About: Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by Support Vector and Gaussian Process Regression Methods

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About: Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by Support Vector and Gaussian Process Regression Methods Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by Support Vector and Gaussian Process Regression Methods
Creator	Cherukuri, Harish Hashemitaheri, Maryam Manish, Sai Mekarthy, Reddy
Source	Elsevier
abstract	Abstract In this paper, machine learning (ML) models, namely Support Vector Regression (SVR) and Gaussian Process Regression (GPR), are presented for the prediction of specific cutting forces and maximum tool temperatures during orthogonal machining processes. The training/test data for building the ML models is generated from finite element (FE) simulations. The simulations are performed using the commercial FE package Abaqus/Explicit and validated using experimental results. The FE generated data consists of cutting speed, uncut chip thickness, and rake angle as the input parameters. The response variables are the cutting force and maximum tool temperature. The data is split into training and test sets using 80-20 split. The optimal SVR and GPR models are selected using grid search on the training data. The predictions on the test data sets show that both the models perform well with high accuracy in predicting cutting force and maximum tool temperature. Between the two models, the mean square errors (MSE) for SVR are less than those for GPR.
has issue date	2020-12-31(xsd:dateTime)
bibo:doi	10.1016/j.promfg.2020.05.139
has license	els-covid
sha1sum (hex)	d5c252b1a930a381433f05eae6bed6c49a70068d
schema:url	https://doi.org/10.1016/j.promfg.2020.05.139
resource representing a document's title	Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by Support Vector and Gaussian Process Regression Methods
schema:publication	Procedia Manufacturing
resource representing a document's body	covid:d5c252b1a930a381433f05eae6bed6c49a70068d#body_text
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