Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model
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....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2021-03-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.ijimai.org/journal/bibcite/reference/2835 |
| _version_ | 1818725488624926720 |
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| author | Paulino José García Nieto Esperanza García-Gonzalo Fernando Sánchez Lasheras Antonio Bernardo Sánchez |
| author_facet | Paulino José García Nieto Esperanza García-Gonzalo Fernando Sánchez Lasheras Antonio Bernardo Sánchez |
| author_sort | Paulino José García Nieto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T21:43:07Z |
| format | Article |
| id | doaj.art-71f64426a4d241478fb19f42af731aa2 |
| institution | Directory Open Access Journal |
| issn | 1989-1660 1989-1660 |
| language | English |
| last_indexed | 2024-12-17T21:43:07Z |
| publishDate | 2021-03-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-71f64426a4d241478fb19f42af731aa22022-12-21T21:31:33ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602021-03-0164394810.9781/ijimai.2020.11.004ijimai.2020.11.004Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based ModelPaulino José García NietoEsperanza García-GonzaloFernando Sánchez LasherasAntonio Bernardo SánchezThe 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.https://www.ijimai.org/journal/bibcite/reference/2835support vector machineparticle swarm optimizationmachine learningregressionhard chromium plating process |
| spellingShingle | Paulino José García Nieto Esperanza García-Gonzalo Fernando Sánchez Lasheras Antonio Bernardo Sánchez Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model International Journal of Interactive Multimedia and Artificial Intelligence support vector machine particle swarm optimization machine learning regression hard chromium plating process |
| title | Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model |
| title_full | Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model |
| title_fullStr | Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model |
| title_full_unstemmed | Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model |
| title_short | Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model |
| title_sort | chrome layer thickness modelling in a hard chromium plating process using a hybrid pso rbf svm based model |
| topic | support vector machine particle swarm optimization machine learning regression hard chromium plating process |
| url | https://www.ijimai.org/journal/bibcite/reference/2835 |
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