In this paper, the process control of a magnetostrictive-actuator-based dual-stage microforming system is studied. Microforming has recently become an emerging advanced manufacturing technique for fabricating miniaturized products. Particularly, miniaturized desktop microforming system based on unconventional actuators possesses great potential in attaining both high productivity and low cost. Process control of such miniaturized microforming systems, however, is challenging and still at its early stage. The challenge arises from the complicated behaviors of the actuators used, the switching and transition of the actuation/motion, and the uncertainty of the system dynamics during the entire microforming process. During the microforming process, the dynamics and the hysteresis effects of magnetostrictive actuator can be excited, resulting in positioning errors of the workpieces in both trajectory tracking and output transition. Rapid transition between tracking and transition is also accompanied with substantial variation of the system dynamics. Additional challenges exist due to the use of multi-stage actuators and the augmentation of ultrasonic vibrations to the microforming process. In this paper, a control framework integrating iterative learning control and optimal transition trajectory design along with feedforward-feedback control is proposed to achieve high speed and high quality in microforming. The efficacy of the proposed control strategies is demonstrated through experimental implementation.
- Dynamic Systems and Control Division
Control of a Dual-Stage Magnetostrictive Actuator-Based Micromachining System for Optimal High-Speed Microforming Process
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Wang, Z, Witthauer, A, Zou, Q, Kim, G, & Faidley, L. "Control of a Dual-Stage Magnetostrictive Actuator-Based Micromachining System for Optimal High-Speed Microforming Process." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems. San Antonio, Texas, USA. October 22–24, 2014. V001T13A005. ASME. https://doi.org/10.1115/DSCC2014-6318
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