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Coatings
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The Present Status of Thermally Sprayed Composite Coatings
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The Present Status of Thermally Sprayed Composite Coatings

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Plasma Coatings, Surfaces & Interfaces".

Deadline for manuscript submissions: 30 October 2024 | Viewed by 2329

Special Issue Editors

Prof. Dr. Liutauras Marcinauskas

E-Mail Website
Guest Editor
1. Department of Physics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentų Str. 50, LT-51368 Kaunas, Lithuania
2. Plasma Processing Laboratory, Lithuanian Energy Institute, Breslaujos Str. 3, LT-44403 Kaunas, Lithuania
Interests: plasma technologies; plasma spraying; ceramic materials and coatings; thin films and nanotechnology; tribology; surface and material characterization; surface engineering
Dr. Mindaugas Milieška

E-Mail Website
Guest Editor
Plasma Processing Laboratory, Lithuanian Energy Institute, Breslaujos Str. 3, LT-44403 Kaunas, Lithuania
Interests: plasma spraying; metal oxide coatings; heat-mass transfer; particle in-flight behavior analysis; surface characterization

Special Issue Information

Dear Colleagues,

The creation of novel composite coatings with enhanced mechanical, tribological and thermal properties is a continuous challenge for various research groups and companies. Various thermal spraying techniques such as plasma spraying, high-velocity oxy-fuel, flame or cold gas spraying are used to create novel composite coatings. The main advantage of thermal spraying technologies is the possibility to spray any metallic, carbide or ceramic materials. It allows the formation of composite coatings with unique properties that can be widely applied in the automotive, aeronautic, marine, mechanical, electronic, or biomedical fields. Many factors play a key role in the final properties of composite coatings, such as type of spaying techniques used, size, shape and chemical composition of feedstock powder, process conditions, etc. Therefore, it is very important to broaden the knowledge about the creation of new coatings and to understand the relationship between the process parameters and the properties of as-sprayed coatings, as well as to expand their application areas.

Research areas of this Special Issue may include the following: different thermal spraying techniques, deposition of coatings, plasma–particle in-flight interactions, structure, morphology, physical and tribological properties, and technological application of novel composite coatings.

Manuscripts should be on, but are not limited to, the following topics of interest:

  • Novel ceramic composite coatings produced by various thermal spraying techniques (plasma spraying, high velocity oxy-fuel spraying, flame spraying, etc);
  • Suspension and solution precursor plasma sprayed coatings;
  • Experimental research and/or theoretical studies of plasma–particle interactions in thermal plasma processing;
  • Composite coatings with improved mechanical and/or tribological properties;
  • Corrosion and erosion behavior of sprayed composite coatings;
  • Applications of sprayed composite coatings in high-temperature environments.

Prof. Dr. Liutauras Marcinauskas
Dr. Mindaugas Milieška
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

  • thermal spraying
  • plasma spraying
  • ceramic composite coatings
  • tribology
  • mechanical and thermal properties
  • structure characterization
  • application of coatings

Published Papers (3 papers)

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Research

20 pages, 8227 KiB  
Article
A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN
by Henan Bu, Teng Yang, Changzhou Hu, Xianpeng Zhu, Zikang Ge, Zhuwen Yan and Yingxin Tang
Coatings 2024, 14(4), 464; https://doi.org/10.3390/coatings14040464 - 11 Apr 2024
Viewed by 422
Abstract
In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However, in real applications, due [...] Read more.
In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However, in real applications, due to the complexity and uncertainty of ship painting conditions, it is very likely that there are unknown classes of painting defects, and the traditional deep learning model cannot identify a few classes, which leads to model overfitting and reduces its generalization ability. In this paper, a zero-shot Image classification method for ship painting defects based on IDATLWGAN is proposed to identify new unknown classes of defects in the target domain. The method is based on a deep convolutional neural network combined with adversarial transfer learning. First, a preprocessed ship painting defect dataset is used as input for the domain-invariant feature extractor. Then, the domain invariant feature extractor takes domain invariant features from the source and target domains. Finally, Defect discriminators and domain alignment discriminators are employed to classify the known categories of unlabeled defects and unknown categories of unlabeled defects in the target domain and to further reduce the distance between the edge distributions of the source and target domains. The experimental results show that the proposed model in this paper extracts a better distribution of invariant features in the source and target domains compared to other existing transfer learning models. It can successfully complete the migration task and accurately recognize the painting defects of known categories and new unknown categories, which is a perfect combination of intelligent algorithms and engineering practice. Full article
(This article belongs to the Special Issue The Present Status of Thermally Sprayed Composite Coatings)
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16 pages, 11669 KiB  
Article
Effects of Laser Remelting on Frictional Properties of Supersonic Flame-Sprayed Coatings
by Fengbo Li, Conghui Zhang, Yan Li and Qingtao Pang
Coatings 2024, 14(3), 325; https://doi.org/10.3390/coatings14030325 - 9 Mar 2024
Viewed by 832
Abstract
In this study, Cr3C2-Al2O3-NiCr coatings were prepared on INCONEL 600 alloy surfaces using the supersonic flame spraying technique, followed by a laser remelting treatment. In this way, this study further explored what impacts laser remelting [...] Read more.
In this study, Cr3C2-Al2O3-NiCr coatings were prepared on INCONEL 600 alloy surfaces using the supersonic flame spraying technique, followed by a laser remelting treatment. In this way, this study further explored what impacts laser remelting has on coating performance. To this end, optical microscopy (OM), scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD) were employed to carry out microstructural characterization. Energy-dispersive X-ray spectroscopy (EDS) was applied to conduct an analysis of the coatings’ elemental distribution while X-ray diffraction (XRD) was used to determine the coating phases. To measure the microhardness of the coatings, a microhardness tester was applied. In addition, the study investigated the samples’ electrochemical corrosion resistance and friction-wear performance under different surface conditions. According to the results, laser remelting enhanced the coating density, improved metallurgical bonding with the substrate, and optimized the carbide distribution, thereby enhancing corrosion and wear resistance in both air and corrosive media. However, excessive laser power hinders Cr3C2 nucleation, leading to diminished coating hardness and wear resistance in Cr7C3 formation. Full article
(This article belongs to the Special Issue The Present Status of Thermally Sprayed Composite Coatings)
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23 pages, 5073 KiB  
Article
Prediction of Ship Painting Man-Hours Based on Selective Ensemble Learning
by Henan Bu, Zikang Ge, Xianpeng Zhu, Teng Yang and Honggen Zhou
Coatings 2024, 14(3), 318; https://doi.org/10.3390/coatings14030318 - 6 Mar 2024
Viewed by 791
Abstract
The precise prediction of painting man-hours is significant to ensure the efficient scheduling of shipyard production and maintain a stable production pace, which directly impacts shipbuilding cycles and costs. However, traditional forecasting methods suffer from issues such as low efficiency and poor accuracy. [...] Read more.
The precise prediction of painting man-hours is significant to ensure the efficient scheduling of shipyard production and maintain a stable production pace, which directly impacts shipbuilding cycles and costs. However, traditional forecasting methods suffer from issues such as low efficiency and poor accuracy. To solve this problem, this paper proposes a selective integrated learning model (ISA-SE) based on an improved simulated annealing algorithm to predict ship painting man-hours. Firstly, the improved particle swarm optimization (MPSO) algorithm and data grouping techniques are employed to achieve the optimal selection and hyperparameter optimization of base learners, constructing a candidate set of base learners. Subsequently, the simulated annealing algorithm is improved by adding random perturbations and using a parallel perturbation search mechanism to enhance the algorithm’s global search capability. Finally, an optimal set of base learners is composed of the candidate set utilizing the ISA-SE model, and a heterogeneous ensemble learning model is constructed with the optimal set of base learners to achieve the precise prediction of ship painting man-hours. The results indicate that the proposed ISA-SE model demonstrates improvements in accuracy, mean absolute error, and root mean square error compared to other models, validating the effectiveness and robustness of ISA-SE in predicting ship painting man-hours. Full article
(This article belongs to the Special Issue The Present Status of Thermally Sprayed Composite Coatings)
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