Surface textural analysis using acousto optic emission- and vision-based 3D surface topography—a base for online tool condition monitoring in face turning

In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. Thus, by monitoring the machined surface topography of the workpiece and extracting the relevant information the cutting process and tool wear st...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of advanced manufacturing technology 2011-08, Vol.55 (9-12), p.1025-1035
Hauptverfasser: Prasad, Balla Srinivasa, Sarcar, M. M. M., Ben, B. Satish
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. Thus, by monitoring the machined surface topography of the workpiece and extracting the relevant information the cutting process and tool wear state should be able to be monitored and quantified. But the effects of vibrations have been paid less attention. The work in the present paper is divided into two parts. First part consists of a data acquisition and signal processing using acousto optic emission sensor (i.e., laser Doppler vibrometer) for online tool condition monitoring and the second part of the work presents the surface topography analysis of machined surfaces during the progression of the tool wear. Most of the work presented is also a study where surface metrology is being used to measure all aspects of the machining in combination with an online metrology tool. The encouraging results of the work pave the way for the development of a real-time, low cost, and reliable tool–condition–monitoring system. A high degree of correlation is established between the results of the acousto optic emission signal- and vision-based surface textural analysis in identification of tool wear state.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-010-3127-z