Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling

An Evolving Interval Type-2 Neurofuzzy Inference System and Its Metacognitive Sequential Learning Algorithm

“…In these algorithms, different variants of evolutionary algorithms are employed to tune the parameters of the network [22]. Another set of algorithms presented in literature use subtractive clustering based Type-2 TSK fuzzy systems [23] for tool condition monitoring. These approaches, however, cannot effectively handle temporally varying data due to the fixed network structure.…”

Analysis of acoustic emission amplitude parameters when increasing the machining depth of a composite

“…In this case, authors consider methods for processing registered AE signals. To reduce uncertainty in the interpretation of AE (evaluation of wear of machining tools), article [2] investigates the principles and algorithm of AE signal processing during a turning operation. The proposed algorithm of data processing ensures the possibility to monitor and automate processes to achieve the specified quality of the products.…”

A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller

“…The proposed AMFFMC is different from existing intelligent and model-free controls. This kind of control combines a hybrid Mamdani fuzzy proportional with a conventional integral plus derivative (fuzzy P+ID) controller [15] for the feedback part of the system, and a TakagiSugeno-Kang (TSK) fuzzy controller [28] based on extended subtractive clustering [29] for the feed-forward nonlinear part.…”