FUZZY ADAPTIVE-CONTROL OF MACHINING PROCESSES WITH A SELF-LEARNING ALGORITHM

When machining conditions change significantly, applying parameter-ada ptive control to the cutting system by varying the table feedrate allo ws a constant cutting force to be maintained Although several controll er schemes have been proposed, their cutting control performance is li mited especially when the cutting conditions vary significantly. This paper presents an adaptive fuzzy logic control (FLC) developed for cut ting processes under various cutting conditions, The controller adopts opt-line scaling factors for cases with varied cutting parameters. In addition, a reliable self-learning (SL) algorithm is proposed to achi eve even better cutting performance by modifying the adaptive FLC rule base according to properly weighted performance measurements. Both si mulation and experimental results show that given a sufficient number of learning cases, the adaptive SL-FLC is effective for a wide range o f applications. The successful implementation of the proposed adaptive SL-FLC algorithm on an industrial heavy-duty machining center indicat es that the proposed adaptive SL-FLC is feasible for use in manufactur ing industries.