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<p>We investigate the synthesis of crank-driven four-bar linkages by learning the inverse problem of predicting the mechanism parameters from given coupler-point paths. This problem exhibits an ambiguity in feature space, where different mechanisms may produce very similar paths. This paper proposes a dual-network approach to combat this issue and compares it to a naive single-network approach. Furthermore, different feature extraction methods, normalizations, and feature lengths are evaluated. We show that the dual-network approach generally leads to better and more robust predictions. Lastly, we show that a neuro-initialized post-optimization can locally refine the proposed designs and achieves results comparable to intermediate solutions of a global optimization algorithm but requires a much lower computation effort.</p>

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A dual-network approach for avoiding feature ambiguity in the synthesis of crank-driven four-bar linkages

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月12日

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  • Benedict Röder,
  • Henrik Ebel,
  • Manuel Berkemeier,
  • Peter Eberhard

摘要

We investigate the synthesis of crank-driven four-bar linkages by learning the inverse problem of predicting the mechanism parameters from given coupler-point paths. This problem exhibits an ambiguity in feature space, where different mechanisms may produce very similar paths. This paper proposes a dual-network approach to combat this issue and compares it to a naive single-network approach. Furthermore, different feature extraction methods, normalizations, and feature lengths are evaluated. We show that the dual-network approach generally leads to better and more robust predictions. Lastly, we show that a neuro-initialized post-optimization can locally refine the proposed designs and achieves results comparable to intermediate solutions of a global optimization algorithm but requires a much lower computation effort.