An Accurate Model of the Corrosion Current Density of Coatings Using an Adaptive Network-Based Fuzzy Inference System

Hesham Alhumade, Hegazy Rezk
<span title="2022-02-24">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/54rwmy3lgfe7fbkfa7e74fa6fi" style="color: black;">Metals</a> </i> &nbsp;
Corrosion resistance coating is fabricated using epoxy/glass flake (E/GF) composites and is utilized to prolong the lifespan of cold-rolled steel (CRS) metal substrates. An in situ synthesis approach was adopted to prepare the composite coating at different levels of synthesis parameters, including a load of filler and coating thickness. In addition, this work shows the effects of the chemical functionalization of the filler on the corrosion protection property of the epoxy/functional glass
more &raquo; ... e (E/FGF) composite coatings. The effects of the modification of the filler, as well as the other synthesis parameters, on the corrosion resistance property are evaluated using a potentiodynamic polarization technique. Here, the corrosion resistance property is evaluated based on the observed current density. The primary goal of this work is to present an accurate model of corrosion current density (CCD). By using measured data, a precise model, which simulates the corrosion resistance properties of the coatings, has been created by an adaptive network-based fuzzy inference system (ANFIS) in terms of glass flake loading, chemical functionalization, and coating thickness. The obtained results revealed good agreement between ANFIS-based modelling and the measured dataset. The root mean square errors of the prediction model were 8.1391 × 10−8 and 0.0104 for training and testing, respectively. The coefficient of determination (R2) values of the ANFIS output were found to be 1.0 and 0.9997 for training and testing, respectively. To prove the superiority of the ANFIS-based model of CCD, the achieved results were compared with an analysis of variance (ANOVA). ANOVA utilizes a linear regression approach to get the model. Thanks to ANFIS, compared with ANOVA, the values of R2 are increased by 10% and 18.6% for the training and testing phases, respectively. Finally, the accuracy of the ANFIS model of corrosion current density is validated experimentally.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/met12030392">doi:10.3390/met12030392</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t6nfjbqm4fbsnjajnk6icffzhu">fatcat:t6nfjbqm4fbsnjajnk6icffzhu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220504012922/https://mdpi-res.com/d_attachment/metals/metals-12-00392/article_deploy/metals-12-00392-v2.pdf?version=1646043752" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e8/99/e899a141ec668f00e49dd5ce2231d199bb25e728.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/met12030392"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>