Deep generative model-based synthesis framework of four-bar linkage mechanisms with target conditions

Abstract This paper proposes a deep generative model-based framework for synthesizing four-bar linkage mechanisms that satisfy specified kinematic and quasi-static conditions. We define two objective functions for crank-rocker mechanisms using kinematic workspaces and geometric configurations. Our approach utilizes a conditional generative adversarial network (cGAN) modified for mechanism synthesis, which learns the relationship between mechanism requirements and linkage lengths. The results demonstrate that the proposed model successfully generates multiple distinct mechanisms meeting specific kinematic and quasi-static requirements. We compare our cGAN approach to traditional optimization methods and other deep learning-based generative models. Our method offers several advantages over traditional design approaches, enabling efficient generation of diverse yet feasible design candidates while exploring a large design space. By considering both kinematic and quasi-static requirements, the proposed model can produce more effective mechanisms for real-world applications. This makes it a promising tool for linkage mechanism design, offering designers a way to efficiently generate multiple viable design options that satisfy key performance criteria.