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.

翻译：机构是机械系统中执行特定任务的关键组成部分。然而，设计满足特定运动学或准静态要求的机构是一项具有挑战性的任务。运动学要求可能包括机构的工作空间，而准静态要求可能涵盖机构扭矩传递能力，即机构有效传递功率和扭矩的性能。本文提出了一种基于深度学习的生成模型，用于生成同时满足上述运动学与准静态要求的多个曲柄摇杆四杆机构。该模型基于改进的条件生成对抗网络（cGAN）进行机构综合，通过训练学习机构需求与杆长之间的映射关系。实验结果表明，所提模型能够成功生成多个满足特定运动学与准静态要求的差异化机构。为评估方法创新性，我们将提出的cGAN生成样本与传统条件变分自编码器（cVAE）及NSGA-II进行了对比。相较于传统设计方法，本方法具备多项优势：它使设计者能够高效生成多样化且可行的设计方案，同时探索广阔的设计空间；此外，该模型同时考虑运动学与准静态约束，可提升实际应用场景中机构设计的效率与性能，使其成为连杆机构设计领域颇具前景的工具。