| Flank wear regulation using artificial neural networks
Danko Brezak, Dubravko Majetic, Toma Udiljak, Josip Kasac
The Journal of Mechanical Science and Technology, vol. 24, no. 5, pp.1041-1052, 2010
Abstract : 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.
Keyword : Control; Machining; Neural network; Productivity maximisation; Tool wear regulation |
