2012 12th International Conference on Control Automation Robotics &Amp; Vision (ICARCV) 2012
DOI: 10.1109/icarcv.2012.6485408
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Design and optimization of a long-stroke compliant micropositioning stage driven by voice coil motor

Abstract: A micro-/nanopositioning system with both large motion range and compact size is highly desired in various precision engineering applications. In this paper, the design of a new compliant micropositioning stage with translational motion is proposed based on flexure mechanisms. The stage parameters are optimized by using the genetic algorithm (GA) to achieve a large natural frequency under the constraints on motion range (i.e., over 10 mm) and compact physical dimension along with a high safety factor for the m… Show more

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Cited by 11 publications
(3 citation statements)
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“…Step 5: The inner beams are under compression, which may lead to buckling failure of inner beams if not designed for buckling. After the selection of length and thickness, find out the minimum value for the width of inner beams to avoid buckling using (15):…”
Section: Proposed Design Steps For Optimized Dpcmmentioning
confidence: 99%
“…Step 5: The inner beams are under compression, which may lead to buckling failure of inner beams if not designed for buckling. After the selection of length and thickness, find out the minimum value for the width of inner beams to avoid buckling using (15):…”
Section: Proposed Design Steps For Optimized Dpcmmentioning
confidence: 99%
“…The challenge is not only create a multi-axis flexure mechanism which able to provide large motion range, but also in the mechanical integration of the flexure mechanism with ground-mounted actuators.Since flexure mechanisms provide the motion guidance attributes of mechanisms with the elastic elements of structures [7]. In this optimization was been carried out on the motion stage in micro scale [8]. Synthesis is based on a systematic and symmetric layout of constraints that are realized by means of common flexure building blocks.…”
Section: Introductionmentioning
confidence: 99%
“…In the literature, Xu et al [59,100,[118][119][120] used particle swarm optimization (PSO) to maximize the frequency of the designed stages. Lin et al [121] and Liu et al [122] applied the genetic algorithm (GA) to optimize their designs. In addition, Tian et al [92] solved the optimization problem by using both PSO and GA, and obtained two similar results.…”
Section: Heuristic Algorithmmentioning
confidence: 99%