  Mechanisms are essential components designed to perform specific tasks in
various mechanical systems. However, designing a mechanism that satisfies
certain kinematic or quasi-static requirements is a challenging task. The
kinematic requirements may include the workspace of a mechanism, while the
quasi-static requirements of a mechanism may include its torque transmission,
which refers to the ability of the mechanism to transfer power and torque
effectively. In this paper, we propose a deep learning-based generative model
for generating multiple crank-rocker four-bar linkage mechanisms that satisfy
both the kinematic and quasi-static requirements aforementioned. The proposed
model is based on a conditional generative adversarial network (cGAN) with
modifications for mechanism synthesis, which is trained to learn the
relationship between the requirements of a mechanism with respect to linkage
lengths. The results demonstrate that the proposed model successfully generates
multiple distinct mechanisms that satisfy specific kinematic and quasi-static
requirements. To evaluate the novelty of our approach, we provide a comparison
of the samples synthesized by the proposed cGAN, traditional cVAE and NSGA-II.
Our approach has several advantages over traditional design methods. It enables
designers to efficiently generate multiple diverse and feasible design
candidates while exploring a large design space. Also, the proposed model
considers both the kinematic and quasi-static requirements, which can lead to
more efficient and effective mechanisms for real-world use, making it a
promising tool for linkage mechanism design.
