Tool condition monitoring using artificial intelligence methods
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TL;DR
A comparison of usability of these methods in practice is discussed, including a new artificial neural network based-fuzzy inference system with moving consequents in if–then rules.
Abstract
This paper describes an application of three artificial intelligence (AI) methods to estimate tool wear in lathe turning. The first two are "conventional" AI methods—the feed forward back propagation neural network and the fuzzy decision support system. The third is a new artificial neural network based-fuzzy inference system with moving consequents in if–then rules. Tool wear estimation is based on the measurement of cutting force components. This paper discusses a comparison of usability of these methods in practice.