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Surface Coatings: Materials and Techniques

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Materials Science and Engineering".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 1483

Special Issue Editors


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Guest Editor
CONAHCYT-CIATEQ A.C., Av. Manantiales 23-A, Parque Industrial Bernardo Quintana, El Marqués, Queretaro 76246, Mexico
Interests: coatings; materials
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
CIATEQ A.C., Av. Manantiales 23-A, Parque Industrial Bernardo Quintana, El Marqués, Queretaro 76246, Mexico
Interests: functional coatings; materials science
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
CONAHCYT—Centro de Ingeniería y Desarrollo Industrial, CIDESI, Av. Pie de la Cuesta 702, Querétaro 76125, Mexico
Interests: thin films; coating techniques

Special Issue Information

Dear Colleagues,

Surface coatings are an important part of engineering components and are applied in many fields to provide surface protection, thermal/electric insulation, magnetic behavior, abrasion resistance, biocompatible properties, and wear resistance, among others. In view of global emerging technologies, there is an increasing interest in materials and surface coating methods that provide high-performance, functional, eco-friendly, and cost-effective coatings for applications in different fields such as aeronautics, biomedical, energy, nuclear, repairing, automobile, and others. This Special Issue focuses on the development of materials and coating techniques that result in the preparation of engineering coatings with functional and/or protective properties. It encompasses a wide coverage of topics including feedstock materials preparation, surface pretreatments, and coating fabrication techniques such as PVD, CVD, laser cladding, thermal spray processes, electrophoretic deposition, aerosol deposition, and many others employed in the fabrication of coatings. Overall, this Special Issue aims to deal with the most recent advances in the following:

  • Novel routes or recent advances for the preparation of feedstock powders or precursor materials for applications in specific surface coating processes.
  • Development of surface processes focused on obtaining high-performance coatings either by improving current coating technologies or creating game-changing methodologies.
  • In-depth reviews about well-established or new coating fabrication techniques and on the development of cutting-edge coating applications.
  • Disruptive and advanced characterization techniques for gaining understanding about coating properties and protection mechanisms for state-of-the-art applications.

Dr. John Henao
Dr. Carlos A. Poblano-Salas
Dr. Diego Germán Espinosa-Arbeláez
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • coatings
  • thin films
  • thermal spray
  • chemical vapor deposition
  • physical vapor deposition
  • aerosol deposition
  • cold spray
  • characterization techniques
  • functional applications
  • materials precursors

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Published Papers (2 papers)

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Research

20 pages, 4016 KiB  
Article
Division and Sealing Surfaces in Flow Energy Machines: An Analysis of Diverse Flatness Deviation Measurement Techniques
by Grzegorz Włażewski, Konrad Stefanowicz, Ryszard Konieczny, Adam Koniuszy and Grzegorz Wałowski
Appl. Sci. 2025, 15(6), 3099; https://doi.org/10.3390/app15063099 - 12 Mar 2025
Viewed by 525
Abstract
This paper presents a comparative analysis of the results obtained from three different measuring machines using tactile measurement techniques. The influence of the number and method of collecting measurement points on the detection of shape errors of the tested object is investigated in [...] Read more.
This paper presents a comparative analysis of the results obtained from three different measuring machines using tactile measurement techniques. The influence of the number and method of collecting measurement points on the detection of shape errors of the tested object is investigated in particular. The conducted studies using three different devices introduce a new, practical method for acquiring and processing measurement points. In addition, the effectiveness of three methods for measuring flatness deviations is analyzed, focusing on how the selection and number of measurement points can significantly affect the accuracy and efficiency of inspection operations. The aim of this study is to illustrate the influence of coordinate measuring techniques on the precision of determining shape errors, offering insight into optimizing measurement practices to improve accuracy and operational efficiency. The novelty of the study lies in the detailed analysis of the impact of the number of measurement points and measurement methods on the final results. Such a detailed approach is rare in the literature and provides significant insights into the possibility of replacing precise devices with less accurate ones under specific conditions, that is, devices with a higher measurement error, while achieving the required measurement accuracy through a greater number of measured points. Full article
(This article belongs to the Special Issue Surface Coatings: Materials and Techniques)
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24 pages, 6564 KiB  
Article
Optimizing Boride Coating Thickness on Steel Surfaces Through Machine Learning: Development, Validation, and Experimental Insights
by Selim Demirci, Durmuş Özkan Şahin, Sercan Demirci, Armağan Gümüş and Mehmet Masum Tünçay
Appl. Sci. 2025, 15(5), 2540; https://doi.org/10.3390/app15052540 - 27 Feb 2025
Viewed by 605
Abstract
In this study, a comprehensive machine learning (ML) model was developed to predict and optimize boride coating thickness on steel surfaces based on boriding parameters such as temperature, time, boriding media, method, and alloy composition. In a dataset of 375 published experimental results, [...] Read more.
In this study, a comprehensive machine learning (ML) model was developed to predict and optimize boride coating thickness on steel surfaces based on boriding parameters such as temperature, time, boriding media, method, and alloy composition. In a dataset of 375 published experimental results, 19 features were applied as inputs to predict the boride layer thickness in various steel alloys. ML algorithms were evaluated using performance metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2. Among the ML algorithms tested, XGBoost exhibited the highest accuracy. XGBoost achieved an R2 of 0.9152, RMSE of 29.57, and MAE of 18.44. Incorporating feature selection and categorical variables enhanced model precision. Additionally, a deep neural network (DNN) architecture demonstrated robust predictive performance, achieving an R2 of 0.93. Experimental validation was conducted using 316L stainless steel (SS), borided at 900 °C and 950 °C for 2 h and 4 h. The DNN model effectively predicted the boride thickness under these conditions, aligning closely with the observed values and confirming the models’ reliability. The findings underscore the potential of ML to optimize boriding processes, offering valuable insights into the relationships between boriding parameters and coating outcomes, thereby advancing surface modification technologies. Full article
(This article belongs to the Special Issue Surface Coatings: Materials and Techniques)
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