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Machine Learning-Driven Advancements in Coatings
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Machine Learning-Driven Advancements in Coatings

A special issue of Coatings (ISSN 2079-6412).

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 4924

Special Issue Editors

Prof. Dr. Ali Khalfallah

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Guest Editor
CEMMPRE, Department of Mechanical Engineering, University of Coimbra, Rua Luís Reis Santos, 3030-788 Coimbra, Portugal
Interests: multidisciplinary modeling of the mechanical behavior of materials; identification of thin-film properties; combination of computational physics; artificial intelligence; multi-scale simulations and materials characterization; recent exploration into tribology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zohra Benzarti

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Guest Editor
CEMMPRE, ARISE, Department of Mechanical Engineering, University of Coimbra, Rua Luís Reis Santos, 3030-788 Coimbra, Portugal
Interests: fabrication of thin films using metal–organic vapor deposition (MOCVD); molecular-beam epitaxy (MBE) and magnetron sputtering techniques; multiphysics characterization; correlation between the nanomechanical and physical properties of thin-films; development of multifunctional materials, including nanomaterials and piezoelectric nano-composites; ZnO doped with transition metals and rare earths; hybrid and ceramic perovskites for light sensors, photovoltaic cells; white LEDs and energy storage devices; copper corrosion; decorative deposition on polymer substrates; development of Li-ion batteries
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coatings are essential in protecting, enhancing, and extending the lifespan of materials across a wide range of industries, from aerospace and automotive to electronics, healthcare, and beyond. These coatings provide requisite functions such as corrosion resistance, thermal insulation, and surface modification, making them indispensable in ensuring the durability and performance of materials under diverse conditions. The integration of machine learning (ML) into the field of coatings presents unprecedented opportunities for innovation and advancement, driving a significant shift in how we approach the design, development, and application of these materials. In essence, machine learning represents a powerful tool that can revolutionize coatings.

This Special Issue will serve as a comprehensive platform for showcasing the latest research, developments, and applications of ML in the field of coatings, drawing contributions from academia, industry, and research institutions. It will focus on the expansive role of machine learning in advancing coatings, covering a wide range of particular topics including, but not limited to, the following:

  1. Machine learning for material discovery in coatings;
  2. Application of ML for optimizing deposition techniques;
  3. ML-driven approaches for assessing mechanical properties of coatings;
  4. ML models for predicting coating durability and lifespan under various conditions;
  5. Smart and responsive coatings;
  6. Application of ML for the design and development of smart coatings;
  7. Application of ML in the development of multifunctional coatings;
  8. ML approaches to tailoring surface texture and morphology of coatings;
  9. ML-driven techniques for developing new anti-corrosive and anti-fouling coatings;
  10. Case studies of ML-enabled functional coatings in industrial applications;
  11. Development of eco-friendly coatings using ML techniques.

Prof. Dr. Ali Khalfallah
Prof. Dr. Zohra Benzarti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Coatings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning in coatings
  • smart coatings
  • functional coatings
  • predictive modeling
  • self-healing coatings
  • sustainable coatings
  • surface engineering
  • corrosion resistance
  • ceramic surfaces
  • polymer surfaces
  • thermal protection
  • wear protection
  • process optimization
  • data-driven coating design

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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10 pages, 6229 KiB  
Article
Synthesis and Evaluation of Porous Nanosynt Block (FGM®) as Synthetic Bone Substitute for Bone Tissue Engineering
by Jaqueline Silva dos Santos, Ana Carla Gonçales Souza, Ricardo Fantasia, Rafael Cury Cecato, Gabriela Aline Dias, Victor Eduardo de Souza Batista, Roberta Okamoto and Fellippo Ramos Verri
Coatings 2025, 15(3), 297; https://doi.org/10.3390/coatings15030297 - 4 Mar 2025
Viewed by 675
Abstract
Synthetic bone substitutes based on hydroxyapatite (HA) and β-tricalcium phosphate (β-TCP) are widely used in regenerative dentistry due to their favorable biocompatibility and osteoconductive properties. This study aimed to evaluate, through laboratory-based analyses, the porosity and surface characteristics of the Nanosynt Block (FGM [...] Read more.
Synthetic bone substitutes based on hydroxyapatite (HA) and β-tricalcium phosphate (β-TCP) are widely used in regenerative dentistry due to their favorable biocompatibility and osteoconductive properties. This study aimed to evaluate, through laboratory-based analyses, the porosity and surface characteristics of the Nanosynt Block (FGM Dental Group®) for bone regeneration applications. The Nanosynt Block, consisting of 60% HA and 40% β-TCP, was analyzed using scanning electron microscopy (SEM) for surface morphology characterization, micro-computed tomography (Micro-CT) for internal structure evaluation, and mercury intrusion porosimetry for porosity assessment. SEM imaging followed the ASTM E1829-02 standard, while Micro-CT and porosimetry provided detailed quantitative data. SEM analysis revealed a homogeneous pore distribution on the surface. Micro-CT indicated high structural stability and consistent volumetric porosity, ranging from 73.27% to 77.08%. Porosimetry indicated a total porosity of 94.9%, with a median pore diameter of 799 nm, characteristics suitable for promoting cellular adhesion and fluid infiltration. The structural and morphological properties of the Nanosynt Block highlight its potential to support initial bone formation and mechanical stability in clinical applications. These findings provide a robust basis for subsequent in vivo investigations to validate its clinical efficacy. Full article
(This article belongs to the Special Issue Machine Learning-Driven Advancements in Coatings)
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20 pages, 606 KiB  
Article
Dynamic Response and Dispersion Analysis of a Damped Heterogeneous Coating over a Homogeneous Elastic Half-Space
by Sadia Munir, Fiazud Din Zaman, Ashfaque H. Bokhari, Ali M. Mubaraki and Rahmatullah Ibrahim Nuruddeen
Coatings 2025, 15(2), 188; https://doi.org/10.3390/coatings15020188 - 6 Feb 2025
Viewed by 707
Abstract
This study models the dynamic response of a damped heterogeneous coating layer over a homogeneous elastic half-space via the shear horizontal equation of motion. The so-called partial nonhomogeneous has been considered in the coating, where only the density of the material features the [...] Read more.
This study models the dynamic response of a damped heterogeneous coating layer over a homogeneous elastic half-space via the shear horizontal equation of motion. The so-called partial nonhomogeneous has been considered in the coating, where only the density of the material features the inhomogeneity parameter. This unusual consideration, motivated by the viscoelasticity setting, gives rise to the realization of Airy’s differential equation in the coating layer that poses Airy’s functions of the first and the second kinds, respectively. Moreover, the resulting dispersion relation has been utilized and analyzed, assessing the impact of the involved parameters. The study realized that an increase in both the damping coefficient and the inhomogeneity parameter accelerates the dispersion of waves in the media. Additionally, once the case of the doubly coated half-space is analyzed, as an extension of the earlier setup, it is noted that the case of a doubly coated half-space is more responsive to the excitations, which is pretty geared by the composition of different layers. In addition, more modes are noted when more coatings are wrapped over the half-space. Full article
(This article belongs to the Special Issue Machine Learning-Driven Advancements in Coatings)
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16 pages, 5270 KiB  
Article
Application of Finite Volume Method Based on the C-N Format with Parallel Algorithms in Metal Thermal Phase Transition Coupled Field
by Bing Su, Zeyu Gong and Han Li
Coatings 2025, 15(1), 56; https://doi.org/10.3390/coatings15010056 - 6 Jan 2025
Viewed by 636
Abstract
The numerical simulation of metal heat treatment is of great significance for the development of new materials or processes. The complex coupling relationship in metal heat treatment poses a huge challenge to the overall solution. A finite volume method based on the Crack–Nicolson [...] Read more.
The numerical simulation of metal heat treatment is of great significance for the development of new materials or processes. The complex coupling relationship in metal heat treatment poses a huge challenge to the overall solution. A finite volume method based on the Crack–Nicolson (C-N) scheme with parallel algorithms is proposed for solving the coupling of metal thermal phase transition. Taking the quenching simulation problem of bearing rings as an example, the calculation accuracy and speed of this method were evaluated by comparing the results of the temperature field and phase transformation field obtained from the literature. In addition, grid independence analysis was conducted to demonstrate the rationality of the established model. The finite volume method using a hexahedral mesh C-N format improves the accuracy of calculations. Optimizing the equation form and using parallel algorithms have improved computational speed. In the trial example, in the 850 °C quenching simulation of GCr15, the outer ring of the bearing is composed of 89.1% martensite and 10.9% residual austenite. The results compare with test verification, which shows that the model performs well in the numerical simulation of metal heat treatment. Full article
(This article belongs to the Special Issue Machine Learning-Driven Advancements in Coatings)
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Review

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26 pages, 2867 KiB  
Review
A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics
by Jinxing Wu, Yi Zhang, Pengfei Hu and Yanying Wu
Coatings 2024, 14(10), 1285; https://doi.org/10.3390/coatings14101285 - 9 Oct 2024
Cited by 1 | Viewed by 1920
Abstract
China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the [...] Read more.
China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China. Full article
(This article belongs to the Special Issue Machine Learning-Driven Advancements in Coatings)
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