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Online Tool Condition Monitoring Based on Parsimonious Ensemble+ by Pratama, Mahardhika; Dimla, Eric; Lughofer, Edwin; Pedrycz, Witold; Tjahjowidowo, Tegoeh is a scholarly article available to read on EtoBox.

What is Online Tool Condition Monitoring Based on Parsimonious Ensemble+ about?

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator

Author

Pratama, Mahardhika; Dimla, Eric; Lughofer, Edwin; Pedrycz, Witold; Tjahjowidowo, Tegoeh

Published

2017

Language

EN