Generative Design of Mechanical Systems Using Deep Learning Algorithms

Abstract

The integration of generative design and deep learning algorithms is revolutionizing the development of mechanical
systems. This paper explores the synergy between these advanced technologies to automate and optimize the design
process. Generative design leverages algorithmic approaches to generate a vast array of design options based on
predefined constraints and objectives. When coupled with deep learning, a subset of artificial intelligence, the
system gains the ability to learn from past designs, predict performance, and refine solutions iteratively. This study
examines the methodologies for implementing deep learning in generative design, highlighting key algorithms such
as convolutional neural networks (CNNs) and generative adversarial networks (GANs). Case studies demonstrate
the effectiveness of this approach in creating innovative and efficient mechanical systems, reducing design time, and
enhancing performance. The results indicate a significant improvement in design quality and feasibility, showcasing
the potential for deep learning to transform the field of mechanical engineering. Future research directions are
proposed to further enhance the integration and capabilities of these technologies, aiming for more intelligent,
autonomous, and robust design processes.