Abstract
ABSTRACT In metal cutting processes, tool condition monitoring has a great importance to prevent surface damage and maintaining the quality of surface finishing. With the development of digitalisation and connection of industrial machines, it has become possible to collect real-time data from various types of sensors (e.g. vibration, acoustic or emission) during the process execution. However, information fusion from multiple sensor signals and tool health prediction still present a big challenge. The aim of this paper is to present a data-driven framework to estimate the tool wear status and predict its remaining useful life by using machine learning techniques. The first part of the framework is dedicated to sensor data preprocessing and feature engineering, while the second part deals with the development of prediction models. Different types of machine learning algorithms are used and compared to find the best result. A case study in a milling process is presented to illustrate the potentialities of the proposed framework for tool condition monitoring.
Published Version
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