Review on machine learning-based approaches for the kinematic analysis and synthesis of mechanisms

Abstract

Kinematic analysis and synthesis are two key topics for the study of mechanisms, and they are also important foundations of the practical application of mechanical design and control. Machine learning (ML), as a data-driven approach, enables the kinematic analysis and synthesis of different types of mechanisms while avoiding complex analytical and numerical methods. In this review, we summarize the various applications of ML algorithms and different types of data representations in the kinematic analysis and synthesis of mechanisms. A comprehensive literature review and brief analysis of current advances in ML-based approaches for the kinematic analysis of serial and parallel mechanisms, as well as their kinematic synthesis, are presented. The advantages of applying single, modular, and hybrid neural networks in the kinematics of mechanisms are discussed and compared. The future integration of ML and the kinematics of mechanisms is proposed, and the potential challenges involved are addressed.

Graphical abstract

Keywords

machine learning / kinematic analysis / serial mechanism / parallel mechanism / kinematic synthesis

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 51775155). All findings and results presented in this paper are by those of the authors and do not represent the funding agency.

Conflict of Interest

The authors declare no conflict of interest.

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