A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials

Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the re...

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Autores principales:	Shibaprasad Bhattacharya, Kanak Kalita, Robert Čep, Shankar Chakraborty
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	regression model turning drilling composite material Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/31f9b72c15a04073895838be3d6df592
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spelling	oai:doaj.org-article:31f9b72c15a04073895838be3d6df5922021-11-11T18:11:39ZA Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials10.3390/ma142166891996-1944https://doaj.org/article/31f9b72c15a04073895838be3d6df5922021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6689https://doaj.org/toc/1996-1944Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the responses for given sets of input parameters. In this paper, the application potentialities of nine different regression models, such as linear regression (LR), polynomial regression (PR), support vector regression (SVR), principal component regression (PCR), quantile regression, median regression, ridge regression, lasso regression and elastic net regression are explored in accurately predicting response values during turning and drilling operations of composite materials. Their prediction performance is also contrasted using four statistical metrics, i.e., mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error and root relative squared error. Based on the lower values of those metrics and Friedman rank and aligned rank tests, SVR emerges out as the best performing model, whereas the prediction performance of median regression is worst. The results of the Wilcoxon test based on the drilling dataset identify the existence of statistically significant differences between the performances of LR and PCR, and PR and median regression models.Shibaprasad BhattacharyaKanak KalitaRobert ČepShankar ChakrabortyMDPI AGarticleregressionmodelturningdrillingcomposite materialTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6689, p 6689 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	regression model turning drilling composite material Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	regression model turning drilling composite material Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Shibaprasad Bhattacharya Kanak Kalita Robert Čep Shankar Chakraborty A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials
description	Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the responses for given sets of input parameters. In this paper, the application potentialities of nine different regression models, such as linear regression (LR), polynomial regression (PR), support vector regression (SVR), principal component regression (PCR), quantile regression, median regression, ridge regression, lasso regression and elastic net regression are explored in accurately predicting response values during turning and drilling operations of composite materials. Their prediction performance is also contrasted using four statistical metrics, i.e., mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error and root relative squared error. Based on the lower values of those metrics and Friedman rank and aligned rank tests, SVR emerges out as the best performing model, whereas the prediction performance of median regression is worst. The results of the Wilcoxon test based on the drilling dataset identify the existence of statistically significant differences between the performances of LR and PCR, and PR and median regression models.
format	article
author	Shibaprasad Bhattacharya Kanak Kalita Robert Čep Shankar Chakraborty
author_facet	Shibaprasad Bhattacharya Kanak Kalita Robert Čep Shankar Chakraborty
author_sort	Shibaprasad Bhattacharya
title	A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials
title_short	A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials
title_full	A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials
title_fullStr	A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials
title_full_unstemmed	A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials
title_sort	comparative analysis on prediction performance of regression models during machining of composite materials
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/31f9b72c15a04073895838be3d6df592
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A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials

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