Ensemble learning with support vector machines algorithm for surface roughness prediction in longitudinal vibratory ultrasound-assisted grinding

It is critical to have an accurate prediction of surface roughness (Sa) in order to improve grinding productivity, reduce costs, and minimize the period of time required for trials and testing. Although many prediction methods have been developed, fewer studies have been conducted on the prediction...

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Veröffentlicht in:	Precision engineering Jg. 88; S. 382 - 400
Hauptverfasser:	Zhao, Mingli, Xue, Boxi, Li, Bohan, Zhu, Junming, Song, Wenbin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.06.2024
Schlagworte:	Alumina ceramic Ensemble learning algorithm Longitudinal ultrasonic grinding Support vector machine Surface roughness prediction Ensemble learning algorithm Longitudinal ultrasonic grinding Support vector machine Alumina ceramic Surface roughness prediction
ISSN:	0141-6359, 1873-2372
Online-Zugang:	Volltext
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