PARAMETER-ADAPTIVE CONTROL OF MACHINING PROCESS USING FUZZY TECHNIQUE WITH A SELF LEA

[B][COLOR="Red"]PARAMETER-ADAPTIVE CONTROL OF MACHINING PROCESS USING FUZZY TECHNIQUE WITH A SELF LEARNING ALGORITHM[/COLOR][/B]

[B]Abstract:[/B]

This paper presents a general scheme of a fuzzy controller and an adaptive fuzzy logic control (FLC) developed for cutting processes under various cutting conditions. The controller adopts on line scaling factors for cases with varied cutting parameters. In addition, a reliable self learning (SL) algorithm is proposed to achieve even better cutting performance by modifying the adaptive FLC rule base according to properly weighted performance measurements. Applications of fuzzy systems in other commercial products are also mentioned. Both simulation and experimental results show that given a sufficient number of learning cases, the adaptive SL-FLC is effective for a wide range of applications. When machining conditions change significantly, applying parameter-adaptive control to the cutting system by varying the table feed rate allows a constant cutting force to be maintained. Although several controller schemes have been proposed, their cutting control performance is limited especially when cutting conditions vary significantly. The successful implementation of the proposed adaptive SL-FLC algorithm on an industrial heavy-duty machining center indicates that the proposed adaptive SL-FLC is feasible for use in manufacturing industries.

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