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11 August 2026

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Editorial: Advances in Alloy Surface Mechanics—The Effects of Coating Technologies on Performance

School of Materials Science and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Coatings2026, 16(8), 952;https://doi.org/10.3390/coatings16080952
This article belongs to the Special Issue Advances in Alloy Surface Mechanics: The Effects of Coating Technologies on Performance
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Coating technologies have emerged as a core element of modern alloy design. Nevertheless, there is currently no consistent academic consensus regarding the specific mechanisms through which such technologies modify the surface mechanical properties of alloys. This Special Issue is developed to bridge this knowledge gap by integrating the latest research progress in experimental characterization, theoretical modeling, and data-driven methodologies covering all research directions of alloy surface mechanics. The mechanical stability of alloy surfaces determines the reliability and service duration of key engineering components applied in aerospace, automotive, energy, and biomedical sectors. Surface degradation processes, such as wear, contact fatigue, fretting, erosion, and the coupling interaction between corrosion and wear, fundamentally restrict the allowable operating range of structural alloys. Coating technologies have been recognized as one of the most efficient approaches to alleviate these degradation mechanisms and facilitate the fabrication of surfaces with customized mechanical, tribological, and chemical properties [1,2]. However, the correlation among coating structure, interfacial mechanical properties, and macroscopic functional performance is still a complicated multi-scale issue that requires further in-depth academic investigation.
In the context of coating technology, the discipline of alloy surface mechanics covers a diverse range of physical phenomena, which include stress fields formed beneath sliding and indentation contacts, the initiation and growth of cracks at the coating–substrate interface, the morphological and compositional evolution of tribological layers under different thermal and chemical conditions, as well as the coupling effect of mechanical loading and environmental exposure on the structural integrity of coatings. Investigation of these phenomena necessitates an integrated research framework that integrates high-resolution experimental characterization; rigorous mechanical modeling; and, with growing importance, data-driven research methods [3,4].
The issue brings together contributions that address fundamental questions about how coating technologies influence the mechanical behavior of alloy surfaces, spanning from atomistic simulations of interfacial failure to high-throughput experimental screening of coating performance. Among the diverse contributions in this issue, the growing role of machine learning (ML) and data-driven approaches emerged as a particularly notable theme. This editorial highlights this emerging paradigm while also providing a comprehensive overview of the full range of contributions that constitute this Special Issue.
One of the most transformative developments in materials science over the past decade has been the integration of machine learning into the materials discovery and characterization workflow [5,6]. The field of coating mechanics is no exception. Traditional approaches to evaluating coating–substrate interfacial toughness rely on a suite of experimental methods including indentation, scratch testing, four-point bending, and blister tests [7]. While these methods provide valuable data, they are often time-consuming, require specialized specimen preparation, and yield results that are sensitive to test conditions and data interpretation methods. Chen et al. [7] systematically reviewed the mechanical methods for evaluating coating adhesion, highlighting the critical role of interfacial fracture mechanics in quantifying the adhesion strength and fracture toughness of coating/substrate systems. Their comprehensive analysis examined both sandwich specimen-based methods (double cantilever beam, four-point bend, Brazil-nut, and double cleavage drilled compression tests) and bimaterial specimen-based methods (indentation, scratch, peel, blister, and tensile tests), concluding that no single method is universally applicable and that the selection of appropriate testing techniques must be tailored to the specific characteristics of each coating/substrate system and its intended service conditions. The need for more efficient and robust characterization approaches has motivated the exploration of ML-based alternatives [8].
Nanoindentation is one of the most widely used techniques for probing the mechanical properties of coatings and thin films. However, extracting intrinsic coating properties from indentation data is complicated by the substrate effect—the measured response always contains contributions from both the coating and the underlying substrate. Sun et al. [9] addressed this challenge by developing a multi-output multilayer perceptron (MLP) neural network that establishes the relationship between film/substrate elastic moduli, composite modulus, and normalized indentation depth. Using finite element simulations to generate training data, their MLP model successfully predicted the elastic moduli of both hard-film/soft-substrate (Ni/304 stainless steel) and soft-film/hard-substrate (Cu/304 stainless steel) systems, with predictions closely matching experimental results. This work demonstrates that ML can effectively deconvolute the substrate effect that has long plagued indentation analysis of coated systems.
In a more recent study, Li et al. [10] developed a machine learning framework empowered by finite element simulations and back-propagation neural networks to simultaneously determine the Young’s modulus, pre-stress, and thickness of freestanding piezoelectric thin films from nanoindentation force–displacement curves. This method attains a computational efficiency that is roughly two orders of magnitude greater than that of conventional curve-fitting approaches. Through this method, the research team identified an abnormally high apparent Young’s modulus in ultrathin PZT films with a thickness of 2.8 nm, a phenomenon that originates from flexoelectric effects. This study demonstrates that machine learning can not only speed up the process of property extraction but also facilitate the discovery of physical phenomena that would otherwise go undetected.
Zhong and his research team [11] utilized machine learning techniques to achieve co-optimization of hardness and toughness for high-entropy alloy coatings. A De-Heterogeneous Conditional Generative Adversarial Network was employed to augment the original small-scale dataset, expanding the total number of samples from fewer than 140 to more than 400. Extra Trees models were subsequently trained on the augmented dataset. The proposed method achieved superior prediction performance, where the coefficient of determination R2 reached 0.93 for hardness prediction and 0.88 for elastic modulus prediction. Across all alloy systems investigated in the study, the Al–Fe–V–Co–Nb–Ti–Mo–W–Ta alloy was identified as a high-potential candidate material, featuring a hardness of 26.2 GPa and an H/E ratio of 0.24. This work confirms that machine learning approaches can effectively guide the synergistic performance optimization of advanced coating materials.
Gadek-Moszczak and co-researchers have previously demonstrated the effectiveness of machine learning techniques in developing digital surface models for electro-spark deposited WC-Co-Al2O3 coatings, a category of materials that is widely utilized in protective coating configurations and tribological applications [12]. By training a recurrent neural network incorporating Monte Carlo simulation using profilometric measurement data, the research group achieved a minimum root-mean-square error of 2.595 μm in the prediction of surface topography. Their work demonstrated that the developed hybrid machine learning-stochastic model can precisely capture both the deterministic structural characteristics and stochastic geometric variations of coating surfaces. The geometric parameters of the digitally generated surfaces deviate by no more than 15% from those of actual measured surfaces, which offers a promising method for the predictive modeling of coating surface topography.
Mandol and Nunthavarawong [13] published a critical systematic review of machine learning methodologies applied to the prediction of the tribological properties of thermally sprayed coatings in a Springer-hosted academic journal. Through quantitative analysis of 144 peer-reviewed studies published between 2019 and 2024, the authors concluded that boosting tree models, artificial neural networks, and random forest algorithms are the most widely adopted models for predicting three key performance indicators: wear resistance, friction coefficient, and hardness. Their analysis further indicates that boosting tree models are utilized most frequently for predicting wear and durability performance. Additionally, aggregated feature importance analyses across included studies confirm that coating process parameters including spray temperature and particle velocity, alongside material composition, are the dominant factors, influencing tribological behavior. The review substantiates that machine learning constitutes an effective tool for accelerating the design and optimization of coated systems, as it facilitates accurate property prediction without requiring large-scale exhaustive experimental campaigns.
Kouraytem et al. [3] investigate modeling approaches for process–structure–property (p-s-p) relationships in metal additive manufacturing (AM), classifying existing methods into two main paradigms: physics-driven and data-driven modeling. They note that due to AM’s inherent multi-scale, multi-phase characteristics—with physical phenomena ranging from powder-level melt pool dynamics to part-level full-component residual stress distributions—optimization relying solely on experiments is unfeasible. Their work systematically compares established mesoscale computational fluid dynamics models, macroscale thermomechanical models, and emerging machine learning methods including Gaussian process regression and convolutional neural networks. A core conclusion is that physics-driven models generate interpretable, high-accuracy results but suffer from excessive computational overhead. In contrast, data-driven models enable fast inference but have limited interpretability and perform poorly when extrapolating beyond training data distributions. The authors argue that future development should prioritize hybrid modeling frameworks, where physics-based simulations generate training datasets for machine learning models. This approach integrates the mechanistic rigor of physics-driven modeling with the computational efficiency of data-driven methods. This perspective, emphasizing synergistic integration rather than opposition between the two paradigms, offers a well-developed, practical stance on applying digital twin technologies for AM performance qualification.
From the research domain of metal fatigue life prediction, Shi et al. [4] investigate the identical methodological contradiction. This work sorts out the historical development trajectory of relevant research, which starts from traditional empirical methods including Basquin’s power law, the Coffin–Manson strain-life relationship, and the Paris crack growth law; passes through various modern machine learning architectures; and finally reaches the current research hotspot of physics-informed neural networks (PINNs). Three key shortcomings of fully data-driven fatigue models are summarized in this study: first, the problem of serious data scarcity (the median size of datasets in the reviewed studies is only between 30 and 217 samples); second, the “black box” problem that prevents such models from obtaining certification in safety-critical application scenarios; and third, the failure to achieve effective generalization under complex multiaxial loading or variable amplitude loading conditions. The most prominent contribution of this review is that it establishes a systematic classification for the five existing methods of integrating physical knowledge into machine learning frameworks, covering the construction of loss functions with physical constraints, feature engineering guided by physical principles, and Kalman filter-based information fusion. The authors found that introducing linear elastic fracture mechanics constraints into the training process of PINNs can improve the model’s prediction ability by 83% compared with unconstrained neural networks. For additively manufactured metals, the authors emphasize a key conclusion: compared with the size or morphology of defects, the position of defects relative to the free surface is usually a better predictor of fatigue failure. This conclusion conforms to physical mechanisms, and purely statistical models tend to ignore this finding, while physics-informed frameworks can capture this pattern naturally.
Machine learning is not only applied to predict material properties in interfacial mechanics. Following this direction, Liu et al. [14] developed an innovative design evaluation method combining molecular dynamics simulation and machine learning to predict the erosion and wear resistance of CoCrFeMnNi high-entropy alloy coatings. Researchers built a quantitative dataset linking elemental composition to six key performance indicators, including contact pressure, friction coefficient, shear deformation, and number of worn atoms. After training models like CatBoost and Ridge Regression through 10-fold cross-validation, this method achieved high prediction accuracy with a coefficient of determination R up to 0.93. The optimal coating composition obtained from the model was further verified by molecular dynamics nano-scratch simulation. The verification results show that dense dislocation locks and dislocation loops endow the alloy with excellent abrasive wear resistance. This research indicates that the synergistic application of molecular dynamics simulation and machine learning provides an effective way to accelerate the design of erosion-resistant high-entropy alloy coatings and eliminates the dependence on large-scale iterative trial-and-error experiments.
Despite these significant achievements, some challenges remain in the current field of research. To begin with, there is a clear lack of high-quality, standardized experimental data focusing on coating–substrate systems. Most current machine learning models are trained on datasets aggregated from diverse sources that utilize inconsistent testing procedures. This inconsistency in data degrades the predictive capability of the final models. The establishment of unified community-wide databases implementing standardized testing protocols will effectively promote the progress of relevant research.
Furthermore, the intrinsic “black box” nature of the majority of machine learning models limits their application in engineering fields. To ensure the reliability of models and support evidence-based coating design, it is essential to develop interpretable machine learning frameworks—such as SHAP analysis [4] and physics-informed neural networks [13]—capable of providing both mechanistic interpretability and prediction outcomes.
Thirdly, current machine learning models show inadequate extrapolation performance when processing data that falls outside the scope of their training domains. For coating development contexts where experimental data is inherently limited, integrating proven physical principles—including contact mechanics, fracture mechanics, and diffusion kinetics—into the structural design of machine learning is an effective strategy to enhance both prediction accuracy and model generalizability.
In addition to the machine learning-focused research discussed above, the wider academic field of alloy surface mechanics and coating technology includes a large volume of experimental and theoretical studies. Subsequent sections of this paper summarize selected studies from both this Special Issue and the broader academic community, which together reflect the scope of current research in this area.
Multiple studies in this collection center on the fabrication and characterization of advanced hard coatings with enhanced wear resistance. In the broader literature, Meng et al. [15] conducted a systematic review of wear-resistant coatings developed for copper and copper alloys, covering mainstream preparation technologies including laser cladding, electrodeposition, thermal spraying, cold spraying, and electro-spark deposition. This review concludes that introducing hard ceramic reinforcing phases (e.g., TiC, WC, and SiC) and refining grain structure via nanoparticle additives are both proven strategies to improve coating hardness and wear resistance. Depending on the preparation process and material composition, the hardness of the resulting coating can reach 6 to 14 times that of the copper substrate.
In a complementary investigation, Liang et al. [16] explored the fretting wear behavior of self-mated Ti-6Al-4V friction pairs under flat-on-flat contact conditions via orthogonal experimental design. They identified temperature and frequency as the primary influencing factors for friction and wear performance. The study confirmed that at elevated temperatures, a protective third-body layer generated through oxidative wear markedly reduces wear damage. At the same time, at high frequencies, retained abrasive debris produces a “micro-roller” effect that lowers the friction coefficient of the contact interface.
Separately, in the research field of high-entropy alloy (HEA) coatings, Mundotiya et al. [17] fabricated FeCoNiMoW and FeCoNiMoWCr HEA coatings via aqueous electrolyte-based electrodeposition, and obtained a single-phase FCC solid solution coating with a flaky microstructure. The Cr-doped FeCoNiMoWCr coating demonstrated superior corrosion resistance, with a measured corrosion current density of 3.53 ± 0.72 μA/cm2 and a protection efficiency of 48.69% for the NAB substrate. This performance improvement is attributed to the formation of a protective Cr2O3-rich oxide film that blocks the diffusion of Cl− ions.
Guo et al. [18] carried out a systematic investigation into the performance trade-off among cavitation erosion resistance, corrosion resistance, and antifouling performance of HVOF-sprayed WC-CoCr coatings. Their work further verified that adding Cu endows the coating with excellent antimicrobial and anti-bioadhesion capabilities, while introducing 316L stainless steel improves both corrosion resistance and cavitation erosion resistance. The combined addition of Cu and 316L stainless steel achieves a balanced performance across the three property metrics, expanding the applicable scope of WC-CoCr coatings in marine service environments.
From a modeling perspective, Luo et al. [19] targeted the core problem of interfacial integrity in coating–substrate systems. They established an innovative analytical approach based on interfacial mechanics and the image method to predict stress distribution in coated coating-reinforced cylindrical gear systems. The study found that appropriately increasing coating thickness reduces interfacial stress magnitude, and setting the coating-to-substrate elastic modulus ratio close to 1 minimizes stress discontinuity at the interface, which in turn improves coating adhesion and wear resistance.
In the field of thermal barrier coatings (TBCs), Bie et al. [20] developed a coupled thermo-mechanical dual-horizon bond-based peridynamics (TM-DH-BPD) model to investigate thermally induced fractures in TBCs. By implementing the model in Abaqus UEL/VUEL subroutines, they successfully captured transverse cracking propagation, kinking in the top coat, and its interaction with interfacial cracking at the TC/BC interface, demonstrating the model’s capability to reproduce complex damage mechanisms observed in thermal shock experiments.
Wang et al. [21] investigated the cross-scale mechanical discrepancy and ultra-precision machinability of electroless Ni-P coatings on high-silicon AlSi40 substrates for space optical mirrors. They discovered that as phosphorus content increased, macroscopic Vickers hardness declined. In contrast, microscopic nanoindentation hardness increased—a mismatch attributed to the indentation size effect—and achieved an ultra-smooth surface roughness of Sa 1.233 nm via single-point diamond turning guided by the structure–property–machinability mapping.
Kara et al. [22] examined the tribological performance of Ti/MoS2-Ti composite coatings deposited by pulsed-DC closed-field unbalanced magnetron sputtering under atmospheric and vacuum conditions. Their results revealed that coatings with higher sulfur retention exhibited significantly improved wear resistance in vacuum due to stable MoS2-based tribofilm formation. In contrast, sulfur-deficient coatings suffered severe wear, demonstrating that high hardness alone does not guarantee wear resistance in vacuum environments.
Asadi Peyghan et al. [23] synthesized novel ethanolamine-based surface-active ionic liquids (SAILs) as functional additives for electrophoretic deposition of hydroxyapatite coatings on Ti-6Al-4V alloys for biomedical applications. The SAIL-assisted coatings exhibited drastically enhanced particle dispersion and zeta potential stability, achieving total impedance values of 107–1012 Ω·cm2 in simulated body fluid and reducing viable bacterial counts by over 99.9% against both E. coli and S. aureus.
Within this Special Issue, Muktanova et al. [24] systematically investigated multilayer Cr/CrN coatings with two-, four-, and six-layer architectures deposited on E110 zirconium alloy substrates by reactive magnetron sputtering. They found that increasing the number of layers from two to six progressively increased hardness from 2.59 ± 0.09 GPa to 5.0 ± 0.27 GPa and Young’s modulus from 97 ± 2 GPa to 110 ± 4 GPa. At the same time, the six-layer coating exhibited the lowest wear rate and the highest critical adhesion load (Lc ≈ 20.5 N).
Beyond the scope of this Special Issue, Zhang et al. [25] further demonstrated that at a modulation period of 20 nm, coherent interfaces between fcc-CrN(200) and bcc-Cr(110) in Cr/CrN multilayer coatings generated alternating stress fields that hindered dislocation motion, yielding a hardness of 26.83 GPa and a friction coefficient as low as 0.1073. Together, these studies highlight that multilayer architecture design—whether through varying the number of layers or the modulation period—is a powerful strategy for optimizing the mechanical and tribological performance of Cr/CrN coating systems.
Outside this Special Issue, Xue et al. [26] prepared NiW nanocrystalline coatings incorporated with carbon nanoparticles on Q235B steel via electrodeposition, systematically varying nanoparticle concentration from 0 to 15 g/L. The coating with 10 g/L carbon nanoparticles achieved the greatest thickness (23.7 μm), lowest surface roughness (0.416 μm), and most positive corrosion potential (−0.49 V) with the lowest corrosion current density (9.7 μA/cm2). In contrast, excessive addition led to agglomeration and diminished corrosion performance.
This Special Issue encompasses a number of additional research contributions that further broaden the research scope of coating and surface engineering. Qi et al. [27] developed a comprehensive “element screening–process synergy–classification prediction” framework for coatings fabricated via high-energy ball milling (HEBM). Their work identified the synergistic interactions among core process parameters, namely ball-to-powder ratio, rotational speed, and milling duration, and established a quantitative database integrating 11 key performance indicators to provide guidance for the development of high-performance HEBM coatings. Li et al. [28] conducted a systematic review of the fabrication processes and core functional properties of micro-arc oxidation (MAO) coatings deposited on substrates including aluminum-based alloys, magnesium-based alloys, titanium-based alloys, high-entropy alloys, and steel. Their review emphasized the core influence of electrolyte composition, electrical parameters, and post-treatment protocols in regulating the corrosion resistance, wear resistance, and biocompatibility of MAO coatings.
Addressing the surface integrity of welded joints—a topic closely related to alloy surface mechanics—Liu et al. [29] conducted a systematic investigation into how welding speed influences the microstructure, mechanical characteristics, and corrosion performance of pulsed metal inert-gas (MIG) welded joints of 7075-T6 aluminum alloy. The research found that when welding was performed at the optimal speed of 550 mm/min, the dendritic structure of the joint was refined, the tensile strength reached the maximum value of 257.7 MPa, and the elongation reached 8.1%. Wu et al. [30] further explored the influence of welding current on P-MIG welded joints of identical 7075/7075 aluminum alloy and confirmed that when the welding current was set to 200 A, the welded joint obtained the optimal comprehensive corrosion resistance, with the intergranular corrosion depth reaching the minimum value of 47.8 μm and the charge transfer resistance reaching the highest level, while maintaining satisfactory mechanical properties. Yang et al. [31] carried out a comparative analysis on single-sided and double-sided friction stir welding (FSW) processes for 3 mm thick 7075-T6 aluminum alloy sheets. The results showed that the double-sided co-directional FSW joint obtained a tensile strength of 388 MPa and an elongation of 7.09%, and its mechanical properties were significantly superior to those of joints obtained via other welding paths. This performance advantage originates from the continuous dynamic recrystallization process, which produces uniformly refined equiaxed grains in the weld nugget zone, with an average grain size of only 1.10 μm. From the research perspective of high-temperature surface degradation of alloys, Wu et al. [32] studied the growth process and formation mechanism of oxide films on FSX-414 cobalt-based superalloy after exposure to a 900 °C environment for up to 1000 h. It was found that oxide films formed on both the outer surface and inside narrow crevices are dominated by oxygen and chromium elements, while the pores enriched with tungsten form diffusion channels that accelerate the progression of localized oxidation. In another research direction, Zhang et al. [33] systematically summarized the latest research progress in the magnetocaloric effect of high-entropy alloys (HEAs), covering rare-earth based, non-rare-earth based, and composite HEA systems. It further clarified that the multi-principal-element design strategy can realize the adjustable regulation of functional properties, which expands the application scope of alloy systems beyond the traditional role as structural materials.
The studies discussed above, from this Special Issue and the broader literature, collectively indicate several promising directions for future research on alloy surface mechanics and coating technologies.
First, integrating machine learning with advanced characterization techniques (especially nanoindentation, scratch testing, and in situ microscopy) provides a way for high-throughput screening of coating mechanical properties. Developing reliable ML models that can predict interfacial toughness, wear resistance, and fatigue life from limited experimental data will greatly speed up the coating design cycle.
Second, continuous development of physics-informed machine learning frameworks that embed known physical laws into model structures helps improve prediction accuracy and extrapolation capability beyond the training data range. This is especially important for coating applications where experimental data are scarce and costly to obtain.
Third, multi-scale modeling approaches that connect atomistic simulations, mesoscale models, and continuum mechanics via machine learning surrogates have become a frontier for understanding and predicting coating performance across different length and time scales. These approaches can support the rational design of coating architectures with optimized combinations of hardness, toughness, adhesion, and thermal stability.
Fourth, applying machine learning to analyze in situ and operando characterization data (such as acoustic emission signals during scratch testing, or electrochemical noise during corrosion-wear testing) can enable real-time prediction of coating failure, supporting condition-based maintenance and extending component service life.
This editorial reviewed diverse high-quality studies from this Special Issue and the wider literature that deepen our understanding of how coating technologies influence alloy surface mechanics. Covering fundamental research on contact mechanics and interfacial failure; applied research on wear, erosion, and corrosion protection; as well as experimental characterization, computational modeling, and machine learning, the work discussed here reflects the breadth and vitality of current research in this field.
The guest editor of this Special Issue expresses sincere gratitude to all the authors for their excellent contributions, to the reviewers for their rigorous and constructive evaluations that have significantly improved the quality of the manuscripts, and to the editorial office of Coatings for their professional and efficient support throughout the publication process.

Funding

This work was financially supported by the Key R&D and Promotion Project of the Henan Province (Science and Technology Research) (262102230055, 252102220078), the “Deputy General Manager of Technology” of Henan Province (HNFZ 20240193), the Natural Science Foundation Project of Henan Province (262300421367), and the Zhengzhou Collaborative Innovation Special Project (2025XTCX016).

Conflicts of Interest

The author declares no conflicts of interest.

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