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<p>The detent force in a linear motor refers to irregular force fluctuations arising from the interactions between permanent magnets and the iron core, which can adversely affect the performance and efficiency of a system. This paper proposes a neural network-based surrogate model to reduce the computational burden associated with minimizing the detent force in linear motors. The proposed surrogate model is identical to the one used in 2D analyses and is analyzed in terms of the model’s bias-variance trade-off. The proposed approach showed a significantly shorter analysis time than conventional sensitivity analysis and interpolation-based methods, particularly in computationally expensive 3D simulations.</p>

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Study of Reducing Analysis Time on Minimizing Detent Force in Linear Motors Using Surrogate Model

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Ji-Hoon Han,
  • Jong-Hoon Park,
  • Seung-Min Song,
  • Sun-Ki Hong

摘要

The detent force in a linear motor refers to irregular force fluctuations arising from the interactions between permanent magnets and the iron core, which can adversely affect the performance and efficiency of a system. This paper proposes a neural network-based surrogate model to reduce the computational burden associated with minimizing the detent force in linear motors. The proposed surrogate model is identical to the one used in 2D analyses and is analyzed in terms of the model’s bias-variance trade-off. The proposed approach showed a significantly shorter analysis time than conventional sensitivity analysis and interpolation-based methods, particularly in computationally expensive 3D simulations.