A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining

Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs invo...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	G. Shanmugasundar, M. Vanitha, Robert Čep, Vikas Kumar, Kanak Kalita, M. Ramachandran
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning linear regression predictive models response surface machining Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/125d9201782f4d0ca8cb9c758b6003b2
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:125d9201782f4d0ca8cb9c758b6003b2
record_format	dspace
spelling	oai:doaj.org-article:125d9201782f4d0ca8cb9c758b6003b22021-11-25T18:51:30ZA Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining10.3390/pr91120152227-9717https://doaj.org/article/125d9201782f4d0ca8cb9c758b6003b22021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/2015https://doaj.org/toc/2227-9717Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.G. ShanmugasundarM. VanithaRobert ČepVikas KumarKanak KalitaM. RamachandranMDPI AGarticlemachine learninglinear regressionpredictive modelsresponse surfacemachiningChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 2015, p 2015 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning linear regression predictive models response surface machining Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	machine learning linear regression predictive models response surface machining Chemical technology TP1-1185 Chemistry QD1-999 G. Shanmugasundar M. Vanitha Robert Čep Vikas Kumar Kanak Kalita M. Ramachandran A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining
description	Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.
format	article
author	G. Shanmugasundar M. Vanitha Robert Čep Vikas Kumar Kanak Kalita M. Ramachandran
author_facet	G. Shanmugasundar M. Vanitha Robert Čep Vikas Kumar Kanak Kalita M. Ramachandran
author_sort	G. Shanmugasundar
title	A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining
title_short	A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining
title_full	A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining
title_fullStr	A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining
title_full_unstemmed	A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining
title_sort	comparative study of linear, random forest and adaboost regressions for modeling non-traditional machining
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/125d9201782f4d0ca8cb9c758b6003b2
work_keys_str_mv	AT gshanmugasundar acomparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT mvanitha acomparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT robertcep acomparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT vikaskumar acomparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT kanakkalita acomparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT mramachandran acomparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT gshanmugasundar comparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT mvanitha comparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT robertcep comparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT vikaskumar comparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT kanakkalita comparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining AT mramachandran comparativestudyoflinearrandomforestandadaboostregressionsformodelingnontraditionalmachining
_version_	1718410632113422336

A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining

Ejemplares similares