Bayesian ART-based fuzzy inference system: A new approach to prognosis of machining processes

Abstract

Modeling of machining processes plays a crucial role in manufacturing operations, in view of its substantial impacts on the overall cost effectiveness and productivity. To this end, computational intelligence approaches, such as neural networks, fuzzy systems, and hybrid fuzzy neural networks, are increasingly being employed in the recent years. However, most of the existing approaches are based on batched learning procedure, in which all machining data are assumed to be available and can be accessed repeatedly. Such approach is impractical in the face of large data stream, and is not suitable for dynamic, time-varying tasks. In this light, this paper proposes a novel fuzzy neural network called the Bayesian Adaptive Resonance Theory (BART)-Based Fuzzy Inference System, which features a fully online learning scheme employing the BART algorithm and decoupled extended Kalman filter (DEKF) procedure for the construction and parameter optimization of its rule base, respectively. Together, the BART and DEKF mechanisms endow the proposed system with computational efficiency and strong statistical foundation, which are desirable in modeling and prognosis tasks. To further simplify its structure, the system also incorporates a pruning procedure to remove inconsequential rules. Experimental studies on tool wear prognosis and chaotic time series prediction tasks have verified the efficacy of the proposed system as an online modeling tool.