47 research outputs found
An effective sensor for tool wear monitoring in face milling : acoustic emmision
Acoustic Emission (AE) has been widely used for monitoring manufacturing
processes particularly those involving metal cutting. Monitoring the
condition of the cutting tool in the machining process is very important since tool
condition will affect the part size, quality and an unexpected tool failure may damage
the tool, work-piece and sometimes the machine tool itself. AE can be effectively
used for tool condition monitoring applications because the emissions from
process changes like tool wear, chip formation i.e. plastic deformation, etc. can
be directly related to the mechanics of the process. Also AE can very effectively
respond to changes like tool fracture, tool chipping, etc. when compared to cutting
force and since the frequency range is much higher than that of machine vibrations
and environmental noises, a relatively uncontaminated signal can be obtained.
AE signal analysis was applied for sensing tool wear in face milling operations.
Cutting tests were carried out on a vertical milling machine. Tests were carried out
for a given cutting condition, using single insert, two inserts (adjacent and opposite)
and three inserts in the cutter. AE signal parameters like ring down count and rms
voltage were measured and were correlated with flank wear values (VB max). The
results of this investigation indicate that AE can be effectively used for monitoring
tool wear in face milling operations.Fundação para a Ciência e a Tecnologia (FCT
Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process
Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model