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• W4319835705 abstract "Abstract The scientific advances in the machining process inculcate intelligence in computer numerical control (CNC), to enhance reliability and quality production. The study made in this article has been directed towards the metaheuristic‐based intelligent control of a nonlinear uncertain CNC machine for milling purposes. The presence of cutting force and frictional type constraints, along with parametric uncertainties, degrade the production quality in milling operations. Moreover, enhanced performance of milling operation can be achieved via intelligent control of position and velocity of machine table and tool. In this work, the black widow optimization algorithm (BWOA) driven proportional–integral–derivative (PID) and PI controllers are employed for the position and the velocity of machine table and tool, respectively. Servo and regulatory are the two important control problems that are considered, analysed, and addressed in this work. The most challenging task of simultaneous tuning of PI and PID controllers is also addressed in this brief. Finally, a vivid comparative analysis of five state‐of‐the‐art optimization techniques is also performed, and the obtained results demonstrate the superiority of the proposed control scheme. While dealing with the servo performance of x ‐axis, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time, and for regulatory performance, it gives a 98.45% improvement in undershoot. Moreover, for z ‐axis control, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time and for regulatory performance gives a 100% improvement in undershoot compared to published literature." @default.
• W4319835705 created "2023-02-11" @default.
• W4319835705 creator A5027456912 @default.
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• W4319835705 date "2023-01-22" @default.
• W4319835705 modified "2023-10-12" @default.
• W4319835705 title "<scp>BWOA</scp> based metaheuristic approach for uncertain nonlinear milling <scp>CNC</scp> machine system" @default.
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• W4319835705 doi "https://doi.org/10.1111/exsy.13233" @default.
• W4319835705 hasPublicationYear "2023" @default.
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