Hybrid Learning for Tool Wear Monitoring

Abstract

In automated manufacturing systems such as flexible manufacturing systems (FMSs)

Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

“…A spanking new Transductive-Weighted Neuro-Fuzzy Inference Technique (TWNFIS) was proposed (Agustin et al, 2009) to model tool wear in turning and proved the accuracy by comparing with experimental values. Li et al (2002) used vibration signals to find out drill wear and proposed a relationship between the vibration and the tool wear with fuzzy neural network model. It was also demonstrated that features of vibration signals can be used to determine the drill wear with greater accuracy.…”

Drill flank wear estimation using supervised vector quantization neural networks

“…The thrust and torque signals were also used by Lin and Ting [9] for comparing several structures and parameters of a cumulative back-propagation algorithm with regression models for drill flank wear monitoring. Li et al [10] used the root mean square (RMS) of the vibration signal frequency bands to train a neural network with fuzzy logic (FNN) for drill flank wear monitoring and found their method to be faster than a back-propagation neural network. Liu and Chen [11] used a back propagation neural network for on-line detection of drill wear to decide a usable or a failure drill.…”

An automated flank wear measurement of microdrills using machine vision

“…Ertunc and Loparo [4] developed a decision fusion center algorithm, which combines the outputs of the individual methods to make a global decision for the wear status of the drill. Li et al [10] presented a hybrid learning method to map the relationship between the features of cutting vibration and the tool wear condition.…”