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.

提出了基于深度学习的生成模型，用于生成满足运动学和准静态要求的多种曲柄摇杆四杆机构。该模型通过训练条件生成对抗网络（cGAN）学习了机构与链杆长度的关系，在生成多种满足特定运动学和准静态要求的不同机构方面取得了成功。