Special Issue "Characterization of Coating: Experimental and Computational Approach with Emphasis on Artificial Intelligence Approach"

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Corrosion, Wear and Erosion".

Deadline for manuscript submissions: 31 July 2022.

Special Issue Editor

Dr. Rajesh Jha
E-Mail Website
Guest Editor
Department of Mechanical and Materials Engineering (MME), Florida International University, Miami, FL, USA
Interests: materials informatics; nanomechanics and nanotribology; coatings; cladding; additive manufacturing; materials/alloy design; CALPHAD; physical metallurgy; process metallurgy: blast furnace iron making; LD steel making; artificial intelligence algorithms; data-driven modeling; multi-objective optimization
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Special Issue Information

Dear Colleagues,

Coating is an essential component of most equipment in application. A protective coating on an automobile’s body protects the substrate from wear and corrosion, while a coating on an orthopedic implant enhances its biocompatibility and osteointegration. Depending on the application, coatings can be single or multi-layer and can vary in thickness from a few microns to a few millimeters.

The characterization of coatings is an important step prior to its application. Data generated from characterization can be numerical values like concentration of various elements in coating material, mechanical properties, data from corrosion experiments, data in the form of images obtained from optical or electron microscopy etc. Data like processing routes including plating, galvanizing, PVD, CVD, cold spray, additive manufacturing, etc., and post processing routes like heat-treatment cycles etc. are not numerical and can be used for classification purpose. Thus, the characterization of coatings generates a significant amount of data that can be utilized in developing new coatings as well as improving multiple targeted properties of existing coatings. Artificial intelligence algorithms can be helpful in determining various correlations among different parameters involved in development of a coating material.

The scope of this issue can be summarized as follows:

  • All types of coating materials and coating methods.
  • Experimental characterization including but not limited to nanoindentation, wear, corrosion, erosion, cavitation, etc.
  • Composition–processing–structure–property relations.
  • Computational simulations with special emphasis on development of models capable of simulating experiments as well as predicting properties.
  • Artificial intelligence and machine learning algorithm application.
  • New coating methods and characterization techniques.
  • Calculation of PHAse Diagram (CALPHAD) approach.

Prospective authors are welcome to consult the editor regarding utilizing nanoindentation data and ways to apply artificial intelligence algorithms while developing predictive models.

Dr. Rajesh Jha
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 papers will be 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 1800 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

  • protective coatings
  • corrosion prevention
  • materials characterization
  • coating methods
  • cladding
  • additive manufacturing
  • artificial intelligence
  • CALPHAD

Published Papers (1 paper)

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Research

Article
Software (GUI/APP) for Developing AI-Based Models Capable of Predicting Load-Displacement Curve and AFM Image during Nanoindentation
Coatings 2021, 11(3), 299; https://doi.org/10.3390/coatings11030299 - 05 Mar 2021
Viewed by 878
Abstract
During nanoindentation tests, the load-displacement curve is used for estimating mechanical properties, while an indent image obtained through atomic force microscopy (AFM) is used for studying deformation of a material. We present a computational platform for developing artificial intelligence-based models for predicting indentation [...] Read more.
During nanoindentation tests, the load-displacement curve is used for estimating mechanical properties, while an indent image obtained through atomic force microscopy (AFM) is used for studying deformation of a material. We present a computational platform for developing artificial intelligence-based models for predicting indentation depth (load-displacement curve) and AFM image as a function of test parameters like maximum applied load, loading rate, and holding time. A user can directly use machine generated data in text (.txt) and hierarchical data format (HDF, hdf) format for developing the AI-based models for indentation depth and AFM image, respectively. The software was tested on three different coatings/materials for indentation depth: heat-treated (HT) sample of cold sprayed aluminum-based bulk metallic glass (Al-BMG) coating, carbon nanotube reinforced aluminum composite (Al-5CNT) coating, and spark-plasma-sintered hydroxyapatite (SPS HA) sample. For AFM imaging, a heat-treated (HT) sample of cold sprayed aluminum-based bulk metallic glass (Al-BMG) coating was considered. Correlation or R-values are close to 1 for all the models developed in this work. Predicted load-displacement curve and AFM image are in good agreement with the experimental findings. Our approach will be helpful in virtual simulation of load-displacement curves and AFM indent images for a large number of new test parameters, thus significantly reducing the number of indents needed for characterizing/analyzing a material. Full article
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