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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 of surface roughness in longitudinal ultrasonic vibration-assisted grinding (LUVAG). In this paper, a surface roughness prediction model algorithm based on ensemble learning of support vector machines (ELSVM) is proposed that can be used for surface roughness prediction of LUVAG alumina ceramics. This paper first details the development of the ELSVM surface roughness prediction model, which consists of four modules: the prepossessing module, the multi-algorithm regression module, the support vector machine algorithm (SVM) module, and the ensemble module. In addition, ELSVM was compared with four other machine learning methods based on experimental results for surface roughness prediction modeling. The error of ELSVM model was reduced by 6.3%, 7.9%, 8.9%, and 7.5%, respectively, compared to the individual prediction models such as I-AISPSO, I-AIS, SPSO, and KBaNN. Comparison results show that the ELSVM model has the lowest average ratio of Mean Absolute Error (MAE) for surface roughness prediction in LUVAG. •The artificial immune particle swarm optimization is improved.•The adaptive particle swarm optimization algorithm and the manual free algorithm are combined in parallel.•A surface roughness prediction model of support vector machine based on ensemble learning algorithm is proposed.•Longitudinal ultrasonic assisted grinding can reduce surface roughness.
ISSN:0141-6359
1873-2372
DOI:10.1016/j.precisioneng.2024.02.018