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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F21%3A10248310" target="_blank" >RIV/61989100:27230/21:10248310 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000726659400001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000726659400001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr9112015" target="_blank" >10.3390/pr9112015</a>

Result language

angličtina

Original language name

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

Original language 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20300 - Mechanical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Processes

ISSN

2227-9717

e-ISSN

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Volume of the periodical

9

Issue of the periodical within the volume

11

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

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UT code for WoS article

000726659400001

EID of the result in the Scopus database

2-s2.0-85119623886