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Coatings
Special Issues
AI-Driven Surface Engineering and Coating
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AI-Driven Surface Engineering and Coating

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Surface Characterization, Deposition and Modification".

Deadline for manuscript submissions: closed (28 March 2025) | Viewed by 4260

Special Issue Editor

Dr. Yilei Zhang

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Guest Editor
Department of Mechanical Engineering, University of Canterbury, Kirkwood Ave., Christchurch 8140, New Zealand
Interests: intelligent design and manufacturing; spiking neural network; bioprinting; neuroengineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent developments in artificial intelligence (AI) have changed many industries and will eventually change research on surface engineering and coating as well. Obviously, AI-driven surface engineering and coating research is very challenging and still in its infancy; thus, a Special Issue in this area will promote research and encourage communication among researchers from different domains. Therefore, I would like to invite you to submit your work to this Special Issue on "AI-Driven Surface Engineering and Coating". The aim is to present the latest developments of AI in the field surface engineering and coating via a combination of original research papers and review articles from leading groups around the world.

This scope of this Special Issue will serve as a forum for papers on the following concepts:

  • Theoretical and experimental research, knowledge and new ideas in AI-driven surface engineering and coating;
  • Recent developments in AI technologies relevant to design and manufacturing for surface engineering and coating;
  • AI technologies used to predict properties, performance, durability and reliability for surface engineering and coating under various external conditions, such as friction, wear, under different pressures/temperatures;
  • AI technologies used for different surface engineering and coating processes, including but not limit to polishing, plating, additive manufacturing, laser and plasma processing, etc.;
  • AI technologies used to help understand degradation mechanisms for surface engineering and coatings under various external conditions such as corrosion.

Dr. Yilei Zhang
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.

Keywords

  • intelligent design
  • biomimetic design
  • smart manufacturing
  • surface engineering
  • coating

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

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Research

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20 pages, 6756 KiB  
Article
Optimization of Film Thickness Uniformity in Hemispherical Resonator Coating Process Based on Simulation and Reinforcement Learning Algorithms
by Jingyu Pan, Dongsheng Zhang, Shijie Liu, Jianguo Wang and Jianda Shao
Coatings 2025, 15(6), 700; https://doi.org/10.3390/coatings15060700 - 10 Jun 2025
Viewed by 489
Abstract
Hemispherical resonator gyroscopes (HRGs) are critical components in high-precision inertial navigation systems, typically used in fields such as navigation, weaponry, and deep space exploration. Film thickness uniformity affects device performance through its impact on the resonator’s Q value. Due to the irregular structure [...] Read more.
Hemispherical resonator gyroscopes (HRGs) are critical components in high-precision inertial navigation systems, typically used in fields such as navigation, weaponry, and deep space exploration. Film thickness uniformity affects device performance through its impact on the resonator’s Q value. Due to the irregular structure of the resonator, there has been limited research on the uniformity of film thickness on the inner wall of the resonator. This study addresses the challenge of thickness non-uniformity in metallization coatings, particularly in the meridional direction of the resonator. By integrating COMSOL-based finite element simulations with reinforcement learning-driven optimization through the Proximal Policy Optimization (PPO) algorithm, a new paradigm for coating process optimization is established. Furthermore, a correction mask is designed to address the issue of low coating rate. Finally, a Zygo white-light interferometer is used to measure film thickness uniformity. The results show that the optimized coating process achieves a film thickness uniformity of 11.0% in the meridional direction across the resonator. This study provides useful information and guidelines for the design and optimization of the coating process for hemispherical resonators, and the presented optimization method constitutes a process flow framework that can also be used for precision coating engineering in semiconductor components and optical elements. Full article
(This article belongs to the Special Issue AI-Driven Surface Engineering and Coating)
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Review

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26 pages, 3681 KiB  
Review
Intelligent Recognition of Tool Wear with Artificial Intelligence Agent
by Jiaming Gao, Han Qiao and Yilei Zhang
Coatings 2024, 14(7), 827; https://doi.org/10.3390/coatings14070827 - 2 Jul 2024
Cited by 1 | Viewed by 2864
Abstract
Tool wear, closely linked to operational efficiency and economic viability, must be detected and managed promptly to prevent significant losses. Traditional methods for tool wear detection, though somewhat effective, often lack precision and require extensive manual effort. Advancements in artificial intelligence (AI), especially [...] Read more.
Tool wear, closely linked to operational efficiency and economic viability, must be detected and managed promptly to prevent significant losses. Traditional methods for tool wear detection, though somewhat effective, often lack precision and require extensive manual effort. Advancements in artificial intelligence (AI), especially through deep learning, have significantly progressed, providing enhanced performance when combined with tool wear management systems. Recent developments have seen a notable increase in the use of AI agents that utilise large language models (LLMs) for specific tasks, indicating a shift towards their integration into manufacturing processes. This paper provides a comprehensive review of the latest advancements in AI-driven tool wear recognition and explores the integration of AI agents in manufacturing. It highlights the LLMS and the various types of AI agents that enhance AI’s autonomous capabilities, discusses the potential benefits, and examines the challenges of this integrative approach. Finally, it outlines future research directions in this rapidly evolving field. Full article
(This article belongs to the Special Issue AI-Driven Surface Engineering and Coating)
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