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Coatings
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Manufacturing and Surface Engineering III
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Manufacturing and Surface Engineering III

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

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 5835

Special Issue Editor

Dr. Alicia Esther Ares

E-Mail Website
Guest Editor
Instituto de Materiales de Misiones, Universidad Nacional de Misiones, Buenos Aires C1417DSE, Argentina
Interests: solidification of metals; processing of metals; mechanical properties of metals; nanotechnology applied to metals products; modelization; corrosion of metals and alloys
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The desired properties of surface components include the improvement of different properties, such as aesthetic appearance, oxidation resistance, wear resistance, mechanical properties, electronic or electrical properties, thermal insulation, and corrosion resistance through barriers.

These properties can be enhanced using different methods, such as by adding a coating. Nevertheless, the bulk of the material or substrate cannot be considered independent of the surface treatment.

Potential topics for this Special Issue include, but are not limited to, the full range of surface engineering aspects, i.e., surface integrity, contact mechanics, friction and wear, coatings and surface treatments, multiscale tribology, computational methods, and optimization techniques applied in surface engineering.

Contributions to this Special Issue are welcomed on all subjects of manufacturing and surface engineering. We especially welcome are papers that raise new questions and new possibilities, or examine old problems from a new angle.

Dr. Alicia Esther Ares
Guest Editor

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.

Published Papers (3 papers)

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Research

14 pages, 3099 KiB  
Article
Simulation Analysis of Organic–Inorganic Interface Failure of Scallop under Ultra-High Pressure
by Jiang Chang, Xue Gong, Yinglei Zhang, Zhihui Sun, Ning Xia, Huajiang Zhang, Jing Wang and Xiang Zhang
Coatings 2022, 12(7), 963; https://doi.org/10.3390/coatings12070963 - 07 Jul 2022
Cited by 1 | Viewed by 1157
Abstract
Shell is a typical biomineralized inorganic–organic composite material. The essence of scallop deshelling is caused by the fracture failure at the interface of the organic and inorganic–organic matter composites. The constitutive equations were solved so that the stress distributions of the adductor in [...] Read more.
Shell is a typical biomineralized inorganic–organic composite material. The essence of scallop deshelling is caused by the fracture failure at the interface of the organic and inorganic–organic matter composites. The constitutive equations were solved so that the stress distributions of the adductor in the radial, circumferential, and axial directions were obtained as σr = σθ = P, σz = 2(2 − ν)P/(2ν − 1), and the shear stress was τzr = 0. Using the method of finite element simulation analysis, the stress distribution laws at different interface states were obtained. The experimental results show that when the amplitude is constant, the undulation period is smaller than the diameter of the adductor or the angle between the bus of the adductor, and the reference horizontal plane gradually decreases, so the interface is more likely to yield. After the analysis, the maximum stress for the yielding of the scallop interface was about 247 MPa, and the whole deshelling process was gradually spread from the outer edge of the interface to the center. The study analyzed the scallop organic–inorganic material interface from the perspective of mechanics, and the mechanical model and simulation analysis results were consistent with the parameter optimization results, which can provide some theoretical basis for the composite material interface failure and in-depth research. Full article
(This article belongs to the Special Issue Manufacturing and Surface Engineering III)
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19 pages, 2597 KiB  
Article
A Knowledge Acquisition Method of Ship Coating Defects Based on IHQGA-RS
by Henan Bu, Xingyu Ji, Jiatao Zhang, Hongyu Lyu, Xin Yuan, Bo Pang and Honggen Zhou
Coatings 2022, 12(3), 292; https://doi.org/10.3390/coatings12030292 - 22 Feb 2022
Cited by 3 | Viewed by 1525
Abstract
Coating defects are caused by a series of factors such as the improper operation of workers and the quality of the coating itself. At present, the coating process of all shipyards is inspected and recorded at a specific time after construction, which cannot [...] Read more.
Coating defects are caused by a series of factors such as the improper operation of workers and the quality of the coating itself. At present, the coating process of all shipyards is inspected and recorded at a specific time after construction, which cannot prevent and control defects scientifically. As a result, coating quality decreases, and production costs increase. Therefore, this paper proposes a knowledge acquisition method based on a rough set (RS) optimized by an improved hybrid quantum genetic algorithm (IHQGA) to guide the ship-coating construction process. Firstly, the probability amplitude is determined according to the individual position of the population, and the adaptive value k is proposed to determine the rotation angle of the quantum gate. On this basis, the simulated annealing algorithm is combined to enhance the local search ability of the algorithm. Finally, the algorithm is applied to rough set attribute reduction to improve the efficiency and accuracy of rough set attribute reduction. The data of 600 painted examples of 210-KBC bulk carriers from a shipyard between 2015 and 2020 are randomly selected to test the knowledge acquisition method proposed in the paper and other knowledge acquisition methods. The results show that the IHQGA attribute approximate reduction algorithm proposed in this paper is the first to reach the optimal adaptation degree of 0.847, the average adaptation degree is better than other algorithms, and the average consumption time is about 10% less than different algorithms, so the IHQGA has more vital and more efficient seeking ability. The knowledge acquisition result based on the IHQGA optimization rough set has 20–50% fewer rules and 5–10% higher accuracy than other methods, and the industry experts have high recognition. The knowledge acquisition method of this paper is validated on a hull segment. The obtained results are consistent with the expert diagnosis results, indicating that the method proposed in this paper has certain practicability. Full article
(This article belongs to the Special Issue Manufacturing and Surface Engineering III)
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20 pages, 2137 KiB  
Article
Ship Painting Process Design Based on IDBSACN-RF
by Henan Bu, Xin Yuan, Jianmin Niu, Wenjuan Yu, Xingyu Ji, Hongyu Lyu and Honggen Zhou
Coatings 2021, 11(12), 1458; https://doi.org/10.3390/coatings11121458 - 28 Nov 2021
Cited by 9 | Viewed by 2444
Abstract
The painting process is an essential part of the shipbuilding process. Its quality is directly related to the service life and maintenance cost of the ship. Currently, the design of the painting process relies on the experience of technologists. It is not conducive [...] Read more.
The painting process is an essential part of the shipbuilding process. Its quality is directly related to the service life and maintenance cost of the ship. Currently, the design of the painting process relies on the experience of technologists. It is not conducive to scientific management of the painting process and effective control of painting cost. Therefore, an intelligent design algorithm for the ship painting process is proposed in this paper. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to form categories of painting objects by cluster analysis. The grey wolf optimization (GWO) is introduced to realize the adaptive determination of clustering parameters and avoid the deviation of clustering results. Then, a painting object classification model is constructed based on the random forest (RF). Finally, the recommendation of the painting process is realized based on the multi-objective evaluation function. Effectiveness is verified by taking the outer plate above the waterline of a shipyard H1127/7 as the object. The results show that the performance of DBSCAN is significantly improved. Furthermore, the accurate classification of painting objects by RF is achieved. The experiment proves that the dry film thickness qualification rate obtained by the painting process designed by IDBSCAN-RF is 92.3%, which meets the requirements of the performance standard of protective coatings (PSPC). Full article
(This article belongs to the Special Issue Manufacturing and Surface Engineering III)
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