An analytical model for predicting the depth of subsurface plastic deformation during cutting titanium alloy

The cutting subsurface plastic deformation layer of titanium alloys has a serious influence on fatigue performance. Hence, it is necessary to establish a predicting model of the depth of subsurface plastic deformation. However, empirical models are difficult to be applied for different cutting metho...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of advanced manufacturing technology 2024-05, Vol.132 (5-6), p.2359-2368
Hauptverfasser: Hou, Ning, Bai, Lidong, Ye, Chao, Niu, Xiaoxia, Wang, Minghai, Huang, Shutao, Wang, Qijia
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The cutting subsurface plastic deformation layer of titanium alloys has a serious influence on fatigue performance. Hence, it is necessary to establish a predicting model of the depth of subsurface plastic deformation. However, empirical models are difficult to be applied for different cutting methods and are controlled by various cutting parameters. This paper establishes an analytical model for predicting the depth of subsurface plastic deformation based on cutting force. In this case, as long as the cutting force is known, the analytical model can be used to predict the depth of subsurface plastic deformation layer for various cutting conditions. In experiments, the depth of subsurface plastic deformation was measured by using a scanning electron microscope (SEM) and electron back-scatter diffraction (EBSD). The measured and predicted values are closed, and the average prediction error is only 16.01%. Therefore, the analytical model is reliable and useful to predict the depth of subsurface plastic deformation during cutting titanium alloys. This study will have an important application value to control the depth of subsurface plastic deformation to improve fatigue performance.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-024-13449-3