Holarchic-attributive modeling of the machining systems – a new approach

Abstract

Manufacturing machine control implies the following aspects: geometry control, dimensional control, stability control, cost control, adaptability and predictability assurance, optimal, adaptive and predictive features of the control system, as well as models structuring and building. The paper subject is the manufacturing machines dedicated to workpiece processing. The research goal is the increasing of manufacturing machine competitivity through: i) lead time reducing; ii) fast programming; iii) deviation minimizing iv) productivity increasing; v) cost minimization and, finally, vi) stability control. This research is based on the following four key ideas: a) attributive modeling instead of phenomenon approach aiming the integrated control of the physical, economic, commercial, trading and organizational aspects of the manufacturing machine operation; b) holarhic control system with an open architecture instead of hierarchical control with closed architecture c) knowledge acquisition from current operation and its immediate using for manufacturing machine control; d) control based on simple localized models, built with recent data instead of complex and general model built with historical data. The research is aiming the development of a new concept for manufacturing machine control based on holarhic attributive modeling and online supervised learning.