Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Multibody System Dynamics

DOI: 10.1007/s11044-025-10104-x

Authors: Benedict Röder, Henrik Ebel, Manuel Berkemeier, Peter Eberhard

Institutions: University of Stuttgart, Lappeenranta-Lahti University of Technology, TU Dortmund University

Language: English

Topic: Robotic Mechanisms and Dynamics

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,190