Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model.

Paulino José García Nieto; Esperanza García Gonzalo; Fernando Sánchez Lasheras; Antonio Bernardo Sánchez

doi:10.9781/ijimai.2020.11.004

Authors

Paulino José García Nieto Universidad de Oviedo
Esperanza García Gonzalo Universidad de Oviedo
Fernando Sánchez Lasheras Universidad de Oviedo
Antonio Bernardo Sánchez Universidad de León

DOI:

https://doi.org/10.9781/ijimai.2020.11.004

Keywords:

Support Vector Machine, Particle Swarm Optimization, Machine Learning, Regression, Hard Chromium Plating Process

Supporting Agencies

Authors wish to acknowledge the computational support provided by the Department of Mathematics at University of Oviedo. Additionally, we would like to thank Anthony Ashworth for his revision of English grammar and spelling of the manuscript. Moreover, this research was funded by the Foundation for the Promotion of Applied Scientific Research and Technology in Asturias (FICYT) through the GRUPIN project Reference IDI/2018/000221, co-financed with EU FEDER funds.

Abstract

The purpose of chromium plating is the creation of a hard and wear-resistant layer of chromium over a metallic surface. The principal feature of chromium plating is its endurance in the face of the wear and corrosion. This industrial process has a vast range of applications in many different areas. In the performance of this process, some difficulties can be found. Some of the most common are melt deposition, milky white chromium deposition, rough or sandy chromium deposition and lack of toughness of the layer or wear and lack of thickness of the layer deposited. This study builds a novel nonparametric method relied on the statistical machine learning that employs a hybrid support vector machines (SVMs) model for the hard chromium layer thickness forecast. The SVM hyperparameters optimization was made with the help of the Particle Swarm Optimizer (PSO). The outcomes indicate that PSO/SVM–based model together with radial basis function (RBF) kernel has permitted to foretell the thickness of the chromium layer created in this industrial process satisfactorily. Thus, two kinds of outcomes have been obtained: firstly, this model permits to determine the ranking of relevance of the seven independent input variables investigated in this industrial process. Finally, the high achievement and lack of complexity of the model indicate that the PSO/SVM method is very interesting compared to other conventional foretelling techniques, since a coefficient of determination of 0.9952 is acquired.

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