Drill flank wear estimation using supervised vector quantization neural networks

Abstract

Drill wear detection and prognosis is one of the most important considerations in reducing the cost of rework and scrap and to optimize tool utilization in hole making industry. This study presents the development and implementation of two supervised vector quantization neural networks for estimating the flankland wear size of a twist drill. The two algorithms are; the learning vector quantization (LVQ) and the fuzzy learning vector quantization (FLVQ). The input features to the neural networks were extracted from the vibration signals using power spectral analysis and continuous wavelet transform techniques. Training and testing were performed under a variety of speeds and feeds in the dry drilling of steel plates. It was found that the FLVQ is more efficient in assessing the flank wear size than the LVQ. The experimental procedure for acquiring vibration data and extracting features in the time-frequency domain using the wavelet transform is detailed. Experimental results demonstrated that the proposed neural network algorithms were effective in estimating the size of the drill flank wear.

Towards the identification of worn picks on cutterdrums based on torque and power signals using Artificial Neural Networks

“…Use of fuzzy logic and evolution algorithms (such as genetic algorithms), recurrence plots technique and the presented neuron networks are a perspective tool for building intelligent systems, that can generalise the obtained information, thus making them suitable for use in a given case (e.g. Alvarez Grima and Verhoef, 1999;Abu-Mahfouz, 2005;Uraikul et al, 2007;Dimla, 1999;Litak et al, 2008).…”

Vector Quantization by Minimizing Kullback-Leibler Divergence

“…But it's better to learn it by combining the class labels in a supervised way. The ideal quantization we are seeking is the one that can contains all the information needed for the classification [16,50,1,42].…”

Predicting the hardness of carbon nanotube reinforced copper matrix nanocomposites using two adaptive fuzzy inference system identifiers

“…Using expert systems to automatically predict the important characteristics of fabricated materials is of high concern to engineers. [12][13][14][15][16] The main motivation to this refers to generalization capabilities of expert intelligent predictive tools. It has been proved that an intelligent system of constant architecture, for example, a Takagi Sugeno Kang fuzzy inference system (TSK-FIS) or a neural network could easily learn the characteristics of various manufacturing procedures.…”