Comparative Study of LS-SVM, RVM and ELM for Modelling of Electro-Discharge Coating Process

ADMT Journal

دوره 14، شماره 1، خرداد 2021، صفحه 19-32   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/admt.2021.1897266.1190

نویسندگان

Morteza Taheri^{* 1}؛ Nader Mollayi²؛ Seyyed Amin Seyyedbarzani¹؛ Abolfazl Foorginejad¹؛ Vahide Babaiyan²

¹Department of Mechanical Engineering, Birjand University of Technology, Birjand, Iran

²Department of Computer Engineering, Birjand University of Technology, Birjand, Iran

چکیده

The Electro-discharge coating process is an efficient method for improvement of the surface quality of the parts used in molds. In this process, Material Transfer Rate (MTR), an average Layer Thickness (LT) are important factors, and tuning the input process parameters to obtain the desired value of them is a crucial issue. Due to the wide range of the input parameters and nonlinearity of this system, the establishment of a mathematical model is a complicated mathematical problem. Although many efforts have been made to model this process, research is still ongoing to improve the modeling of this process. To this end, in the present study, three powerful machine learning algorithms, namely, Relevance Vector Machine (RVM), Extreme Learning Machine (ELM) and the Least Squares Support Vector Machine (LS-SVM) that have not been used to model this process, have been used. The values R² above 0.99 for the training data and above 0.97 for the test data show the high accuracy and generalization capability degree related to the LS-SVM models, which can be applied for the input parameters tuning in order to attain a preferred value of the outputs.

کلیدواژه‌ها

Average Layer Thickness؛ Electro-Discharge Coating؛ Material Removal Rate؛ Support Vector Machine

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