Adaptive control optimisation system for minimising production cost in hard milling operations

Abstract

Abstract This paper proposes an adaptive control with optimisation (ACO) methodology for optimising the production cost subjected to quality constraints in high-performance milling operations of hardened steel (58–62 HRC). Unlike traditional approaches for optimising production cost, this paper deals with optimising the cutting operation considering the current state of the cutting tool. Artificial intelligence techniques for modelling (artificial neural networks) and optimising (genetic algorithms and mesh adaptive direct search algorithms) are applied for this purpose. As a result, the production cost estimation from the proposed approach is 13% lower than the one obtained by the traditional approach with 76% less uncertainty. Keywords: artificial neural networksproduction costadaptive control with optimisation (ACO)genetic algorithmsmesh adaptive direct search algorithmshard milling Acknowledgements The authors would like to express their gratitude to the Department of Engineering Systems and Design at the Universitat Jaume I, to the Intelligent Machines Research Group at Tecnológico de Monterrey, to CONACYT exchange scholarship program for its support and to Miguel Aymerich for his help in gathering the experimental data.