In this paper, the process control of a magnetostrictive-actuator-based microforming system is studied. Microforming has recently become an emerging advanced manufacturing technique for fabricating miniaturized products for applications including medical devices and microelectronics. Particularly, miniaturized desktop microforming systems based on unconventional actuators possess great potential in both high productivity and low cost. Process control of these miniaturized microforming systems, however, is challenging and still at its early stage. The challenge arises from the complicated behaviors of the actuators employed, the switching and the transition involved in the actuation/motion, and the uncertainty of the system dynamics during the entire microforming process. The dynamics and the hysteresis effects of the magnetostrictive actuator can be excited, resulting in positioning errors of the workpiece during both the trajectory tracking and the output transition phases. The rapid tracking-transition switching is also accompanied with substantial variation of the system dynamics. Moreover, the process control is further complicated by the use of multistage actuators and the augmentation of ultrasonic vibrations to the microforming process. In this paper, a control framework integrating iterative learning control and an optimal transition trajectory design along with feedforward-feedback control is proposed to achieve high-speed, high-quality microforming. The efficacy of the proposed control approach is demonstrated through experiments.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
- Iterative learning control
- Magnetostrictive actuator
- Optimal output tracking transition