Flank Wear Regulation Using Artificial Neural Networks

Tool wear regulation highly influences product quality and the safety and productivity of machining processes. Hence, it is one of the most important elements in the supervisory control of machine tools. The development of this type of machine tool adaptive control is practically at its infancy because there are still no industrial solutions concerning robust, reliable, and highly precise continuous tool wear estimators. Therefore, this paper primarily aims at the determination of a tool wear regulation model that can ensure the maximum allowed amount of tool wear rate within a predefined machining time, while simultaneously maintaining a high level of process productivity. The proposed model is structured using Radial Basis Function Neural Network controller and Modified Dynamical Neural Network filter. It is analysed using an analytical tool wear model with experimentally adjusted parameters.