Abstract
Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for the effective monitoring of tool conditions, which is the most crucial feedback information to the process controller. Interestingly, the abundance of data collected from multiple sensors allows us to employ various techniques such as feature extraction, selection, and classification methods for generating such crucial information. While the use of multiple sensors has improved the accuracy in the classification of tool conditions, design of tool condition monitoring system (TCM) for reduced complexity and increased robustness has been rarely studied. Therefore, this paper studies the design of effective multisensor-based TCM when machining 4340 steel by using a multilayer-coated and multiflute carbide end mill cutter. Multiple sensors tested in this paper include force, vibration, acoustic emission, and spindle power sensor for the time and frequency domain data. In addition, two feature selection methods and three classifiers with a machine ensemble technique are considered as design components. Importantly, different fusion methods are evaluated in this paper: (1) decision level fusion and (2) feature level fusion. The experimental results show that the design of TCM based on the feature level fusion can significantly improve the accuracy of the tool condition classification. It is also shown that the highest accuracy can be achieved by using force, vibration, and acoustic emission sensor together with correlation-based feature selection method and majority voting machine ensemble.
