples considered. It is found that for 2 of 3 samples, the U-Net segmentation algorithm detects spatter particles that are missed by the manual segmentation approach. It is concluded that further training of the U-Net approach is required before it can fully supersede manual segmentation. In the future, it may be possible to replace human operators that subjectively segment surface features with robust machine learning-based surface feature segmentation algorithms. This novel application of U-Net for AM surface feature segmentation has the potential to automate surface characterisation for metal AM process optimisation, and for quality control in production environments.

Additive manufacturing (AM) is rapidly developing technology which provide opportunity to generate 3D complex geometries without using any conventional tools. However, it was initially used frequently for rapid prototyping, it has now begun to be used for manufacturing functional machine parts. Wear is a critical phenomenon encountered in functional engineering systems and must be well understood for developing predictive and preventive approach. In this study, it was aimed to determine measurement procedure for additive manufactured AlSi10Mg metal part by using both standard and new technology wear measurement methods such as gravimetric, 2D and 3D optical profilometry, x-ray computed tomography (X-CT) and image processing. Minimum wear volume was measured by gravimetric method as 0,9268 mm³ while maximum was recorded as 1,6403 mm³ by 2D mechanical profilometer. X-CT and image processing methods wear volumes were close to each other and lie between gravimetric and 2D profilometric methods. This study aimed to provide basic understanding about the differences between the wear measurement methods on AM parts and serve further studies on measuring, predicting and preventing wear with more technological methods.

This study investigates how ultrasonic shot peening (USP) influences compressive residual stress (CRS) in the material surface layer. By combining experimental and simulation analyses, the research explores the distribution and variability of CRS. The results suggest that the residual stress (RS) curve from shot peening may not always conform the typical '√' shape. The experimental results show that, compared to the unpeened specimens, each group curve of the USP specimens exhibits a deeper CRS depth. Notably, within a 30 μm surface layer, certain data points exhibit CRS relaxation, which deviates from the expected behavior. Further research was conducted to simulate the mutual effects of the spheres impact on the material surface, using parameters from the USP treatment experimental. Simulations with a small number of spheres and a model of multiple-shot USP both indicate that higher velocities intensify RS accumulation and accentuate mutual influence between pit locations. Strong interactions among spheres during impact can extend from the surface to the subsurface. The stress vectors of adjacent craters exert compression and tension effects on each other, leading to the formation of tensile residual stress (TRS) in certain local areas. This TRS is often observed at the stacking regions. However, relaxation of CRS at the crater locations can also occur due to the influence of stress vectors. This may potentially cause relaxation in the subsurface RS field due to sphere interactions and pit stress vectors, although this relaxed state is not consistently observed.

To address the essential problem in surface metrology of establishing functional correlations spatial, frequencies in topographic measurements are progressively decomposed into a large number of narrow bands. Bandpass filters and commercially available software are used. These bands can be analyzed with conventional surface texture parameters, like average roughness, Sa, or other parameters, for detailed, multiscale topographic characterizations. Earlier kinds of multiscale characterization, like relative area, required specialized software performing multiple triangular tiling exercises. Multiscale regression analyses can test strengths of functional correlations over a range of scales. Here, friction coefficients are regressed against standard surface texture parameters over the range of scales available in a measurement. Correlation strengths trend with the scales of the bandpass filters. Using bandpass frequency, i.e., wavelength or scale, decompositions, the R² at 25 μm, exceeds 0.9 for Sa compared with an R² of only 0.2 using the broader band of conventional roughness filtering. These improved, scale-specific functional correlations can facilitate scientific understandings and specifications of topographies in product and process design and in designs of quality assurance systems.

We have developed a system that makes it possible to derive parameters of a Kuramoto-Sivashinsky (KS) model from a single given two-dimensional profile of surface structures, such as those produced by ion and plasma irradiation. The numerical method is inspired by well-known approaches to facial recognition. Starting from a scaled version of a KS Model to describe surface erosion, a training set of surface profiles is created. Each profile is assigned an appropriate feature in Fourier space and a Singular Value Decomposition is used to determine an orthogonal set of eigenfeatures that allow each profile to be assigned a point in the space of this basis and to determine the distances between them. It turns out that the profiles belonging to different model parameters are clearly separated from each other in this feature space, which enables very good identification. We explain the basic relationships using a synthetic data set and discuss the possibilities for applications to experimental results.