Fuzzy Logic-Based Torque Control System for Milling Process Optimization

• Published in: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 37; no. 5; pp. 941 - 950
• Main Authors: Haber, R.E., Alique, J.T.
• Format: Journal Article
• Language: English
• Published: New-York, NY IEEE 01.09.2007; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Computer numerical control; Computer science; control theory; systems; Control system synthesis; Control systems; Control theory. Systems; Design optimization; Error correction; Errors; Exact sciences and technology; Feed rate; Feeds; Fuzzy control; Fuzzy logic; Fuzzy systems; Mechanical engineering. Machine design; Milling; milling process; Optimization; Process control. Computer integrated manufacturing; Strategy; Torque; Torque control; System architecture; Input output; Rate function; Machining; Control synthesis; Logical programming; Optimization; Fuzzy control; Cutting; Computer architecture; Logic control; Penetration rate; Peak; Speed control; Erosion; Selftuning control; Transient response; Computer digital control; Output error; Fuzzy system; Process planning; Process control; milling process; Fuzzy logic; Boarded computer; Torque control; Milling; Open systems
• ISSN: 1094-6977; 1558-2442
• DOI: 10.1109/TSMCC.2007.900654

This paper focuses on the design and implementation of a fuzzy-logic-based torque control system, embedded in an open-architecture computer numerical control (CNC), in order to provide an optimization function for the material removal rate. The control system adjusts the feed rate and spindle speed simultaneously as needed, to regulate the cutting torque using the CNC's own resources without requiring additional hardware overheads. The control system consists of two inputs (i.e., torque error and change of error), two outputs (i.e., the feed rate and spindle speed increment) fuzzy controller, and a self-tuning mechanism, all of which are embedded within the kernel of a standard open control. The self-tuning strategy is based on the measured peaks in the torque error signal of the closed-loop system response. The self-tuning fuzzy controller is applied to the milling process in a production environment in order to demonstrate the improvements in performance and effectiveness. Two approaches are tested, and their performance is assessed using several performance measurements. These approaches are the two-input/two-output for the fuzzy controller and a single-output fuzzy controller (i.e., only feed-rate modification), with and without the self-tuning mechanism. The results demonstrate that the proposed control strategy provides better transient performance, accuracy, and machining cycle time than the others, thus, increasing the metal removal rate.